Computational Intelligence in Machine Learning: Proceedings of the 2nd International Conference ICCIML 2022 (Lecture Notes in Electrical Engineering, 1106) 9819979536, 9789819979530

Table of contents :
Contents
About the Editors
A Deep Learning Method for Autism Spectrum Disorder
1 Introduction
2 Transfer Learning
3 Experiments
3.1 Data Set
3.2 VGG-16
4 Results and Discussion
5 Conclusion
References
Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning—A Survey
1 Introduction
2 Taxonomy on Anomaly Detection of Cyberattacks in Networking
2.1 Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Semi-supervised Learning
3 Literature
4 Comparative Analysis
5 Problem Statement
6 Conclusion
References
AI-Enabled Analysis of Climate Change on Agriculture and Yield Prediction for Coastal Area
1 Introduction
2 Literature Survey
3 Implementation
3.1 LSTM Model
3.2 Random Forest Model
3.3 Predicted Temperature and Actual Temperature
3.4 Shows the Baseline Predicted Example
3.5 Shows the Prediction Weather
3.6 Shows the Alternate Crop
4 Result Analysis
5 Conclusion and Future Scope
References
Deep Learning Methods for Predicting Severity for Diabetic Retinopathy on Retinal Fundus Images
1 Introduction
2 DR Features
3 Literature Review
4 Proposed Methodology
5 Conclusion
References
Hate Text Finder Using Logistic Regression
1 Introduction
2 Related Work
3 Proposed System
4 Working of Proposed System
5 Results
6 Conclusion
7 Future Scope
References
Automated Revealing and Warning System for Pits and Blockades on Roads to Assist Carters
1 Introduction
2 Pothole and Obstacle Detection Working Model
3 Implementation of Pothole and Obstacle Detection
3.1 Stage 1: When an Obstacle is Detected
3.2 Stage 2: When the Working Model Detects Potholes
4 Conclusion
References
Green Data Center Power Flow Management with Renewable Energy Sources and Interlinking Converter
1 Introduction
2 Uninterruptible Power Supply
3 Proposed Framework for Green Data Center
4 Simulation Result
5 Conclusion
References
Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid Renewable Power Generation
1 Introduction
2 Problem Formulation
2.1 Scenario 1: Rising Voltage Profile
2.2 Scenario 2: Backward Power Flow
2.3 Scenario 3: An Increase in the Fault Current's Size
2.4 Scenario 4: The Traditional Method is to Estimate the EG's Penetration Limitations
3 Proposed Solutions
References
Power Quality Enhancement with PSO-Based Optimisation of PI-Based Controller for Active Power Filter
1 Introduction
2 Power Quality
2.1 Power Quality Problems
2.2 The Benefits of Power Quality
3 PSO
4 Optimisation of PI Controller By Using PSO
5 Conclusion
References
Monitoring and Control of Motor Drive Parameters Using Internet of Things Protocol for Industrial Automation
1 Introduction
1.1 Automation
1.2 Material and Method
1.3 Relay and Contactor
2 Architecture of SCADA
2.1 Human–Machine Interface (HMI)
2.2 Internet of Things SCADA System
2.3 Data Communication
2.4 Data Presentation
2.5 Data Acquisition
3 Conclusion
References
Switching Loss Comparison of a Cascaded Diode-Clamped Inverter with Conventional Multilevel Inverters
1 Introduction
2 Inverter Topologies
2.1 Cascaded H-Bridge Inverter (CHBI)—(9-Level)
2.2 Diode-Clamped Multilevel Inverter (DCMLI)—(Five-Level)
2.3 Hybrid Inverter—Cascaded Diode-Clamped Inverter (CDCI)—(Nine-Level)
3 Switching Loss Calculation
4 Simulation and Results
4.1 Cascaded H-Bridge Inverter (Nine-Level)—Results
4.2 Diode-Clamped Multilevel Inverter (FiveLevel)—Results
4.3 Cascaded Diode-Clamped Inverter (Nine-Level)—Results
4.4 Comparision of CHBI, DCMLI and CDCI
5 Conclusion
References
An Energy-Efficient Mechanism Using Blockchain Technology for Machine-Type Communication in LTE Network
1 Introduction
2 Related Work
3 Proposed Cluster-Based Energy-Efficient Cluster Head Using Blockchain
3.1 Distance (fdistance)
3.2 Delay (fdelay)
3.3 Energy (fenergy)
3.4 Objective Function
3.5 WTL Algorithm for Optimal Cluster Head Selection
3.6 Blockchain Technology
4 Results and Discussions
4.1 Simulation Procedure
5 Conclusion
References
Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting
1 Introduction
2 Methodology
2.1 Artificial Neural Network
2.2 Echo State Network
3 Statistical Methods
4 Result and Discussion
5 Conclusion
References
A Grey Wolf Integrated with Jaya Optimization Based Route Selection in IoT Network
1 Introduction
2 Literature Review
3 Proposed Routing Strategy in IoT
3.1 Computation of Fitness Value
3.2 Optimal Node Selection Using GWJO Model
3.3 Route Establishment
4 Results
4.1 Simulation Set-Up
5 Conclusion
References
Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins
1 Introduction
2 Literature Survey
2.1 Cyber Physical Systems and Their Attacks?
2.2 Advantages of Digital Twin Over Cyber Physical System
2.3 Disadvantages of DT
2.4 Cyber Digital Twin
2.5 How Secure Are CDTs?
3 Methodology
3.1 Secure Software Development Life Cycle (Proposed Solution)
3.2 Why SSDLC and not SDLC?
3.3 Detailed Look at the SSDLC
4 Conclusion
References
Social Networks and Time Taken for Adoption of Organic Food Product in Virudhunagar District—An Empirical Study
1 Introduction
2 Related Works
3 Conceptual Framework
4 Objectives of the Study
5 Area of the Study, Sample Framework and Procedure
6 Days to Purchase
7 Members of the Social Group
8 Reason to Join in the Group
9 The Frequency of Participation in the Group
10 Information Trust by the Respondents
11 Convey Information About New Product
12 Cost Spend by the Respondents for Passing the Information
13 Like to Spread the Information
14 Quantity of Passing of Information
15 Hypothesis
16 Kruskal–Wallis Test
17 Living Place and Adoption of New Product in the Market
18 Chi-Square Tests
19 Age Group and Information Passing
20 Chi-Square Test
21 Exponential Smoothing for Applying Roger’s Model in Identifying the Adoption of Organic Food Product
22 Exponential Growth
23 Findings and Discussion
References
Usage of Generative Adversarial Network to Improve Text to Image Synthesis
1 Introduction
2 Literature Survey
2.1 Generative Adversarial Networks
2.2 Text to Photo-Realistic Image Synthesis with Stacked GAN
2.3 Image Generation from Scene Graphs
2.4 Fine Grained Text to Image Generation with Attentional Generative Adversarial Networks (Attn GAN)
2.5 Realistic Image Synthesis with Stacked Generative Adversarial Networks (Stack GAN++)
3 Methodology
3.1 Defining Goal
3.2 Researching Previous Attempts
3.3 Defining Approach
3.4 Algorithm
4 Experiments and Results
4.1 Experiment Setting
4.2 Effectiveness of New Modules
4.3 Component Analysis of AATM
4.4 Component Analysis of SDM
4.5 Comparison of KT GAN with Other GAN Models
4.6 Visualization
5 Conclusion
References
Recurrent Neural Network-Based Solar Power Generation Forecasting Model in Comparison with ANN
1 Introduction
2 Methodology
2.1 Artificial Neural Network (ANN)
2.2 Recurrent Neural Network (RNN)
3 Statistical Measures
4 Result and Discussion
5 Conclusion
References
Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning
1 Introduction
1.1 A Subsection Sample
2 Proposed Method
2.1 Supervised Classification (Training Dataset)
2.2 Supervised Classification (Test Dataset)
2.3 System Design
2.4 Use Case Diagram
3 Testing and Implementation
4 Results and Discussion
5 Conclusion
References
Mental Health Disorder Predication Using Machine Learning for Online Social Media
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Data Collection
3.3 Check Category
3.4 Check Wordlist
4 Machine Learning Models
5 Result and Discussion
6 Conclusion
References
An Application on Sweeping Machines Detection Using YOLOv5 Custom Object Detection
1 Introduction
2 Related Work
2.1 YOLOv5
2.2 Model Description
3 Proposed System
3.1 Dataset
3.2 Manual Labeling
3.3 Augmentation
4 Training
5 Results
6 Command
7 Conclusion and Future Work
References
Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review
1 Introduction
2 State-of-Art: Overview
3 Dataset Availability
4 Methodology
5 Conclusions and Future Work
References
A Study on Predicting Skilled Employees’ Using Machine Learning Techniques
1 Introduction
2 Related Work
3 Classifier Construction
3.1 Objectives and Problem Definition
3.2 Data Collection and Understanding Process
3.3 Data Preparation and Pre-processing
4 Modeling and Experiments
5 Comparative Analysis and Discussion:
6 Results
7 Conclusion and Future Scope
References
Forensic Analysis of the Uncompressed Image Data Cluster for Estimating the Image Width
1 Introduction
2 The Principle and Limitation of Greedy Path Algorithms
2.1 The Significance of Image Width
2.2 The Smoothness Property of Images
2.3 The Drawback of Greedy Path Algorithms
3 Importance of Width Attribute
3.1 The Image Width is Important in Image Recovery
3.2 The Problem Statement
4 Analysis of Disk Block Data for Finding Width
5 Experiments and Results
6 Conclusion
References
Performance Comparison of Various Supervised Learning Algorithms for Credit Card Fraud Detection
1 Introduction
2 Methodology
2.1 Dataset
2.2 Data Preprocessing
2.3 Data Cleaning (DC)
2.4 Data Exploration (DE)
2.5 Data Manipulation (DM)
2.6 Data Modeling (DMD)
3 Classifiers
3.1 Logistic Regression
3.2 Decision Tree
3.3 Artificial Neural Networks
3.4 Gradient Boosting
4 Experimental Results
5 Conclusion
References
Simple-X a General-Purpose Image Classification Model Through Investigative Approach
1 Introduction
2 Related Work
3 Proposed System
3.1 Dataset
3.2 Augmentation
4 Training
5 Testing
6 Visuals
7 Conclusions and Future Scope
References
Blockchain-Based Source Tracing System Using Deep Learning: A Review
1 Introduction
2 Literature Review
3 Types of Blockchains
4 Deep Learning Models
5 Conclusion
References
Geospatial Data Visualization of an Energy Landscape and Geographical Mapping of a Power Transmission Line Distribution Network
1 Introduction
1.1 Related Works
2 Research Design
2.1 Energy Supply Chain Network
2.2 Data Description
3 Methodology
3.1 Experimental Setup and Implementation Process Metrics
4 Results and Discussion
5 Conclusion and Future Enhancement
References
Sign Language Identification Using Deep Learning Methods
1 Introduction
2 Literature Survey
2.1 Novelty
3 Methodology
3.1 Data Pre-processing
3.2 CNN Architecture
4 Implementation
4.1 Creating and Training the Model
4.2 Deployment
5 Results and Discussion
5.1 Class-Wise Classification Report
5.2 Class-Wise Heat Map
5.3 Loss and Val Loss Per Epochs
5.4 Accuracy and Val Accuracy Per Epochs
6 Deployment on Web App Using Flask
7 Conclusion
References
Development of LoRa-Based Weather Monitoring and Lightning Alert System in Rural Areas Using IoT
1 Introduction
2 Literature Review
3 Hardware Requirements
3.1 ESP 32 and Lightning Detector
3.2 DHT11 and BMP180
3.3 Rain Sensor and LM 386 Audio Amplifier
3.4 LoRa Ra 02 Module and Speaker
4 Software Requirements
4.1 Arduino IDE
4.2 ThingSpeak and IFTTT Platform
5 Proposed System Design
6 Results and Discussion
7 Conclusion and Future Scope
References
Automatic Text Recognition from Image Dataset Using Optical Character Recognition and Deep Learning Techniques
1 Introduction
2 Literature Review
3 Algorithmic Survey
3.1 SVM
3.2 Convolutional Neural Network (CNN)
4 Datasets
4.1 MNIST
4.2 EMNIST
4.3 IAM
5 Methodology
6 Accuracy Measured Used
6.1 Confusion Matrix
7 Analysis of Accuracy of Algorithms
8 Conclusion
References
Securing IoT Networks Using Machine Learning, Deep Learning Solutions: A Review
1 Introduction
2 Literature Survey
3 Discussions
3.1 The Role of Machine Learning in the Security of Internet of Things Devices
4 Suggestions
5 Conclusion
References
Smart Warehouse Management System
1 Introduction
2 Literature Survey
3 Working
4 Experimental Results
5 Conclusion
References
IoT Enabled LoRa-Based Patrolling Robot
1 Introduction
2 Empirical Survey
3 System Design
4 Methodology
4.1 Transmitter Section
4.2 Receiver Section
4.3 LoRa SX1276 Transceiver
4.4 ESP32 Camera (Ai-Thinker Module)
4.5 Interfacing Blynk Application (IoT Dashboard)
5 Flow Chart
6 Results and Discussions
7 Conclusion
References
Single Image Dehazing Using CNN
1 Introduction
2 Related Work
2.1 Atmospheric Scattering Model (ASM):
2.2 Image Enhancement Based Methods
2.3 Methods Based on Learning Models
3 Proposed Model
4 Results
5 Conclusion
References
Artificial Intelligence in Healthcare and Medicine
1 Introduction
2 Literature Survey
3 Uses of AI
3.1 Diagnosis and Treatment
3.2 Management
4 Challenges and Solutions
4.1 Privacy
4.2 Accountability
4.3 Unemployment
5 Conclusion
References
Share Market Application Using Machine Learning
1 Introduction
2 Literature Survey
3 Objective
4 Methodology
5 Proposed Algorithm
5.1 Data Flow Diagram
6 Implementation
6.1 Create Account Page
6.2 Home Page
6.3 Company Search Page
6.4 Company List
6.5 Sentimental Analysis
6.6 News Update
7 Conclusion
References
PollX—Polling Assistant
1 Introduction
2 Literature Survey
2.1 Analysis of Electronic Voting Systems
2.2 The Indian Electoral Process and Negative Voting [4]
2.3 On What Basis Indian People Vote [5]
2.4 Handbook for Presiding Officers [6]
2.5 Election Material and Electronic Voting in India [7, 8]
3 Problem Statement and Objectives
3.1 Problem Statement
3.2 Specific Objectives
4 Methodology
4.1 System Architecture
4.2 Modular Diagram
4.3 Flow Charts and Working of Each Process
5 Results, Analysis, and Discussion
5.1 Results
5.2 Analysis and Discussion
6 Conclusion
References
A Security Framework Design for Generating Abnormal Activities Report of Bring Your Own Devices (BYODs)
1 Introduction
2 Literature Review
3 System Model
3.1 Access Context Information
3.2 Usage Context Information
3.3 Finish Context Information
4 Results
4.1 Analysis on Use Behaviour of Overall Transaction
4.2 Occurrence Sequence Analysis of Initial Use Behaviour
5 Conclusion
References
Fake News Detection Using Machine Learning
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Datasets
3.2 Methodology
3.3 Algorithms
3.4 Metrics of Performance
4 Results and Discussion
4.1 Confusion Matrix and Classification Report
4.2 Naïve Bayes
4.3 Logistic Regression
4.4 Decision Tree
5 Conclusion
References
Advanced Intelligent Tutoring Systems: Featuring the Learning Management Systems of the Future
1 Introduction
2 What is an ITS?
3 Architecture of ITS
3.1 The Knowledge Domain
3.2 The Student Model
3.3 The Pedagogical Model/Teaching Strategies
3.4 The User Interface Model
4 Literature Review of Some Existing Intelligent Tutoring Systems
5 Advanced Artificial Intelligence Tutors in Education
6 ITS Versus LMS—Future of Education
7 Conclusion
References
Stock Prediction Using Machine Learning and Sentiment Analysis
1 Introduction
2 Literature Survey
3 Methodology
3.1 Dataset
3.2 Fetching Data
3.3 LSTM
3.4 Linear Regression
3.5 ARIMA
3.6 Sentiment Analysis
3.7 Root Mean Square Value
4 Results
4.1 Stock Prediction Using Machine Learning
4.2 Sentiment Analysis
5 Comparative Analysis
6 Conclusion
7 Future Scope
References
Parkinson Disease Screening Using UNET Neural Network and BWO Based on Hand Drawn Pattern
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Result and Discussions
5 Conclusion and Future Scope
References
A Comprehensive Review for Optical Character Recognition of Handwritten Devanagari Script
1 Introduction
2 Literature Survey
2.1 Traditional Methods
2.2 Deep Learning Methods
3 Discussion
4 Conclusion
References
Emotion Detection Using Machine Learning Algorithms: A Multiclass Sentiment Analysis Approach
1 Introduction
2 Literature Survey
3 Binary Versus Multiclass Sentiment Analysis
4 Experimental Analysis
4.1 Importing Dataset
4.2 Text Pre-processing
4.3 Text Representation
4.4 Classification
4.5 Model Evaluation
5 Conclusion
References
Deep Learning-Based Methods for Automatic License Plate Recognition: A Survey
1 Introduction
2 Localization Techniques for AVPR
2.1 Localization Using Signature Analysis
2.2 Localization Using Characteristics of Alphanumeric Characters
2.3 Localization Using Novel Approach
3 Pre-processing Methods in AVPR
4 Segmentation Process
5 Feature Extraction in AVPR
5.1 Color Feature
5.2 Deep Features
6 Classification and Recognition
7 Dataset
8 Performance Measures
9 Summary
10 Conclusion
References
Modern Predictive Modelling of Energy Consumption and Nitrogen Content in Wastewater Management
1 Introduction
2 Literature Review
3 Methodology
3.1 Case Study on Melbourne
3.2 Data Collection
3.3 Data Pre-processing and Visualization
3.4 Modelling Approach
3.5 Workflow of the Predictive Model
4 Results and Discussions
4.1 Insights from Energy Consumption Prediction
4.2 Insights from Total Nitrogen Prediction
4.3 Impact of Input Parameters on Model Performance
5 Conclusion
References
Automatic Text Document Classification by Using Semantic Analysis and Lion Optimization Algorithm
1 Background
2 Major Challenges
3 Gap Analysis
4 Methodology
5 Algorithm
6 Results and Discussions
7 Comparative Analysis
8 Conclusions
References
Text-Based Emotion Recognition: A Review
1 Introduction
2 Machine Learning Types
2.1 Supervised
2.2 Semi-supervised
2.3 Unsupervised
2.4 Hybrid/Reinforcement
3 Literature Review
4 Comparative Study
5 Discussion
5.1 Generalized Linear Model
6 Limitations and Future Works
7 Conclusion
References
AURA—Your Virtual Assistant, at Your Service
1 Introduction
2 Research Work
3 Proposed Methodology
4 Result and Discussion
4.1 Commands Executed
5 Conclusion
References
Design and Develop Sign Language to English Script Convertor Using AI and Machine Learning
1 Introduction
2 Literature Survey
3 Research Methodology
4 Observations and Applications
5 Conclusion
References
Akademy: A Unique Online Examination Software Using ML
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Tab Switch Detection
3.2 Random Question Generation
3.3 Examination
3.4 Analysis
4 Evaluation Methods
5 Results
5.1 Face Detection and Verification
5.2 Tab Switch
5.3 Test Analysis
6 Conclusion and Future Scope
References
Selection of Reactive Load to Correct Power Factor, Cost-Effectiveness
1 Introduction
2 Related Work
3 System Architecture
3.1 Block Diagram
3.2 System Flowchart
3.3 Results
3.4 Cost-Effectiveness
4 Conclusion
References
Machine Learning Algorithm Recommendation System
1 Introduction
1.1 Motivation
1.2 Problem Analysis
1.3 Objectives
1.4 Scope
2 Literature Review
2.1 Related Work
3 Proposed System
3.1 Proposed Approach and Details
3.2 Innovation in Idea
4 Implementation Details and Results
4.1 Technology Stack
4.2 Implementation Parameters
5 Results
6 Conclusions
References
Crop Recommender System
1 Introduction
2 Literature Review
3 Motivation
4 Methodology
4.1 Dataset Retrieval
4.2 Algorithm Building
4.3 Accuracy Prediction and Comparison
5 Result
5.1 Accuracy Prediction Table
5.2 Time Complexity for Accuracy Predicted
6 Conclusion
References
256-Bit AES Encryption Using SubBytes Blocks Optimisation
1 Introduction
2 Related Work
3 AES Algorithm
3.1 Architecture of AES Algorithm
3.2 Existing Methodology
3.3 Key Expansion
4 Proposed Methodology
4.1 Architecture for S-Box
5 Results
6 Conclusion
References
Detection of Food Freshness
1 Introduction
2 Literature Survey
2.1 Aim of the Work
2.2 Objectives
2.3 Scope
2.4 Future Enhancements
3 Proposed Methodology
4 Methodology
5 Results
6 Conclusion
References
COVID-19 Data Analysis and Forecasting for India Using Machine Learning
1 Introduction
2 Literature Survey
3 Methodology
3.1 Column Description
4 Block Diagram
4.1 Import Libraries
4.2 Read Data
4.3 Checking for Missing Values
4.4 Checking for Categorical and Variable Data
4.5 Standardize the Data
4.6 Data Splitting
4.7 Support Vector Machine
4.8 Logistic Regression
4.9 Testing the Classifier
4.10 Evaluating the Classifier
4.11 Random Forest
4.12 K-Nearest Neighbour
4.13 Decision Tree
4.14 Gradient Boosting Classifier
5 Result
6 Conclusion and Future Scope
References
Design of a Smart Safety Design for Women Using IoT
1 Introduction
2 Existing System
3 Proposed System
4 System Design
5 Module Description
5.1 OpenCV
5.2 ARM11 Raspberry Pi 3 Board
5.3 Raspbian OS
6 Methodology
7 Experiment and Results
8 Applications
9 Advantages
10 Conclusion
11 Future Work
References
Ultravoilet Sterilization Disinfectant Robot for Combating the COVID-19 Pandemic
1 Introduction
2 Literature Survey
3 Problem Statement
4 Existing Versus Proposed System
5 Methodology
5.1 Hardware Components
5.2 Circuit Connections
6 Result
7 Conclusion
References
Advancement in Sericulture Using Image Processing
1 Introduction
2 Related Work
3 Proposed Approach
4 Design and Implementation
5 Result and Discussion
6 Conclusion and Future Work
References
Model Based on Credit Card Fraud Detection Using Machine Learning
1 Introduction
1.1 Problem Statement
2 Methodology
2.1 Data Preparation
3 Result Output
4 Conclusion and Future Enhancement
References
Implementation of Smart Mobile Health Application for COVID-19 Pandemic
1 Introduction
2 Proposed System
3 Implementation and Results
4 Conclusion
References
IoT-Based Weather Monitoring System
1 Introduction
2 Literature Survey
3 Working
4 Design Methodology
5 Implementation and Result
6 Conclusion and Future Scope
References

Citation preview

Lecture Notes in Electrical Engineering 1106

Vinit Kumar Gunjan Amit Kumar Jacek M. Zurada Sri Niwas Singh   Editors

Computational Intelligence in Machine Learning Proceedings of the 2nd International Conference ICCIML 2022

Lecture Notes in Electrical Engineering Volume 1106

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, University of Karlsruhe (TH) IAIM, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, Department of Mechatronics Engineering, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Intrinsic Innovation, Mountain View, CA, USA Yong Li, College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Subhas Mukhopadhyay, School of Engineering, Macquarie University, Sydney, NSW, Australia Cun-Zheng Ning, Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Federica Pascucci, Department di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, University of Stuttgart, Stuttgart, Germany Germano Veiga, FEUP Campus, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Haidian District Beijing, China Walter Zamboni, Department of Computer Engineering, Electrical Engineering and Applied Mathematics, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA Kay Chen Tan, Department of Computing, Hong Kong Polytechnic University, Kowloon Tong, Hong Kong

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Vinit Kumar Gunjan · Amit Kumar · Jacek M. Zurada · Sri Niwas Singh Editors

Computational Intelligence in Machine Learning Proceedings of the 2nd International Conference ICCIML 2022

Editors Vinit Kumar Gunjan Department of Computer Science and Engineering CMR Institute of Technology Hyderabad, India Jacek M. Zurada Department of Electrical and Computer Engineering University of Louisville Louisville, KY, USA

Amit Kumar BioAxis DNA Research Centre Hyderabad, India Sri Niwas Singh Indian Institute of Technology Kanpur Kanpur, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-7953-0 ISBN 978-981-99-7954-7 (eBook) https://doi.org/10.1007/978-981-99-7954-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Contents

A Deep Learning Method for Autism Spectrum Disorder . . . . . . . . . . . . . . Bindu George, E. Chandra Blessie, and K. R. Resmi Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning—A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Swathi and G. Narsimha AI-Enabled Analysis of Climate Change on Agriculture and Yield Prediction for Coastal Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Manikavelan, Swapna Thouti, M. Ashok, N. Chandiraprakash, and N. Rajeswaran Deep Learning Methods for Predicting Severity for Diabetic Retinopathy on Retinal Fundus Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minakshee Chandankhede and Amol Zade Hate Text Finder Using Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . Kumbam VenkatReddy, Ravikanti Vaishnavi, and Chidurala Ramana Maharshi Automated Revealing and Warning System for Pits and Blockades on Roads to Assist Carters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vijay Raviprabhakaran, Prasanth Dharavathu, Dhanush Adithya Gopaluni, and Abhinav Reddy Jale

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Green Data Center Power Flow Management with Renewable Energy Sources and Interlinking Converter . . . . . . . . . . . . . . . . . . . . . . . . . . Syed Abdul Razzaq and Vairavasamy Jayasankar

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Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid Renewable Power Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Sunanda and M. Lakshmi Swarupa

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Power Quality Enhancement with PSO-Based Optimisation of PI-Based Controller for Active Power Filter . . . . . . . . . . . . . . . . . . . . . . . G. Dhulsingh and M. Lakshmi Swarupa Monitoring and Control of Motor Drive Parameters Using Internet of Things Protocol for Industrial Automation . . . . . . . . . . . . . . . . . . . . . . . . G. MadhusudhanaRao, Srinivas Dasam, M. Pala Prasad Reddy, and B. Rajagopal Reddy

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Switching Loss Comparison of a Cascaded Diode-Clamped Inverter with Conventional Multilevel Inverters . . . . . . . . . . . . . . . . . . . . . . 103 Vinodh Kumar Pandraka and Venkateshwarlu Sonnati An Energy-Efficient Mechanism Using Blockchain Technology for Machine-Type Communication in LTE Network . . . . . . . . . . . . . . . . . . 119 K. Krishna Jyothi, G. Kalyani, K. Srilakshmi, Shilpa Chaudhari, M. Likhitha, and K. Sriya Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting . . . . . . . . . . . . . . . . . . . . . . . 133 Shashikant, Binod Shaw, and Jyoti Ranjan Nayak A Grey Wolf Integrated with Jaya Optimization Based Route Selection in IoT Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 G. Kalyani, K. Krishna Jyothi, K. Srilakshmi, and Shilpa Chaudhari Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Radha Seelaboyina, Sai Prakash Chary Vadla, Sree Alekhya Teerthala, and Veena Vani Pedduri Social Networks and Time Taken for Adoption of Organic Food Product in Virudhunagar District—An Empirical Study . . . . . . . . . . . . . . 163 Dhanalakshmi Thiyagarajan and Maria Ponreka Usage of Generative Adversarial Network to Improve Text to Image Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 D. Baswaraj and K. Srinivas Recurrent Neural Network-Based Solar Power Generation Forecasting Model in Comparison with ANN . . . . . . . . . . . . . . . . . . . . . . . . . 197 Shashikant, Binod Shaw, and Jyoti Ranjan Nayak Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning . . . . . . . . . . . . . . . . . . 207 M. Sonia, Chaganti B. N. Lakshmi, Shaik Jakeer Hussain, M. Lakshmi Swarupa, and N. Rajeswaran

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Mental Health Disorder Predication Using Machine Learning for Online Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 S. A. Patinge and V. K. Shandilya An Application on Sweeping Machines Detection Using YOLOv5 Custom Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 K. Balagangadhar, N. N. S. S. S. Adithya, Cheryl Dsouza, and R. Sudha Dharani Reddy Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Sirikonda Shwetha and N. Ramana A Study on Predicting Skilled Employees’ Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 C. Madana Kumar Reddy and J. Krishna Forensic Analysis of the Uncompressed Image Data Cluster for Estimating the Image Width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 K. Srinivas and D. Baswaraj Performance Comparison of Various Supervised Learning Algorithms for Credit Card Fraud Detection . . . . . . . . . . . . . . . . . . . . . . . . . 273 Chandana Gouri Tekkali, Karthika Natarajan, and Thota Guruteja Reddy Simple-X a General-Purpose Image Classification Model Through Investigative Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Rella Usha Rani and N. N. S. S. S. Adithya Blockchain-Based Source Tracing System Using Deep Learning: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Hemlata Kosare and Amol Zade Geospatial Data Visualization of an Energy Landscape and Geographical Mapping of a Power Transmission Line Distribution Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 J. Dhanalakshmi and N. Ayyanathan Sign Language Identification Using Deep Learning Methods . . . . . . . . . . . 315 Ishaan C. Saxena, Rohan Anand, and M. Monica Subashini Development of LoRa-Based Weather Monitoring and Lightning Alert System in Rural Areas Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Ome Nerella and Syed Musthak Ahmed Automatic Text Recognition from Image Dataset Using Optical Character Recognition and Deep Learning Techniques . . . . . . . . . . . . . . . . 339 Ishan Rao, Prathmesh Shirgire, Sanket Sanganwar, Kedar Vyawhare, and S. R. Vispute

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Securing IoT Networks Using Machine Learning, Deep Learning Solutions: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Vivek Nikam and S. Renuka Devi Smart Warehouse Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 S. V. Aswin Kumer, Nirbhay Jha, Karishma Begum, and Kodali Brahmani IoT Enabled LoRa-Based Patrolling Robot . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Miriyala Sridhar, P. Kanakaraja, L. Yaswanth, Sk. Yakub Pasha, and P. Sailesh Chowdary Single Image Dehazing Using CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 Samarth Bhadane, Ranjeet Vasant Bidwe, and Bhushan Zope Artificial Intelligence in Healthcare and Medicine . . . . . . . . . . . . . . . . . . . . 397 Aakriti Sethi, Tushar Gupta, Ruchi Ranjan, Varun Srivastava, and G. V. Bhole Share Market Application Using Machine Learning . . . . . . . . . . . . . . . . . . 405 Shraddha S. Tanawade and S. V. Pingale PollX—Polling Assistant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Neel Shah, Suhas G. Sapate, Ajit Pradnyavant, Vishal Kamble, and Anis Fatima Mulla A Security Framework Design for Generating Abnormal Activities Report of Bring Your Own Devices (BYODs) . . . . . . . . . . . . . . . . . . . . . . . . . 429 Gaikwad Sarita Sushil, Rajesh K. Deshmukh, and Aparna A. Junnarkar Fake News Detection Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . 443 N. Pavitha, Anuja Dargode, Amit Jaisinghani, Jayesh Deshmukh, Madhuri Jadhav, and Aditya Nimbalkar Advanced Intelligent Tutoring Systems: Featuring the Learning Management Systems of the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 Trishna Paul and Mukesh Kumar Rohil Stock Prediction Using Machine Learning and Sentiment Analysis . . . . . 465 Preeti Bailke, Onkar Kunte, Sayali Bitke, and Pulkit Karwa Parkinson Disease Screening Using UNET Neural Network and BWO Based on Hand Drawn Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 Pooja Gautam Waware and P. S. Game A Comprehensive Review for Optical Character Recognition of Handwritten Devanagari Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Pragati Hirugade, Rutwija Phadke, Radhika Bhagwat, Smita Rajput, and Nidhi Suryavanshi

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Emotion Detection Using Machine Learning Algorithms: A Multiclass Sentiment Analysis Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Sumit Shinde and Archana Ghotkar Deep Learning-Based Methods for Automatic License Plate Recognition: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Ishwari Kulkarni, Dipmala Salunke, Rutuja Chintalwar, Neha Awhad, and Abhishekh Patil Modern Predictive Modelling of Energy Consumption and Nitrogen Content in Wastewater Management . . . . . . . . . . . . . . . . . . . 527 Makarand Upkare, Jeni Mathew, Aneesh Panse, Archis Mahore, and Vedanti Gohokar Automatic Text Document Classification by Using Semantic Analysis and Lion Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Nihar M. Ranjan, Rajesh S. Prasad, and Deepak T. Mane Text-Based Emotion Recognition: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Heer Shah, Heli Shah, and Madhuri Chopade AURA—Your Virtual Assistant, at Your Service . . . . . . . . . . . . . . . . . . . . . . 563 Janhvi Pawar, Disha Shetty, Aparna Ajith, and Rohini Patil Design and Develop Sign Language to English Script Convertor Using AI and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Bhausaheb Khamat, Mrunal Bewoor, and Sheetal Patil Akademy: A Unique Online Examination Software Using ML . . . . . . . . . 581 Hartik Suhagiya, Hardik Pithadiya, Hrithik Mistry, Harshal Jain, and Kiran Bhowmick Selection of Reactive Load to Correct Power Factor, Cost-Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Somnath Lambe and Kailash Karande Machine Learning Algorithm Recommendation System . . . . . . . . . . . . . . . 599 Haider Nakara, Prashant Mishra, and Hariram Chavan Crop Recommender System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Shivanoori Sai Samhith, T. V. Rajinikanth, Burma Kavya, and Alley Yashwanth Sai Krishna 256-Bit AES Encryption Using SubBytes Blocks Optimisation . . . . . . . . . 621 R. Kishor Kumar, M. H. Yogesh, K. Raghavendra Prasad, Sharankumar, and S. Sabareesh Detection of Food Freshness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 H. R. Poorvitha and R. Ruthu

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COVID-19 Data Analysis and Forecasting for India Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643 K. T. Rashmi, S. Hamsa, M. G. Thejuganesh, and S. Yashaswini Design of a Smart Safety Design for Women Using IoT . . . . . . . . . . . . . . . . 657 G. Shruthi, R. Chandana, and P. Gagana Ultravoilet Sterilization Disinfectant Robot for Combating the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Padmashree V. Kulkarni, K. M. Akash, G. Deepika, Kamal Nayan Singh, and K. Madhura Advancement in Sericulture Using Image Processing . . . . . . . . . . . . . . . . . . 675 Kishor Kumar, N. Pavan, R. Yashas, R. Rajesh, and B. G. Rakshith Model Based on Credit Card Fraud Detection Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 T. R. Lakshmi Devi, S. Keerthana, Akshata B. Menasagi, and V. Akshatha Implementation of Smart Mobile Health Application for COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 M. J. Akshath, M. B. Sudha, Y. S. Sahana, S. Spurthi, and S. Sahana IoT-Based Weather Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 M. J. Akshath, L. Amrutha, and Praveen S. Yarali

About the Editors

Vinit Kumar Gunjan is an Associate Professor in the Department of Computer Science & Engineering at CMR Institute of Technology India (affiliated with Jawaharlal Nehru Technological University, Hyderabad). Dr. Gunjan is an active researcher and published research papers with high-quality conferences authored several books and edited volumes. He was awarded the prestigious Early Career Research Award in 2016 by the Science Engineering Research Board, Department of Science & Technology, Government of India. He has been involved in several technical and non-technical workshops, seminars, and conferences. During his tenure, worked with top leaders of IEEE and was awarded the best IEEE Young Professional award in 2017 by IEEE Hyderabad Section. Amit Kumar is a DNA forensics professional, entrepreneur, engineer, bioinformatician, and an IEEE volunteer. In 2005, he founded the first private DNA testing Company Bio Axis DNA Research Centre (P.) Ltd in Hyderabad, India, with a US collaborator. He has vast experience in training 1000+ crime investigating officers and helped 750+ criminal and non-criminal cases to reach justice by offering analytical services in his laboratory. His group also works extensively on genetic predisposition risk studies of cancers and has been helping many cancer patients since 2012 to fight and win the battle against cancer. He was a member of the IEEE Strategy Development and Environmental Assessment Committee (SDEA) of IEEE MGA. He has driven several conferences, conference leadership programs, entrepreneurship development workshops, innovation, and internship-related events. Currently, he is Managing Director of BioAxis DNA Research Centre (P) Ltd and IEEE MGA Nominations and Appointments committee member. Jacek M. Zurada is a Professor of Electrical and Computer Engineering and Director of the Computational Intelligence Laboratory at the University of Louisville, USA, where he served as Department Chair and Distinguished University Scholar. He received his M.S. and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. He has published over 420 journal and conference papers in neural networks, deep learning, computational intelligence, xi

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data mining, image processing, and VLSI circuits. He has authored or co-authored three books. In addition to his pioneering neural networks textbook, his most recognized achievements include an extension of complex-valued neurons to associative memories and perception networks; sensitivity concepts applied to multilayer neural networks; application of networks to clustering, biomedical image classification, and drug dosing; blind sources separation; and rule extraction as a tool for prediction of protein secondary structure. S. N. Singh obtained his M. Tech. and Ph.D. in Electrical Engineering from the Indian Institute of Technology (IIT) Kanpur, in 1989 and 1995, respectively. Presently, Prof. Singh is Director, Atal Bihari Bajpayee- Indian Institute of Information Technology and Management Gwalior (MP), India (on leave from Professor (HAG), Department of Electrical Engineering, Indian Institute of Technology Kanpur, India). His research interests include power system restructuring, FACTS, power system optimization & control, security analysis, wind power, etc. Prof Singh has published more than 500 papers in international/national journals/conferences and supervised 40 Ph.D. (8 Ph.D. under progress). He has also written 30 book chapters, 8 edited books, and 2 textbooks. Prof. Singh has completed three dozen technical projects in India and abroad.

A Deep Learning Method for Autism Spectrum Disorder Bindu George, E. Chandra Blessie, and K. R. Resmi

1 Introduction ASD is a neurodevelopmental disorder characterised by a wide range of behavioural and developmental abnormalities. It can be detected at any point in its development. It is a disorder that affects brain development. A person with ASD is often unable to interact socially or converse with others. ASD is becoming a growing concern in developing countries like India. It provides a considerably higher and serious issue due to the intensity of the impact on affected persons and their families, as well as the economic burden it imposes, as well as a lack of scientific information about the sickness. Currently, deep learning models are used in the prediction of various diseases in medical applications and Fig. 1 shows various modalities and deep learning frameworks used in ASD. The non-invasive technology of magnetic resonance imaging (MRI) has been broadly used to study the brain regions [1]. As a result, MRI data can be utilised to highlight small differences in brain patterns/network, that can help in the identification of ASD biomarkers. Electrical pluses are used in MRI technology to create a graphical picture of specific brain tissue. Figure 2 shows several cross sections view an MRI scan [2]. Based on the type of scanning employed, MRI scans are classified as structural MRI (sMRI) or functional MRI (fMRI) [3].

B. George Santhigiri College, Vazhithala, India E. Chandra Blessie Department of Artificial Intelligence and Machine Learning, Coimbatore Institute of Technology, Coimbatore, India K. R. Resmi (B) CHRIST(Deemed to be) University, Bengaluru, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_1

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Fig. 1 Different modalities and various methods in deep learning

Fig. 2 MRI scan in various cross-sectional view

The structure and neurology of the brain are examined using structural MRI (sMRI) studies. sMRI images are also used to determine the brain volume, including regional white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) [4] as well as the sub-regions and localised lesions volume. T1-weighted MRI and T2weighted MRI are the two sequences of sMRI, where sequence refers to the number of radio-frequency pulses and gradients that produce a series of pictures with a specific style [5]. The parameters Echo Time (ET) and Repetition Time (TR) used for scanning determine the sequences. The picture contrast and weighting of an MRI image are controlled using the TR and TE parameters [6]. The active brain areas linked with brain function are shown using functional MRI (fMRI) scans. By observing blood flow variance across distinct cognitive regions, fMRI computes synchronised brain activity. MRI scans have been used by many researchers to show that specific brain regions are linked to ASD [7]. Figure 3 shows functional and structural MRI of brain [8]. The Autism Brain Imaging Data Exchange (ABIDE) was established in 2012 to provide the scientific community with an open-source database for studying ASD using brain imaging data, such as MRI [9]. The ABIDE data set contains rsfMRI (resting state functional magnetic resonance imaging) data from 1112 subjects (autism and healthy controls). rs-fMRI data is fMRI data collected in a resting or task-free state. Phenotypical1 and anatomical scans data are also available through ABIDE [9]. Figure 4 shows an example of segmentation of corpus callosum region [10].

A Deep Learning Method for Autism Spectrum Disorder

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Fig. 3 Brain mapping based on sMRI and fMRI techniques

Fig. 4 Example of corpus callosum region detection

On the ABIDE data set, average autism detection accuracy is between 52 and 55% for several literature studies based on machine learning. We used a transfer learning approach with the VGG-16 model to improve recognition accuracy using deep learning. In most machine learning algorithms, training and test data are obtained from the same distribution. Transfer learning, on the other hand, allows for different distributions in training and testing. Building a deep learning network from scratch needs a vast amount of data and huge resources. The use of a transfer learning strategy is motivated by the fact that existing model trained on large image sample can be reused for a new problem.

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2 Transfer Learning Transfer learning is the practice of using a model created for one problem to solve another. Building deep models from the ground is both costly and time-consuming. The same parameter (like weight) can be utilised to build another model for the same or a different application once it has been trained for one application. In general, when creating a new model, a set of previously learnt parameters is preserved, while other parameters are adjusted to fit the new data set or requirement. Transfer learning saves time during training and is effective with small data sets. Several models based on a huge number of image sets are available on CNN. They have been taught how to extract the most distinguishing attributes from a particular image. Popular networks of CNN models suitable for transfer learning include AlexNet, VGG-16, and ResNet-50. The essential steps in transfer learning for classification are summarised below: 1: Load the data set for classification. 2: Split the data for training, testing, and validation. 3: If the data set for deep learning is small use data augmentation if required. 4: Select a deep learning base model like AlexNet, VGG-16, GooleNet, etc. 5: Modification of the architecture is done by adding and/removing layers or neurons. 6: Freeze some selected parameters in the original model and fine tune some parameters with the new data set to increase the accuracy. 7: Save the new model. Among the different steps in the algorithm, building the new model is the most critical one. There are two model building strategies used in transfer learning. Feature Extraction: In CNN, the initial layers in the model are considered as feature extractors. In feature extraction, the weights of all the layers of feature extraction are frozen and layers closer to the output are removed. The high-dimensional features obtained from feature extraction can either be sent to a fully connected model or it can be used as input to traditional algorithms like SVM, Random forest, KNN, etc., for classification. Fine Tuning: In this method, new model is built on the top of already trained base model. Freeze only few layers and fine tune some layers in base network using the new data set. Figure 5 explains the basics of transfer learning. The convolutional layers in Fig. 5 consists of convolution, ReLU, and Pooling blocks. Theses layers perform the feature extraction like traditional machine learning models. A convolutional layer consists of a set of predefined filters that is used to convolve with the original input image. After each convolutional layer, there is an activation layer or nonlinearity layer ReLU, which helps to train the network faster by alleviating the vanishing gradient problem. ReLU layer changes all negative activations to zero. After ReLU, there is down sampling layer called Pooling layer. Pooling layer performs feature selection process

A Deep Learning Method for Autism Spectrum Disorder

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Fig. 5 Transfer learning basic

and perform dimensionality reduction of data. Max pooling, average pooling, and random pooling are different pooling methods available in CNN. The fully connected layer (FC) in CNN performs the final classification decision.

3 Experiments 3.1 Data Set The Autism Brain Imaging Data Exchange (ABIDE) is a publically available structural and functional brain imaging data from laboratories all over the world [9]. It includes two collections: ABIDE I and ABIDE II. ABIDE database contain a total of 1112 subjects with rs-fMRI samples, where 539 are autism people and 573 healthy control participants (HC). Figure 6 shows fMRI sample of ABIDE data set.

3.2 VGG-16 VGG-16 is one of the simple and effective deep CNN architectures which contain twelve convolutional layers and three fully connected (FC) layers [11]. This architecture uses filters with size 3 × 3. It is one of the widely used architectures for feature extraction from images. VGG-16 detailed architecture is given in Table 1.

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Fig. 6 fMRI sample Table 1 Details of VGG-16 architecture Layer

Feature map

Kernel size

Activation

Stride

Input

1

-

Conv 1–1 Conv 1–2

64

Pooling Conv 2–1 Conv 2–2

Output size

3×3

ReLU

1

[224 × 224 × 64]

64

3×3

ReLU

2

[112 × 112 × 64]

128

3×3

relu

1

[112 × 112 × 128]

Pooling

128

3×3

ReLU

2

[56 × 56 × 128]

Conv 3–1 Conv 3–2 Conv 3–3

256

3×3

ReLU

1

[56 × 56 × 256]

Pooling

256

3×3

ReLU

2

[28 × 28 × 256]

Conv 4–1 Conv 4–2 Conv 4–3

512

3×3

ReLU

1

[28 × 28 × 512]

Pooling

512

3×3

ReLU

2

[14 × 14 × 512]

Conv 5–1 Conv 5–2 Conv 5–3

512

3×3

ReLU

1

[14 × 14 × 512]

Max pooling

512

3×3

ReLU

2

[7 × 7 × 512]

FC

–

ReLU

25,088

FC

–

ReLU

4096

FC

–

ReLU

4096

FC

–

Softmax

1000

[224 × 224 × 3]

A Deep Learning Method for Autism Spectrum Disorder

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Table 2 Rank 1 classification accuracy Rank 1 classification accuracy (%) Network

Deep features without data augmentation

Deep features using augmented data

VGG-16

76.25%

79.29%

Table 3 Summary of different ASD prediction based on deep learning studies References

Data sample size

Deep learning architecture

Technique

Prediction accuracy (%)

Li et al. [12]

Healthy Control (HC) = 209 ASD = 55

Multi-channel CNN

sMRI images

76.24

Li et al. [13]

Healthy Control (HC) = 215 ASD = 61

Dilated-Dense U-Net

sMRI images

79.9–92.3

Sidhu et al. [14] Healthy Control (HC) = 124 SPR = 55 ADHD = 19 ASD = 31

Principal Component analysis (PCA)

rfMRI and tfMRI images

> 80

Xiao et al. [15]

Healthy Control (HC) = 81 ASD = 117

SAE

rfMRI images

96.26

Aghdam et al. [16]

ABIDE I & ABIDE II data†

CNN

rfMRI images

72.73

Jung et al. [17]

Healthy Control (HC) = 125 ASD = 86

SVM-RFE

rfMRI images

76.3–84.1

Xu et al. [18]

HC = 22 ASD = 25

LSTM, CNN

fnIRS images

95

Xu et al. [19]

HC = 22 ASD = 25

CGRNN

fnIRS images

90–92.2

Proposed Work

ABIDE I

VGG-16

rfMRI images

79.29

4 Results and Discussion The pre-trained networks are adjusted to fit the ABIDE class size. VGG-16 network was trained for 1000 class classification problem and ABIDE data set is a two-class classification problem. In the models, the output layer is reduced from 1000 to two. Other than the FC layers, the weights are frozen. After that, the network is trained using the pre-processed and resized image samples, with an 80:20 training-test split. A mini-batch size of 20 is chosen. We tested with different numbers of epochs. With 100 epochs, the best performance was found. The Rank1 accuracy of the selected CNN model with deep features is given in Table 2. Data augmentation is done using

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Imgaug tool. Table 3 gives comparison of proposed method using different deep learning methods. It is not possible to compare the result because different methods use different data sets and different architectures.

5 Conclusion Deep learning results using CNN are promising for predicting ASD. One of the difficulties in properly utilising CNN’s learning/data modelling capabilities is the need for a big database to learn an idea, which makes it unsuitable for applications where labelled data is difficult to record. This paper discusses the usage of deep learning-based CNN for autism prediction on ABIDE brain image data set. Transfer learning using VGG-16 architecture is experimented.

References 1. Sharif H, Khan RA (2022) A novel machine learning based framework for detection of autism spectrum disorder (ASD). Appl Artif Intell 36(1):2004655 2. Schnitzlein HN, Reed Murtagh F (1985) J Neurol Neurosurg Psychiatry 3. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186. https://doi.org/10.1038/nrn2575 4. Giedd JN (2004) Structural magnetic resonance imaging of the adolescent brain. Ann N Y Acad Sci 1021(1):077–085. https://doi.org/10.1196/annals.1308.009 5. Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng Y-CN (2009) Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. Am J Neuroradiol 30(1):19–30 6. Rutherford MA, Bydder GM (2002) MRI of the Neonatal Brain. WB Saunders, London 7. Huettel SA, Song AW, McCarthy G (2004) Functional magnetic resonance imaging, vol 1. Sinauer Associates, Sunderland, MA 8. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186 9. Di Martino A, Yan C-G, Qingyang L, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M (2014) The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19(6):659. https://doi.org/10.1038/mp.2013.78 10. Hiess RK, Alter R, Sojoudi S, Ardekani BA, Kuzniecky R, Pardoe HR (2015) Corpus callosum area and brain volume in autism spectrum disorder: quantitative analysis of structural MRI from the abide database. J Autism Development Disord 45(10):3107–3114 11. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition 12. Li G, Liu M, Sun Q, Shen WL (2018) Early diagnosis of autism disease by multi-channel CNNs. Mach Learn Med Imaging 11046:303–309 13. Li G, Chen MH, Li G, Wu D, Lian C, Sun Q (2019) A longitudinal MRI study of amygdala and hippocampal subfields for infants with risk of autism. Graph Learn Med Imaging 2019(11849):164–171 14. Sidhu G (2019) Locally linear embedding and f MRI feature selection in psychiatric classification. IEEE J Transl Eng Health Med 7:2200211

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15. Xiao Z, Wu J, Wang C, Jia N, Yang X (2019) Computer-aided diagnosis of school-aged children with ASD using full frequency bands and enhanced SAE: a multi-institution study. Exp Ther Med 17:4055–4063 16. Aghdam MA, Sharifi A, Pedram MM (2019) Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging 32:899–918 17. Jung M, Tu Y, Park J, Jorgenson K, Lang C, Song W (2019) Surface-based shared and distinct resting functional connectivity in attention deficit hyperactivity disorder and autism spectrum disorder. Br J Psychiatry 214:339–344 18. Xu L, Liu Y, Yu J, Li X, Yu X, Cheng H (2020) Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy. J Neurosci Methods 331:108538 19. Xu L, Geng X, He X, Li J, Yu J (2019) Prediction in autism by deep learning short-time spontaneous hemodynamic fluctuations. Front Neurosci 13:1120

Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning—A Survey K. Swathi and G. Narsimha

1 Introduction As there is rapid development in Internet technology, it has become daily routine in our life, affecting people’s life. With the progressive increase of Internet usage, intrusion detection has become a very challenging task in cyber security. In this context, most of existing approaches of attack detection rely mainly on a finite set of attacks. However, these solutions are vulnerable, i.e., they fail in detecting some attacks when sources of information are ambiguous or imperfect. But few approaches started investigating toward this direction. The networks have millions of nodes, due to the presence of millions of nodes cyber-attack takes place in a huge attack area. If a device gets affected by cyber-attack, it acts as a chain effect and affects other devices connected to it [1]. The intelligent anomaly detection is an important way in detecting cyber-attacks especially in security systems and reliability of systems to develop security protection and to control system vulnerabilities [2]. If the cyber-attacks are undetectable, the attackers frequently change the data and there is no trace in change of data at this time anomaly detection system is used. The machine learning algorithm uses artificial intelligence for analyzing the real time application in industrial machineries and acts as a defense for cyber-attacks in critical situations [3, 4]. Moreover, the machine learning algorithm prevents the impact of cyber-attacks by anomaly detection, malicious activities in networks and helps to improve the accuracy rate and high efficiency. This study is about the machine learning algorithms, classifiers used K. Swathi (B) Department of Computer Science and Engineering, CVR College of Engineering, Jawaharlal Nehru Technological University, Hyderabad, India e-mail: [email protected] G. Narsimha Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_2

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in detection of anomaly in networks and helps in finding the malicious activity of cyber-attacks in networking.

2 Taxonomy on Anomaly Detection of Cyberattacks in Networking See Fig. 1.

2.1 Machine Learning The machine learning method helps in extracting the valid and some significant patterns to make recognition in the existing dataset to help and make intelligent decisions. A nontrivial mechanism is a type of learning mechanism that is used in

Fig. 1 Taxonomy on anomaly detection of cyber-attacks in networking

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machine learning technique, which is based on some specific procedures and helps to detect the anomalies of cyber-attacks in networking.

2.2 Supervised Learning The supervised learning utilized the datasets which are labeled to train algorithms and classify accordingly. Supervised learning techniques are applied on datasets of the network and helps to construct the model and a set of attributes are helpful in labeling the data. There are many supervised techniques which detects the intrusions and anomaly in networks. The common algorithms like Support Vector Machine, Naïve Bayes, Neural Network, Nearest Neighbor and Decision Tree were used in supervised machine learning algorithm. Support Vector Machine The Support Vector Machines are associated learning algorithms that are used in classification and analysis of regression. The SVM will accumulate with the data sparsely and generate predictive regulations for the detection of anomaly which cannot classified by using the linear decision functions. It can solve large number of problems in linear and nonlinear regression and helps solving cyber attacking problems [1]. Naive Bayes The Naïve Bayes (NB) model plays an important role in detection of anomaly and intrusion of networks. It is utilized in detecting the cyber-attacks in training data and protects the data related with IoT, and it also helps in reduction of redundancy and false rate. The NB algorithm collects the information from the sensors and detects the anomaly in network nodes. NB helps to prevent the cyber-attacks in less time with low cost of execution [5]. Neural Network The Neural Network in other words known as Artificial Neural Network (ANN). The ANNs consist of more number of interconnected neurons and they were interconnected with each other in a complex way. The ANNs were trained for the detection of anomaly and other activities occur abnormally in networks [6]. Nearest Neighbor The Nearest Neighbor is considered as most widely used supervised machine learning technique for detecting the anomaly. The Nearest Neighbor algorithm helps in anomaly detection in both computer networks and other networks like WSN, IoT, etc. The Nearest Neighbor technique helps to obtain the low error rate and a considerable detection rate.

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Decision Tree The Decision Tree is known as one of the common classifying methods which can be used in all techniques. The Decision Tree helps in reducing the false negative rate and as well as false positive rate. The combination of Decision Tree Algorithm and Intrusion Detection Algorithm helps in classifying the traffics in network then detects the normal and abnormal activities of the networks [7].

2.3 Unsupervised Learning The unsupervised machine learning technique doesn’t require labeled data in training dataset. The unsupervised machine learning algorithm helps to identify the hidden pattern of the data without an involvement of training data and helps in detection of anomalous activities in network traffic. The clustering in the data group helps in analyzing the behavior and the data’s structure without any information about the data. The anomaly detection in unsupervised learning method helps in dividing the data points, whereas the period of observation takes in isolation from other data. K-means Clustering Algorithm K-means clustering algorithm is simply known as straightforward algorithm presented in unsupervised learning which combines the unprocessed dataset into various clusters and most probably used to detect corresponding attack groups. In k-means algorithm, k objects are selected as center of initial clusters, after selection process, the distance between the objects will be measured and the objects will be assigned to the nearby center. Hidden Markov Model (HMM) HMM is a branch of an unsupervised algorithm which manage the huge size of data as well as detect the secular relation of abnormal events. It is also a mathematical model used in engineering as a state-based classification model. First of all, the HMM used for speech recognition and after the deep analysis of this model with better efficient results that have been used in area of mobile network security and in the detection of anomaly activity. Principal Component Analysis (PCA) PCA is an analytical method used to reduce the extension of the dataset containing several variables based on each other preserving dataset. The recorded data is used in various network detection systems of PCA. PCA is used to construct a network intrusion detection system with less system complexity as well as to achieve high accuracy in classification results. Labeled data does not require any other requirement when a PCA is applied on training set.

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Gaussian Mixture Model (GMM) GMM is a deterministic or a probabilistic model which is used to allocate the data into various categories related to probability distribution. The GMM model is used for clustering to group the number of data points into clusters. GMM models are also used to evaluate the probability which each clusters will accept the new data point. Additionally, the GMM model used in various areas including marketing, finance, etc. [8]. Hierarchical Clustering Algorithm Hierarchical algorithm is also known as hierarchical cluster analysis combines identical objects into groups known as clusters. The endpoint consists a collection of clusters, each cluster is well-defined from each other clusters as well as each clusters objects are highly identical to each other. Hierarchical clustering algorithm treats each observation as individual cluster and then identifies the clusters which are close together after identification it starts to merge those nearest clusters until all the clusters merged together.

2.4 Semi-supervised Learning The semi-supervised learning is a combination which consist of both supervised and unsupervised machine learning methods. In other word, it is also known as the algorithm which consist of both labeled and unlabeled data. Majorly, the semisupervised machine learning method consists of unlabeled data because if there is enough number of labeled data it can be used in improving the size of training data. The semi-supervised learning method contains both the classification and clustering algorithms which are related to supervised and unsupervised learning, respectively [2].

3 Literature Sarker [9] has introduced a Cyber Learning technique for anomalous detection and to control the different types of cyber-attacks. In this work, the popular NSL-KDD and UNSW-NB15 security datasets were used to construct the security model. The various ML-based security methods outperforms individually for identifying the cyber-attacks and anomalous activities occurred or appeared in network. While detecting the anomalies for the unknown attacks, the both multiclass classification model and binary classification model were severely impacted.

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Bertoli et al. [10] described the AB-TRAP framework that updates traffic in network and enables deployment of the solution with operational concerns. ABTRAP is a five-step framework tells about attack generation to performance evaluation and it is exercised to LAN and globally to detect TCP port scanning attacks. The LAN got f1-score of 0.96, and an area under the ROC curve of 0.99 using a decision tree with minimum memory. For the Internet case with eight machine learning algorithms with an average f1-score of 0.95, an average area under the ROC curve of 0:98. This framework addresses about models realization and deployment by utilizing the most up-to-date network traffic, attacks. Abdollah et al. [11] has introduced Lower and Upper Bound Estimation (LUBE) used to delivered an effective attainable interval over the smart meter readings and Modified Symbiotic Organisms Search (MSOS) used to accommodate the Neural Network (NN) parameters. The input data contains the MSOS data, micro-grid data and the anomalous activity detection system data. The proposed model resulted a high accuracy and greatly used to detect the anomalous activity occurred in network as well as to secured the micro-grids from data probity attacks. The performance of the developed method or model used to detect the false information injections in the smart meter readings. However, the proposed model was not good enough to identify or detect various attacks severity due to the limits in micro-grids. Siddiqi and Pak [12] have introduced a machine learning-based intrusion detection system (ML-IDS) for identifying the best normalization technique. There were number stages included in detecting the normalization technique in an agile approach. After the completion of data cleaning feature, selection procedure was applied to the dataset. The ML-based IDS played an essential part in delivering emphasized security measures. The ML-IDS agile approach confirmed that it could be able to identify the normalization method with high accuracy to enhance the performance of the ML-IDS. However, the ML-IDS method was not proficient to detect normalization in classification. Shafiq et al. [13] have introduced a Bijective soft set method to identify the cyberattacks in Internet of Things, and this technique was used for selection and decision making. The Bijective soft set technique was used in solving the problems in decision making and problem in selection of Wi-Fi frequency. In this article, the union and intersection operation also used to obtain better performance. The proposed model with the Naïve Bayes ML algorithm was efficient as well as more effective for the identification of anomalous activity and to detect the intrusion occurred in IOT networks. However, the complexity of the model was comparably high and here the two main metrics only considered. Singh and Govindarasu [14] have introduced a Cyber-Physical Anomaly Detection System (CPADS) which exploits the attributes of network packets to identify or detect information integrity and transfer failure attacks in Centralized RAS (CRAS). A CPADS was an individual anomalous activity detector allocated to the particular installation zone which supervised a few crucial installations and observed possible anomalous intrusions in that substation zone. The classification models of CPADS show greater efficiency than the other performed classification models because it

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provides constant performance during Phasor Measurement Unit (PMU) measurements. But the proposed system was not relevant for the detection of communication failure attacks and co-ordination attacks.

4 Comparative Analysis The various methodologies used for anomaly detection in networking based on machine learning algorithms. The comparative analysis of anomalous activity detection in networking presented in Table 1.

5 Problem Statement The problems found from the literature survey along with the general issues faced during the design of anomaly detection are stated as follows: 1. Accuracy and other performance metrics of the proposed models still needs to be improve mainly when various types of intrusions or cyber-attacks are obtained. 2. There is also an essential need to construct a detection system or model which acts in real time without the needs of offline training and also need to develop an adjustable scheme to modify the changes when it is needed. 3. More number of developed models ignores many number of parameters in the input stage of preprocessing which mainly affect the efficiency of anomaly detection. 4. The model only detects the known attack patterns but not the unknown patterns and the model have continuous input data that act as a challenge to store the resource, increase computational cost. 5. Due to the dynamic changes in the network data over time, static intrusion detection systems cannot adapt well to the behavioral characteristics of input network data resulting in reduced detection accuracy. 6. Machine learning algorithms are no longer effective in detecting all new and complex malware variants. So there is a need for robust detection framework which is possible with artificial neural networks.

6 Conclusion This study reviews the ML techniques used for the detection of anomalous activity in network. The ML helps in providing solutions in detection of cyber-attacks, system malwares and security issues based on control in industrial machineries and power system securities. The ML systems greatly removes the functional threats and protects break-time of the process. Additionally, the study reviews about the preprocessing

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Table 1 Anomalous detection of cyber-attacks in networking–comparative analysis References

Methodology

Advantages

Limitations

Performance metrics

Dong et al. [15]

Semi-supervised Double Deep Q-Network (SSDDQN)-based optimization method for network abnormal traffic detection, mainly based on Double Deep Q-Network

It has achieved good results in abnormal traffic detection and unknown attacks are detected well

The training time and prediction time are low. Optimization effect is limited and it has almost no detection ability to detect the lowest number of U2R abnormal attack traffic

Good accuracy and F1-Score metrics

Karimipour The Kalman filter and Leung [4] based on anomaly detector in ensemble data using relaxation-based solution

The Kalman filter based on anomaly was fast in identifying the cyber-attack and minimize computational burden

The Kalman Accuracy filter-based anomaly detector cannot differentiate the cause for the attack

Schneider and The stacked Bottinger [16] de-noising autoencoder-based unsupervised machine learning method for detection of anomaly in different types of fieldbus protocols

The stacked de-noising autoencoder-based unsupervised machine learning method provides overall high accuracy rate and performance while processing the anomaly detection in the industrial network

The stacked de-noising autoencoder-based unsupervised machine learning method cannot able to assign a clear root by utilizing the labeling method

Precision, recall, F-measure

Poornima and An online locally Paramasivan weighted [17] projection regression (OLWPR) for detection of anomaly in Wireless Sensor Networks

The OLWPR method helped in detecting the anomalous data by utilizing the dynamic threshold value

The OLWPR method does not detects any newly introduced cyber-attacks because this method detect only the behavior of attacks that must be defined priorly

Accuracy, F1-score, accuracy error rate, sensitivity, specificity and recall

(continued)

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Table 1 (continued) References

Methodology

Advantages

Limitations

Performance metrics

Ullah and Mahmoud [7]

A two level anomalous activity detection model in IoT networks such as L-1 and L-2

L-1 classifies the network flow and verify whether it was normal flow or abnormal flow L-2 categorizes the detected anomalous activity

Before training the data in IoT Botnet dataset, the non-numeric features must be converted into numeric features before normalization because the format was not appropriate in machine learning algorithm

Accuracy, F score and Precision, Recall

Ullah and Mahmoud [18]

Recursive Feature Elimination (RFE) as a feature selection technique in Botnet dataset for detection of anomalous activities in IoT networks

The feature selection method in implemented technique helps in reduction of overfitting, prediction capability was improved and the training time got reduced

The established technique needs improvement in preprocessing because the format of the features doesn’t suits with the machine learning algorithm

Accuracy, Precision, Recall and F-score

and training related anomalous detection methods which are applicable in various or different application domains. To detect abnormal traffic in network, we need a strong robust deep learning abnormal traffic detection framework. In the future, the new data-driven attacks detection system will be designed with more security for delivering the high automated security services for cyber security community. The anomaly detection process helps in providing the report of malicious activities and gives better results.

References 1. Liu Z, Thapa N, Shaver A, Roy K, Yuan X, Khorsandroo S (2020) Anomaly detection on IoT network intrusion using machine learning. In: 2020 International conference on artificial intelligence, big data, computing and data communication systems (icABCD), pp 1–5. IEEE 2. Zhou X, Liang W, Shimizu S, Ma J, Jin Q (2020) Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems. IEEE Trans Industr Inf 17(8):5790–5798 3. Demertzis K, Iliadis L, Bougoudis I (2020) Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network. Neural Comput Appl 32:4303– 4314

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4. Karimipour H, Leung H (2020) Relaxation-based anomaly detection in cyber-physical systems using ensemble Kalman filter. IET Cyber-Phys Syst Theory Appl 5(1):49–58 5. Schneider P, Böttinger K (2018) High-performance unsupervised anomaly detection for cyberphysical system networks. In: Proceedings on cyber-physical systems security and privacy, pp 1–12 6. Siddiqi MA, Pak W (2021) An agile approach to identify single and hybrid normalization for enhancing machine learning-based network intrusion detection. IEEE Access 9:137494– 137513 7. Ullah I, Mahmoud QH (2020) A two-level flow-based anomalous activity detection system for IoT networks. Electronics 9(3):530 8. Ullah I, Mahmoud QH (2020) A technique for generating a botnet dataset for anomalous activity detection in IoT networks. In: 2020 IEEE transactions on Systems, Man, and Cybernetics (SMC), pp 134–140. IEEE 9. Sarker IH (2021) Cyberlearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet Things 14:100393 10. Bertoli GDC et al (2021) An end-to-end framework for machine learning-based network intrusion detection system. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3101188 11. Kavousi-Fard A, Su W, Jin T (2020) A machine-learning-based cyber-attack detection model for wireless sensor networks in microgrids. IEEE Trans Industr Inf 17(1):650–658 12. Siddiqi MA, Pak W (2021) An agile approach to identify single and hybrid normalization for enhancing machine learning-based network intrusion detection. IEEE Access 9(2021):137494– 137513 13. Shafiq M, Tian Z, Sun Y, Du X, Guizani M (2020) Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city. Futur Gener Comput Syst 107:433–442 14. Singh VK, Govindarasu M (2021) A cyber-physical anomaly detection for wide-area protection using machine learning. IEEE Trans Smart Grid 12(4):3514–3526 15. Dong S, Xia Y, Peng T (2021) Network abnormal traffic detection model based on semisupervised deep reinforcement learning. IEEE Trans Netw Serv Manage 18(4):4197–4212 16. Schneider P, Böttinger B (2018) High-performance unsupervised anomaly detection for cyberphysical system networks. In: Proceedings on cyber-physical systems security and privacy, pp 1–12 17. Poornima IGA, Paramasivan B (2020) Anomaly detection in wireless sensor network using machine learning algorithm. Comput Commun 151:331–337 18. Ullah I, Mahmoud QH (2020) A technique for generating a botnet dataset for anomalous activity detection in IoT networks. In: 2020 IEEE transactions on Systems, Man, and Cybernetics (SMC). IEEE, pp 134–140 19. Panthi M (2020) Anomaly detection in smart grids using machine learning techniques. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE

AI-Enabled Analysis of Climate Change on Agriculture and Yield Prediction for Coastal Area D. Manikavelan, Swapna Thouti, M. Ashok, N. Chandiraprakash, and N. Rajeswaran

1 Introduction Long-term changes in temperature and weather patterns are referred to as climate change [1–5]. Climate change is endangering the future of food supply because of the increased frequency of extreme weather events like droughts [6], as well as the link between rising global temperatures [7, 8] and decreasing food crop yields. This creates a major problem given that global populations are expected to grow to greater than 10bn people by 2050 a lot must be done to ensure agriculture can meet this anticipated larger demand. Human-related climate change will affect the agricultural sector more directly than many others because of its direct dependence on weather [9]. The nature and extent of these impacts are determined by both the evolution of the climate system and the relationship between crop yields and weather.

D. Manikavelan Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India e-mail: [email protected] S. Thouti Department of ECE, CVR College of Engineering, Hyderabad, India M. Ashok Malla Reddy Institute of Engineering and Technology (Autonomous), Secunderabad, Telangana, India N. Chandiraprakash (B) Department of CSE (AIML), Malla Reddy Institute of Engineering and Technology (Autonomous), Secunderabad, Telangana, India e-mail: [email protected] N. Rajeswaran Department of EEE, Malla Reddy Institute of Engineering and Technology (Autonomous), Secunderabad, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_3

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Accurate weather-to-crop-yield models are essential not just for predicting agricultural impacts, but also for projecting climate change’s impact on linked economic and environmental outcomes [10], and therefore for policy mitigation and adaptation. A substantial portion of the work of modeling yield for climate change (impact assessment relies on deterministic, biophysical crop models). These models are based on detailed representations of plant physiology and remain important, particularly for assessing response mechanisms and adaptation options. However, they are generally outperformed by statistical models in prediction over larger spatial scales. In parallel, machine learning (ML) techniques have advanced considerably over the past several decades [2]. ML differs philosophically from most of traditional statistics, owing to the fact that its goals are different: it is primarily concerned with the prediction of outcomes rather than inference into the nature of the mechanical processes that produce those results. Early and accurate crop yield estimation is critical in quantitative and financial evaluation at the field level for defining agricultural commodity strategic plans for import–export policies and increasing farmer incomes. Crop yield predictions use machine learning algorithms to estimate higher crop yields, which is one of the most difficult issues in the agricultural sector. Crop yield processes [2, 3] and methods are time-dependent and fundamentally nonlinear. These strategies are also complex due to the inclusion of a wide range of interconnected factors that are described and influenced by non-arbitration and external factors. Previously, farmers relied on their own experiences and accurate historical data to predict crop yields and make important cultivation decisions based on the prediction [2]. Even though, advancements in innovation, such as crop model simulation and machine learning, have appeared in recent years to predict yield more precisely, as well as the ability to analyze large amounts of data using high performance computing. Due to the fact that it provides a significant amount of the world’s food, agriculture is one of the most crucial fields of study. Yield forecasts help cultivators and farmers make financial and management decisions. Agricultural supervision, particularly crop yield monitoring, is critical for determining a region’s food security. Crop yield forecasting [2–4], on the other hand, is extremely difficult due to numerous complex factors. Crop yield is primarily determined by climatic conditions, soil quality, landscapes, pest infestations, water quality and availability, genotype, harvest activity planning, and so on. Crop yield processes and strategies change over time and are profoundly nonlinear and intricate as a result of the integration of a wide range of correlated factors characterized and influenced by non-arbitrate runs and external factors. Typically, a significant portion of the agricultural framework cannot be delineated in a basic stepwise calculation, especially when dealing with complex, incomplete, ambiguous, and strident datasets. Many studies currently show that machine learning algorithms have a higher potential than traditional statistics [2]. Machine learning is a branch of artificial intelligence, in which computers can be instructed without explicit programming.

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2 Literature Survey The LSTM model produced improved accuracy by being exposed to additional historical trends that were not present in the IoT observations by using previous air temperature data from weather underground [2]. In this, it contributed to their research by adopting several approaches to forecast agricultural yield [4]. Logistic Regression, Naive Bayes, and Random Forest are the classifier models were used and Random Forest produced the higher accuracy prediction. Here, the project used stacked long short-term memory recurrent neural network model to predict rice yield which produced superior performance and effectiveness on increasing depth [1]. They made comparison between stacked LSTM with other approaches such as ARIMA, GRU, and ANN This system considered five climatic factors to train the model using the Random Forest approach. The model is trained and developed using 20 decision trees, which are used to create the random forest method, which improves the model’s accuracy. The model’s accuracy was improved using a tenfold cross-validation procedure. The model produced a decent accuracy of 87%. In order to compare the LSTM’s accuracy to that of the well-known Weather Research and Forecasting model, their study set out to build and evaluate a short-term weather forecasting model using the LSTM (WRF) [5]. With the help of surface meteorological [9] characteristics over a given time period, this suggested model’s stacks of LSTM layers predict the weather. By experimenting with different numbers of LSTM layers, optimizers, and learning rates, the model is made to be effective at making short-term weather forecasts. In comparison to the well-known and sophisticated WRF model, their experiment reveals that the suggested lightweight model delivers better results, with accurate short-term weather forecasting. India is mostly a farming country. Agriculture is the most important emerging field in the world today, and it is our country’s main occupation and backbone. Recent advancements in information technology for agriculture have sparked an interest in crop yield prediction research [8]. Crop yield prediction is a technique for forecasting crop yields based on a range of variables, including weather, fertilizers, pesticides, and other environmental conditions, such as rainfall and temperature. In the field of agriculture, data mining [11] techniques are increasingly prevalent. Data mining techniques are applied and evaluated in agriculture to predict future crop yield. This work gives a brief investigation of crop yield prediction using the K-Nearest Neighbor (KNN) Algorithm for the chosen region of Mangalore, Kasaragod, Hassan, and the surrounding areas.

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3 Implementation 3.1 LSTM Model Long short-term memory abbreviated as LSTM. LSTM cells are used in recurrent neural networks that learn to predict the future using sequences of various lengths. Recurrent neural networks are not time series-specific and, unlike ARIMA and Prophet, can handle any kind of sequential data. Learning to store information over long time intervals with recurrent back propagation takes a long time, owing to insufficient, decaying error backflow. We briefly review Hoch Reiter’s (1991) analysis of this problem, and then address it by introducing long short-term memory, a novel, efficient, gradient-based method (LSTM). By enforcing constant error flow through constant error carousels within special units and truncating the gradient where it does not harm, LSTM can learn to bridge minimal time lags in excess of 1000 discretetime steps. Multiplicative gate units figure out how to open and close access to a constant error flow. In both space and time, the LSTM is local; its computational complexity per time step and weight is O. (1) Local, distributed, real-valued, and noisy pattern representations are used in our simulated data investigations. LSTM outperforms serial-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking in terms of number of successful runs and time to learn. LSTM also addresses difficult, artificial longtime-lag problems that earlier recurrent network algorithms have never been able to tackle. Sequential data can come in a variety of formats. This issue was greatly alleviated by the “long short-term memory” model. The LSTM includes “gates,” which are sigmoid-activated neurons that multiply the output of other neurons. The LSTM can ignore certain inputs adaptively using gates.

3.2 Random Forest Model When dealing with datasets that contain a large number of variables, feature subset selection becomes quite crucial and prevalent. It eliminates inconsequential factors, resulting in more efficient and enhanced prediction performance on class variables as well as a more cost-effective and trustworthy data comprehension. Random forest [4] has proven to be a very efficient and reliable approach for handling feature selection problems with a large number of variables [2–4]. It is also particularly good at managing with challenges like missing data imputation, categorization, and regression. It also does a great job with outliers and noisy data. In this research, we used the random forest approach to undertake a comparative analysis on feature subset selection, classification, and regression. It also does a great job with outliers and noisy data. In this research, we used the random forest algorithm to do a comparative examination of the random forest algorithm from many angles, including feature subset selection, classification, and regression. Random forest is an ensemble technique that

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Table 1 Test cases for yield prediction No. Test procedure

Pre-condition

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Click on inputs

Enter only district, Selected is played Input Passed region, season, and crop

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Click on Submit button Train the dataset

Shows the result

Passed

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Click on exit

Exits from application

Passed

––

Expected result

Passed/failed

Fig. 1 Predicted and actual temperature v/s month

uses several decision trees with a technique called Bootstrap and Aggregation, sometimes known as bagging, to solve both regression and classification tasks. Instead than depending on individual decision trees, the main idea is to aggregate numerous decision trees to determine the final outcome. As a fundamental learning model, random forest uses several decision trees. R owl and feature sampling are doing neat random from the dataset resulting in sample datasets for each model. Bootstrap is the name of this section (Table 1).

3.3 Predicted Temperature and Actual Temperature Figure 1 shows the predicted temperature and actual temperature. In this step, user can predict the temperature by using actual temperature. It shows predicted and actual temperature in different lines and colors.

3.4 Shows the Baseline Predicted Example Figure 2 shows the Baseline prediction example. In this figure, some symbols are present to predict the weather, like blue-colored line indicates the history of the

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Fig. 2 Baseline prediction for mean temperature

weather and wrong symbol indicates the true future and green-colored circle indicates the prediction of model.

3.5 Shows the Prediction Weather Figure 3 shows the predicted weather [8] like temperature, rainfall, and growth. After selecting the values, user clicks the submit button, after that if the selected crop is yes, it means the crop can grow in that region and user will get this page.

Fig. 3 Prediction weather

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3.6 Shows the Alternate Crop After selecting the values from the input page, the user clicks the submit button, after that if this selected crop is 0, it means crop cannot grow in that region and user will get the page that will show the alternate crop.

4 Result Analysis For study, we have taken the Bajpe region of the Dakshina Kannada district. We took weather data from 1983 to 2017 which is used for training and we predict the monthly mean temperature of 2018 we look into the original weather data of 2018 to check its accuracy. The model produced an accuracy of 94.8386% and the RMSE score is 1.008. The only thing that truly discloses the status of affairs is a true positive trial result. A true negative trial result is one that only obscures the absence of the scenario. Only a false positive trial result can show a circumstance where the state is absent. A false negative trial result is the only one that fails to recognize the reality of the circumstance. Assume that TP stands for true positives, TN for true negatives, FP for false positives, and FN for false negatives. Sensitivity keeps an eye on a trial’s ability to alert the scenario when the state is present. Sensitivity is therefore defined as TP/ (TP + FN). The ability of a trial to precisely reject the circumstance (rather than identify the condition) when the state is absent is measured by specificity. As a result, Specificity = TN/ (TN + FP). The percentage of positives that fit the current circumstance is known as the predictive value positive. Predictive value positive hence equals TP/(TP + FP).

5 Conclusion and Future Scope This model helps farmers and inexperienced user to predict weather patterns such as temperature and rainfall, which could affect crop yields. It helps the end-user to obtained crop that could be cultivated, also get alternate crop. This model would also help those with agricultural experiences, such as researchers, agriculturists, and others, figure out what’s causing low or high yield predictions based on whether forecasts. Farmers will be able to forecast weather patterns such as temperature and rainfall, which may have an impact on agricultural output. It also assists the end-user who is unfamiliar with the kind of crops that might or might not grow in coastal Karnataka, as well as agricultural production. Researchers, agriculturists, and others with agricultural knowledge might use this strategy to figure out what’s driving low or high production projections based on weather forecasts.

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References 1. Yu M, Xu FC, Hu W, Sun J, Cervone G (2021) Using long short-term memory (LSTM) and internet of things (IoT) for localized surface temperature forecasting in an urban environment. IEEE J 9:137406–137418 2. Nigam A, Garg S, Agrawal A, Agrawal P (2021) Crop yield prediction using machine learning algorithms. Int J Eng Res Technol (IJERT) 9(13):23–26 3. Meng X, Liu M, Wu Q (2020) Prediction of rice yield via stacked LSTM. Intl J Agric Environ Inform Syst 11(1):86–95 4. Moraye K, Pavate A, Nikam S, Thakkar S (2021) Crop yield prediction using random forest algorithm for major cities in Maharashtra State. Intl J Innov Res Comput Sci Technol (IJIRCST) 9(2):2347–5552 5. Hewage P, Behera A, Trovati M, Pereira E (2019) Long-short term memory for an effective short-term weather forecasting model using surface weather data. Springer, Cham 6. Barichivich J, Osborn TJ, Harris I, van der Schrier G, Jones PD (2019) Drought [in “State of the Climate in 2018”]. Bull Amer Meteor Soc 100(9):S39–S40. https://doi.org/10.1175/201 9BAMSStateoftheClimate.1 7. Dunn RJH, Stanitski DM, Gobron N, Willett KM (2020) Global climate. Bull Amer Meteorol Soc 101(8):127–129 8. Adler R et al (2018) The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9:138. https://doi. org/10.3390/atmos9040138 9. Dunn RJH, Mears CA, Berrisford P, McVicar TR, Nicolas JP (2019) Surface winds [in “State of the Climate in 2018”]. Bull Amer Meteor Soc 100(9):S43–S45. https://doi.org/10.1175/201 9BAMSStateoftheClimate.1 10. Arguez A, Hurley S, Inamdar A, Mahoney L, Sanchez-Lugo A, Yang L (2020) Should we expect each year in the next decade (2019–2028) to be ranked among the top 10 warmest years globally? Bull Amer Meteor Soc 101:E655–E663. https://doi.org/10.1175/BAMS-D-190215.1 11. Fathima, Sowmya K, Barker S, Kulkarni S (2015) Analysis of crop yield prediction using data mining technique 12. Moraye K, Pavate A, Nikam S, Thakkar S (2021) Crop yield prediction using random forest algorithm for major cities in Maharashtra state 13. Gergis J, D’Arrigo RD (2019) Placing the 2014–2016 ’protracted’ El Niño episode into a long-term context. Holocene 30:90–105. https://doi.org/10.1177/0959683619875788 14. Arosio C, Rozanov A, Malinina E, Weber M, Burrows JP (2019) Merging of ozone profiles from SCIAMACHY, OMPS and SAGE II observations to study stratospheric ozone changes. Atmos Meas Tech 12:2423–2444. https://doi.org/10.5194/amt-12-2423-2019

Deep Learning Methods for Predicting Severity for Diabetic Retinopathy on Retinal Fundus Images Minakshee Chandankhede and Amol Zade

1 Introduction Diabetes problems can lead to diabetic retinopathy, an eye condition that can cause blindness. Up to 80% of persons with diabetes who have had it for 10 years or more will develop DR, an advanced stage of the disease. Research suggests that if effective and watchful treatment and regular eye monitoring were provided, at least 90% of these new cases might be decreased, according to the observation drawn from the statistics of the literature evaluation. The longer a person has had diabetes, the greater are their risks of getting diabetic retinopathy. In the United States, DR causes 13% of all new cases of blindness each year. According to research, diabetic retinopathy is the one of the major causes of blindness in adults between the 20 and 65 of ages. Ophthalmologists use human retinal fundus images extensively in the identification and diagnosis of numerous eye disorders. Some conditions, such glaucoma, macular degeneration, and diabetic retinopathy, can result in blindness if they are not identified and treated properly, making them exceedingly risky. As a result, retinal pictures must be detected, and among these, the recognition of blood vessels is crucial. Blood vessel modifications, such as length, breadth, and branching pattern, can not only reveal information about pathological changes but also aid in grading the severity of diseases. The use of digital retinal image analysis techniques for the detection and prediction of various diseases in eye fundus images offers significant potential advantages, allowing for the quick and inexpensive inspection of a large number of fundus images (Fig. 1).

M. Chandankhede (B) · A. Zade G H Raisoni University Amravati, Amravati, India e-mail: [email protected] A. Zade e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_4

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Fig. 1 a Healthy retinal image. b Diabetic retinal image

2 DR Features Due to diabetes retinal blood vessels gets affected, some abnormalities be the indicator or key features responsible for the detection, diagnosis of the diabetic retinopathy. Early diagnosis of diabetic retinopathy is essential; we can use these features as an indicator for the diagnosis. Hence, identification of these features is very important point while taking research for the DR. Here, we discussed some crucial features in this section. A. Microaneurysms A tiny spot of a blood that bulges from an artery or vein at the posterior part of the eye is known as a retinal microaneurysm. The retinal tissue nearby these protrusions could become blood-soaked if they break free. A retinal microaneurysm can be

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Fig. 2 Microaneurysms

caused by any vascular illness or excessive blood pressure, but diabetes mellitus is the most common culprit (Fig. 2). B. Haemorrhages Haemorrhage in a retina is an eye condition which contains the light-sensitive tissue found on the rear wall of the eye, bleeds. Rods and cones are the photoreceptor cells that present in the retina that convert light energy into nerve impulses that the brain can interpret to create visual images. Infant and young child abuse is significantly linked to retinal haemorrhage, which frequently renders such infants permanently blind. Retinal haemorrhage in elder children and grown-ups can be brought on by a number of illnesses, including diabetes and hypertension (Fig. 3). C. Exudates In diabetic retinopathy, hard exudates of the retina are frequently observed. The damaged blood retinal barrier allows lipid and proteinaceous substances, including albumin and fibrinogen, to flow into the hard exudates. They are typically deposited in the retina’s outer plexiform layer (Fig. 4). D. Cotton wool spots Retinal cotton wool like spots are hazy (fluffy) greyish white areas of discolouration in the layer of nerve fibres. They arise from the interruption of axoplasmic flow caused by local ischaemia. One or two cotton wool patches are not dangerous and are quite common. Multiple cotton wool spots, or more than six in a single eye, however, are thought to be a sign of generalised retinal disorder and a pre-proliferative condition (Fig. 5).

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Fig. 3 Haemorrhages

Fig. 4 Exudates

E. Optic disc In a typical eye fundus image, OD, i.e. optic disc is a homogenous, brightest and a circular structure and has a yellowish appearance. The OD centre and its diameter provide details about the macula region and the blood vessel origin location. Finding

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Fig. 5 Cotton wool spots

any anomaly in the OD’s area, size, shape or structure should raise the possibility of early changes leading to vision loss (Fig. 6). F. Neovascularization Fine loops or the networks of blood vessels that extend into the vitreous cavity and lie on the surface of the retina are the clinical sign of neovascularization. On a slit

Fig. 6 Optic disc in DR

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Fig. 7 Neovascularization in DR

lamp examination, they are typically simple to spot, but they can be missed in the early stages. Not only must the vessels be visible to detect NV, but they also must be distinguished from intraretinal microvascular anomalies (IRMAs) (Fig. 7).

3 Literature Review Multiple sources have been used to conduct the thorough literature review. The comprehensive review of literature is presented below. Convolution neural networks are specifically used by Aswin et al. [1] to diagnose diabetic retinopathy. Automatic feature extraction and effective computation are the distinguishing characteristics of the CNN and any Deep Learning methodology. These benefits of CNN served as inspiration for this paper’s approach. Three layers are there, i.e. convolution layer, a pooling layer and an activation layer can be stacked in many ways to make up a convolution neural network model. The network that convolved with different filters was fed with the input retinal image data. These filters were comparable to conventional image processing system filters, with the exception that they were automatically learned rather than explicitly defined. Here, the model was developed and validated using 413 images from the training set. On the 103 image test set, the CNN model was put to the test. The fundamental objective of the problem architecture, which was broken down into numerous pieces, is the

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results of Convolution Neural Networks for the prediction and diagnosis of a Diabetic Retinopathy. In the Hadoop framework, Hatua et al. [2] developed a Diabetic Retinopathy diagnosis technique that is quick and accurate and can recognise the earliest indications of diabetes from the retinal images of the eye. Here, retinal images in this study are classified into five classes: no DR, mild DR, moderate DR, severe DR, and proliferative DR. Here, they classify the images of diabetic retinopathy using three distinct phases: firstly feature extraction then reduction of feature and image classification. The Histogram Oriented Gradients (HOG) are employed in this early stage of the algorithm as a feature signifier to represent an each retinal image for diabetic retinopathy. Here, dimensional reduction of HOG characteristics is achieved by the application of Principal Component Analysis (PCA). The retinal images are classified into several classifications in this instance using the K-Nearest Neighbours (KNN) classifier, the algorithm’s last stage, in a distributed setting. In this study, studies were conducted on a sizable high-resolution retinal image obtained under various imaging circumstances. For each implementation, retinal images of the left eye and right eye are taken here. MapReduce and the Mahout programming framework are employed with the Hadoop platform to manage very large datasets. According to Chandrasekar et al. [3], the authors evaluated OST using infrared thermal imaging for a set of 150 volunteers in various age groups. In this study, there were a 80 healthy participants (40 men and 40 women), 50 NPDR (25 men and 25 women), and 20 PDR (10 men and 10 women). Clinical testing was used to guarantee the health of the controls. To rule out the potential of cardiovascular illnesses, glaucoma, or other visual pathologies, the three of them underwent examinations. They were also checked to see if they were taking any medicine. Through fundus imaging, NPDR and PDR patients were evaluated and proven in this case with the aid of an ophthalmologist. Clinical testing revealed that the diabetic retinopathy participants in this study had no other ocular pathologies or cardiovascular disorders. Patients with diabetic retinopathy were neither treated or diagnosed outside of routine procedures in this study. It was established during imaging that the participants were not taking any medications and did not have a temperature. In order to investigate the potential applications of using a thermal imaging camera for diagnosis, pre- and post-dilatation experiments were conducted in eyes of controls and diabetic retinopathy patients. Since ambient temperature has an impact on OST, a 25 °C constant temperature was kept in the imaging area. Huang et al. [4], diabetes patients run the risk of acquiring the eye condition diabetic retinopathy (DR). When the retinal blood vessels are harmed by high blood glucose levels, diabetic retinopathy develops. Because of significant success of deep learning, computer-aided detection of DR has now developed into a viable tool to detect and for grading severity of diabetic retinopathy in early stages. In this research, they create a large perfect annotated diabetic retinopathy dataset of 2842 images to solve this issue (FGADR). FGADR has 1000 photos with image-level labels rated via ophthalmologists using the intra-level consistency and 1842 images with each pixel level-lesion explanations. This dataset will allow for in-depth research on DR analysis. They also design 3 benchmark tasks for assessment:

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1. Diabetic retinopathy segmentation of fundus lesion. 2. Diabetic retinopathy severity classification through segmentation and joint classification. 3. Use of transfer learning method for the ocular multiple disease identification. Sreedevi et al. [5] used graph theory technique and dynamic programming to automatically segment the seven layers of retina in the OCT image. In place of the previous edge detection algorithm, that algorithm segmented the layers with gaps, the method of graph-cut segmentation is adopted. Eight OCT retinal pictures with normal resolution and five with diabetic retinopathy are used here to implement the graph-cut algorithm. Following features from the segmented OCT retinal layers include neovascularization and retinal layer thickness. The NFL and INL of the thickness of the diabetic retinopathy patient are wider and narrower, respectively. Neovascularization is found in PDR topic. These exacted attributes are applied to identify that provided input fundus image is affected by diabetic retinopathy or not. Li et al. [6] here operators may quickly identify image quality issues by given realtime retinal image quality capability feedback, avoiding the need to call the patient back. The real-time feedback function for retinal image quality is a promising tool for lowering the percentage of fundus images that cannot be graded, which will boost the effectiveness of DR screening. First and foremost, this study focuses on the single ethnic cohort that was used to create the system. Though, they achieved acceptable sensitivity and specificity using the publicly accessible EyePACS. Due to the lack of observations in lesion for external cohorts, the lesion aware subnetwork was therefore they tested on the available local validation set of data. The robustness of lesion detection and DR grading of the DeepDR method requires additional external validation in multi-ethnic and multi-centre populations. They created an automated, comprehensible, and tested method that can grade early- to late-stage DR, diagnose retinal lesions, and provide real-time feedback on image quality. With the help of those features, the DeepDR model is able to increase the calibre of image collecting, offer clinical references, and expedite DR screening. In their study, Mushtaq and Siddiqui [7] trained a proposed model using DenseNet-169 utilising data from the APTOS 2019 blindness detection and Diabetic Retinopathy Detection 2015 competitions on Kaggle. Pre-processing was required since the retinal images in the datasets had a huge noise associated with them. In order to concentrate more on the fundus picture alone during pre-processing, the black border and corners of the photographs were first eliminated. The images were then downsized to a standard format. Finally, the Gaussian noise was subtracted from the photos using a Gaussian blur. The majority of the data, which fitted to class ‘0’, or no DR, was found to be significantly uneven among the severity classes after pre-processing. To find out this problem, they employed augmentation, which gave them 7000 retinal images from each grading class and balanced the data. Data was eventually given to the DenseNet-169 for model training after pre-processing and picture augmentation.

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The three-phase paradigm for automating the identification and grading of DR is presented in this study [8]. The employed method entails feature extraction, preprocessing, and classification. Here, experimental results represents that they can suggestively improve the system’s performance by augmenting and balancing the data. The results on the IDRiD dataset were compared to other cutting-edge research, which supports the idea that the proposed system for grading DR is more accurate. They will initially have an exceptional possibility for better results thanks to a larger architecture and increasing the number of training epochs. Concurrently, this would require more resources and processing capacity, which seems unworkable given how often they employ an automated system. Second, there is need for improvement in target image data pre-processing techniques. Paul [9], the author claims that one of the main causes of blindness or vision loss is diabetic retinopathy, a serious complication of eye which can result in harm to the retina’s blood vessels. Generally, Diabetic Retinopathy is divided into two categories non-proliferative (NPDR), here essentially no symptoms other than a rare microaneurysms, and proliferative, which involves a significant no. of microaneurysms, haemorrhages, exudates, cotton wool spots, neovascularization, or a grouping of these can make it easier to diagnose. More definitely, DR is typically broken down into 5 levels, numbered 0–4, with 4 being the most severe. Here, the authors discuss the disease’s risk factors before reviewing recent study on the topic and examining at a few methods that have been proven to be quite successful in enhancing prediction accuracy. Lastly, a convolutional neural network model for DR detection on a edge microcontroller with low memory is developed. Butt et al. [10] to classify DR into five DR groups based on DR severity, authors offer three models of Deep CNN as: no DR, mild DR, moderate, severe DR, and proliferative DR. It displays the results for the Blue channel, the results for all models through red channel separation of input fundus images, also for the results from these models that through green channel separation of DR images, also the results for all models using grey scale images. They noticed that significant variations in test accuracy only start to happen for all created models at epoch sizes 32 and above. Because of this, the connection between accuracy and epoch size is not linear. Higher epoch sizes result in better performance. Here second model achieve the best overall accuracy, according to the authors’ observation. When analysing the three suggested models, second model had the highest level of accuracy, followed by first model and third model. Ayala et al. [11] here, researchers used the Adam algorithm to maximise the categorical cross-entropy loss function and learning rate of the proposal method training. In order to avoid overfitting, all of the model parameters were changed over the course of 50 epochs using the early stop method. Datasets were employed in this instance for cross-testing, validation, and training purposes. The same percentage is used for training and validation if Messidor is used to improve the model, leaving the APTOS dataset for testing. Here authors have taken precision, accuracy, recall, receiver operating characteristic (ROC) curve, f1-score, and area of ROC under the ROC curve to compare the results of each training dataset.

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Mateen et al. [12] have taken the previously trained CNN based detection of microaneurysms for diabetic retinopathy which has been carried out in this research effort framework using an approach of feature embedding. Pre-trained deep neural network models are used in this work’s methodology for feature extraction. Additionally, a fully linked layer is added to the accepted DNN architectures to facilitate the classification of fundus images. When features are merged, different types of features that describe compactness, circularity and roundness may be produced from a single shape descriptor. Megala and Subashini [13] According to this study, microaneurysms and haemorrhages are among the earliest indications of diabetic retinopathy. A bulge known as a microaneurysm is caused by an accumulation of leaky fluid in the weak blood vessel. This bulge bursts, resulting in a haemorrhage from blood leakage. After diabetic retinopathy, haemorrhage is a serious issue because it causes bleeding to enter the interior of the eye. Here are a few of the main causes of haemorrhages, including high blood pressure, diabetes, and blocked veins in the retina. For the protection of diabetic patients’ vision and an earlier diagnosis of diabetic retinopathy, accurate haemorrhage detection is more important than ever. In this research, authors proposes a very novel system for exposing and demonstrating diabetic retinopathy in fundus colour images. Shaban and Ogur [14] to analyse retinal images and automatically differentiate between severity classes no diabetic retinopathy, mild, moderate, non-proliferative DR and severe DR and proliferative DR. In this research work, authors proposed deep convolutional neural network having eighteen layers of convolutional and three layers of fully connected. They were applied fivefold and tenfold cross validation techniques, respectively. Firstly, a pre-processing stage implemented, using methods for class-specific augmentation and image resizing. The proposed method significantly increases access to retinal care by eliminating the need for a retina specialist and accurately identifying and rating the severity of diabetic retinopathy. This technique permits early illness detection and the monitoring of illness progression, that may help tailor medical treatment to reduce the likelihood of visual loss. In this study, Colomer et al. [15] concentrate on diabetic retinopathy, one of the most prevalent disorders in contemporary society. The proposed method does not need candidate map creation or lesion segmentation prior to classification. Local binary patterns and profiles of granulometric are created in order to extract textural also morphological info from retinal fundus images. Multiple tests taken using a range of public datasets by a variability high degree and short of image exclusion demonstrate the suggested method’s ability to identify signs of diabetic retinopathy. Tufail et al. [16] employed several deep learning-based three-dimensional convolutional neural network, i.e. 3D-CNN architecture for binary and multiple classes. They looked at the categories of no DR, mild, moderate, proliferate, and severe. To complete these goals in the field of spatial domain, they used two types of artificial enhancement or data augmentation methods they are second-random shift and random weak Gaussian blurring, as well as used its combination. It has been discovered that DL algorithms that operate with big amounts of data perform better than approaches that work with small amounts of data.

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One of the main roots of visual abnormalities in the expanding population that affects the portion of the retina responsible for light perception is diabetic retinopathy. Both forms of diabetes mellitus are impacted. High blood sugar levels harm the retinal blood vessels, causing them to enlarge and leak or block the passage of blood through them, which causes DR. If left untreated, it initially presents as minor vision issues and eventually results in blindness. Early stage DR detection and therapy will be aided by contemporary technological breakthroughs, automated detection, and analysis of the stage of diabetic retinopathy. Therefore, analysing the colour fundus image of the retina requires a competent technician. In order to stop blindness, DR detection is in great demand due to the constantly expanding population. In this research, Bhakat and Singh [17] aim to review the existing methodologies and techniques for detection. Automated screening of diabetic retinopathy patients offers a chance to recover their intermediate outcome while reducing the public spending related with the direct and indirect costs of frequently occurring diabetes diseases that are sight-threatening. This study’s objective was to develop and test an automated deep learning-based method for identifying referable and non-referable diabetic retinopathy cases in retinal fundus images from foreign and the patients from Mexica. Noriega et al. [18] tested the effectiveness of automated retina image analysis system below an independent scheme and two assistive schemes, determining and contrasting the sensitivity and specificity of the three schemes. They done this through a web-based platform for remote retinal image analysis. One of the most prevalent diseases of the eyes is diabetic retinopathy. Complete blindness could occur if the infected eyes are not identified and treated quickly. The recommended technique extracts a feature from the fundus image by fusing local extrema data with quantized Haralick features. The suggested method analyses the retinal vasculature and hard-exudate signs of diabetic retinopathy using two separate publicly available datasets. Promising indices have been identified in experiments using performance matrices for specificity, accuracy, and sensitivity. Similar to this, the suggested strategy’s validity is shown by associating it to pertinent literature studies. The bulk of the papers used as comparisons were outperformed by the suggested strategy [19]. Diabetes frequently causes diabetic retinopathy, which affects the retina’s blood vessels. It is the greatest cause of visual damage and vision loss in the working age adults as well as the most frequent reason of blindness in those with diabetes. In their study, Pak et al. [20] compare the newly optimised architecture to two extensively used conventional ones (DenseNet and ResNet) (EfficientNet). On the basis of gathered database from the APTOS Symposium, the authors of this article developed methods to identify the retinal fundus image as one of five class situations.

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Fig. 8 Proposed flow graph

4 Proposed Methodology The proposed method in this research attempts to assess the patients’ levels of diabetic retinopathy, whether they have it or not, and at what stage, if they do because this is a major complication or disorder of diabetes and because it can cause eyesight loss. For this reason, it is crucial to accurately and quickly classify the individuals. Capsule networks are trained to classify diabetic retinopathy by using fundus images as input in order to provide extremely accurate findings (Fig. 8).

5 Conclusion Deep learning techniques are very helpful for advancing research in the field of medical image segmentation, feature extraction, classification especially for the diagnosis of eye complication as diabetic retinopathy. Diabetic retinopathy, i.e. DR is a very serious medical health disorder that can result in vision loss or blindness. Here, for the purpose of DR early detection, many approaches are examined and investigated. It is clear and obvious that machine learning techniques are significantly less scalable with respect to big data and needed more time for model training and its analysis than deep learning techniques. When there are more features and data, ML models come to suboptimal conclusions, while DL models work to produce the

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best results possible. In this work, we identify and reviews a sufficient deep learning models to understand the working, also its evolution and combination of hybrid techniques, also how these models can be work on shortage of data and resources to yield an effective outcome. This is grounded on the enormous applications of deep learning in currently projected models. Deep learning techniques can help us increase the efficiency and precision of DR prediction model.

References 1. Thiagarajan AS, Adikesavan J, Balachandran S, Ramamoorthy BG (2020) Diabetic retinopathy detection using deep learning techniques. J Comput Sci 16(3):305–313 2. Hatua A, Subudhi BN, Veerakumar T, Ghosh A (2021) Early detection of diabetic retinopathy from big data in Hadoop framework. Displays 70:102061 3. Chandrasekar B, Rao AP, Murugesan M, Subramanian S, Sharath D, Manoharan U et al (2021) Ocular surface temperature measurement in diabetic retinopathy. Exp Eye Res 211:108749 4. Zhou Y, Wang B, Huang L, Cui S, Shao L (2020) A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE Trans Med Imaging 40(3):818–828 5. Ramkumar S, Sasi G (2021) Detection of diabetic retinopathy using OCT image. Mater Today Proc 47:185–190 6. Dai L, Wu L, Li H, Cai C, Wu Q, Kong H et al (2021) A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun 12(1):3242 7. Mushtaq G, Siddiqui F (2020) Detection of diabetic retinopathy using deep learning methodology. Mater Sci Eng 1070:012049 8. Yasin S, Iqbal N, Ali T, Draz U, Alqahtani A, Irfan M et al (2021) Severity grading and early retinopathy lesion detection through hybrid inception-ResNet architecture. Sensors 21(20):6933 9. Paul AJ (2021) Advances in classifying the stages of diabetic retinopathy using convolutional neural networks in low memory edge devices. IEEE 10. Butt MM, Latif G, Iskandar DA, Alghazo J, Khan AH (2019) Multi-channel convolutions neural network based diabetic retinopathy detection from fundus images. Proc Comput Sci 163:283–291 11. Ayala A, Ortiz Figueroa T, Fernandes B, Cruz F (2021) Diabetic retinopathy improved detection using deep learning. Appl Sci 11(24):11970 12. Mateen M, Malik TS (2022) Deep learning approach for automatic microaneurysms detection. Sensors 22(2):542 13. Megala S, Subashini TS (2020) Haemorrhages and micro-aneurysms diseases detection using eye fundus images with image processing techniques. Intl J Recent Technol Eng 9(1):28–33 14. Shaban M, Ogur Z (2020) A convolutional neural network for the screening and staging of diabetic retinopathy. PLoS ONE 15(6):e0233514 15. Colomer A, Igual J, Naranjo V (2020) Detection of early signs of diabetic retinopathy based on textural and morphological information in fundus images. Sensors 20:1005. https://doi.org/ 10.3390/s20041005 16. Tufail AB, Ullah I (2021) Diagnosis of diabetic retinopathy through retinal fundus images and 3D convolutional neural networks with limited number of samples. Hindawi Wirel Commun Mobile Comput 2021:1–15. https://doi.org/10.1155/2021/6013448 17. Bhakata A, Singh V (2021) A generic study on diabetic retinopathy detection. Turk J Comput Math Educ 12(3):4274–4283 18. Noriega A, Meizner D, Camacho D (20221) Screening diabetic retinopathy using an automated retinal image analysis system in independent and assistive use cases in Mexico: randomized controlled trial. JMIR Form Res 5(8), E25290

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19. Ashir AM, Ibrahim S (2021) Diabetic retinopathy detection using local extrema quantized Haralick features with long short-term memory network. Hindawi Intl J Biomed Imag 2021:6618666. https://doi.org/10.1155/2021/6618666 20. Pak A, Ziyaden A, Tukeshev K (2020) Comparative analysis of deep learning methods of detection of diabetic retinopathy. Cogent Eng 7:1805144. https://doi.org/10.1080/23311916. 2020.1805144

Hate Text Finder Using Logistic Regression Kumbam VenkatReddy, Ravikanti Vaishnavi, and Chidurala Ramana Maharshi

1 Introduction The hostile substance via web-predicated networking media destinations might be as stag, defilement, provocation, prejudice and foul. This hostile substance can make by the customer to differ the others individualities allowed through that the misconception between the general populations can do and can prompt mischief different people groups through their ill-bred substance on the web-predicated life destinations. According to reports, the general public is not confined to talk anything they ask and to posts whatever they feel, the general population use these Internet-grounded life locales in an exceedingly way. It is decreasingly hard to deal with or to characterize that content and to detect the hostile terms regarding their implicit customer who start the application of the hostile terms in the converse. Lately, Online Social Network has demonstrated to be a feasible vehicle for individualities to uninhibitedly convey what needs be. The guests can within much of a stretch conduct among themselves exercising talk errand people and offer or include posts, film land, textbooks and soon on other customer’s profile. A portion of these dispatches conceivably allowed to be hostile by certain guests. In UK, an overview was led. Its perceptivity shows 28 of the youths progressed nearly in the range of 11 and 16 with a profile on a person to person communication web runner have encountered commodity disquieting on that point of which 18 have encountered vicious language and 3 were prompted to hurt themselves. Individualities are permitted to banner similar reflections still there is no distinct answer for this issue. As the amounts of guests on informal communication have expanded snappily because of K. VenkatReddy Vignana Bharathi Institute of Technology, Ghatkesar, Hyderabad, India R. Vaishnavi (B) · C. R. Maharshi Department of Information Technology, Vignana Bharathi Institute of Technology, Ghatkesar, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_5

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the expanded access to web, it is demonstrating to be a test for the current fabrics to arrange similar dispatches successfully. In this paper, a frame is proposed, which identifies similar reflections. Logistic retrogression is a bracket algorithm. It is used to read a double outgrowth grounded on a set of independent variables. Okay, so what does this mean? A double outgrowth is one where there are only two possible scripts—either an event occurs (1) or it doesn’t (0). Independent variables are those variables or factors which may impact the outgrowth (or dependent variable).

2 Related Work A significant number of the scientists have effectively characterized the different methods for distinguishing the hostile language in the online networking organizing destinations. They utilized the current procedure like Natural Language Filtering, Blacklist Moderation and text classification for sorting the material via web-based networking sites. In the Data Mining, by utilizing a regulated methodology of order, the hostile terms can be distinguished effectively and intelligently with the constant unique information. Under administered procedure, different grouping algorithms can be characterized as Naive Bayes, choice tree, K-Nearest neighbor and bolster vector machine. From this method, the SVM is more supported than different systems. The SVM gives worldwide information arrangement and anticipate the high exactness result than the Naive Bayes and other characterization strategy. The paper by author [8] illustrates the less computational unpredictability of the calculation and expressed that the paper can deal with the enormous dataset than existing scale-up techniques.

3 Proposed System The proposed framework can utilize the Logistic Regression to precisely order and distinguish the offensive and the protective sentence with high exactness. The proposed framework can recognize the potential client by methods for whom the offensive language is utilized. We direct the principal near investigation of different learning models on Hate and Abusive Speech on Twitter, and talk about the likelihood of utilizing extra highlights and context information for upgrades. This task applies machine learning methods to perform computerized hostile language identification. Hostile language can be characterized as communicating outrageous subjectivity and this investigation for the most part centers around two classes ‘sensual’ and ‘bigot’. Advantages of Proposed System 1. More accurate and high performance. 2. It works on huge datasets and online social networks like twitter, Facebook, etc.

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3. Its easy to implement and detect the offensive language. 4. Although logistic regression is less vulnerable to overfitting, it can overfit in high-dimensional datasets. 5. It performs well when the dataset is linearly separable.

4 Working of Proposed System 1. 2. 3. 4.

User authentication. Blog posting and viewing. To detect the offensive words, utilize the Logistic Regression ML algorithm. Validate the results.

Step 1 User authentication, often known as signing in, is typically used to access a certain page, website or application that is unavailable to others. The login is occasionally used to refer to the user credentials, which are normally some kind of username and a password that matches (or logon, sign-in, sign-on). Step 2 In Blog Posting and Viewing, a user can create a post and user can just view the other users post. But user cannot have access to perform the actions on posts like update, delete, etc. Only Super User (admin) has right to perform actions on the other users posts. Step 3 Based on a set of independent factors, a binary result can be predicted using logistic regression. Logistic regression is used to classify the probability of a binary event occurring, and to deal with issues of classification. When dealing with binary data, the proper method of analysis to use is logistic regression. Step 4 Validation of result is performed using the output from step 3. Usually, in this step, it stops user to post in blog if any offensive or hostile text exists. Otherwise, user can post it successfully. User can post any of the content he likes and also he can comment on others content until and unless it contains some abusive or hate spreading content. This developed algorithm will help users to stop posting negative or abusive comments if they are willing to do so.

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5 Results Few captured images of the output screen when different inputs are passed (Figs. 1, 2, 3, 4, 5, 6).

Fig. 1 Sign up/registration page for user

Fig. 2 Login page for user

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Fig. 3 User created a post

Fig. 4 Posted successfully (non-offensive)

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Fig. 5 User trying to post offensive words

Fig. 6 Cannot be posted due to offensive words

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6 Conclusion In this examination, we research existing text-mining strategies in recognizing hostile substance for securing juvenile online well-being. Explicitly to distinguish hostile substance in online networking, and further foresee a client’s probability to convey hostile substance. ‘Our examination has a few commitments. In the first place, we for all intents and purposes concept the thought of online hostile substance, and further recognize the commitment of pejoratives/obscenities and obscenities in deciding hostile substance, and present hand creating syntactic principles in distinguishing verbally abusing badgering’. Second, we improved the customary ML techniques by not just utilizing lexical highlights to recognize hostile dialects, yet additionally fusing style highlights, structure highlights and context-explicit highlights to all the more likely foresee a client’s probability to convey hostile substance in Internet-based life.

7 Future Scope In the future, we can implement more classifications on hate speech. And also we can implement on other languages like Hindi, Tamil, etc. We can still improve the performance of algorithm and can implement the artificial intelligence for automatic detection of hate speech.

References 1. Badjatiya P, Gupta S, Gupta M, Varma V (2017) Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp 759–760 2. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022 3. Burnap P, Williams ML (2014) Hate speech, machine classification and statistical modelling of information flows on Twitter: Interpretation and communication for policy decision making 4. Chatzakou D, Kourtellis N, Blackburn J, De Cristofaro E, Stringhini G, Vakali A (2017) Mean birds: detecting aggression and bullying on twitter. In: Proceedings of the 2017 ACM, pp 13–22. ACM 5. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation 6. Djuric N, Zhou J, Morris R, Grbovic M, Radosavljevic V, Bhamidipati N (2015) Hate speech detection with comment embeddings, pp 29–30. ACM 7. Duggan M (2017) Online harassment 2017. Pew Research Center, Washington 8. Founta A, Djouvas C, Chatzakou D, Leontiadis I, Blackburn J, Stringhini G, Vakali A, Sirivianos M, Kourtellis N (2018) Large scale crowdsourcing and characterization of twitter abusive behavior. ICWSM. https://doi.org/10.1609/icwsm.v12i1.14991

Automated Revealing and Warning System for Pits and Blockades on Roads to Assist Carters Vijay Raviprabhakaran , Prasanth Dharavathu, Dhanush Adithya Gopaluni, and Abhinav Reddy Jale

1 Introduction Nowadays because of few pavements, distress number of accidents is increasing, and pothole is one of the main types of pavement distress. Potholes are formed because of bad weather conditions and poor maintenance. Roads are the main mode of transportation in India nowadays. Nevertheless, the maximum number of pathways in India is narrow and clogged with a modest exterior nature and roadway preservation requirements are not agreeably seen. Conferring to a survey by The Indian Highway Ministry, nearly 5000 crashes happened in 2019 and around 3500 such crashes were reported in 2020 due to potholes. Furthermore, the ministry reported the overall number of accidents caused by potholes in the past three years is approximately 21,000. Based on the records accessible from the highway agency, the aggregate number of highway crashes in 2020 was 366,138. The foremost struggle these days in budding countries is the safeguarding of highways. A perfectly preserved pathway commits a key portion of the nation’s economy. Potholes are formed by bad weather conditions and poor maintenance. Not only do they cause discomfort for citizens, but they also cause fatalities due to road accidents. A cost-effective solution for the problems mentioned above is proposed to help drivers to drive securely. The purpose of this system is to encourage operators to avoid accidents caused by potholes and obstacles. This system model proposes a solution for examining geographical roadway visions. It requires a Kinect sensor, which provides straight depth proportions. This V. Raviprabhakaran (B) CVR College of Engineering, Hyderabad 501 510, India e-mail: [email protected] P. Dharavathu · A. R. Jale University of Central Missouri, Lee’s Summit 64093, USA D. A. Gopaluni The University of Memphis, Memphis 38152, USA © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_6

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sensor embraces an Infrared (IR) and a Red Green Blue (RGB) camera. These pictures are examined by eradicating calibration and distinguishing geographies, to figure out the depth of potholes [1, 2]. The geographical representation of roadway potholes is noticed by the system. This technique uses Light Emitting Diode (LED) and two charge-coupled cameras to find out the street imageries. Conversely, outcomes get disturbed by LED light strength and ecological aspects [3]. The procedure for pit detection is established on a Support Vector Machine (SVM). The whole process is carried out by distinguishing potholes from extra flaws like cracks. These images are disconnected employing partial differential equations. This process guides the SVM by a group of pathway pictures to notice potholes. Yet, the model fails to sense the roadway flaws if the pictures are not accurately brightened [4]. The system was developed on an Android platform to identify roadway dangers. The entire collected data is saved in a central depository for extra administering [5, 6]. Even though this process interconnects traffic flow proceedings with other drivers, it escalates the price and difficulty of execution [7]. The pothole finding exemplary uses of Android mobile phones with accelerometers. The accelerometer information is utilized to sense potholes. The different procedures, e.g., z-thresh, compute the acceleration amplitude at the z-axis and zdiff to assess the difference concerning the dualistic amplitude assessments [8, 9]. The model uses a camera embedded with algorithms so that whenever there is a pothole, the camera captures it and the location coordinates are also captured and saved in the database [10, 11]. Accelerometers are used to detect defects on the road, and it also customs the Global Positioning System (GPS) to know the meticulous coordinates or position of the defects. The data which is sensed is transferred in the direction of the chief catalogue by exhausting main and secondary entree opinions. This project ends up being expensive as it requires the installation of wireless routers and local computers and the development of access points [12, 13]. The arrangement that perceives pits is all about a visualization-constructed tactic. The depictions of the highway are apprehended by the camera which is fixed properly. The metaphors are then checked to identify the manifestation of pits. Since it is a visualizationgrounded technology, it slogs in proper illumination conditions which means in the no-light like at night time, it is not that effective also system does not give any kind of warning. The above solution has its limitations as it only identifies the potholes, and it will not provide any information about potholes or obstacles to the vehicle user [14, 15]. In this system, Wi-Fi is equipped in the vehicles which helps in collecting information about the surface of the highway and sending it to the Wi-Fi entree spot. This retrieves end spreads this evidence to all automobiles in the outline of cautions. This arrangement bears to be pricier and almost every vehicle ought to be established with Wi-Fi bases resulting in the construction of more access points [16]. Similarly, the same system can be incorporated into the shipping system [17]. The sensors are situated on the vehicle in such a way that it record both horizontal and vertical accelerations experienced by the vehicle on its route. There is a GPS installed in the project which helps to locate the coordinates of the potholes, the vehicle user experiences and it also stores information about experienced potholes in the past when the driver travelled. Recently, optimization algorithms have been incorporated

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for pothole detection [18–22]. A Firebird V robot is employed in the project aimed at investigating through uniform rate. This robot is positioned through the servo motor. This research is hampered by gathering data only concerning the pits [5]. In this manuscript, the concept of pothole detection is achieved by the ultrasonic sensor and obstacle detection is accomplished by an infrared sensor. The whole module works with the external battery supply given to the Regulated Power Supply (RPS) and this RPS supplies the power throughout the model. Initially, the Bluetooth model is set so that the automobile is commanded by the commands and sensors are placed in their respective positions. Whenever an obstacle or pothole is observed, there are few signals generated from the sensors. Signals from both sensors are sent to Arduino UNO, and the output from the Arduino is given to the motor driver which controls the motor, this helps to stop the motors. Early detection of potholes and obstacles are two critical techniques to quickly stop the vehicle and prevent accidents. The instructions to control the working model are given through predefined American Standard Code for Information Interchange (ASCII) commands from the Bluetooth application, which is open-source software. The user gets alerted by the notifications when the working model detects any pothole or obstacle.

2 Pothole and Obstacle Detection Working Model The block diagram provides the outlook of the pothole and obstacle notification as shown in Fig. 1. The Arduino UNO acts as the central part, where sensors, Bluetooth module, motor driver, and regulated power supply are connected to it. RPS provides the power supply to all the components like Arduino, infrared sensor, ultrasonic sensor, buzzer, Bluetooth module, motor driver, and motors. When an obstacle is detected, the infrared sensor shoots an indication to the Arduino. It sends signals to the buzzer and motor simultaneously, from which the buzzer informs the user by a beep sound and the motors stop automatically. Similarly, when an ultrasonic sensor detects a pothole, it sends signals to the Arduino, causing the same functions as the IR sensor. The ASCII commands are given through the Bluetooth module (HC-05) to control the working model. The Bluetooth component takes the instructions from the module application, which is an open-source application. Whenever the vehicle is about to approach a pothole or obstacle, the sensors will detect and alert the necessary components concurrently through Arduino. Hence, the working model gets stopped alerting the user through a beeping sound and a notification is also sent to the mobile application.

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Battery

Regulated Power Supply

Ultrasonic sensor

Bluetooth Module

Bluetooth server

Motor Driver

Wheels (DC Motors)

Arduino UNO

Infrared sensor

Fig. 1 Block illustration of pothole and obstacle revealing

3 Implementation of Pothole and Obstacle Detection The top view of the working model of the Pothole and Obstacle detection system is demonstrated in Fig. 2. The RPS unit gets the power supply from a battery through which Arduino and other components get the supply. Arduino UNO is the central part of the module which manages every other component of the module by receiving and sending the signals. There are two sensors used: ultrasonic and infrared. The ultrasonic sensor sends the sound waves through the transmitter and when these touch an object they will get reflected and will be received by the receiver. Through this process, the sensor will calculate the distance. If the distance is more than that of the ceiling value, it will remit an indication to the Arduino. The infrared beam comprises an emitter and a detector. The emitter is an IR LED, whereas the indicator is an IR photodiode. This diode is susceptible to the IR light which is radiated by the IR LED. When there is an object near the sensor, the IR light from the emitter reflects the detector and triggers and will send the signal to the Arduino and a buzzer is used for giving an alarm. It will receive a signal from the Arduino and beeps when there is a pothole or obstacle detected. A motor driver which is associated with the motors receives a signal from Arduino whenever a pothole or obstacle is detected, it stops the motors. A Bluetooth module is used which takes input and gives output where the inputs are the commands given by the mobile application which are used to control the vehicle. The outputs are alerts from the sensors which are displayed in the mobile application.

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Fig. 2 Top view of the working model of the pothole and obstacle detection system

3.1 Stage 1: When an Obstacle is Detected When an obstacle is detected by the infrared sensor, the light on the sensor glows and it broadcasts the communication to the beeper, mechanical carter, and Bluetooth component rapidly via Arduino is demonstrated in Fig. 3 from which the buzzer informs the user by a beep sound and the motor driver stops the motors automatically and the user gets notified about the obstacle through the mobile application. When an obstruction is revealed, the infrared sensor instructs the indications to the Bluetooth element over Arduino, this module sends the alerts to the mobile

Fig. 3 Working model when an obstacle and pothole is detected

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Fig. 4 Mobile application interface when an obstacle and pothole is detected

application. The application displays a notification as “Obstacle robot” as indicated in Fig. 4.

3.2 Stage 2: When the Working Model Detects Potholes When a pothole is detected, the ultrasonic sensor guides the indicator to the beeper, motor carter, and Bluetooth segment contemporarily over Arduino, from which the buzzer informs the user by a beep sound and the motor driver stops the motors automatically and the user gets notified about the obstacle through the mobile application is displayed in Fig. 3. When a pothole is detected, the ultrasonic radar casts the indication to the Bluetooth unit via Arduino, and this module sends the alerts to the mobile application. The application displays a notification as “Pothole robot” shown in Fig. 4.

4 Conclusion This research paper concludes with a model which facilitates automobile operators with pre-indications of pits and obstacles, for a hazel-free ride. This methodology is an affordable result for the revealing of awful pits and obstacles, while it exploits an affordable-priced ultrasonic sensor and infrared sensor. There is alternative assistance, the mobile application in this approach cautions against hindrances. It will be beneficial in the drizzly period once pits are crammed with filthy water. The intended

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model will save many lives from horrible accidents caused by obstructions on the pathways.

References 1. Lion KM, Kwong KH, Lai WK (2018) Smart speed bump detection and estimation with Kinect. In: 2018 4th international conference on control, automation and robotics (ICCAR). IEEE, pp 465–469 2. Kamal K, Mathavan S, Zafar T, Moazzam I, Ali A, Ahmad SU, Rahman M (2018) Performance assessment of Kinect as a sensor for pothole imaging and metrology. Int J Pavement Eng 19(7):565–576 3. Youquan H, Jian W, Hanxing Q, Zhang W, Jianfang X (2011) A research of pavement potholes detection based on three-dimensional projection transformation. In: 2011 4th international congress on image and signal processing, vol 4. IEEE, pp 1805–1808 4. Hoang ND (2018) An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction. Adv Civ Eng 5. Madli R, Hebbar S, Pattar P, Golla V (2015) Automatic detection and notification of potholes and humps on roads to aid drivers. IEEE Sens J 15(8):4313–4318 6. Fernandes B, Alam M, Gomes V, Ferreira J, Oliveira A (2016) Automatic accident detection with multi-modal alert system implementation for ITS. Vehic Commun 3:1–11 7. Mednis A, Strazdins G, Zviedris R, Kanonirs G, Selavo L (2011) Real time pothole detection using android smartphones with accelerometers. In: International conference on distributed computing in sensor systems and workshops (DCOSS). IEEE, pp 1–6 8. Wang HW, Chen CH, Cheng DY, Lin CH, Lo CC (2015) A real-time pothole detection approach for intelligent transportation system. Math Probl Eng 9. Yik YK, Alias NE, Yusof Y, Isaak S (2021) A real-time pothole detection based on deep learning approach. J Phys Conf Ser 1828(1):012001 10. Li Y, Papachristou C, Weyer D (2018) Road pothole detection system based on stereo vision. In: NAECON 2018—IEEE national aerospace and electronics conference. IEEE, pp 292–297 11. Arjapure S, Kalbande DR (2020) Review on analysis techniques for road pothole detection. In: Soft computing: theories and applications. Springer, Singapore, pp 1189–1197 12. Mohamed A, Fouad MMM, Elhariri E, El-Bendary N, Zawbaa HM, Tahoun M, Hassanien AE (2015) RoadMonitor: an intelligent road surface condition monitoring system. In: Intelligent systems’. Springer, Cham, pp 377–387 13. Basudan S, Lin X, Sankaranarayanan K (2017) A privacy-preserving vehicular crowdsensingbased road surface condition monitoring system using fog computing. IEEE Internet Things J 4(3):772–782 14. Bharadwaj Sundra Murthy S, Varaprasad G (2014) Detection of potholes in autonomous vehicle. IET Intell Transp Syst 8(6):543–549 15. Tsai JC, Lai KT, Dai TC, Su JJ, Siao CY, Hsu YC (2020) Learning pothole detection in virtual environment. In: 2020 international automatic control conference (CACS). IEEE, pp 1–5 16. Sharma SK, Sharma RC (2019) Pothole detection and warning system for Indian roads. In: Advances in interdisciplinary Engineering. Springer, Singapore, pp 511–519 17. Raviprabhakaran V, Mummadi TS (2020) Optimal scheme and power controlling aspects in shipboard system. In: Innovations in electrical and electronics engineering. Springer, Singapore, pp 367–379 18. Vijay R (2018) Quorum sensing driven bacterial swarm optimization to solve practical dynamic power ecological emission economic dispatch. Int J Comput Methods 15(03):1850089 19. Raviprabhakaran V (2022) Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm. COMPEL Int J Comput Math Electr Electron Eng 41(5):1971–1990

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20. Raviprabhakaran V (2023) Economical modelling and manufacturing of a prosthetic ARM. Wireless Pers Commun 130(3):1819–1832 21. Raviprabhakaran V (2023) Quorum sensing centered bacterial horde algorithm for global optimization. Concurr Comput Pract Exper 35(8):e7627 22. Raviprabhakaran V (2023) Clonal assortment optimization procedure to unravel cost-effective power dispatch problem. In: Soft computing applications in modern power and energy systems: select proceedings of EPREC 2022. Springer Nature, Singapore, pp 39–53

Green Data Center Power Flow Management with Renewable Energy Sources and Interlinking Converter Syed Abdul Razzaq

and Vairavasamy Jayasankar

1 Introduction A detailed survey on energy consumption in data center (DC) is discussed in term of hardware and software power models [1]. Data centers are controlled with centralized and decentralized techniques like data center management (DCM), data center infrastructure management (DCIM), critical facilities management (CFM). DC includes IT racks/cabinets, captive power generation, distribution through switchgears power distribution units (PDU), transformers, power control center (PCC), low voltages for fire and security, HVAC chiller, CRAH/CRAC units battery chargers and battery energy storage system (BESS), direct current power supplies for battery charging. Three phase to single phase supplies for each rack/cabinet, two power supplies serving IT racks for power redundancy. With the combination of software, smart meters and intelligent sensors the complete data center real-time monitoring and controlling can be performed with a critical management tool termed as DCIM [2]. Use of non-conventional source of energy (NCSE) reduces the carbon dioxide (CO2 ) emission. Energy storage system (ESS) like pumped hydro, flywheels, thermal storage, compressed air, batteries, super capacitors and distributed generation (DG) like combined heat and power (CHP), fuel cell (FC), diesel generator, PV, micro turbine, tidal energy and wind turbine are being adopted in AC/DC hybrid micro grids (HMGS). Anyhow the integration of renewable energy sources (RES) depends on the geographical situations and location allotted for DC. The sustainability criteria in green DC from United States green building council (USGBC) in terms of leadership in energy and environmental design (LEED) are discussed [3]. A similar paper on assessment of DC is discussed for improving the overall efficiency [4]. In search of tier-IV DC and achieving certified energy efficiency for data center awards (CEEDA), gold category DC are improving and upgrading S. A. Razzaq (B) · V. Jayasankar Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_7

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their power generation sources with RES and enhancing the power flow with fault interruptions for tier-IV certification. A detailed paper on energy Internet (EI) is proposed concluding the challenges for electrification and integrating of RES [5]. Using REDUX, the renewable energy manager tool for on site for energy efficient integrated with RES and UPS to reduce the electricity cost in DC is proposed [6]. The EI and energy router (ER) have simplified the power utilization in terms of improved efficiency. ER operates with three modules power electronics devices, information and communication networks and third smart grid intelligence to make DC more reliable and robust [7]. The investigation on optimal utilizing the electricity, water and reducing the CO2 emission by adopting alternating direction method of multipliers (ADMM) algorithm is elaborated [8]. This paper discusses the importance of direct current network in DC with the multi stage integration of ESS and DG for reducing the total energy consumption in DC [9]. Already to obtain green data centers, cold aisle containment (CAC) and hot aisle containment (HAC) are used to separate the air and improve the cooling in the containment by increasing the efficiency and decreasing the electricity bills. Increasing the thermostat temperature to its maximum limits and utilizing the fresh outside air for cooling, adopting the smart meters for monitoring and control of energy, humidity and temperature are productive for green data center. Using the separate DC network for DC-loads which reduces the power conversions. Equal power distribution for every phase is to be assigned for this phase balance algorithm is proposed which also beneficial in reducing the energy cost [10]. The paper discusses when the power grid attains the maximum capacity, the term energy gentrification is introduced for electricity deficits in power grid [11]. PV and wind generation depend on the climatic conditions, a discrete wavelet transformation and long short-term method are used to find the patterns for atmospheric conditions and electricity usage/production due to unpredictable and variable nature of renewable energies [12]. The proposed paper addresses for integrating RES and a fault interruption power supply with a bidirectional power flow from AC/DC power sources to AC/DC-loads through an interlinking converter (ILC) which operates on droop control principle. Finally, the power system constructed for DC is of Tier-IV and CEEDA gold category.

2 Uninterruptible Power Supply Uninterruptible power supply (UPS) provides the backup power during outages or emergency shutdowns. The recommendation from international electrotechnical commission (IEC) IEC-62040-3, UPS are classified in three modes the first is offline UPS classified as resultant voltage and frequency dependent (VFD) on fed input, the second is line interactive UPS classified as resultant voltage and frequency independent (VFI) from fed input and the third is online double conversion UPS this also classified as VFI. The online double conversion UPS is much familiar in DC as different frequency can be obtained at output compared to input. In case of emergency

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Fig. 1 Online double conversion UPS integrated with DG and ESS

shutdown, the BESS should have the capacity of supplying power demand to entire IT-cabinets minimum 5–10 min till emergency generator attains its full speed. The output power of UPS is basically served to IT-cabinets, and for DC other utilities, separated power scheme is designed. Figure 1 shows the concept of integrating UPS with DG and ESS for online double conversion. EI is a network of AC/DC nano grid or small micro grids (MG) referred as energy hubs (EH). Every EH is composed of ESS and DG with its respective loads. EH are connected in ring main system subject to intermittency. The main challenge in designing an efficient power management scheme is calculating the exact power bank, loads and throughout 24/7 power delivery to critical facilities in case of interruptions. ER is nothing but multichannel converter with capability of delivering power to either side.

3 Proposed Framework for Green Data Center The electrical design of DC is of three topologies N, N + 1, 2N, where N defines the exact number of equipment without any built in redundancy and 2N provides the highest and maximum redundancy. The proposed single line diagram of power flow management in green DC integrated with DG, ESS and ILC compiled with uptime tier-IV DC standards is shown in Fig. 2, whereas tier-IV has the same feature of tier-III concurrently maintainable with an additional feature of fault tolerance. ILC is controlled through a droop control to optimal bidirectional power transfer, reduce circulating currents, burdening the converter [13]. ILC installation is becoming essential for Grids because of integration of AC/DC power sources, this in fact reduces the power losses, switching losses and reduces the number of conversions. ILC in modular DC majorly benefits with the space, cooling cost as no room is required for installing additional rectifiers and inverters, reduces the maintenance, capability of overloading IT-cabinets or other facilities. Figure 3 shows the multi-port ILC which works on decentralized droop control scheme with no communication link and is independent and with autonomous control. The proposed ILC

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Fig. 2 Power flow management for green data center with integration of DG, ESS and ILC

is voltage source converter (VSC) with six IGBT/diode switches for bidirectional power sharing. Based on the frequency, AC/DC voltages, AC/DC active/reactive powers the converter operates and behaves as rectifier or inverter. Constant 48 V at IT-cabinets is supplied with the help of buck/boost converters. (Δ1 (u − u r )) + (Δ2 (Pilc − Pilc−r )) + (Δ3 ( f − fr )) = 0

(1)

where u, Pilc , f is DC-voltage, power of ILC and frequency, respectively, and ur , Pilc-r , f r is reference voltage, reference power of ILC and reference frequency, respectively. Δ1 , Δ2 , Δ3 are predefined.

Fig. 3 Structure of bidirectional power flow through ILC

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Table 1 Design parameters of system ER-AC side

ER-DC side

Voltage:380 V, F = 50 Hz Voltage: 800 V Line impedance: 0.01 Ω; 0.02 mH LC filter: 5 mH; 40 µF AC LCL filter, L = 0.2 mH

DC bus, L = 470 µH Boost, L = 0.1 µH

L = 5 mH; C = 2200 µF BESS, C = 1000 µF Boost, C = 500 mF

LCL filter: 2 mH, 4.2 mH; 60 µF C = 2200 µF, LCL filter, C = 80 µF

PV: Irradiance, G = 1000 w/m2 , T = 25 °C

By tuning the Δ1 , Δ2 , Δ3 , operators, the converter performance can be identified. Power flowing from through AC-grid to DC-loads the converter performs as rectifier and the power flown is positive and during the DC-grid to AC-loads converter performs as inverter and power flown is negative. Variation in AC-loads will impact the power generation which causes frequency disturbance and moreover varies DC-voltage, given by below equation: ∑

Pac =

Δ3 Δ f − Δ1 Δu dc 2π Δf − k Δ2

(2)

The load variation in DC-loads will impact on its DC power generation given by: ∑

Pdc =

Δ3 Δ f − Δ1 Δu dc Δu dc + l Δ2

(3)

where k and l are the droop coefficients. Variation in AC/DC-load will have its impacts on voltages and frequency. Table.1 gives the various parameters for constructing the power system for green DC.

4 Simulation Result Constructing a modular data center is more beneficial, instead of over sizing which causes the loss in capital expenditure (CAPEX) and operating expenses (OPEX) [14]. Model predictive control algorithm is proposed for obtaining the real-time energy values in RES powered DC [15]. Integration of RES and clean energy in DC is observed in terms of two divisions power usage effectiveness (PUE) and carbon usage effectiveness (CUE). (PUE)∗ =

totalfacilityenergy ITequipmentenergy

(4)

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Matlab/simulink is used to simulate the proposed model. AC/DC linear loads are varied chronologically. ER of DC-grid is combination of PV, FC, BESS which supplies the power to DC-loads of 12, 85 kW and charges the BESS of alternating source feeding the automatic transfer switch (ATS). Figure 4 shows the frequency variations due to loads demand. Figures 5 and 6 show the AC and DC voltages, respectively. ER of AC-grid is combination of PV, FC and batteries which feeds the AC-loads of non-critical DC facilities. The power transferred through PV, FC and BESS sources via ILC is shown in Fig. 7. The case study shows where the converter behaves as rectifier for 0–1.5 s with varying the DC-loads, where both AC and DC power sources contributes power. For 1.5–3 s, AC-loads increase due to limited power generation capability of AC-grid, the DC power is shared via ILC, dramatically making converter perform as inverter by feeding to AC-loads. The capability of power sources is designed to feed individually the entire IT-cabinets in DC.

Fig. 4 Frequency variation during AC-loads

Fig. 6 DC-voltage

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Fig. 5 AC-voltage

Fig. 7 ILC as rectifier and inverter

5 Conclusion The proposed scheme for green DC with RES and ILC serves the proposal of integrating the DG and ESS sources with ILC in the higher level of power system or by connecting at UPS section for reducing the CO2 emission with reducing greenhouse gases also by improving the overall efficiency of DC. The ILC scheme works on simple droop control scheme with voltage, frequency and ILC power as outer loop for sensing the power demand on either side of converter. The following valid points can extract from the proposed paper: • • • •

ILC provides autonomous bidirectional power sharing among AC/DC ER. Surplus power can be sold during high electricity prices. Plug and play feature with enabled future expansion. Certainly a good approach for upgrading to 100% green modular data center.

With use of RES and double conversion UPS, the frequency deviations are restricted. The proposed scheme provides reliable AC and DC buses.

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References 1. Dayarathna M, Wen Y, Fan R (2016) Data center energy consumption modeling: a survey. IEEE Commun Surv Tutor 18(1):732–794 2. Harris M (2014) Data center infrastructure management. In: Data center handbook [Internet]. Wiley, Hoboken, NJ [cited 29 Sept 2022], pp 601–618. Available from https://doi.org/10.1002/ 9781118937563.ch33 3. Moud HI, Hariharan J, Hakim H, Kibert C (2022) Sustainability assessment of data centers beyond LEED. In: 2020 IEEE green technologies conference (GreenTech) [Internet]. IEEE, Oklahoma City, OK, USA [cited 29 Sept 2022], pp 62–64. Available from https://ieeexplore. ieee.org/document/9289793/ 4. Dumitrescu C, Plesca A, Dumitrescu L, Adam M, Nituca C, Dragomir A (2022) Assessment of data center energy efficiency. Methods and metrics. In: 2018 international conference and exposition on electrical and power engineering (EPE) [Internet]. IEEE, Iasi, Romania [cited 29 Sept 2022], pp 0487–0492. Available from https://ieeexplore.ieee.org/document/8559745/ 5. Hussain HM, Narayanan A, Nardelli PHJ, Yang Y (2020) What is energy internet? Concepts, technologies, and future directions. IEEE Access 8:183127–183145 6. Peng X, Qin X (2020) Energy efficient data centers powered by on-site renewable energy and UPS devices. In: 2020 11th international green and sustainable computing workshops (IGSC) [Internet]. IEEE, Pullman, WA, USA [cited 29 Sept 2022], pp 1–3. Available from https://iee explore.ieee.org/document/9291205/ 7. Xu Y, Zhang J, Wang W, Juneja A, Bhattacharya S (2011) Energy router: architectures and functionalities toward energy internet. In: 2011 IEEE international conference on smart grid communications (SmartGridComm) [Internet]. IEEE, Brussels, Belgium [cited 29 Sept 2022], pp 31–6. Available from http://ieeexplore.ieee.org/document/6102340/ 8. Zhang G, Zhang S, Zhang W, Shen Z, Wang L (2020) Distributed energy management for multiple data centers with renewable resources and energy storages. IEEE Trans Cloud Comput 2020:1 9. Bai X (2021) Research on the application of renewable energy in DC data center. J Phys Conf Ser 2108(1):012054 10. Wang W, Yu N (2017) Phase balancing in power distribution network with data center. ACM SIGMETRICS Perform Eval Rev 45(2):64–69 11. Libertson F, Velkova J, Palm J (2021) Data-center infrastructure and energy gentrification: perspectives from Sweden. Sustain Sci Pract Policy 17(1):152–161 12. Nabavi SA, Motlagh NH, Zaidan MA, Aslani A, Zakeri B (2021) Deep learning in energy modeling: application in smart buildings with distributed energy generation. IEEE Access 9:125439–125461 13. Peyghami S, Mokhtari H, Blaabjerg F (2018) Autonomous operation of a hybrid AC/DC microgrid with multiple interlinking converters. IEEE Trans Smart Grid 9(6):6480–6488 14. Wiboonrat M (2020) Energy management in data centers from design to operations and maintenance. In: 2020 international conference and utility exhibition on energy, environment and climate change (ICUE) [Internet]. IEEE, Pattaya, Thailand [cited 29 Sept 2022], pp 1–7. Available from https://ieeexplore.ieee.org/document/9307075/ 15. Wu Y, Xue X, Le L, Ai X, Fang J (2020) Real-time energy management of large-scale data centers: a model predictive control approach. In: 2020 IEEE sustainable power and energy conference (iSPEC) [Internet]. IEEE, Chengdu, China [cited 29 Sept 2022], pp 2695–2701. Available from https://ieeexplore.ieee.org/document/9351010/

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid Renewable Power Generation S. Sunanda and M. Lakshmi Swarupa

1 Introduction In close proximity to end customers, embedded generation (EG) produces energy on a modest scale (10 kW–10 MW) and is straightforwardly connected to the conveyance organization (DN). The entrance of EG affects how the appropriation framework (DS) works [1], regardless of whether the reconciliation of EG into DS is intended to offer dependability, reactive power compensation, loss reduction, and voltage support [2, 4]. The integration of EG has a significant influence on the operation of the grid [6], the voltage profile [7–9], the power quality [10, 11], the lattice misfortunes [12], the issue current level [13], and the ongoing security framework [14–18]. It may be deduced from the literature that researchers are primarily interested in how to safeguard distribution systems from failures by creating protective equipment including fuses, relays, surge diverters, surge arresters, circuit breakers, and hybrid protective systems [19–24]. To fill an unmistakable exploration hole recognized to relieve (settle) the adverse consequences on conveyance framework security with EG incorporation, this paper surveys the benefits of inserted age on the matrix [25] and examines how implanted age will present effects [26] on insurance of dissemination system [27]. Advantages of Embedded Generation to the Grid • Decreased dispersion and transmission misfortunes. Contingent upon the area and execution during busy times, it may be possible to postpone network augmentation. • Voltage support increased power system robustness. S. Sunanda St. Martins Engineering College (A), Dullapally, Secunderabad, India M. Lakshmi Swarupa (B) CVR College of Engineering, Ibrahimpatnam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_8

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• Possibility of reducing emissions. Pros of the Grid for Deployed Embedded Generation • • • •

Empowers admittance to precursor markets Supports the safeguarding of unwavering quality for inconsistent inserted age Advances voltage quality, which is urgent for end-use gadgets Reinforces the functional adequacy of implanted creating since yield need not represent nearby burden • Upholds the client’s beginning up power needs when top current might climb recognizably. Significant Effects of the Integrated Generation The following difficulties, which are listed in [28, 29], are the key ones raised in this study about the entrance of implanted ages into current dispersion frameworks. • • • • • • • •

Reverse energy flow Energy losses Coil voltage Operation islands battles Increasing fault current Power quality issues Stability The few performance index factors that need to be taken into account for analysis include safe penetration limits estimate, etc.

In this article, four important concerns from the previously mentioned challenges with EG integration have been taken into consideration for the distribution network analysis. estimate of the penetration level, reverse power flow, voltage profile, and fault current rise. in the event that DN and EG are combined. Identifying power flows in both directions is critical for DN protection in cases when local generation exceeds local demand. For this reason, reverse power relay [30] is used in this article; it will recognize the bearing where shortcoming emerges according to the place of hand-off and assist in finding a solution to circumstances when protection is compromised. Because of the overvoltage that results from EG penetrations into the DN, problems in these areas are more difficult to diagnose and repair, and EG integration is what ultimately causes the DN to transform from radial to bidirectional. Therefore, protection of the DN is required in terms of voltage profile and fault current reduction because to the higher fault current brought on by the contribution from EGs and further worsened during times of fault. When failures occur in power distribution systems, fault currents and over voltages are introduced that must be mitigated. This article is the first to examine the use of a superconducting fault current limiter (SFCL) for this purpose, and an active type SFCL is now commercially available [31, 32]. Additionally, SFCL is included to enhance communication amongst protective devices [33] and testify to its effectiveness. After conducting experimental studies [34] to decide what amount of time it requires for an issue to become obvious in

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid …

69

a perplexing framework, leading tests [35] to determine how well SFCLs perform at different locations within the power system, and conducting cost-benefit analyses [36] of the power dissipation [37] factor during SFCL operation, the team arrives at the conclusion that multiple resistive SFCLs can be effectively applied. Practical application issues [38] are then recognized. Finally, SFCL [39]’s properties are also discussed. From the aforementioned literature, it was clear that there was a research gap that needed to be filled SFCL functioning as a passive resonance carrier-based (PRCB) Over voltage and a larger fault current may be mitigated by using SFCL and inverse current injection CB (I-CB) during fault periods. The following are the writers’ contributions to this paper’s goals. • To distinguish between symmetrical and unsymmetrical errors that are severe, a test procedure has been built up. • As determined by monitoring the fluctuations in active and reactive power, the least impacted EG among all linked EGs. • By planning the settings for the reverse power relay, a technique is established to locate and isolate EGs at the time of problems. • Modern methods: The use of SFCL as a passive resonance CB (PRCB) and inverse current injection (I-CB) CBs is recommended as a method for diminishing the enormous shortcoming flows and over voltages welcomed on by EG entrances and blames, separately. • The recreation aftereffects of the two proposed techniques under extreme shortcoming (LLLG) with the most un-impacted EG (SOFCEG) were thought about, ideas were made for the plan of defensive gadgets (circuit breakers), and the penetration levels of various EGs were computed and analyzed using standard mathematical formulas.

2 Problem Formulation The sort of EG unit is being used has a significant effect on planning and operational considerations such voltage profile, power quality, power misfortunes, dependability, and defensive framework. Direct connections to the distribution grid are possible for both synchronous and asynchronous generators, as well as other EG units. Power electronic converters are another option. The distribution grid’s power flows and the aforementioned features are impacted in each scenario. Types and Capacity of Embedded Generation Different Embedded Generation Models: The two main categories of EGs are rotating machine EGs and inverter-based EGs. Since the produced voltage may take either a DC or AC form, inverters are often utilized in EG systems after the generating process. Because it must first be converted to DC before being transformed back into AC, it must be fine-tuned using the rectifier to the ostensible voltage and recurrence with the ostensible boundaries. According to their terminal properties in terms of their ability to generate real and reactive power [41], EG may be roughly categorized

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Table 1 Major EG kinds depending on their capacity to produce power [42] EG type

Type description

Example

Type 1

EG has the capacity to inject both reactive and actual power

Synchronous power plants

Type 2

While EG may inject actual power, it also consumes reactive power

Wind power farms, for example, use induction generators

Type 3

Only EG has the capacity to provide PV, micro-turbines, and fuel cells integrated with genuine power converters and inverters into the main grid

Type 4

Only reactive power may be introduced via EG

Synchronous compensators

Fig. 1 Single-line schematic of a power system with integrated generation units

into four broad groups; several kinds of EG, as displayed in Table 1, have already been taken into account for this study. Figure 1 illustrates how an EG is connected to a distribution network. This connection will undoubtedly cause some local alterations to the network’s properties. Section 1 of this paper lists the primary difficulties. Of the issues presented, four have been taken into account for study and have pertinent remedies provided to secure the distribution system. A few of the difficulties include: assessment of penetration levels; voltage profile; invert power stream; expanded issue current; and reverse power flow.

2.1 Scenario 1: Rising Voltage Profile The incorporation of EG into the DN will increase the voltage, which must be kept below legal limits for the DN to function properly (nearby voltage in addition to EG-based voltage should be not exactly the neighborhood interest).

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid …

71

2.2 Scenario 2: Backward Power Flow Recommended in this present circumstance pivot power moves be acquainted with safeguard the dispersing framework against power inversions achieved by circumstance 1. In most cases, a radial distribution network will be set up such that power flows in just one direction, from the source down the distribution lines and into the loads. This assumption is reflected in the design of conventional protection systems, such as relays that trip in the event of an overcurrent. The load flow status might alter if there is an age on the dissemination feeder. The direction of the power flow will alter if local output is greater than local consumption.

2.3 Scenario 3: An Increase in the Fault Current’s Size At the point when EG is associated with a dissemination organization, the network’s nearby point of connection experiences an increase in the fault current when there is a problem. When fault current values are surpassed, there is a potential that the DN may be damaged and fail, which increases the chance that people will be hurt and that supplies will be interrupted. For the system’s protection equipment, the new shortcoming current and setting ought to be registered.

2.4 Scenario 4: The Traditional Method is to Estimate the EG’s Penetration Limitations To check the trial discoveries for the boundaries recently referenced, IEEE-14 base has been taken into account with four EGs (PVCELLS, MICRO TURBINE, SOFC FUEL CELL, and WIND TURBINE) in each example at picked transport no entrance levels. Distribution network characteristics have also been examined.

3 Proposed Solutions Comprehensive hybrid system simulation in Simulink with lattice associated VSI is shown. The arranged cross breed framework comprises five significant parts: a photovoltaic exhibit, an on-board charger (OWC), a battery bank, a voltage source inverter, and a buck-help converter (BBDC) on the pile side with a relative fundamental (PI) control commitment cycle. A PV group, DC converter, and MPPT combined calculation comprises a sunlight-based PV framework. The OWC framework was constructed utilizing a bidirectional turbine controlled by a simultaneous generator (SG) and an air conditioner DC three-stage rectifier. Regularly, an inexhaustible PV

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S. Sunanda and M. Lakshmi Swarupa Load Discrete, s =5e-005 s powergui

A a

Aa

A B

Bb

C

Cc

wave system

A B

B

C

C

Three-Phase Series RLC Branch

Three-Phase V-I Measurement

A a

A

A Bb

Cc

A

B

B

C

C

Three-Phase Series RLC Branch1

Three-Phase V-I Measurement1

Bb

A B C

Cc

Three-Phase V-I Measurement2

A Goto

A

3-phase Instantaneous Active & Reactive Power

Three-Phase V-I Measurement3

b B

Out1

Subsystem

aA

From2

B

Three-Phase Series RLC Branch2

PQ

Iabc

C

[Ps]

C

Vabc

Iabc

cC

From1

B

Vabc

Ppv

Universal Bridge1 A

+ input

+

g

Series RLC Branch1

Series RLC Branch B

-input

+

-K-

From3

Gain1

[Ps]

-K-

From4

Gain2

Scope

A B

-

C Pv model

100Vdc DC/DC Converter3

Fig. 2 Simulation model of hybrid system

and wave energy system will be the main power producing source in an HRES, while a battery bank will go about as a reinforcement energy capacity system to meet the system’s load needs in the event of a power outage. In a hybrid setup, the PV, wave, and battery bank must all be connected by a DC link with a stable voltage. Thusly, the HRES utilizes a BBDC with a PI controller to keep the DC-interface voltage stable. A three-stage voltage source inverter (VSI) is used at the stack side to control the voltage’s plentifulness and recurrence (Figs. 2, 3 and 4). Parameters of ocean wave chamber [5] OWC chamber length (L ch )

1.5 m

Water surface area inside the chamber (A1 )

1.4 m2

Turbine inlet area (A2 )

0.012 m2

Water depth (d)

WH (m)

WP (s)

0.98

4.9

16.47

0.9

4.79

15.75

0.88

4.79

15.73

d (m)

Parameters of PV array [5] Maximum rated power (Pmax )

87 W

Maximum voltage (V max )

17.4 V

Maximum current (I max )

5.02 A

Open circuit voltage (V oc )

21.7 V

Short circuit voltage (I sc )

5.34 A (continued)

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid …

73

(continued) Maximum rated power (Pmax )

87 W

Number of module required

5

Simulation Results of Ocean Wave Chamber See Figs. 5, 6, 7 and 8. PV System Modeling Results See Figs. 9, 10, 11, 12 and 13. A Study of Hybrid-Grid System Simulation Results See Figs. 14, 15, and 16. Subsystem of SOFC System See Figs. 17, 18 and Table 2. Conclusions: Most of sustainable power sources are erratic and heavily depend on climatic conditions, making it difficult to build a hybrid renewable power producing system. To meet the high energy demands in this challenging environment, researchers have been working on a mixture power creating framework that coordinates matrix associated battery capacity with PV, wave, and SOFC renewable energy sources, all controlled by an efficient power management algorithm. Maximum power output voltage of 650 V is exactly the same as the system’s reference voltage, and therefore, it is clear that the system can operate properly at this voltage.

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Ipv s

Out1

+

+

Subsystem

Controlled Current Source

Diode

Series RLC Branch

i -

Current Measurement

1 A

+ v Voltage Measurement

Timer s

-K-

+ Add

-

Gain2

Controlled Voltage Source 25 Constant2

Fig. 3 Subsystem of solar PV

Fig. 4 Subsystem of ocean wave

B 2

Goto1

Goto

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid … Fig. 5 Air velocity power of chamber Pa

Fig. 6 Chamber pressure power Pp

Fig. 7 Total power of chamber Pch

75

76 Fig. 8 Total power (Pw ) generated by OWC system

Fig. 9 Total output current (I pv ) of PV array

Fig. 10 Total output voltage (V pv ) of PV array

S. Sunanda and M. Lakshmi Swarupa

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid … Fig. 11 Total output power Ppv of PV array

Fig. 12 Total DC link voltage

Fig. 13 Battery voltage

77

78 Fig. 14 Three-phase grid voltage versus time (s)

Fig. 15 Three-phase grid current versus time (s)

S. Sunanda and M. Lakshmi Swarupa

Design of Grid-Connected Battery Storage Wave Energy and PV Hybrid …

Fig. 16 Simulation results of SOFC system

Fig. 17 Three-phase grid voltage versus time (s)

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Fig. 18 Three-phase grid voltage versus time (s)

Table 2 Comparison table

Generating Voltage (V)

Current (A)

Ocean wave system

410

0.18

Solar PV system

450

0.19

SOFC system

510

0.22

Hybrid system

650

0.21

References 1. Podder S, Khan RS, Alam Mohon SMA (2015) The technical and economic study of solar-wind hybrid energy system in coastal area of Chittagong, Bangladesh. J Renew Energy 2015:1–10 2. Baek S, Kim H, Chang H (2015) Optimal hybrid renewable power system for an emerging island of South Korea: the case of Yeongjong island. Sustainability 7(10):13985–14001 3. Ahmad S, Albatsh FM, Mekhilef S, Mokhlis H (2014) A placement method of fuzzy based unified power flow controller to enhance voltage stability margin. In: Power electronics and applications (EPE’14-ECCE Europe), Aug 2014, pp 1–10 4. Mahamudul H, Silakhori M, Henk Metselaar I, Ahmad S, Mekhilef S (2014) Development of a temperature regulated photovoltaic module using phase change material for Malaysian weather condition. Optoelectron Adv Mater Rapid Commun 8(11–12):1243–1245 5. Samrat NH, Ahmad NB, Choudhury IA, Taha ZB (2014) Modeling, control, and simulation of battery storage photovoltaic wave energy hybrid renewable power generation systems for island electrification in Malaysia. Sci World J 2014:1–21 6. Ozaki Y, Miyatake M, Iwaki D (2010) Power control of a stand-alone photovoltaic/wind/ energy storage hybrid generation system with maximum power point tracker. In: International conference of electrical machines and systems (ICEMS), Nov 2010 7. Jian C, Yanbo C, Lihua Z (2011) Design and research of off-grid wind solar hybrid power generation systems. Power Electron Syst Appl 8. Lund H (2006) Large-scale integration of optimal combinations of PV, wind and wave power into the electricity supply. Renew Energy 31(4):503–515 9. Rehman S, Mahbub Alam M, Meyer JP, Al-Hadhrami LM (2012) Feasibility study of a wind– PV–diesel hybrid power system for a village. Renew Energy 38(1):258–268 10. Bhende CN, Mishra S, Malla SG (2011) Permanent magnet synchronous generator-based Standalone wind energy supply system. IEEE Trans Sustain Energy 2(4):361–373

Power Quality Enhancement with PSO-Based Optimisation of PI-Based Controller for Active Power Filter G. Dhulsingh and M. Lakshmi Swarupa

1 Introduction Harmonic disturbances in power systems have increased as a result of the recent widespread expansion of power electronic equipment. Some of the loads connected to the AC power grid are nonlinear and hence need harmonic and reactive power. Nonlinear loads, together with the current harmonics they produce, have been connected to issues such equipment overheating, capacitors blowing, motor vibration, and poor power factor [1]. Researchers and the power industry have worked to enhance power quality. Passive filters are effective in reducing power factor and current harmonics. Passive filters, on the other hand, have a number of drawbacks, including significant scale resonance and a fixed compensating behaviour, making them ineffectual [2]. By injecting compensating currents [4] and correcting reactive power of nonlinear loads [5], the shunt active is often employed rather than inactive channels to further develop power quality and to compensate for current harmonics generated by distorting loads [3]. The present-day standard-setting signals have been determined using a p-q theory of instantaneous power. This idea was first presented in Japanese in 1983 [6] by Akagi, Kanazawa, and Nabae. A new approach of compensatory current control, the PI-PSO optimised PI controller employing the particle swarm algorithm, is the focus of the work that has just been presented. The PI regulator’s parameters need to be optimised [7]. In this study, we frame the existing difficulty of PI controller design as an optimisation issue. There are two performance directories used in this study, the integral absolute error of step response and maximum overshoot, to establish PI control settings for a given system’s optimal performance. With the goal of improving compensation performance and reducing harmonic distortion through the distribution of electrical lines under all voltage situations, we present an optimisation G. Dhulsingh · M. Lakshmi Swarupa (B) Department of EEE, CVR College of Engineering, Ibrahimpatnam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_9

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method for SAPF [8–10]. You can reach your objectives by minimising the fitness function. The proposed methodology for finding a solution is based on the particle swarm optimisation (PSO) method, which uses the social interaction paradigm to solve optimisation problems. It does a pursuit by changing the headings of discrete vectors (likewise called “particles”) that are imagined to move in a spherical fashion in a higher dimensional space. Randomly, each particle is attracted to the spots where it has previously performed the best and where it has previously performed the best of its neighbours. The original PSO has undergone two notable revisions since its creation, each of which sought to create a balance between two opposing requirements. As Shi and Eberhart [11] explained, each particle’s velocity is scaled down by an additional “inertia weight” term that decreases in a linear fashion throughout a run. Clerc and Kennedy [12] developed the concept of a “constriction factor,” which employs a coefficient to apply a second time to the full right side of the formula. Their generalised molecule swarm model permits a boundless number of strategies for striking a balance between discovery and organisation.

2 Power Quality Any problem with voltage, current, or frequency that leads to malfunctioning client equipment is considered a power quality issue. Many types of highly sensitive electronic and electric equipment experience a decline in performance as a result of the poor facility quality. This is because the supplied voltage must be within the reported value’s assured tolerance, so that the excellent quality of power may be provided. Within acceptable distortion bounds, the wave shape should be a smooth undulation. All three phases should have equal voltage. Availability of supplies should be consistent, meaning they would not run out in the middle of a project. These days, there are a plethora of potential points of failure in both commercial and industrial computer networks and machines. Electrical power quality is commonly questioned when mechanical systems fail or computer networks break for no apparent cause. It is easy to point the finger at since it is hard to see and even harder to fight against. It is not easy to figure out what is causing power quality issues, and sometimes the electricity does not seem to care. In a factory, for instance, a malfunctioning automatic assembly machine might have been caused by a leak in the pressurised gas supply or a malfunctioning hydraulic valve. Transmission line tee placements that are overly approximative, creating reflections and signal loss, might be the root cause of problems on a local area network in a building. Like many other modern business fields, the container crane sector is fascinated by the possibilities presented by increasing degrees of automation and incorporating flashy features like colour diagnostic screens and lightning-fast speeds. While these components and the computer-based improvements to which they contribute are certainly important to the smooth running of the terminal, it is essential not to lose sight of the original motivation. The cement that holds the building blocks of inspiration together is the quality of the power they employ. Terminal running costs, crane dependability, environmental impact, and

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upfront interest in power dispersion framework to help new crane establishments are undeniably affected by power quality. Utilizing energy keenly “might be a savvy natural and business procedure which sets aside you cash, minimises emissions from producing facilities, and conserves our Natural resources,” according to the utility company newsletter that came with my most recent monthly utility bill. We are all aware that the demands placed on container crane performance continue to rise at a staggering pace. Power requirements for the next generation of container cranes now up for bid are estimated to be between 1500 and 2000 kW per year, which is about twice the absolute typical demand from quite a while back. Quick development in power utilisation, an expansion in the quantity of compartment cranes, retrofits to existing crane drives utilizing SCR converters, and the enormous AC and DC drives expected to control and work these cranes will before long raise public familiarity with the capacity quality issue.

2.1 Power Quality Problems Therefore, the following definition of power quality concerns will be used throughout this book: “any power problem that leads to failure or disoperation of client equipment appears as an economic expense to the user or has bad effects on the environment.” Ability concerns that impair power quality, as they pertain to the container crane sector, include: • • • • •

The power factor Harmonic distortion Rapid changes in voltage Sagging or dropping voltage The voltage increases.

1 Voltage sag (or dip)

Description: The voltage drops from 10 to 90% of the normal rms voltage at the power frequency, and they may last anywhere from half a cycle to a minute Causes: Issues with the distribution or transmission system (usually on parallel feeds) and an issue that has arisen because of the customer’s configuration. Connecting massive weights and cranking up enormous motors is a heavy-duty operation Consequences: Failure of microprocessor-based control systems (personal computers, programmable logic controllers, automatic shutdown devices, etc.) may halt production. The tripping of electrical and mechanical relays and contactors. In electric rotating machinery, disconnection causes a drop in performance (continued)

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(continued) 2 Very short interruptions

Description: Momentary or temporary loss of all electrical power, lasting from milliseconds to seconds Causes: In order to decommission a problematic piece of the network, protective devices must be opened and then automatically reclosed. Insulation failure, lightning, and insulator flashover are the primary sources of faults Consequences: Repercussions include information loss, malfunctioning data processing equipment, and the triggering of safety mechanisms Refusal to operate high-value machinery if it is not equipped to handle with the issue, including ASDs, computers, and programmable logic controllers

3 Long interruptions

Description: An whole blackout lasting more than a couple of seconds Causes: Storms and items (trees, automobiles, etc.) impacting wires or poles, fire, human error, improper coordination, or failed protective mechanisms are all potential reasons for a power outage in a power grid Consequences: Machines will stop working as a result

4 Voltage spike

Description: Voltage values fluctuate very rapidly over times ranging from microseconds to milliseconds. Low-voltage fluctuations may nevertheless reach several thousand volts Causes: Several factors may disrupt electrical power, including: lightning, switching lines or power factor correction capacitors, and disconnecting large loads Consequences: Component destruction (especially electronic components), insulating material destruction, processing mistake or data loss, and electromagnetic interference are all possible outcomes

5 Voltage swell

Description: Voltage spikes are temporary, power–frequency voltage increases that last more than one cycle but usually less than a few seconds and fall outside of the acceptable range Causes: Heavy loads starting and stopping, undersized power supplies, and poorly controlled transformers (especially during off-peak times) are the major culprits behind this phenomenon Consequences: If the voltage levels are too high, it might cause problems including data loss, flashing lights and displays, and the complete shutdown or destruction of delicate machinery (continued)

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(continued) 6 Harmonic distortion

Description: Waveforms of voltage or current are not sinusoidal, as described. The shape of the wave is proportional to the addition of sine waves that vary in amplitude and phase and have frequencies that are multiples of the power-system frequency Causes: Electric hardware working over the knee of the charge bend (attractive immersion), like curve heaters, welding machines, rectifiers, and DC brush engines, are the traditional examples of this kind of power generation. All nonlinear loads typical of modern sources, such as computers, data centres, and even high-efficiency light bulbs, are examples of power electronics Consequences: Consequences include an increase in the likelihood of reverberation, nonpartisan over-burden in three-stage frameworks, link and hardware overheating, electric machine proficiency misfortune, electromagnetic obstruction with correspondence frameworks, metre reading errors under average conditions, and nuisance thermal protections tripping

7 Description: Amplitude-modulated voltage oscillation at a rate of Voltage fluctuation 0–30 Hz Causes: The most common culprits are arc furnaces, frequently started and stopped electric motors (such those found in elevators), and oscillating loads Consequences: As for the repercussions, they are rather standard for undervoltage. The most noticeable effect is the flashing of lights and displays, which may cause a feeling of insecurity in one’s vision 8 Noise

Description: Signals of a high frequency are superimposed over the power frequency waveform Causes: Electromagnetic interferences induced by Hertzian waves, such as those emitted by microwave ovens, televisions, and the radiation emitted by welding equipment, arc furnaces, and electronic devices. A possible contributing factor is poor grounding Consequences: The results are disruptions to sensitive electronic equipment, although they are typically harmless. Potential for causing both data loss and processing mistakes

9 Voltage unbalance

Description: When the magnitudes or phase angle discrepancies of the three voltages in a three-phase system are unequal, we say that there is a voltage variation Causes: Large single-phase loads (such as those produced by induction furnaces or traction loads) and improper three-phase distribution of all single-phase loads are to blame (this may be also due to a fault) Consequences: In an unbalanced system, the three-phase loads are subject to injury from a potentially catastrophic negative sequence. Three-phase induction motors are the most sensitive loads

Complete consonant current and voltage twisting are exacerbated by the AC and DC variable speed drives utilised in compartment cranes. However, DC SCR drives only function at this power factor, whereas the more ideal average power factor is generated through SCR phase control. Another issue is line notching, which happens when SCRs commutate and results in transient apex recovery voltages that may be three to various times the common line voltage, dependent upon the system

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impedance and thusly the size of the drives. Both the repeat and force of these framework disruptions change as the drive’s speed increases. When running at low speeds, AC and DC drives tend to emit the most harmful harmonic currents. As the SCRs are staged on to create evaluated or base speed, the power element will ascend from its base worth while the DC drives are running at low velocities or during early speed increase and deceleration stages. Once you go over your starting speed, your ability factor stays fairly consistent. Cranes used to move containers around ports can move slowly while the operator searches for and places containers. Low power factor increases the load on the utility or engine alternator in terms of kilowatt-hours. The voltage stability may be negatively impacted by low power factor loads, which can abbreviate the existence of delicate hardware or prompt it to bomb discontinuously. High recurrence symphonious voltages and flows, as well as voltage homeless people brought about by DC drive SCR line indenting and AC drive voltage hacking, may cause severe noise and disruption to sensitive equipment. In our experience, end users seldom consider power quality concerns when purchasing a container crane since they are either completely oblivious to them or see no economic consequence to ignoring them. As a result, power factor was acceptable, and the introduction of harmonic current was negligible, until the advent of solid-state power supply. However, problems with power quality did not appear until the population of Grus exploded, cranes required more energy, and static power conversion became the norm. Nobody was ready for the simultaneous emergence of harmonic distortion and power factor concerns. When bidding on new crane construction projects, crane manufacturers and electrical drive system suppliers still sidestep the issue. Facility quality is either deliberately or accidentally overlooked in favour of focusing on other, more pressing concerns. There are options for resolving power quality issues. These solutions will not set you back nothing, but they should provide a fair return on your money. But if power quality is not specified, it is unlikely to be provided. As a result of these measures, power quality may be enhanced: • • • • •

Correction of the power factor, Harmonic filtering, Notch filtering for certain lines, Suppression of transient voltage surges, Equipped with the right earthing systems.

The person responsible for the container crane’s specification and/or purchase may not always be aware of the possible power quality concerns that may arise. It is possible that the people responsible for the container cranes’ specifications and purchases are either unaware of the problem and do not want to deal with it since it is not their responsibility to pay the utilities’ bills, or both. As a result, power quality boundaries, for example, power factor revision as well as symphonious sifting could not be expressly expressed in holder crane necessities. Not only that, but many of these requirements for power quality equipment fail to adequately identify the elements that must be met. At an early stage in developing the Crane standard,

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• Identify any contractual or regulatory stipulations with the utility business via discussion. • Discuss the suggested drive sizes and technology with the electrical drive providers to establish facility quality profiles that might be normal from the task. • Consider the impact of future utility liberation on the terminal’s long-term growth plans as you assess the financial viability of power quality correction in this specific instance.

2.2 The Benefits of Power Quality The costs of running a container terminal, the dependability of the terminal’s equipment, and the experience of other customers using the same utility service are all impacted by the power quality in the terminal’s surrounding environment.

3 PSO Inspired by the cooperative nature of animal societies like bird flocking and fish schooling, particle swarm optimisation (PSO) may be a population-based stochastic optimisation method. Using what it has learned from the case, PSO then applies its knowledge to the optimisation issues at hand. Every possible answer in PSO might be a "bird" somewhere in the search area. Particle is the term that has been adopted for it. Particles have velocities that guide their flight and fitness values that are assessed by the fitness function that has to be optimised. By aligning with the best particles at the moment, the particles are propelled swiftly through the material universe. PSO starts with a large number of random particles (solutions), and it updates its generational pool in order to find the optimal solution. Every particle receives an update at each iteration based on the average of the two "best" values. Its key advantage is that it has already attained its optimal solution (level of fitness). (The fitness rating is also recorded.) This parameter is denoted as Pbest . The particle swarm optimiser also keeps track of the best value ever attained by any particle in the population. It is possible that this top score is the finest in the whole world and that we should refer to it as gbest . For example, the ith particle is represented as xi = (xi1, xi2, … xid)in the ddimensional space. The most accurate prior location of the particle I is stored and shown as:   Pbesti = Pbesti1 , Pbesti2 , . . . Pbestid

(1)

Among the particles in the set, gbest is all the file for the best molecule. Particle i’s speed is denoted by the formula vi = (vi1, vi2, … vid). Utilizing the ongoing speed and the separation from Pbest-id to gbest-id , we can get the changed velocity and

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location of each particle, as illustrated in the following formulas.     (t+1) (t) (t) (t) + c2 rand() gbestm − xi,m vi,m = wvi,m + c1 rand() Pbesti,m − xi,m

(2)

(t+1) (t) (t+1) xi,m = xi,m + vi,m

i = 1, 2. . . . n; m = 1, 2, . . . d;

(3)

where n d t (t) vi,m w c1 , c2 rand() (t) vi,d Pbesti gbesti

Number of particles in the group, Dimension, Pointer of iterations (generations), Velocity of particle i at iteration t, Inertia weight factor, Acceleration constant. Random number between 0 and 1. Current position of particle i at iterations, Best previous position of the ith particle. Best particle among all the particles in the population.

4 Optimisation of PI Controller By Using PSO In this work, SAPF is treated as a managed plant, and its control structure is shown in Fig. 1. Following is the typical format for a PI controller: t y(t) = k p ∗ e(t) + ki

e(t).dt

(4)

0

where y The control output. pk Proportional gain. ik Integral gain. Inverter PWM signal is generated from the control output. Error signal is interpreted as the voltage or current that is different from the reference current and the injected current. Planning a standard PI controller with an expert’s expertise in mind is exciting, but in our work, we have relied on trial and error to figure out the values for K p and K i . The main contribution of this work is a method for determining the optimal values of the PI parameters shown in Fig. 2 for minimising the steady-state

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Fig. 1 Control diagram of SAPF system

error of the system. The motivation behind ideal plan of a flows PI regulator is to decide the upsides of K p and K i for a given plant to such an extent that the presentation lists for the transient reaction are limited. It is shown in Fig. 3 how PSO algorithms evolve over time. The first step in PSO is to generate seed populations. True genetic blueprints, or chromosomes, make up the population. The “fitness function,” which measures the population’s overall effectiveness, is the analogous assessment. The higher fitness index indicates a more robust level of performance. When the wellness capability not set in stone, the worth of the wellness capability and the age number conclude whether the advancement cycle proceeds (Maximum emphasis number came to?). The best molecule likelihood, Pbest , and populace best gbest may not entirely set in stone (the best development, all

Fig. 2 Optimised PI controller-based PSO

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Fig. 3 Flow chart of the PSO algorithm

things considered). Particles’ velocities, locations, best g, and best P values are all updated, resulting in a revised best P value.

5 Conclusion In this project, inverter-based AF, namely shunt active power filter (SAPF), is used to mitigate the harmonics triggered by nonlinear loads/unbalanced loads in the source voltage and current by injecting the compensating currents. The suggested SAPF is implemented to reduce the prevalence of harmonics and restore supply equilibrium. The suggested filter uses a PI controller with self-tuning to maintain a constant DC-link voltage, and its parameters are tweaked and improved with the help of an intelligent approach based on particle swarm optimisation (PSO).

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References 1. Essamudin AE, El-Sayed A-H, Abdul-Ghaffar HI (2015) Particle swarm optimization based tuning of PI controller for harmonic compensation of non-linear dynamic loads. In: Fourth international conference on advanced control circuits and systems, ACCS’015, Nov. 2015, Luxor, Egypt 2. Ebrahim EA, El-Sayed A-H, Abdul-Ghaffar HI (2016) Particle swarm optimization-based control of shunt active-power filter with both static and dynamic loads. J Electr Eng 16(1):1–12 3. El-Saady G, El-Sayed A-H, Ebrahim EA, AbdulGhaffar HI (2015) Harmonic compensation using on-line bacterial foraging optimization based three-phase active power filter. WSEAS Trans Power Syst 10:73–81 4. Rabelo RD, Lemos MV, Barbosa D (2012) Power system harmonics estimation using particle swarm optimization. In: IEEE world congress on computational intelligence, Brisbane, Australia, 10–15 June 2012, pp 1–6 5. Uzuka T, Hase S, Mochinaga Y (1996) A static voltage fluctuation compensator for AC electric railway using self-commutated inverter. J Electr Eng Jpn 117(3):1521–1528 6. Takeda M, Murakami S (1997) Development of a three-phase unbalanced voltage fluctuation compensating system using a self-commutated static VAR compensator. J Electr Eng Jpn 12(3):826–834 7. Kalantari M, Sadeghi MJ, Fazel SS, Farshad S (2010) Investigation of power factor behavior in AC railway system based on special traction transformers. J Electromagn Anal Appl 2:618–626 8. Sun J, Duan Y, Xiong Y, Zhang B (2012) Study of reactive power compensation for high speed railway design. Energy Procedia Sci Direct 17(Part A):414–421; Dai NY, Lao KW, Wong M (2013) A hybrid railway power conditioner for traction power supply system. IEEE Trans Industr Electron 45(2):1326–1331 9. Natesan P, Madhusudanan G (2014) Compensation of power quality problems in traction power system using direct power compensator. Int J Innov Res Sci Eng Technol 3(3):277–280 10. Sangle SV, Reddy CHM (2015) An innovative topology for harmonics and unbalance compensation in electric traction system using direct power control technique. Int J Innov Res Sci Eng Technol 4(12):12213–12220 11. https://www.researchgate.net/publication/228559771_Power_Quality_Problems_and_New_ Solutions 12. Igual R, Medrano C, Arcega FJ, Mantescu G (2018) Integral mathematical model of power quality disturbances. In: 2018 18th international conference on harmonics and quality of power (ICHQP), pp 1–6. https://doi.org/10.1109/ICHQP.2018.8378902 13. Deokar SA, Waghmare LM (2014) Integrated DWT–FFT approach for detection and classification of power quality disturbances. Electr Power Energy Syst 61:594–605 14. Decanini JGMS, Tonelli-Neto MS, Malange FCV, Minussi CR (2011) Detection and classification of voltage disturbances using a fuzzy-ARTMAP-wavelet network. Electr Power Syst Res 81:2057–2065 15. Naderian S, Salemnia A (2016) An implementation of type-2 fuzzy kernel based support vector machine algorithm for power quality events classification. Int Trans Electr Energy Syst 27(5) 16. Borges FAS, Fernandes RAS, Silva IN, Silva CBS (2016) Feature extraction and power quality disturbances classification using smart meters signals. IEEE Trans Industr Inform 12(2) 17. Abdoos AA, Mianaei PK, Ghadikolaei MR (2016) Combined VMD-SVM based feature selection method for classification of power quality events. Appl Soft Comput 38(C):637–646 18. Moravej Z, Pazoki M, Abdoos AA (2011) Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances. Euro Trans Electr Power 21:212–222 19. Pahasa J, Ngamroo I (2012) PSO based kernel principal component analysis and multi-class support vector machine for power quality problem classification. Int J Innov Comput Inf Control 8(3A) 20. Lee CY, Shen YX (2011) Optimal feature selection for power-quality disturbances classification. IEEE Trans Power Del 26(4)

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21. Hooshmand R, Enshaee A (2010) Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm. Electr Power Syst Res 80:1552–1561 22. Kanirajan P, Kumar VS (2015) Power quality disturbance detection and classification using wavelet and RBFNN. Appl Soft Comput 35:470–481 23. Kubendran AKP, Loganathan AK (2017) Detection and classification of complex power quality disturbances using S-transform amplitude matrix–based decision tree for different noise levels. Int Trans Electr Energ Syst 27:e2286

Monitoring and Control of Motor Drive Parameters Using Internet of Things Protocol for Industrial Automation G. MadhusudhanaRao, Srinivas Dasam, M. Pala Prasad Reddy, and B. Rajagopal Reddy

1 Introduction In today’s fast-paced, fiercely competitive industrial climate, a company must be flexible, cost-effective, and efficient to succeed. This has increased the need for industrial control systems and automation in the process and manufacturing industries, which will streamline operations regarding speed, dependability, and output. Automation is becoming more vital to daily life and the global economy. Electric drives are often used in industrial plants due to their efficient electromechanical conversion. A frequency converter is used by electric drives to power an electric motor. The system’s processing unit and industrial network connectivity are included in the latter. The Internet is sporadically used in contemporary manufacturing. Electric drives receive command references from the conventional ones and frequently implement them, such as position, speed, and torque; when problems occur, the electric drive will respond adversely via the network with alarm conditions [1]. To enable new services and applications that could ultimately increase the effectiveness and safety of industrial operations, Industry 4.0 and the industrial Internet efforts seek to deepen the connectivity between the equipment in an industrial plant [2, 3]. Thanks G. MadhusudhanaRao (B) Department of Electrical Engineering, OP Jindal University, Raigarh, India e-mail: [email protected] S. Dasam Department of Electrical and Computer Engineering, Mattu University, Metu, Oromia, Ethiopia e-mail: [email protected] M. Pala Prasad Reddy Department of EEE, Institute of Aeronautical Engineering, Hyderabad, India e-mail: [email protected] B. Rajagopal Reddy Department of EEE, Vardhaman College of Engineering, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_10

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to high-speed industrial networking technology, network nodes can communicate more quickly [4]. But the primary goal of these networks and network protocols is to offer periodic, synchronous traffic with exact delay requirements. As the Industry 4.0 vision requires, whether the same technology could handle various traffic types with varying service requirements is still debatable. IoT-based situational monitoring is starting to take shape. They demonstrate real-time electrical energy monitoring [5].

1.1 Automation Automation replaces human labor’s need to produce goods and services using control systems and information technology. Automation goes beyond mechanization within the context of industrialization. Automation dramatically reduced the need for human sensory and cerebral talents, even though mechanization provided technology to assist human operators with the physically demanding portions of their tasks. Automation control system: A system that can initiate, modify, act, display, or measure the variables of the process and stop it from generating the desired outcomes.

1.2 Material and Method The design of DC motor control based on IoT is shown in Fig. 1. There are four components in the system. The DC converter is the initial component; its job is to supply the motor with 30 V. The control of DC motors is discussed in the following passage. In this project, the speed of a DC motor is controlled using the PID algorithm. The smartphone serves as the control device thanks to the IoT architecture of this DC motor control. IoT nodes can only connect to the Internet and communicate with users through routers. Data transfer from node to node is the last stage. This research, however, only investigates one node, IoT-based DC motor control. In this project, messaging applications are used to control and monitor the speed of DC motors.

1.3 Relay and Contactor An electrically controlled switch is a relay. A magnetic field produced by current flowing through the relay coil pulls a lever and modifies the switch contacts. Relays have two switch positions since the coil current can be on or off, and most feature double-throw (changeover) switch contacts, as illustrated in the diagram. A digitally operating electronic device has a programming memory to perform certain operations like logic, sequencing, timing, counting, and arithmetic to operate various machines or processes using digital or analog modules.

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Fig. 1 System block diagram

(a) Sensors: Switches, flow, level, pressure, temp, transmitters, etc. (b) Output devices/actuators: Motors, valves, solenoids, lamps, or audible devices.

2 Architecture of SCADA This phrase is SCADA or supervisory control and data acquisition. SCADA systems monitor and manage a facility or equipment in the telecommunications, water and waste management, energy, oil and gas refining, and transportation industries. These systems comprise a SCADA central host computer, several remote terminal units (RTU) or programmable logic controllers (PLCs) [6], and another device for data transmission between the primary host and operator terminals. As an illustration, a. A SCADA system gathers information, relays it to the leading site, and then tells the home station about a leak. It then carries out the required analysis and control, including determining whether the leak is critical and effectively and logically presenting the data. These systems can be very intricate, like one that monitors every activity in a nuclear power plant or the performance of a public water system, or they can be very straightforward, like one that monitors the environmental conditions of a small office building. Historically, SCADA systems have used the public switched network (PSN) for monitoring. In addition, several techniques are currently monitored using the corporate local area network (LAN)/wide area network (WAN) design. These days, wireless technologies are very often utilized for surveillance (Fig. 2).

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Fig. 2 Architecture of the SCADA

2.1 Human–Machine Interface (HMI) The SCADA system is mainly made to monitor and manage the process or plant automatically [7, 8]. However, for various reasons, provisions are established for human operators to monitor their operation continuously and to take action as and when necessary. An interface between the SCADA system and the human operators is essential. The MTU in the control room provides the same usual procedure. A computer is the center of the MTU’s design and operation. Consequently, a human– computer interface (HCI), also known as a human–machine interface (HMI) or a visual operator interface, is used to implement the human-SCADA interface (GOI).

2.2 Internet of Things SCADA System The fourth generation of SCADA systems adopted the Internet of things and commercial cloud computing, lowering the SCADA systems’ infrastructure costs. As a result, the fourth-generation SCADA systems are much easier to maintain and integrate than prior ones. Additionally, utilizing the horizontal scale offered by cloud computing, these SCADA systems can report statuses in real time, making it feasible

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and adequate to build more complex control algorithms on traditional PLCs. Additionally, by offering a transparent and manageable security barrier, open network protocols like TLS embedded into the Internet of things can be utilized to solve the security problems associated with decentralized SCADA implementations, such as a heterogeneous mix of proprietary network protocols [9]. IoT-based situational monitoring is starting to take shape. It has implemented real-time electrical energy monitoring [10]. Voltage, current, and neutral current are all measured during data monitoring. To track the amount of electricity utilized, the data is used. The authors [11] have created IoT-based monitoring systems for solar power plant efficiency at the same time. They use the Raspberry Pi and the message communication protocol (MCP) for smartphone access. Improved DC motor control performance is rising alongside the advancement of IoT-based monitoring. The author created a control system that uses a PID algorithm [12]. The majority of organizations have adopted the Internet of things as a technology. It facilitates completing tasks devoid of manual labor and human assistance. This integrated technology enables the object to make better decisions by interacting with internal states or the outside world (Figs. 3 and 4). The PID controller will operate by using the following equation Ds = K p +

Ki + KD s

Fig. 3 Equivalent circuit of DC motor

Fig. 4 Block diagram of PID controllers for the monitoring of DC motor

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The proposal applies to the speed of a DC motor. Designing a motor speed control with great precision and a quick reaction was done [13]. Although the findings of earlier studies were noteworthy, the PID algorithm is necessary to achieve satisfactory results when using DC motors with a full load. The development of Internet of things (IoT)-based DC motor control sources DC motors from the supply. This project helps to develop PID algorithms and IoT-based rules for controlling the speed of DC motors [14]. It is a method of linking physical objects to other components over the Internet. The term “things” refers to items, such as people or vehicles, that have embedded sensors and can collect and transfer data over the Internet. It mainly comprises intelligent devices with embedded CPUs, sensors, and connectivity to gather and deliver data from various contexts. The IoT hub or gateway is connected to devices that exchange locally collected and processed data. These devices might also establish connections with other devices and take action based on the data they get from those devices. Most of the time, these technologies operate independently of human input. Since everything in the modern world is connected to the Internet, its scope is extensive. It links the various systems’ devices to the Internet, allowing for remote control of the objects representing the different systems’ devices. It aids in obtaining more information and locations, as well as other methods of boosting productivity and enhancing safety and security. IoT is an analytics and security aid for the firm in improving performance to provide positive outcomes. Numerous businesses, including those in the manufacturing, transportation, infrastructure, oil and gas, insurance, and other retail sectors, stand to gain from it, and some have already started doing so. Without assistance from people or other human interaction, the Internet of things platform often enables the device or object being observed to recognize and comprehend the scenario. The Internet of things connects billions of devices, and information is collected, transferred, and sent from many data points. The security and privacy of IoT are significant problems for various enterprises because of its extensive structure and surface area. Since these devices are connected, an attacker can use one weakness to modify all the data simultaneously. Regular device updates are the leading cause of these assaults (Fig. 5).

2.3 Data Communication SCADA systems have traditionally employed radio and direct-wired connections, whereas extensive systems like railways and power plants also frequently use SONET/SDH. The term “communication” is commonly used to describe the function of a SCADA system’s remote management or monitoring. Some customers favor having SCADA data travel through their current corporate networks or sharing the network with other programs. The legacy of the original low-bandwidth protocols is still evident, nevertheless. SCADA protocols are designed to be very concise. Most of the time, the RTU is only polled by the master station when data is sent. Standard

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Fig. 5 Internet of things-based SCADA system

historical SCADA protocols include RP-570, Control, Profibus, and Modbus RTU. Despite being widely used and adopted, each communication protocol is unique to a particular SCADA manufacturer. Acceptable protocols are IEC 61850, DNP3, and IEC 60870-5-101 or -104. All significant SCADA vendors accept and standardize these communication protocols. Several of these protocols can operate thanks to TCP/IP extensions. Although standard networking specifications like TCP/IP blur the line between traditional and industrial networking, each meets fundamentally different requirements. The infrastructure can be designed to meet the availability and reliability requirements defined by the SCADA system operator, be self-contained, and have built-in encryption. The key advantages of this are these. First, consumer-grade VSAT had a poor performance history. Modern carrier-class systems provide the level of service needed by SCADA. Second, RTU and other autonomous controller devices were developed before widespread industry interoperability standards. As a result, developers and their management produced many control protocols. The more prominent vendors were also incentivized to develop their policies to “lock in” their clientele. Here is a list of automation protocols. OLE for process control (OPC), which was initially not meant to be a part of an industrial network, has recently gained popularity as a solution for intercommunicating various hardware and software.

2.4 Data Presentation The SCADA system typically presents the data to the operating personnel graphically in a mock diagram. This suggests that the operator can visualize the control system conceptually. The operator can then turn the pump off. The HMI software will also

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show the fluid flow rate in the pipe as it decreases in real time. Mimic diagrams may also use digital images, line graphics, and schematic symbols to represent various process components. The operators or system maintenance staff can modify how these points are described in the interface using the drawing software customarily included in the SCADA system’s HMI package.

2.5 Data Acquisition The back-EMF technique is not used in this project, which is sensorless. Instead, the voltage sensor on our IoT nodes detects the back EMF, which ADC converts to a digital value. The DC motor will move if it receives power from a voltage source. The voltage changes to opposite the permanent magnet coil when the motor turns. The term "EMF technique" refers to this. Knowing the EMF value allows you to compute the motor speed because EMF and motor speed are intimately related. Figure 3 represents a DC motor’s equivalent. For data analysis or auditing purposes, the SCADA system maintains a log of the past condition of these inputs and outputs. Although SCADA systems can be built to control specific field variables, this is typically a bad scenario. In some situations, operator input is necessary for these semi-autonomous systems to operate well. As a result, the operator’s level of “control” over the SCADA screen (i.e., HMI) is typically far lower than the control carried out by a PLC. Therefore, SCADA systems are primarily used for data collecting and monitoring, with control capabilities used in unusual or complicated situations. SCADA enables operators (and control systems engineers) to monitor a plant overview remotely and react to any abnormal conditions. One thing to remember is that a SCADA system technically consists of PLCs with which SCADA will communicate.

3 Conclusion To summarize this article, a framework called supervisory control and data acquisition uses computers, communication channels, and software to remotely monitor and control devices in a control framework that often has a vast scope. Frameworks for supervisory control and data acquisition often focus on data and decisionmaking. When an input device’s state changes, SCADA control frameworks will function immediately. However, alternatively, immediate modifications that drive programmable logic controllers frameworks may be done. But, by implementing PLC-based drive monitoring and sending emails for every drive status, such as communication problems, overload, rotor stalls, etc. The 50W loaded IoT-based DC motor control system was constructed and tested. This paper outlines the creation of PID-based DC motor control based on the Internet

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of things. The motor input voltage affects motor speed. Therefore, the voltage automatically increases until it reaches the reference value when the motor input voltage is lower than the reference voltage. This effort aims to assess how a wide range of novel applications with various performance needs may be served by current industrial network technology. However, the peculiarities of the protocols and the requirement for recurring delay-constrained transfers may severely restrict the use of the communication resource. Nevertheless, given the high demands of some of these applications, such as multi-drive control, additional research and development are required to support them with wireless connectivity.

References 1. Benzi F, Buja GS, Felser M (2005) Communication architectures for electrical drives. IEEE Trans Industr Inform 1(1):47–53 2. Wollschlaeger M, Sauter T, Jasperneite J (2017) The future of industrial communication: automation networks in the era of the internet of things and industry 4.0. IEEE Industr Electron Mag 11(1):17–27 3. Colombo AW, Karnouskos S, Kaynak O, Shi Y, Yin S (2017) Industrial cyber-physical systems: a backbone of the fourth industrial revolution. IEEE Industr Electron Mag 11(1):6–16 4. Galloway B, Hancke GP (2012) Introduction to industrial control networks. IEEE Commun Surv Tutor 15(2):860–880 5. Sreenivasulu C, Girish Kumar ANO, Madhusudhana Rao G (2013) Position control for Digital DC drives and PLC. IJERD 67:61–68 6. Ioannides MG (2004) Design and implementation of PLC-based monitoring control system for induction motor. IEEE Trans Energy Convers 19(3):469–476 7. Elsaid RAS, Mohamed WA, Ramadan SG (2016) Speed control of induction motor using PLC and SCADA system. Int J Eng Res Appl 6(1):98–104 8. Rathore RS, Sharma AK, Dubey HK (2015) PLC-based PID implementation in process control of temperature flow and level. Int J Adv Res Eng Technol 6(1):19–26 9. Nakiya AN, Makwana MA (2012) An overview of PLC based control panel system for external plunge grinding machine and CNC machine. Int J Mod Eng Res 2(6) 10. Phung MD, De La Villefromoy M, Ha Q (2017) Management of solar energy in microgrids using IoT-based dependable control. In: 2017 20th international conference on electrical machines and systems (ICEMS). IEEE, pp 1–6 11. Choi CS, Jeong JD, Han J, Park WK, Lee IW (2017) Implementation of IoT based PV monitoring system with message queuing telemetry transfer protocol and smart utility network. In: 2017 international conference on information and communication technology convergence (ICTC). IEEE, pp 1077–1079 12. Flores-Morán E, Yánez-Pazmiño W, Barzola-Monteses J (2018) Genetic algorithm and fuzzy self-tuning PID for DC motor position controllers. In: 2018 19th international Carpathian control conference (ICCC). IEEE, pp 162–168 13. Jing J, Wang Y, Huang Y (2016) The fuzzy-PID control of brushless DC motor. In: 2016 IEEE international conference on mechatronics and automation. IEEE, pp 1440–1444 14. MadhusudhanaRao G, SankerRam BV (2009) Speed control of BLDC motor with common current. Int J Recent Trends Eng 2(6):182

Switching Loss Comparison of a Cascaded Diode-Clamped Inverter with Conventional Multilevel Inverters Vinodh Kumar Pandraka and Venkateshwarlu Sonnati

1 Introduction Multilevel inverters are the best choice for the applications used in industries to meet high voltage and high current requirements [1]. Increasing the levels in the output AC voltage of the inverter and minimizing the harmonics has become a regular practice in research. These inverters are intended to produce a sinusoidal voltage/current from DC input. This DC voltage can be split into different levels using capacitors. There are mainly three types of multilevel inverters in the literature and in use, namely cascaded H-bridge inverter, diode-clamped inverter and flying capacitor inverter. There are many new topologies available in the literature such as reduced number of switches [2], and there are symmetric and asymmetric converters are also proposed in the literature [3–8]. The quality of the sine wave output can be improved by increasing the number of levels. But different topologies use different number of switches for same levels. Increasing the number of levels will affect the efficiency [9, 10]. To avoid the filter in the main drive line, the operating/switching frequency should be increased. All these aspects finally come to the discussion of efficient inverter for specific applications. This requires analysing the losses in different inverter topologies [11, 12]. A hybrid model of cascading two diode-clamped inverters is taken for simulation reference, and a hybrid PWM technique is also implemented in this paper [13].

V. K. Pandraka (B) · V. Sonnati Electrical and Electronics Engineering, CVR College of Engineering, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_11

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2 Inverter Topologies 2.1 Cascaded H-Bridge Inverter (CHBI)—(9-Level) A H-bridge inverter can produce two-level or three-level output voltage depending on the gap maintained during the switching transition from positive group of switches to negative group of switches. Four full bridge inverters when connected in cascaded mode can produce nine levels in the output voltage as shown in Fig. 1. Four DC sources are required to power up the nine-level cascaded H-bridge inverter. Sinusoidal pulse width modulation technique is used to generate the gating pulses. The standard switching states of IGBTs are shown in Table 1. Fig. 1 Cascaded H-bridge inverter schematic (9-level)

Switching Loss Comparison of a Cascaded Diode-Clamped Inverter … Table 1 Switching table for NINE-level CHBI

V0

Active switches

4V

13, 16, 12, 8, 4, 9, 5, 1

3V

13, 16, 12, 8, 4, 9, 5

2V

13, 16, 12, 8, 4, 9

V

13, 16, 12, 8, 4

0

–

−V

14, 15, 10, 6, 2

−2V

14, 15, 10, 6, 2, 11

−3V

14, 15, 10, 6, 2, 11, 7

−4V

14, 15, 10, 6, 2, 11, 7, 3

105

2.2 Diode-Clamped Multilevel Inverter (DCMLI)—(Five-Level) The most used multilevel inverter topology is the diode-clamped inverter. The diodes are used to clamp the DC bus voltages to achieve steps in the output voltage. The concept can be extended to any number of levels by increasing the number of capacitors or DC sources. The voltage across the capacitor creates an imbalance in its voltages. Hence, the usage of DCMLI is limited to three levels in industries. Total 16 switches are used (8 per each leg) with four DC sources will produce 5-level output. The DCMLI inverter circuit is shown in Fig. 2 (MOSFETs as switches). The actual level count should be done for line voltage, and it is not a proper way to compare with single-phase systems. But for this paper, the main concentration is the number of switches and number of DC sources should be same for the comparison of topologies. Number of levels is not considered as an important objective in this paper. The IGBT/MOSFET switching states are shown in Table 2.

2.3 Hybrid Inverter—Cascaded Diode-Clamped Inverter (CDCI)—(Nine-Level) The hybrid inverter schematic is shown in Fig. 3. The leg of a three-level diodeclamped inverter acts as a half bridge module. The two diode-clamped multilevel inverters (DCMLI) are connected in series (cascaded) to form the hybrid inverter. So, it be called as cascaded diode-clamped inverter (CDCI). The output of both CHBI and CDCI will have the same number of levels in the output. The voltage quality, i.e. THD, switching losses and RMS voltage for different switching frequencies are simulated. The top inverter is named as inverter-A and the bottom one as inverter-B. The switches Sa1, Sa2, Sa3 and Sa4 form a leg in inverter-A, and Sb1, Sb2, Sb3 and Sb4

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Fig. 2 Five-level diode-clamped multilevel inverter (DCMLI)

Table 2 Switching table for FIVE-level DCMLI V0

Sa1

Sa2

Sa3

Sa4

Sa5

Sa6

Sa7

Sa8

2V

1

1

1

1

0

0

0

0

V

0

1

1

1

1

0

0

0

0

0

0

1

1

1

1

0

0

−V

0

0

0

1

1

1

1

0

−2V

0

0

0

0

1

1

1

1

form a leg in inverter-B. As usual the switches Sa3 and Sa4 are compliment to Sa1 and Sa2, respectively. Similarly, the inverter-B switches. But if same pulse sequence is used for both inverter-A and inverter-B, there will be a loss in number of levels. Here in this case, the output voltage will ∏ have five levels instead of nine levels. The inverter-B carrier signals are delayed by /4. The reference and carrier signals and the inverter output are shown in Fig. 4.

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Fig. 3 Cascaded diode-clamped inverter (CDCI)

3 Switching Loss Calculation The heat sink and IGBT thermal models taken from the Simulink/MATLAB can generate the switching loss, conduction loss and total losses. Conduction losses are proportional to the load current, and switching losses are considerably high for high switching frequencies. Conduction losses include switch loss and diode loss. The conduction loss in switch and diode loss can be calculated using the Eqs. (1) and (2), respectively.

where

Wcs = UCE0 ∗ ICAV + rc ∗ I2Crms

(1)

Wcd = UD0 ∗ IDAV + rD ∗ I2Drms

(2)

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Fig. 4 Reference and carrier signals for hybrid PWM (top) and inverter output voltage with ninelevel (bottom)

UCE0 ICav Rc ICrms UD0 IDav rD IDrms

On-state zero current collector emitter voltage Average switch current Collector emitter on-state resistance RMS switch current Diode approximation with a series conduction of DC voltage sources Average diode current Diode on-state resistance RMS diode current.

Similarly, the switching losses in switch and antiparallel diode can be calculated using the Eqs. (3) and (4), respectively. Wsws = (EonSw + Eoffsw) ∗ fsw

(3)

Switching Loss Comparison of a Cascaded Diode-Clamped Inverter …

WSwd = (EonD + EoffD) ∗ fsw

109

(4)

where EonSw Eoffsw EonD EoffD Fsw

turn-on energy losses in switch turn-off energy losses in switch turn-on energy losses in diode turn-off energy losses in diode Switching frequency.

4 Simulation and Results In this paper, the switching losses and conduction losses of nine-level cascaded and five-level diode-clamped inverter are compared with hybrid inverter which uses the same number of switches. A special half bridge model from Simulink-MATLAB is taken as reference which facilitates to access its heat sink model and thermal model of IGBT pair to analyse the switching and conduction losses [14]. Infineon IGBT half bridge module FF800R17KE3 data is taken for reference. Four DC sources of each 1800 V are used which suits the maximum voltage IGBT module [15]. The thermal model of half bridge IGBT diode (top) and the heat sink Simscape model (bottom) are shown in Fig. 5. The turn on losses are calculated using the voltage before and current after the switching transition. Similarly, the turn off losses are calculated with the voltage after and current before the switching transition. The junction temperature is considered during the rise time and fall time of the switching pulse. Conduction loss of the IGBT is equal to the product of collector emitter voltage and collector current during the switch conduction. For switching frequencies like 500, 1000, 2000 and 5000 Hz, the switching losses are obtained from the simulation. All the inverter examples considered in this paper had same number of switches and DC sources. Technical details used for simulation are given in Table 3. It is focused on the same number of switches for different topologies rather than same number of levels. So, the number of DC sources and IGBT modules used for all the three topologies is same. IGBTs used are 16 in number, and four number of DC sources are used for the simulation.

4.1 Cascaded H-Bridge Inverter (Nine-Level)—Results SPWM control technique is chosen to simulate the nine-level cascaded H-bridge inverter. Switching and conduction losses are obtained for the switching frequencies 500 and 2000 Hz. Losses versus switching frequencies for 500 Hz are shown in Fig. 6 and for 2000 Hz is shown in Fig. 7. The switching frequency is directly affecting the inverter switching losses. It is observed in Fig. 8 that the conduction losses are almost negligible compared to switching losses.

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Fig. 5 Thermal models of IGBT half bridge (top) and heat sink (bottom)

Table 3 Specifications of SIMULINK model

DC voltage

1800 V × 4 sources

Load power factor

0.9 and 0.5 lag.

Power

250 kW

Load voltage

5000 V—RMS

A similar analysis is made with 0.5 lagging load, and the results are shown in Figs. 8 and 9. The orange colour curve represents 0.5 lagging load, and blue colour curve represents 0.9 lagging load.

Switching Loss Comparison of a Cascaded Diode-Clamped Inverter …

Fig. 6 Switching losses—CHBI at 500 Hz

Fig. 7 Switching losses—CHBI at 2 kHz Fig. 8 Switching loss versus switching frequency for CHBI

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Fig. 9 THD versus switching frequency for CHBI

Fig. 10 Switching losses—DCMLI at 500 Hz

4.2 Diode-Clamped Multilevel Inverter (FiveLevel)—Results Five-level diode-clamped inverter is simulated with SPWM technique at different switching frequencies, and the losses are obtained. Figures 10 and 11 give the switching and conduction losses of diode-clamped five-level inverter at 500 and 2000 Hz. Switching losses versus switching frequencies and THD versus switching frequencies are shown in Figs. 12 and 13.

4.3 Cascaded Diode-Clamped Inverter (Nine-Level)—Results The proposed hybrid inverter has same number of switches and power sources as in the case of above two inverter topologies. Two three-level diode-clamped inverters are cascaded to form a single-phase cascaded diode-clamped inverters. The switching losses are obtained from the simulation for the switching frequencies of 50 Hz and

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Fig. 11 Switching losses—DCMLI at 2 kHz Fig. 12 Switching loss versus switching frequency for DCMLI

Fig. 13 THD versus switching frequency for DCMLI

2000 Hz and are shown in Figs 14 and 15, respectively. The switching losses for different switching frequencies are shown in Fig. 16. In addition, total harmonic distortion for different frequencies and output voltages for different modulation index is shown in Fig. 17.

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Fig. 14 Switch losses—CDCI at 500 Hz

Fig. 15 Switch losses—CDCI at 2 kHz Fig. 16 Switch loss versus switch frequency for CDCI

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Fig. 17 THD versus switch frequency for CDCI

Fig. 18 Switching loss versus switching frequencies for CHBI, DCMLI and CDCI inverters

4.4 Comparision of CHBI, DCMLI and CDCI The comparison of CHBI, DCMLI and CDCHBI inverters is presented here. The switching losses of hybrid inverter is high compared to the other two conventional inverters. The CDCI has triple the switching losses of CHBI and double the switching losses of DCMLI. This will be a drawback for the hybrid inverter. The comparison of switching losses is shown in Fig. 18. The switching frequency is lightly affecting on the THDs of the output voltages over a wide range from 3 to 5 kHz. But from 500 Hz to 2 kHz, CDCI and DCMLI have very less distortion compared to CHBI. The maximum THD of CHBI is around 12%, whereas CDCI and DCMLI inverter’s maximum THD are 4% and 2.2%, respectively. The comparison of THD versus switching losses is shown in Fig. 19. All the three inverters are producing approximately same RMS voltage with respect to a specific modulation index. The CDCI’s characteristics are lying in between CHBI and DCMLI. Compared to all the three inverters, CHBI inverter is producing more voltage compared to the other three inverters. The RMS voltage of all the three inverters for different modulation index is shown in Fig. 20.

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Fig. 19 THD versus switching frequency for CHBI, DCMLI and CDCI

Fig. 20 Output RMS voltage versus modulation index for CHBI, DCMLI and CDCI

5 Conclusion The aim of the paper is to compare the switching losses that occur in different topologies of multilevel inverters. The important point to be considered while analysing the results is that all the three inverters have same number of switches and DC sources. Along with the switching losses, conduction losses and THD are also obtained from the simulation for a range of switching frequencies (500 Hz–5 kHz). The output RMS voltage is also obtained for different modulation indices. It is observed that the hybrid inverter is causing more switching losses whereas cascaded H-bridge inverter is causing minimum switching losses. But in sine wave quality perspective, diodeclamped inverter and hybrid inverter have shown similar performance. With respect to the sine wave quality, cascaded H-bridge inverter has very poor performance. All the three inverters are producing approximately same RMS voltage for different modulation indices. In conclusion, the diode-clamped multilevel inverter and the hybrid inverters are best suitable for the applications where the quality of sine wave is a priority issue. Cascaded H-bridge inverter is efficient with respect to inverter but because of its dominating harmonic content, the system will not be efficient.

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References 1. Franquelo L (2008) The age of multilevel converters arrives. IEEE Ind Electron Mag 2(2):28–39 2. Koshti AK, Thorat AR, Tejasvi PC, Bhattar L (2017) Multilevel inverter with reduced number of switches. In: 2017 international conference on circuit, power and computing technologies (ICCPCT), pp 1–5. https://doi.org/10.1109/ICCPCT.2017.8074283 3. Su GJ (2005) Multilevel DC-link inverter. IEEE Trans Ind Appl 41(3):848–854 4. Hinago Y, Koizumi H (2009) A single phase multilevel inverter using switched series/parallel DC voltage sources. In: Proceedings of IEEE-ECCE’09, pp 1962–1967 5. Hinago Y, Koizumi H (2010) A single-phase multilevel inverter using switched series/parallel DC voltage sources. IEEE Trans Ind Electron 57(8):2643–2650 6. Choi WK, Kang FS (2009) H-bridge based multilevel inverter using PWM switching function. Paper presented at the INTELEC-31st international telecommunications energy conference, Incheon, pp 1–5 7. Ebrahimi J (2012) A new multilevel converter topology with reduced number of power electronic components. IEEE Trans Ind Electron 59(2):655–667 8. Babaei E et al (2013) Cascaded multilevel inverter using sub-multilevel cells. Electr Power Syst Res 96:101–110 9. Ned M, Undeland TM, Robbins WP (2007) Power electronics converters applications & design, 2nd edn. Wiley 10. Bhuvaneswari G, Nagaraju (2005) Multilevel inverters—a comparative study. IETE J Res 51(2):141–153 11. Derakhshanfar M (2010) Analysis of different topologies of multilevel inverters. Master thesis. Chalmers University of Technology 12. Akagi H (2017) Multilevel converters fundamental circuits & systems. Proc IEEE 105:2048– 2065 13. Rao PN, Saini LM, Nakka J (2021) Cascaded diode clamped inverter-based grid-connected photovoltaic energy conversion system with enhanced power handling capability and efficiency. IET Renew Power Gener 15(3):600–613 14. Mathworks.com. Loss calculation in a 3-phase 3-level inverter using SimPowerSystems and Simscape 15. Infenion IGBT module FF800R17KE3. https://pdf1.alldatasheet.com/datasheet-pdf/view/165 425/EUPEC/FF800R17KE3.html

An Energy-Efficient Mechanism Using Blockchain Technology for Machine-Type Communication in LTE Network K. Krishna Jyothi, G. Kalyani, K. Srilakshmi, Shilpa Chaudhari, M. Likhitha, and K. Sriya

1 Introduction Machine-type communication (MTC) is a network of machine terminals that communicates without human participation. Towards the communication, the air interface of LTE/LTE-A is one of the biggest challenges [1–3]. It was designed for broadband applications even though MTC communication typically transmits and receives little amounts of data, which results in an unreasonable low payload-to-control data ratio due to the usage of non-optimized transmission protocols [4]. Additionally, the other important factors, such as the need for low-energy and lowlatency devices, must be estimated for M2M communications [5]. Currently, MTCbased applications are implemented using short-range radio technologies that operate on unlicensed spectrum, such as Bluetooth, WiFi, and Zigbee [6, 7]. Growing network communities will need MTCs with higher quality of service (QoS), reliability, and security in the future [8, 9]. The availability of ready-to-use cellular infrastructure-based network technologies like LTE, long-term evolution advanced (LTEA), and the future of 5G is crucial for the effective deployment of next-generation MTCs to address these difficulties [10, 11]. Blockchain technology is a new method of keeping record of all the transactions, rather than centralized machine having one entity handling. It is a decentralized K. Krishna Jyothi (B) · G. Kalyani · M. Likhitha · K. Sriya Department of CSE, Geethanjali College of Engineering and Technology, Hyderabad, India e-mail: [email protected] K. Srilakshmi Department of EEE, Sreenidhi Institute of Science and Technology, Hyderabad, India S. Chaudhari 3Department of CSE, M.S. Ramaiah Institute of Technology, Affiliated to VTU, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_12

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method where many computers work together. This is a best method instead of relying on one entity and risk which come with it. The main contribution of the proposed paper is to provide complete information about the cluster nodes by utilizing block chain technology to select the energy efficient node. Selecting the optimal CH based on distance, delay, and energy is identified using whale tri-level (WTL) algorithm. The organization of the paper is given as: Sect. 2 gives the related work; Sect. 3 portrays the optimal cluster head selection: considering distance, energy, and delay. Section 4 illustrates the results obtained, and at last the paper is concluded in Sect. 5.

2 Related Work Author [citation]

Adopted methodology

Features

Challenges

Gupta et al. [12]

DGBES-AKA

✓ Flexible bandwidth ✓ Lower access latency

✓ Costly framework

Singh et al. [13]

EMTC-AKA

✓ Safe from the multiple malicious attacks ✓ Low transmission cost, transmission delay, and signalling overhead

✓ High packet transmission cost ✓ High energy dissipated

Choi et al. [14]

EPS-AKA

✓ Improves grouping optimization ✓ Improve the delay response

✓ Incurs extra computational costs of signing

Srinidhi et al. [15]

HGKA

✓ Resistant against replay attacks ✓ Avoids sharing or pre-storing of the group key

✓ Does not address on group key Generation

Panda et al. [16]

ECC, ECDH and Salsa20 algorithm

✓ Makes the system more ✓ Secure and faster ✓ Secure communication ✓ Mutual authentication is achieved

✓ Reduces the life span of the network

Zhang et al. [17]

GAA

✓ Reduces the service ✓ Add considerable delivery complexity ✓ Consumes lower ✓ Suffers from congestion costs and effort (continued)

An Energy-Efficient Mechanism Using Blockchain Technology …

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(continued) Author [citation]

Adopted methodology

Features

Challenges

Lai et al. [18]

AKA

✓ Low storage ✓ Long delay and large communication, computational overhead computational ✓ And storage overhead ✓ Authentication time is considerably reduced

Cao [19]

EGHR

✓ Fast re-authentication process ✓ Average authentication signalling cost

✓ Lot of handover delays

Liu et al. [20]

BESPTM

✓ Improving the security ✓ Reducing its electricity costs ✓ Reducing grid burden

✓ Need better cluster head selection

Sharma [21]

BBSCM

✓ Improved packet delivery ratio

✓ Reduces the life span of the network

Nguyen [22]

RDAC-BC

✓ Improved network lifetime ✓ Better energy ✓ Improved packet delivery ratio PDR

✓ Need better cluster head selection

Jyothi [8]

BBCHA

✓ Attack-free network ✓ High energy consumption ✓ Improved network lifetime

Shahbazi [23]

ATEAR

✓ Avoiding overheated nodes as forwarders ✓ High throughput ✓ Increase of the network lifetime

✓ Need better cluster head selection

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3 Proposed Cluster-Based Energy-Efficient Cluster Head Using Blockchain The proposed methodology energy-efficient optimal cluster head (CH) selection for MTC network is shown in Fig. 1. Initially, the nodes are initialized with distance, delay, and energy. For every node in the network, fitness is computed. The fitness equations are given below. After that optimal cluster head selection is done using the proposed whale tri-level (WTL) algorithm considering the computed fitness values. Blockchain technology is used to store the nodes energy value. As the quantity of the iterations increases, the CH energy depletes. To enhance the network performance, a new CH is selected based on the energy levels. For each MTC device, the distance, energy, and delay are computed.

3.1 Distance ( fdistance ) (m) The distance is computed as specified in Eq. (1), where f distance tells the distance (n) connecting the nodes in the range 0 and 1. As a result, f (distance) represents the distance computed from normal node to CH and from CH to BS over the packets transmitted. If the distance amid normal node and CH is superior, it gives higher values of f distance .

Fig. 1 Clustering the MTC devices in LTE network

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f distance = (m) f distance =

(m) f distance

123

(1)

(n) f (distance)

Hn L ∑ ∑ || norm || || || || L − Hcq || + || Hcq − Bs || p

(2)

p=1 q=1 (n) f distance =

L ∑ L ∑ || norm || || L − L norm || p

q

(3)

p=1 q=1

3.2 Delay ( fdelay ) The fitness function for delay for a node is given by Eq. (4). The delay is directly relative to the number of nodes in the cluster. As a result, nodes in the cluster have to be reduced to obtain minimum delay. f delay =

Hcn ( q ) Hc Maximumqi=1

Lc

(4)

The f delay should lie from 0 and 1. Equation (4), L c tells the total number of Hcn ( q ) clusters, and Maximumqi=1 Hc determines the maximum number of CH in the network.

3.3 Energy ( fenergy ) The main issue in selecting cluster head is the battery utilization. Once the battery level of the node depletes, it cannot be recharged due to non-availability of power supply. In the process of transferring the data to all the nodes and then to the base station, energy is drained. The energy utilization model drains energy in different operations like sensing, transmission, aggregation, and reception. The entire energy required to transmit the message from cluster head is expressed as follows { E el ∗ N + E f s ∗ N ∗ d 2 , i f d < d0 E T X (N : d) = (5) E el ∗ N + E am ∗ N ∗ d 4 , i f d ≥ d0 In Eq. (5), E T X (N : d) is the whole energy consumed to transfer packets of N bits which are at a distance d, the energy requisite to transmit each bit is E el . For receiving packet of N bits, the entirety energy required.

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E Rx (N : d) = E e N

(6)

The amplification energy is expressed as E am = E f s d 2

(7)

The whole energy in the network represented as: E total = E T X + E Rx + E 1 + E s

(8)

The value obtained from Eq. (8) should be minimal. The energy frenzied during idle is E 1 , and while sensing E S is the energy consumed. E el is the electronic energy, and E ae gives the data aggregation energy as shown in Eq. (9). E el = E T X + E ae

(9)

In LTE Network, more energy is consumed by CH rather than sensor node. After a number of iterations, the energy level of sensor nodes changes relatively. The network average energy is shown in Eq. (14). E avg =

( r) 1 E total 1 − N R

(10)

where E i (r ) is the energy of node, and E total is N nodes total energy for r rounds. The number of iterations is predicted based on available energy, and energy consumed in the current round is defined as R R=

E total E round

(11)

Here, E round is the energy utilized for each iteration. Based on the threshold value at each iteration, the decision is taken whether the node is a normal node or it is a cluster. } { ( ( )) pi τ E sample (12) T (ki ) = 1 − Pi (mod(r, 1/ pi )) Here, Pi is desired probability which lies from 0 and 1, τ is a weighted ration used with the energy-balanced value (E sample ) whose value lies from 0 to 1. The proper value of τ is determined by several simulations by means of random networks. Actually, the value of τ is not possible to identify as the network formation for the next network tour is indefinite. } { E i (r )E 0 Popt b (13) Pi = E avg E Total

An Energy-Efficient Mechanism Using Blockchain Technology …

| | | E avg || | E sample (i ) = |1 − E i (r ) |

125

(14)

Here, the constant limit level Tlimit is initialized. The nodes under this T limit indicate that all the nodes are equally qualified for selecting as CH. Tlimit = τ E 0 The fitness function for energy is expressed in Eq. (15), where the value of energy E f energy becomes more than one if the cumulative energy of the entire cluster head f (a) E and f (b) to be of greatest value and maximum count of cluster head’s f energy =

E f (a) E f (b)

(15)

3.4 Objective Function The selection of optimal CH is done taking into consideration various constraints, namely distance, energy, and delay, to which WTL method is subjugated. In general, the distance and delay of the devices should be minimized, and the energy should be more for enhanced CHS. ψ1 + ψ2 + ψ3 = 1

(16)

Pobjective = τ f m + (1 − τ ) f n ; 0 < τ < 1

(17)

f m = ψ1 ∗ f energy + ψ2 ∗ f distance + ψ3 ∗ f delay

(18)

fn =

n || 1 ∑ || || L norm − Bs || p n p=1

(19)

Here, ψ1 , ψ2 , and ψ3 denote the distance, energy, and delay constant parameters. The constraints defined in Eq. (18) have to reach the state as given in Eq. (16).

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3.5 WTL Algorithm for Optimal Cluster Head Selection Enhanced version of the conventional WOA [24] algorithm is introduced in this work in order to improve the network life time. The mathematical representation of the new WTL algorithm is discussed below.

3.5.1

Prey Encircling

The position the prey can be identified by the whale, and then, they surround them. Mathematically, the behaviour of humpback whales in encircling the prey as given in Eqs. (20) and (21). | | → T S (t) − W → = ||V→2 .W → T (t)|| M

(20)

→ → T (t + 1) = W → T S (t) − V→1 . M W

(21)

In which, V→1 and V→2 represent coefficient vectors. t1 denotes current iteration. The → T and W → T S , respectively. position vector and best position acquired are given by W → → The vectors V1 and V2 are computed using Eqs. (22) and (23), respectively. In Eq. (22), the component a→ diminishes from 2 to 0 in each iteration. The arbitrary vectors ra1 and ra2 lie within the interval [0, 1].

3.5.2

◠ V→1 = 2 a .ra1 − a→

(22)

V→1 = 2ra2

(23)

Bubble-Net Attacking Method

Shrinking encircling method and spiral updating position are used to model the exploitation phase. (a) Shrinking encircling mechanism: This phase is dependent on the value of a→ given in Eq. (22). Generally, V→1 is an arbitrary value in the range [−a, a], where the a changes 2 from 0. (b) Novel Tri-level Spiral updating Mechanism: “Spiral equation is defined to represent the position of whale and prey”. This is expressed mathematically by → is the distance between the ith whale to the prey, and b is Eq. (24), in which M stable denotes logarithmic spiral shape. In addition, x is a random number whose value ranges between [−1, 1]. To compute the G→' , the mathematical equation is expressed in Eq. (25).

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→ ' ebl . cos(2π x) + W → T S (t1 ) WT (t1 + 1) = G

(24)

| | |→ | → G→' = |W T S (t1 ) − WT (t1 )|

(25)

In the optimization process, the position of whale is mathematically given in Eq. (26), where φ takes random number ranges from [0, 1]. { WT (t1 + 1) =

→ f or → T S (t1 ) − V→1 . M φ < 0.5 W bl ' → → G '.e . cos(2π x) + WT S (a) f or φ ≥ 0.5

(26)

Besides the standard updation, a new tri-level update is done. Primarily, the values of φ1 , φ2 , and φ3 are initialized. Considering the value of φ, i.e. if φ < 0.5, then the value of φ1 and φ2 computed using Eqs. (21) and (26). Else, φ3 is calculated using Eq. (24). Afterwards, the value of arbitrary variable r1 is initialized, and if the value of r1 ≤ 0.3, the position of the search agent is updated by Eq. (27). If r1 = 0.3–0.6, then position of the current search agent is updated using Eq. (28). If the two conditions are not fulfilled, then position update of the current search agent is calculated using Eq. (29). As three levels of updation are taking place, the proposed method is named as whale tri-level updation (WTL).

3.5.3

→ T (t1 + 1) = φ1 + φ2 W 2

(27)

→ T (t1 + 1) = φ2 + φ3 W 2

(28)

→ T (t1 + 1) = φ1 + φ3 W 2

(29)

Searching for the Prey

The exploration phase is mathematically computed using Eqs. (30) and (31), respectively. | | → T || → = ||V→2 W → (rand) − W M

(30)

→ → T (t1 + 1) = X→ (rand) − V→1 . M W

(31)

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Fig. 2 Blockchain model with continuous block sequence

3.6 Blockchain Technology In the projected model, the selected optimal cluster head acts as a envoy for all other nodes in the cluster. The cluster head maintains the information (records) about all the nodes in that cluster. The energy values of the nodes are stored using blockchain technology. Figure 2 shows the structure of blockchain.

4 Results and Discussions The proposed approach over the LTE network using various optimization algorithms is done in MATLAB 2019a, and the outcomes are obtained.

4.1 Simulation Procedure The proposed model is compared with the conventional methods like JA [25], FF [26], GWO [27], and WI-JA method [28] in terms of network lifetime and computational time. The designed work is competent to perk up network life and based on the energy level the number of alive nodes.

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Fig. 3 Computation time analysis: with and without using blockchain technology

4.1.1

Time Analysis

Figure 3 depicts the time analysis of the proposed model. The computational time of proposed model is compared with traditional block chain model by varying the number of CH.

4.1.2

Assessment on Network Lifetime

In order to transmit data packets in the network the Lifetime Ratio is computed. The performance of the model is computed based on the Lifetime ratio. Figure 4 depicts the results of network life of the proposed model WTL by varying the number of cluster heads in the network. Figure 5 shows the network life wrt other presented models. It is practical that even increase in the no. of cluster heads, significantly there is increase in the network lifetime compared to other models. In particular, when the count is 10, the proposed work has attained a high network lifetime, which is 55, 70, 75, and 57% better compared with models like GWO, FF, WI-JA, and JA. Fig. 4 Network lifetime of presented model (WTL) works by changing number of CH

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Fig. 5 Network lifetime of presented model (WTL) over traditional works by changing number of CH

5 Conclusion The paper has proposed a model that obtains the selection of optimal cluster head for MTC using WTL scheme. The CH is modified based on the information stored in the blockchain technology. The choice of optimal CH is considered to be the major task that is computed based on parameters, namely distance, delay, and energy. The presented work is compared over the existing methods, and its pre-eminence was proved. From the results, the adopted scheme shows better network lifetime and computational time using blockchain technology. Thus, the proposed WTL model has been confirmed successfully.

References 1. Cao J, Ma M, Li H (2014) A survey on security aspects for LTE and LTE-A networks. IEEE Commun Surveys Tutor 16(1):283–301 2. Krishna Jyothi K, Chaudhari S (2019) A secure cluster-based authentication and key management protocol for machine-type communication in the LTE network. Int J Comput Appl 2019:1–11 3. Jyothi KK, Chaudhari S (2020) Survey on MTC group authentication and key management for MTC in LTE networks. ICDSMLA 2019: 753–759 4. Kalyani G, Chaudhari S (2021) Enhanced privacy preservation in the routing layer with variable-length packet data for attack free IoT sector. J Eng Sci Technol Rev 14(1):95–99 5. Kalyani G, Chaudhari S (2020) Survey on 6LoWPAN security protocols in IoT communication. In: ICDSMLA 2019. Springer, Singapore, 696–702 6. Jyothi KK, Chaudhari S (2020) Optimized neural network model for attack detection in LTE network. Comput Electr Eng 88:106879 7. Zhang A, Chen J, Hu RQ, Qian Y (2016) SeDS: secure data sharing strategy for D2D communication in LTE-advanced networks. IEEE Trans Vehic Technol 65(4):2659–2672 8. Jyothi KK, Chaudhari S (2020) A novel block chain based cluster head authentication protocol for machine-type communication in LTE network: statistical analysis on attack detection. J King Saud Univ Comput Inf Sci 9. Lai C, Lix H, Lu R, Shen X. A unified end-to-end security scheme for machine-type communication in LTE networks. In: 2nd IEEE/CIC international conference on communications in China (ICCC), pp 698–703

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10. Kalyani G, Chaudhari S (2020) An efficient approach for enhancing security in Internet of Things using the optimum authentication key. Int J Comput Appl 42(3):306–314 11. Lai C, Lu R, Zheng D, Li H, Shen XS (2015) Toward secure large-scale machine-to-machine communications in 3GPP networks: challenges and solutions. In: IEEE communications magazine—communications standards supplement, Dec 2015, pp 12–19 12. Gupta S, Parne BL, Chaudhari NS (2018) DGBES: dynamic group based efficient and secure authentication and key agreement protocol for MTC in LTE/LTE-A networks. Wirel Personal Commun 98(3):2867–2899 13. Singh G, Shrimankar DD (2018) Dynamic group based efficient access authentication and key agreement protocol for MTC in LTE-A networks. Wirel Personal Commun 101(2):829–856 14. Choi D, Choi H-K, Lee S-Y (2014) A group-based security protocol for machine-type communications in LTE-advanced. Wirel Netw 15. Srinidhi V, Lakshmy KV, Sethumadhavan M (2019) HGKA: hierarchical dynamic group key agreement protocol for machine type communication in LTE networks. In: Security in computing and communications, pp 231–241, Jan 2019 16. Panda PK, Chattopadhyay S (2019) An improved authentication and security scheme for LTE/ LTE-A networks. J Amb Intell Human Comput 17. Zhang W, Zhang Y, Chen J, Li H, Wang Y (2013) End-to-end security scheme for machine type communication based on generic authentication architecture. Cluster Comput 18. Lai C, Li H, Lu R, Shen XS (2013) SE-AKA: a secure and efficient group authentication and key agreement protocol for LTE networks. Comput Netw 57(17):3492–3510 19. Cao J, Ma M, Li H, Fu Y, Liu X (2018) EGHR: efficient group-based handover authentication protocols for mMTC in 5G wireless networks. J Netw Comput Appl 102:1–16 20. Liu Z et al (2020) A blockchain-enabled secure power trading mechanism for smart grid employing wireless networks. IEEE Access 8:177745–177756 21. Sharma A et al (2020) Blockchain based smart contracts for internet of medical things in e-healthcare. Electronics 9(10):1609 22. Nguyen GN et al (2020) Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks. Sustain Comput Inf Syst 28:100464 23. Shahbazi Z, Byun Y-C (2020) Towards a secure thermal-energy aware routing protocol in wireless body area network based on blockchain technology. Sensors 20(12):3604 24. Mirjalili S, Lewisa A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 25. Venkata Rao R. Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Industr Eng Comput 7:19–34. https://doi.org/10. 5267/j.ijiec.2015.8.004 26. Kalyani G, Chaudhari S (2019) Data privacy preservation in MAC aware Internet of things with optimized key generation. J King Saud Univ Comput Inf Sci 27. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61 28. Jyothi KK, Chaudhari S (2020) Cluster-based authentication for machine type communication in LTE network using elliptic curve cryptography. Int J Cloud Comput 9(2–3):258–284

Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting Shashikant, Binod Shaw, and Jyoti Ranjan Nayak

1 Introduction Over the last few decades, power generation from solar has increased to a great extent, and it is now growing rapidly and has been a great support to the grid. Various challenges associated with solar in support of the grid are coordination, availability of support, reliability, etc. Majority of the portion of the challenges are overcome by forecasting solar power generation. This forecasting helps us to know the availability of power in advance which makes proper planning for the distribution of power to meet demands, which can only be done by scheduling. Thus, it helps in scheduling and planning, which increases the reliability of the system. This makes forecasting an important tool for renewable energy-based systems because renewable energy is nature-dependent which introduces nonlinearity in the system which makes prediction difficult. Various forecasting models have been reported in the literature which works with an efficiency greater than 95%. The forecasting accuracy and efficiency of a model are dependent on the problem and its application. A forecasting model with an efficiency greater than 95% cannot give the same results always; it is dependent on the problems, application, and availability of data. Thus, improvement in the forecasting model is needed every time to deal with uncertainties. Recently, machine learning and deep learning-based forecasting model have shown great performance in forecasting for the prediction of different applications such as financial market forecasting, energy forecasting, solar irradiation forecasting, solar power generation forecasting, weather forecasting, and load demand forecasting. ML and deep learning have been widely used in speech recognition, image processing, controller design, etc. Various ML-based forecasting model has been developed for forecasting Shashikant (B) · B. Shaw · J. R. Nayak National Institute of Technology, Raipur, India e-mail: [email protected] Shashikant OP Jindal University, Raigarh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_13

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since they can handle uncertainties and can be trained to perform a particular task. A deep learning-based forecasting model is applied to crude oil prediction, and also, it is hybridized with a time series model to improve the accuracy of the model empirically [1]. Deep learning has been applied to various other applications for forecasting such as power markets [2], a day-ahead prediction for solar irradiation and wind speed [3], solar irradiation as time series forecasting methods such as the Naïve method, linear method, and nonlinear method illustrated in [4], scheduling for intra-hour solar irradiation forecasting for 10 min of duration [5], research on the selection of forecasting model for solar irradiation is done based on the variability of the data collected from different sites, it is divided into three types, namely low, medium, and high variability, and the conclusion is made that the forecasting reliability is low with higher variability [6]. Apart from DL, an alternate method such as a fuzzy algorithm is introduced for forecasting as illustrated in [7], a hybrid model is developed based on fuzzy and NN for the prediction of solar irradiation, and the model advantages are illustrated in [8]. Hourly ahead forecasting is done for a particular location with memory-based NN such as recurrent neural network (RNN) is illustrated in [9], and simple NN is also used with different algorithms for forecasting as illustrated in [10]. The advancement in ML is done based on the decomposition method concerning its input. Wavelet-based decomposition method is introduced to RNN to improve the accuracy of the model [11], the input of the model, i.e., diagonal recurrent wavelet neural network is applied via fuzzy to enhance forecast precision as illustrated in [12]. From the literature above, it is conspicuous that the improvement in forecasting is done either by enhancing the ML algorithm or by applying some arithmetic logic to extract special feature from the input which enhances the system output and further improves the forecasting accuracy. Similarly, principal component analysis is employed for the identification of crucial features before applying the NN as illustrated in [13]. To improve the performance of RNN, long short-term memory (LSTM) is introduced, which increases the forecasting accuracy [14]. The LSTM is further hybridized with various other NN such as convolution neural network (CNN) for the application of prediction of solar irradiation as illustrated in [15, 16], for the application of solar power generation prediction [17], LSTM hybridized with DL for power generation forecasting application [18], hybrid evolutionary NN-based models [19], and time series model hybridized with ANN [20]. Some other conventional NN models are implemented for forecasting such as kernel extreme learning machine [21], regression tree method [22], and knowledge-based model [23]. In the prediction of solar irradiation or power, the prediction is done for short-term [24, 25], medium-term, and long-term [26]. From the literature, it is conspicuous that the forecasting accuracy or improvement is dependent on the type of NN used, whether it may be alone or hybridized with other NN, and the type of data provided to the NN, i.e., either important parameters from the raw data or by applying some mathematical model to extract important features before feeding to the NN, which is done by decomposition methods, dimension reduction, etc. In this work, a memory-based echo state network (ESN) is used to forecast solar power generation and compared with the conventional NN, i.e., ANN. The highlights of the paper are as follows:

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1. Echo state network-based forecasting model is developed which has echo property. 2. Comparison is made between the memory-based model, i.e., ESN, and the memoryless-based model, i.e., ANN. 3. Statistical analysis is done for both models to evaluate the performance. 4. Percentage improvement is presented based on statistical measures. 5. Future work is discussed to carry out the research further, i.e., a comparison of two memory-based forecasting models. The paper’s objective is to develop a forecasting model which will have minimum errors and reliability in forecasting short-, medium- and long-term forecasting. The rest of the paper is organized into sections, Sect. 2 discussed methodology, i.e., the development of a forecasting model. Section 3 discussed statistical measures to evaluate the performance of models. Section 4 deals with results and discussion. Section 5 deals with the conclusion followed by acknowledgment and references.

2 Methodology The method used in the development of the forecasted model is ANN and ESN. The models are trained with three input parameters as time, irradiation, and temperature to predict power generation as an output. The model development is discussed below:

2.1 Artificial Neural Network ANN was introduced to solve complex problems like image processing, pattern recognition, prediction, optimization, and controls. It is a biological-inspired NN that mimics the human brain, which enables the development of parallel computing, generalization, learning through experience, pattern recognition, mapping, and various other applications which are associated with data. The architecture of ANN is simple as shown in Fig. 1 which consists of three layers named the input layer, hidden layer, and output layer. Each layer consists of neurons that contain information like input layer neurons indicate the number of input parameters, output layer neurons depend on the number of outputs required for prediction or output variables, and hidden layer neurons are user-dependent based on the complexity of the problems. Each neuron in a layer is connected with the next layer of neurons through weights, which increases the steepness of the activation function. The weights are updated iteratively during the training process through a chain rule based on the error generated, as it is propagated backward through the backpropagation algorithm, till the error is minimum or it reaches zero. Zero error represents the ANN model is robust. The whole process of the ANN, how the information is passed, error handling, and weight updates are expressed mathematically from Eq. (1)–(7), which leads to the

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development of the ANN model for prediction (Fig. 1). Z t = W21 ∗ I + b

(1)

  O H t1 = f Z t

(2)

O t = W32 ∗ O H t1 + c

(3)

  yt = f O t

(4)

et = y t − y t

(5)

  T  W32new = W32old − alpha ∗ O H 1 ∗ y t 1 − y t ∗ et

(6)

   T W21new = W21old − alpha ∗ I ∗ O H 1 (1 − O H 1 ) ∗ (W32 )T ∗ y t 1 − y t ∗ et (7) where ‘W 21 ’and ‘W 32 ’ are the weights between the input layer (I) and hidden layer (H1) and weights between the output layer and hidden layer. ‘b’ and ‘c’ are biased, ‘Z t ’ is the input to the hidden layer, and ‘O t ’ is the input to the output layer. ‘O H t1 ’ and ‘y t ’ represent the output of the hidden layer and output layer. ‘y t ’ and ‘y t ’ represent the target value and predicted value. ‘et ’ represents the error, and alpha is the learning rate. ANN was introduced in the 1950s; thereafter, several advancements were made such as the perceptron rule, backpropagation in the 1960s and rediscovered in the 1980s, convolution neural network in 1970s, RNN in 1980s as Hopfield network, and echo state network in 2000s. The advancement takes from feed-forward NN to Fig. 1 Architecture of ANN

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Fig. 2 Architecture of ESN

memory-based NN. One of the memory-based NN is used in this work for prediction, i.e., ESN which is discussed in this subsection.

2.2 Echo State Network Echo state network is a special type of recurrent neural network which uses a reservoir network made from dynamic neurons to compute the output. A reservoir is a large number of randomly and sparsely connected hidden units. Each neuron in this reservoir network receives a nonlinear signal because of its design and work, i.e., random, large, fixed, recurrent neural network. The architecture of ESN consists of three layers, namely the input layer with ‘K’ units, the dynamic reservoir with ‘N’ internal neurons, and the output layer with L neurons as portrayed in Fig. 2. It consists of four weights, namely input weight ‘W in (N × K)’, which represents the connection between the input layer to a reservoir, reservoir weight or internal weight ‘W (N × K)’, which represents the connection between internal neurons in a reservoir, output weight ‘W out (L × (K + N + L))’, which represents the connection between reservoir to the output layer and feedback weight ‘W back (N × L)’, which represents the connection between output layer to a reservoir. All these four weights are initialized randomly. W in , W, and W back are fixed during the whole process, whereas the output weights are calculated through simple linear regression [27], during the training process. The next state and output is calculated by Eqs. (8) and (9), and the readout weights are calculated by Eq. (10). x(i + 1) = f (Win ∗ u(i + 1) + W ∗ x(i ) + Wback ∗ y(i))

(8)

y(i + 1) = g(Wout ∗ [u(i + 1); x(i + 1); y(i )])

(9)

The activation functions of the neurons in the reservoir and output layers, respectively, are denoted in Eqs. (4) and (5) by f = [ f 1; f 2... f N ] and g = [g1; g2...gL],

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respectively. Both linear and nonlinear functions, such as sigmoid and tanh functions, are possible. In general, the output layer neurons have linear activation functions, but the internal neurons in the reservoir have nonlinear activation functions. The sole connection matrix that has to be taught in an ESN is Wout. Usually, an input sequence is fed into an ESN through the output layer of the network to produce a target trivial sequence. A key need for selecting a recurrent neural network is the echo state attribute of the ESN. The steps for the training of readout weights are as follows: Step 1 Set the ESN and the scale of the reservoir N, the internal reservoir’s connection rate a, the spectral radius r, win; W back ; w; and the washout time step I o should be set. The weight matrix W is an arbitrary sparse matrix whose spectral radius equals the predetermined value, and the elements of the vectors win and Wback are created at random while adhering to a [0, 1] uniform distribution. Step 2 Update the reservoir’s status. Calculate the new reservoir state based on the current input, the reservoir state from the previous step, and the output from the previous step using Eq. (4). Step 3 Determine the readout weights. Select the reservoir’s state vectors M = ((I − Io + 1) ∗ N ) and the anticipated outputs Y that fulfill a time step equal to or greater than I 0 , then enter the calculation formula Eq. (6) Step 4 Calculate readout weights  T W out = M −1 ∗ Y

(10)

Now, setting appropriate values for the critical parameters is vital to ensure optimal performance since the reservoir is arbitrarily produced, which is highly significant to the performance of ESN. However, because of the exponential relationship between size and the growth of hidden states, reservoir N’s scale has a significant impact. The size of the reservoir is important given the number of samples and the intricacy of the issue. In general, a larger scale can describe a dynamic system more accurately; however, an excessively large scale will lead to overfitting issues. In this investigation, a trial-and-error methodology is used to determine the magnitude of N. It is noteworthy that this strategy, which is based on several studies, is frequently used to set simple and effective parameters. When not all of the neurons in a reservoir are connected, as is the case with the connectivity rate α, intertwined; correct non-zero connections must exist. Jaeger noted that there may be too few connections made within the reservoir. The reservoir’s condition lost recollection and became lost. However, there are too many connections that make it challenging to interpret the reservoir’s status. Results from experiments have shown that the connectedness rate often falls between 15 and 20%. The spectral radius ρ also denotes the largest absolute w is the weight matrix’s eigenvalue, it is important to note that once a weight matrix is generated at random, its spectral radius is known, and then, it is possible to get a matrix with a unit spectral radius by spectral radius divided by weight matrix.

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Then, by employing the matrix with a unity spectral radius amplified by the earlier determined value, it is simple to obtain a matrix with a known value. In accordance with earlier research, the internal weight matrix w’s spectral radius r is set to a value lower than 1 to ensure the echo state property. This indicates that once the ESN has undergone some iterations, the current network state is exclusively connected to the historical input values and the output of the training data. Therefore, a thorough study is done on the three key parameters, which are the spectral radius r, connection rate a, and reservoir size N.

3 Statistical Methods Statistical measures are used to measure the closeness between actual data (Ai ) to the predicted data (Pi ). Four statistical measures are used in this work, namely mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and correlation of determination (R2 ). MAE is widely used for forecasting accuracy; it measures the average value between the variables. The idea behind the absolute error is to avoid mutual cancellation of signed errors but fails to produce large errors which are essential for minimizing the error for specific engineering applications. To overcome this problem, MSE is introduced which sticks to the idea of absolute errors, i.e., to avoid mutual cancellation and avoid negative errors. Due to squared error, small errors become very less, but large errors produce a greater effect in performance matric. MAPE measures the accuracy of the forecasting system. Therefore, the minimum value of MAE, MSE, and MAPE will be best for the forecasting model. R2 determines the proportion of variance in the dependent variable that can be explained by the independent variable. The maximum value of R2 will be best for any forecasting model. The statistical methods used in the work are expressed mathematically from Eqs. (11–14). MAE =

1 N |Ai − Pi | i=1 N

(11)

MSE =

1 N ( Ai − Pi )2 i=1 N

(12)

 N  1  Ai − Pi  ∗ 100 N i=i  Pi 

(13)

MAPE =

N R = 1 − N 2

i=1 (Ai

i=1 (Ai

− Pi )2

− mean(Pi ))2

where ‘Ai ’ and ‘Pi ’ are the actual data and predicted data.

(14)

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4 Result and Discussion The forecasting model is trained with three input parameters (i.e., time, irradiation, and temperature) and predicted a single output as solar power generation. The model is trained with 3000 samples and predicted based on the application required as a day-ahead prediction, a week-ahead prediction, two-week-ahead prediction, and a month-ahead prediction. The result associated with it is portrayed in Figs. 3,4,5 and 6. The performance of ANN and ESN is compared based on their statistical parameters which are tabulated in Table 1. From the table, it is observed that the statistical parameters MAE, MSE, and MAPE are minimum, and R2 is maximum for ESN for all cases except for a week-ahead prediction. For a week-ahead prediction, it is observed that except MAE all other parameters show good performance for ANN over ESN. This is because of the bad weather condition. Hence, we can say that improvement is needed for the ML-based forecasting model, which can predict accurately during bad weather conditions. This drawback can be overcome by training the ML model with more occurrences of bad weather data. The overall performance of ESN is better than ANN because the MAE is increasing if the prediction is done for long-term prediction.

Fig. 3 SPGF for a day-ahead prediction

Comparison of Echo State Network with ANN-Based Forecasting … Fig. 4 SPGF for a week-ahead prediction

Fig. 5 SPGF for two-week-ahead prediction

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Fig. 6 SPGF for a month-ahead prediction

Table 1 Performance analysis of ANN and ESN MAPE

R2

78.1824

0.1554

0.9539

4.0737

72.2308

0.1245

0.9578

7.5692

204.3490

0.3235

0.8163

7.1062

224.7057

0.3438

0.7980

Duration

Model

MAE

A day ahead

ANN

4.8026

ESN ANN ESN

A week ahead Two weeks ahead A month ahead

MSE

ANN

6.9767

175.9838

0.2718

0.8809

ESN

6.1176

158.2317

0.2652

0.8929

ANN

9.2236

296.5659

0.3703

0.8536

ESN

5.9194

131.5463

0.2622

0.9351

Bold represents best value. Upon comparison Between ANN and ESN.ESN shows better value than ANN which is represented via bold

5 Conclusion Forecasting is necessary for renewable power generation for proper coordination among devices and control. In the present work, forecasting model is developed based on the ML model for forecasting solar power generation based on time, irradiation, and temperature as input. The ESN-based forecasting model is developed and made a comparison with the conventional ML model, i.e., the ANN model. ML model can solve nonlinear problems, and renewable energy is nonlinear due to its dependency. Upon comparison with both models, based on statistical methods, it is found that the ESN-based forecasting model performance is superior to the ANN model.

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A Grey Wolf Integrated with Jaya Optimization Based Route Selection in IoT Network G. Kalyani, K. Krishna Jyothi, K. Srilakshmi, and Shilpa Chaudhari

1 Introduction Kevin Ashton invented the term “Internet of things” at the time of working at Procter and Gamble on a network of nodes utilizing RFID. After ten years the concept has acquired complete popularity [1]. However, in today’s world, the amount of connected devices outnumbers the quantification of people, and these gadgets range from smart wearables to homes in addition to smart cities. In the future, it is expected that network devices are predicted to communicate directly with each other over the Internet [2]. Apart from that, the IoT is a novel pattern in which various IoT network objects interact and establish connection with one another to facilitate common goals. It will allow objects to use social media networks, while also allowing people to access the results of these automate inter-object interactions [3]. IoT network consists of various smart devices with its own identity that are responsible for data exchange and oblige with each other without human intervention through wireless network [4–8]. IoT opened doors for its large-scale deployment to record, monitor, and control the environmental conditions such as smart grids, smart cities, smart buildings, agricultural, manufacturing, transportation, and e-health. IoT network integrity gets concerned in the presence of malicious node [9, 10].

G. Kalyani (B) · K. Krishna Jyothi Department of CSE, Geethanjali College of Engineering and Technology, Hyderabad, India e-mail: [email protected] K. Srilakshmi Department of EEE, Sreenidhi Institute of Science and Technology, Hyderabad, India S. Chaudhari Department of CSE, M.S. Ramaiah Institute of Technology, Affiliated to VTU Bangalore, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_14

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The proposed secure routing strategy is presented for route establishment for data exchange between the nodes. It uses GWJO algorithm for the optimal forwarding node selection. In the proposed work, link quality is used during route establishment. This paper’s mainly focuses on (1) Design and development of GWJO algorithm for selection of optimal node. (2) Identification of best path using link quality. (3) Proposed secure routing scheme analysis based on performance metrics. The following is a breakdown of the paper’s structure: Sect. 2 discusses various existing works on. Section 3 explains the proposed routing strategy for node selection using GWJO algorithm and route selecting using link quality in IoT. Section 4 depicts the results, and Sect. 5 conclusion, respectively.

2 Literature Review In paper [11], the author developed a bio-inspired trust management methodology for monitoring node behaviour. The model was developed on the beta reputation systems (BRS) and ant colony optimization (ACO) approach. By preventing vulnerable devices to enter into the state of data transmission, this model improved the conventional dynamic source routing (DSR) protocol. In [12] fuzzy-integrated particle swarm optimization (Fuzzy-FPSO) is suggested by combining firefly algorithm (FA) and particle swarm optimization (PSO) [14] to select optimal appropriate path for secure routing. All the secured paths are identified based on trust computation and distance as its objective function. The performance is evaluated based on throughput, detection rate, delay, and routing overhead. In [13], the enhanced LOEPRE technique is offered for route construction to transfer data with a high level of security while minimizing network overload. As a outcome, the suggested technique improves network longevity, reduces packet latency, and lowers energy usage in wireless networks. In [14], the author employed butterfly optimization algorithm (BOA) to select the best CH among the given set of nodes. The CH is chosen based on the node’s energy consumption, distance from neighbours, and distance from the BS. Because to generate the energy saving CH election and optimal appropriate path generation for secure data transmission, a network’s lifetime is extended. The performance of the BS is measured based on the live nodes, energy consumption levels, number of dead nodes, and packets arrived. Aghbari et al. [15] conducted a review on various routing protocols for WSNs with optimization techniques. In this article, various optimization strategies for determining a routing path between a source and a destination node are explored. Routing protocols are explored to decrease energy consumption levels and extend the life span of a network (network life time). In [16] FBUCA and TM-ORT are two algorithms are proposed to improve the performance of the network. QOS-based multi-hop routing protocol is used data reliability and energy consumption. To achieve data aggregation and routing, a fuzzybased efficient cluster head was used.

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Bera et al. [17] the optimization technique aids in reducing network traffic on a global scale in addition to node and link energy utilization in the WSN. Consequently, during data transmission, the network evaluates energy consumption by choosing the best path.

3 Proposed Routing Strategy in IoT This section presents the proposed optimal routing based on link quality strategy is shown in Fig. 1. The steps involved in route discovery are as follows. (1) Computing the fitness values considering the distance (Dis), delay (De), energy (E), and link quality (L q ) (2) Optimal node selection using GWJO technique (3) Route discovery phase selects the next node based on the link quality. In the following subsections, the steps are discussed in detail.

3.1 Computation of Fitness Value The optimal node selection is done taking into consideration the constraints, namely distance, energy, and delay to which GWJO method is subjugated. In general, the distance and delay must be minimal, and the energy of the node must be more for the accomplishment of best path identification. Distance: Consider two nodes be p1 and p2 with a and b to be their locations. The distance D( p1 , p2 ) linking the nodes is obtained by using (1) based on the Euclidean distance considering the simulation area (SA). Here, the SA is taken as 100 × 100. √ D=

Fig. 1 Route discovery steps

( p1a − p2a )2 + ( p1b − p2b )2 SA

(1)

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Delay: The delay De is computed by considering the Packet Transmission Time (PTT) and Total Time (TT) actually taken to transmit a packet is given in (2). De = TT − PTT

(2)

Energy: Energy consumed while transmitting the data in IoT network is computed by (3). Where, i.e. is initial energy of the node, and F e is the complete energy left by the node after data transmission. E T = Ie − Fe

(3)

Link Quality: Link quality is computed based on nodes packet loss (PL). The endto-end packet loss is measured as the number of data bits lost to the total number of bits expected in each time interval. L q = PL /Total number of Packets.

(4)

Equation (6) demonstrates the fitness function used for node selection depending on these parameters. Moreover, ψ1 , ψ2 , ψ3 , and ψ4 denote energy, link quality, delay, and distance, respectively. ψ1 + ψ2 + ψ3 + ψ4 = 1 P1 = (ψ1 × D) + (ψ2 × De) + (ψ3 ×

1 ) + (ψ4 × L q ) ET

(5) (6)

3.2 Optimal Node Selection Using GWJO Model This work launches a new fusion model that picks the optimal node for routing. The conventional GWO [18] algorithm depicts the grey wolves’ chasing behaviour and its leadership ladder. The process of hunting of wolves α1 , β1 , and δ1 is focussed. Among these wolves, α1 is treated as the leader which finalize decisions related to attacking process, time to awake, sleeping location, etc., whereas, the remaining two wolves β1 and δ1 hold the second, third levels which support the α1 in making the best choices. The enclosing feature is designed as per (7) and (8), where coefficient vectors are denoted as Mc and L c , prey’s position vectors is J p , J indicates position vectors of GW, and iter denotes present iteration. Equations (9) and (10) denote the model for Mc and L c , where a is a parameter which is minimized gradually to 0 from 2 in the entire iterations. Here, v1 and v2 specify the random vectors that lie in the range [0, 1], and itermax represents the maximum iteration.

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| | D = | L c J p (iter ) − J (iter )|

(7)

J (iter + 1) = J p (iter ) − Mc D

(8)

◠

◠

Mc = 2 a .r1 − a

(9)

L c = 2r2

(10)

The mathematical equation relating the hunting behaviour of wolves is given from (11) to (16), where the final position updating is evaluated using (17). | | Dα1 = | L 1 .Jα1 − J |

(11)

| | Dβ1 = | L 2 .Jβ1 − J |

(12)

| | Dδ1 = | L 3 .Jδ1 − J |

(13)

( ) Ja1 = Jα1 − M1 . Dα1

(14)

) ( Ja2 = Jβ1 − M2 . Dβ1

(15)

( ) Ja3 = Jδ1 − M3 . Dδ1

(16)

J (iter + 1) =

Ja1 + Ja2 + Ja3 3

(17)

In the standard GWO, the location of α1 , β1 , and δ1 gets updated using (14)–(16). JA [19] focuses getting the best appropriate solution. At iterth iteration eliminating the worst case, the modelling constraints number is “V ” (where p = 1, 2, ...V ), and size of population is “P”. If J p,r,i represents the value of pth factor for the rth aspirant throughout the iterth iteration. Further, this value is modified based on (18), where J p,best,i denotes the value of the pth constraint for the best solution, and J p,wor st,i refers to the value of pth constraint on considering the worst solution in the population. | | |) |) ( ( J ' p,r,i = J p,r,i + r1 J p,best,i − | J p,r,i | − r2 J p,wor st,i − | J p,r,i |

(18)

Additionally, J ' p,r,i represents the new value ( of J p,r,i |, and r|)1 , and r2 are random | | numbers with the range [0,1]. The term “r1 J p,best,i − | |) that to ( J p,r,i ” represents move the solution near the best one, and the factor “− r2 J p,wor st,i − | J p,r,i | ” denotes to move the solution far from worst one. J ' p,r,i is accepted if the function value returned by it is enhanced.

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In addition to the proposed approach, the GWO positions are updated based on Eqs. (19–21). JA is incorporated with GWO model, and the proposed method is named as GWJO model. ( ( ) ) J1 = J + r1 Jα1 − |J | − r2 J p,wor st,i − |J |

(19)

( ( ) ) J2 = J + r1 Jβ1 − |J | − r2 J p,wor st,i − |J |

(20)

) ) ( ( J3 = J + r1 Jδ1 − |J | − r2 J p,wor st,i − |J |

(21)

To obtain optimal node, the objective function is given in Eq. (22) Pobj = Min(P1 )

(22)

3.3 Route Establishment To identify the path, we are considering the optimal nodes form GWJO algorithm. To establish a path between the nodes, we are considering link quality (L q ). Every node in the network reports LQ by using beacon packets from its neighbours for every 5 min.

4 Results This segment discusses the simulation environment and outcome of the proposed secure optimal routing.

4.1 Simulation Set-Up The adopted secure routing model is simulated using MATLAB 2019a, and the results were accomplished. The performance of the proposed methodology is compared with other methods such as GA [20], FF [21], CS [22], and LA [23] with respect to the number of nodes. The number of nodes taken is 100, and the analysis was performed in terms of distance, delay, energy, and link quality (Fig. 2).

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Fig. 2 Distance, delay, link quality, and energy are computer taking number of nodes as 100

5 Conclusion Modern IoT networks are complicated, and because users’ private data is being shared, every infringement has a direct negative impact on people’s life. To ensure greater security and privacy, unified architecture, protocols, and technologies are required. The paper has proposed a methodology for selection of shortest route in IoT using GWJO algorithm for data sharing among nodes. The constraints that relied on the route selection are distance, delay, energy, link quality, and PDR, respectively. In the end, the analysis is carried out by taking number of nodes as 100. Especially, the adopted model GWJO attains minimal values over GA, CS, LA, and FF models with the number of devices taken to be 100. As a result, the betterment of proposed model was proved.

References 1. Cao J, Ma M, Li H (2014) A survey on security aspects for LTE and LTE-A networks. IEEE commun Surv Tutor 16(1):283–301 2. Kalyani G, Chaudhari S (2021) Enhanced privacy preservation in the routing layer with variable-length packet data for attack free IoT sector. J Eng Sci Technol Rev 14(1):95–99 3. Kalyani G, Chaudhari S (2020) Survey on 6LoWPAN security protocols in IoT communication. In: ICDSMLA 2019. Springer, Singapore, 696–702

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4. Kalyani G, Chaudhari S (2019) Data privacy preservation in MAC aware Internet of things with optimized key generation. J King Saud Univ Comput Inf Sci 5. Krishna Jyothi K, Chaudhari S (2019) A secure cluster-based authentication and key management protocol for machine-type communication in the LTE network. Int J Comput Appl: 1–11 6. Jyothi KK, Chaudhari S (2020) Survey on MTC group authentication and key management for MTC in LTE networks. ICDSMLA 2019: 753–759 7. Jyothi KK, Chaudhari S (2020) Optimized neural network model for attack detection in LTE network. Comput Electr Eng 88:106879 8. Jyothi KK, Chaudhari S (2020) A novel block chain based cluster head authentication protocol for machine-type communication in LTE network: statistical analysis on attack detection. J King Saud Univ Comput Inf Sci 9. Kalyani G, Chaudhari S (2020) An efficient approach for enhancing security in Internet of Things using the optimum authentication key. Int J Comput Appl 42(3):306–314 10. Jyothi KK, Chaudhari S (2020) Cluster-based authentication for machine type communication in LTE network using elliptic curve cryptography. Int J Cloud Comput 9(2–3):258–284 11. Ourouss K, Naja N, Jamali A (2021) Defending against smart grayhole attack within MANETs: a reputation-based ant colony optimization approach for secure route discovery in DSR protocol. Wireless Pers Commun 116(1):207–226 12. Kondaiah R, Sathyanarayana B (2018) Trust factor and fuzzy-firefly integrated particle swarm optimization based intrusion detection and prevention system for secure routing of MANET. Int J Comput Sci Eng 10(1) 13. Elavarasan R, Chithra K (2021) Enhanced LION optimization with efficient path routing equalization technique against DOS attack in WSN 14. Maheshwari P, Sharma AK, Verma K (2021) Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw 110:102317 15. Aghbari ZA et al (2020) Routing in wireless sensor networks using optimization techniques: a survey. Wirel Pers Commun 111(4):2407–2434 16. Rajaram V, Kumaratharan N (2021) Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks. J Ambient Intell Humaniz Comput 12(3):4281– 4289 17. Bera S, Das SK, Karati A (2020) Intelligent routing in wireless sensor network based on African buffalo optimization. In: Nature inspired computing for wireless sensor networks. Springer, Singapore, 119–142 18. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61 19. Pandey HM (2016) Jaya a novel optimization algorithm: what, how and why? In: 2016 6th international conference—cloud system and big data engineering (confluence), Noida, 728–730 20. Deshpande KV, Rajesh A (2017) Investigation on IMCP based clustering in LTE-M communication for smart metering applications. Eng Sci Technol Int J 20(3):944–955 21. Liang J-M, Chang P-Y, Chen J-J (2019) Energy-efficient scheduling scheme with spatial and temporal aggregation for small and massive transmissions in LTE-M networks. Pervasive Mob Comput 52:29–45 22. Priyadharshini AS, Bhuvaneswari PTV (2018) Regression model for handover control parameter configuration in LTE-A networks. Comput Electr Eng 72:877–893 23. Malandra F, Chiquette LO, Lafontaine-Bédard L-P, Sansò B (2018) Traffic characterization and LTE performance analysis for M2M communications in smart cities. Pervasive Mob Comput 48:59–68

Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins Radha Seelaboyina, Sai Prakash Chary Vadla, Sree Alekhya Teerthala, and Veena Vani Pedduri

1 Introduction Digital Twins cater to magnanimous opportunities and implications in various industries and sectors. Being a congregation of technology and real-time industry, they can be said as the digital portrayal of any kind of industrial component. Hence, their applications are vast. Every industrial component is attached with sensors and actuators, which relay real-time information to the Digital Twin. The data generated from this is further processed using AI and ML algorithms for simulating, analyzing, predicting and generating solutions for present and future aspects. The interlink between the physical systems and Digital Twin is called Digital Thread, which is performed through various networking devices. As the whole physical system is converted into a digital model, the risk and chances of cyberattacks are humongous.

R. Seelaboyina (B) · S. P. C. Vadla · V. V. Pedduri Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, India e-mail: [email protected] S. A. Teerthala Department of Information Technology, Geethanjali College of Engineering and Technology, Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_15

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2 Literature Survey 2.1 Cyber Physical Systems and Their Attacks? Cyber Physical System (CPS) is an intelligent computer system which primarily focuses on communication, computing and controlling to be able to operate and make decisions independently. On a parallel spectrum, the Cyber-Physical System (CPS) is a technology which has made it’s mark in the industry by combining both the computational and physical spheres together. Furthermore, it helps in real-time processing, communication and gathering information from various sub-systems in creating large and complex systems. This opens the door for numerous cyber-attacks on Cyber Physical Systems. Some prominent attacks where the Cyber Physical System has fallen prey are: 1. The Stuxnet worm which attacked the Iranian nuclear facility in Natanz exploited 4 zero-day vulnerabilities and modified the values of the current-electrical frequencies drives [1]. 2. The virus attack on the Istanbul Ataturk Airport focused primarily on the passport control system [1]. Additionally, another airport had also fallen prey to this attack resulting in the complete passport control system shut down and flights being delayed. We know from the cyberattacks the world has witnessed so far that threat actors’ technical prowess has considerably increased and that their desire to do bodily harm is alarming. Stuxnet is the next generation of harmful malware that cyberattacks can have a huge influence on the real world. Stuxnet was an incredibly sophisticated cyberattack carried out using cutting-edge malware that was directed towards a particular IIOT [2]. The most important takeaway from Stuxnet is that a well-funded, highly skilled malicious malware that can probably attack any network, which should worry those who own and operate vital infrastructure. Because it is impossible to completely safeguard all systems from potential attackers, the most crucial lesson for IIOT is to understand how to recognize and recuperate from a cyber-attack. Stuxnet is by far just a small sand particle in the vast oceans of various attacks that occur on cyber-physical systems. As mentioned in Fig. 1, we can see that, on the whole, the types of attacks are broadly categorized into—Worm, Trojan, Virus, DDos, WhistleBlower, Dos, Account Hijacking. Each of these attacks have their own set of target sectors like government, Private, Industries, etc., and their malicious intentions leave a lasting impact on the society or organization [1]. Cybersecurity will become more complex and require even greater attention in order to protect CPS. The incidents described in this study highlight the changing landscape and growing threats to CPS. This makes the implementation of cyber- physical systems questionable and less favorable, creating a path for the widespread adoption of Digital Twins.

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Fig. 1 Threat taxonomy

2.2 Advantages of Digital Twin Over Cyber Physical System The fact that the Digital Twin is a virtual replica of the whole physical system and works extremely efficiently and accurately in comparison to a cyber-physical system. This in turn transforms Digital Twins into extremely versatile options which can be executed on a wide array of algorithms acting as a perfect subset for a Cyber Physical System. Furthermore, Digital Twins can be even implemented way before the actual product is available and can also help in the further improvisation of the physical object if required. The Digital Twin can be accessed and analyzed from a PC or through any of the mobile phones. So, safety of the device can be looked at from anywhere, without having to be present near the physical system. What Digital Twin additionally offers, is a continuous scope for improvement by pondering on many what-if questions and try implementing them, to make the product better equipped. Digital Twins also help compensate for the loss faced by the attacks on CPS, where there is huge damage on the physical objects resulting in huge losses. And the budget used for Digital Twins is meager when compared to Cyber Physical Systems (example: virtual car test drives). In both IT and the IoT, digital twin technology continues to play a key supporting purpose. Product creation, testing of prototypes, analysis, and maintenance routines are a few of the numerous contributions. Incorporating Digital Twins will make it possible to share remote control of manufacturing equipment, placing an ever-increasing demand on IoT management of identities, verification, and authorization—the three main foundations of any industry’s security.

2.3 Disadvantages of DT As the Digital Twins completely maintains the data virtually and everything is going digital the concerns related to cybersecurity are also increasing gradually. Industry 4.0 incorporation into DT technology ought to call for ‘best standard’ cyber security

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legality. Cybersecurity should be a top priority in its execution as one of its key pillars. Though attacks on CPS are limited due to less integration with the internet, the attacks on DT have no limits, the cyber attacks will increase predominantly. As the backend systems may be readily accessible by the twin, the organization may be exposed to breaches if a Digital Twin gets hacked. This threat gives the hacker access to a directory of the backend infrastructure. Now let us probe into the possible cyber-security attacks on Digital Twins: 1. Reconnaissance Attacks: Reconnaissance is also called as information gathering, this the first step that every attacker will follow in order to enumerate the target. This attack includes, network scanning, port scanning, vulnerability scanning, and many other sensitive information. For example, let us look at the Stuxnet malware which has already been discussed before, directed at a plant that enriches uranium and deals with air gaps [3]. So, after doing reconnaissance scans to learn about the infrastructure’s vulnerabilities, a hacker may deploy Stuxnet or other kinds of malware to attack the Digital Twins. 2. Targeting the Digital Twin: The attacker focuses on the Digital Twins, so to eventually strike and demolish the physical system. This is due to the fact that malicious software may intercept and alter the parameters used for simulation in the Digital Twins of (Programmable Logic Controllers) PLCs, making them vulnerable to attacks just like a physical system would be [3].

2.4 Cyber Digital Twin CDT can prevent cyber attacks by providing modeling/prediction capability and through increased visibility of the system behaviors. CDT is a brainchild of DT which in detail analyzes and predicts the statistics of cyber attacks on the DT associated with it. In simple CDT works like a firewall, which analyzes and tracks all the data that is being transmitted to and fro.

2.5 How Secure Are CDTs? CDT’s predict and show the security details of a DT, but are they real? Can a CDT be manipulated? Can CDT’s counter the cyber attacks on itself, before detecting the attacks which can occur on DT? There are several queries that pop up when it comes to the security of CDT and DT. Now let us see some significant cybersecurity risks associated with the implementation of CDT technology. The following reviews some of these challenges and risks created by the use of DT technology and in particular the use of CDTs. Security misconfiguration, the most heard vulnerability these days, which leads to massive cyber attacks and breaches, arises as a result of default or unsafe setups

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[4]. This configuration should be hidden and should not be exposed on the domain, else it would lead the attacker to jump into the system. With respect to the CDT, the security parameters must be transferred to the online setting while constructing the duplicate of the physical setting, increasing the risk that private information may become available to third parties. CPs follow an in-depth security architecture against cyber attacks, the Security design conceals various system components from possible hackers and functions similar to an onion with numerous levels of security [4]. All the resources, whether they are physical or software-based, will be visible while CDT is being executed. If an attacker manages to get into the CDT, they have far more information about the network. Hence, CDT has higher chances of exposing the system which in turn leads to massive cyber attacks than CPs. What transpires if an adversary gains entry to the CDT? The real-world entity’s vulnerabilities can be found by the perpetrator, who is able to evaluate the attack’s viability and execute it with a higher degree of certainty. The security ramifications of CDT must therefore be properly examined during it’s designing and implementation phase itself [4], especially because security impacts safety.

3 Methodology 3.1 Secure Software Development Life Cycle (Proposed Solution) The crucial challenge that we are facing at the moment is the increased cyber attacks on Digital Twins and Cyber Digital Twins. Our focus was mainly on Cyber Digital Twins (CDT), DTs that are specifically designed and implemented for the purposes of cybersecurity defense. We have reviewed and analyzed challenges for CDT technology and the security challenges it presents in and of itself. Every DT which is incorporated with a CDT will be designed keeping the end-goal in mind. The CDT will be application specific and will not be dynamic enough to protect against any zero-day vulnerabilities. This makes the CDT’s again vulnerable to cyber attacks. Hence, the proposed model we are looking at involves the implementation of security in the software development life cycle, broadly known as secure software development life cycle. The security features in this case are embedded, designed and tested at each phase of the development life cycle to make sure that the product is secure from any border-line security breaches and also insider attacks.

3.2 Why SSDLC and not SDLC? When a bug is found later during the SDLC the developer should go back and work on the code which was written long ago, this might be hard and takes lots of time

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Fig. 2 Secure software development life cycle

to make changes. Sometimes this might lead to large changes in the code which has been written until now and should be verified from the beginning again. So this may increase the finance of the system, and also the time and overall delaying the entire SDLC, which is a bad omen.

3.3 Detailed Look at the SSDLC As mentioned in Fig. 2, in an existing software development life cycle, let us look at how security can be integrated at each phase-:

3.3.1

Design Phase

If we are assimilating security in software development, then what we will need is a security specialist team also in our development team. They will focus on fulfilling the security aspects of the software at each phase by creating a basic map to reach the desired goal [5]. As this is the foundation for all the security architecture, various meetings, workshops and analysis are done, based on which the requirements for achieving security are decided and agreed upon. At this stage, a risk assessment is conducted where the threats, vulnerabilities and impacts are identified by creating an elaborate and accurate vulnerability landscape. This is done by assigning parameters

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and elaborately evaluating each of the threats and their mechanisms individually to facilitate the formation of a cost-effective risk mitigation strategy. After a thorough analysis at all the levels keeping the budget in mind, the security technologies, tools and protocols are selected, verified and reviewed.

3.3.2

Development Phase

At this stage, the product is coded with the help of software developers and this is the phase where the security team must be adept to make sure that each of the team members focuses primarily on ensuring that the code provides maximum safety by adhering to the various security policies, standards and guidelines [5]. One of the key vulnerabilities that the team should focus on is the buffer overflow, which results in 50% of security breaches.

3.3.3

Testing Phase

A security test plan is devised whose main aim is to make sure that the code is implemented correctly with the appropriate security countermeasures embedded into it [5]. The given product then goes through four testing stages after which it enters into production. 1. Unit testing—All the source codes, runtime methods and buffer overflows are checked and verified [5]. 2. Integration/Quality Assurance Testing—Method specific tests are conducted like authentication, authorization, implementation of countermeasures. 3. Penetration Testing—Generally a third party professional hacker is assigned the job to check the system vulnerabilities without authorization. 4. Certifications—Software certifications are taken for the product like the FIPS and Common criteria. 3.3.4

Operations and Maintenance Phase

At this point in the life cycle, the product has been officially released and is being used by the users. Constant security tracking, audits, back-up procedures must be done [5]. Patches management and product updation must also be done in a consistent manner.

3.3.5

Disposal Phase

Security is one feature we must take care of not only when the product is created, when the product is being used but also when the product or it’s related information is being disposed of [5]. The legal requirements for the information retention, disposal

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or release are to be analyzed and kept in mind while doing any of the tasks. The data must be sanitized using methods like cryptographic erasure, degaussing or physically destroying it.

4 Conclusion The Cyber Physical System as quoted above has shown high chances of falling prey for cyber-attacks. On the other hand, the Digital Twins-the subset of the cyberphysical system, has also shown some vulnerabilities. Owing to this, the cyber-Digital Twins were introduced to make sure that the Digital Twins are made secure from cyber-attacks. But as we have seen earlier in the paper, Cyber Digital Twins also pose a lot of challenges while combating the attacks on them. This needed a root-cause level solution, rather than just creating a separate entity, CDT. The security measures have to be adapted at a much foundation level i.e., when the product is just being designed and created. This leads us into embedding security features at the software development level itself and this process is called the Secure Software Development Lifecycle (SSDLC) which is our proposed solution. We have then pondered deep into each level of the software development lifecycle and integrated security to each of them. With the introduction of Secure Software Development LifeCycle, the cyberattacks are mitigated at the earliest possible time rather than, at the later stages, where one has to patch and update them. As the saying goes, “Nothing is ever 100% safe, once it is posted on the internet”, the SSDLC model is also not a 100% foolproof model, but around 30–60% of the risks can be mitigated at the fundamental level itself. So, this is a point that can be further researched and looked upon.

References 1. Al-Mhiqani M, Ahmad R, Mohamed W, Hassan A, Zainal Abidin Z, Ali N, Abdulkareem K (2018) Cyber-security incidents: a review cases in cyber-physical systems. Int J Adv Comput Sci Appl 9:499–508. https://doi.org/10.14569/IJACSA.2018.090169 2. Hemsley KE, Fisher E, Ronald D (2018) History of industrial control system cyber incidents. United States. https://doi.org/10.2172/1505628, https://www.osti.gov/servlets/purl/1505628 3. Suhail S, Zeadally S, Jurdak R, Hussain R, Matuleviˇcius R, Svetinovic D (2022) Security attacks and solutions for digital Twins. arXiv preprint arXiv:2202.12501 4. Holmes D, Papathanasaki M, Maglaras L, Ferrag MA, Nepal S, Janicke H (2021) Digital Twins and cyber security-solution or challenge? https://doi.org/10.1109/SEEDA-CECNSM 53056.2021.9566277 5. Jones R, Rastogi A (2004) Secure coding: building security into the software development life cycle. Inf Syst Secur 13:29–39. https://doi.org/10.1201/1086/44797.13.5.20041101/84907.5 6. Lou X, Guo Y, Gao Y, Waedt K, Parekh M (2019) An idea of using digital twin to perform the functional safety and cybersecurity analysis. GI-Jahrestagung 7. Hearn M, Rix S (2019) Cybersecurity considerations for digital twin implementations. IIC J Innov: 107–113

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8. Singh S, Yadav N, Chuarasia PK (2020) A review on cyber physical system attacks: issues and challenges. In: 2020 international conference on communication and signal processing (ICCSP). IEEE, pp 1133–1138 9. Pokhrel A, Katta V, Colomo-Palacios R (2020) Digital Twin for cybersecurity incident prediction: a multivocal literature review. In: Proceedings of the IEEE/ACM 42nd international conference on software engineering workshops, pp 671–678 10. Tao F, Zhang H, Liu A, Nee AYC (2018) Digital Twin in industry: state-of-the-art. IEEE Trans Industr Inform 15(4):2405–2415 11. Baheti R, Gill H (2011) Cyber-physical systems. Impact Control Technol 12(1):161–166 12. Koulamas C, Kalogeras A (2018) Cyber-physical systems and digital twins in the industrial internet of things [cyber-physical systems]. Computer 51(11):95–98. https://doi.org/10.1109/ MC.2018.2876181

Social Networks and Time Taken for Adoption of Organic Food Product in Virudhunagar District—An Empirical Study Dhanalakshmi Thiyagarajan and Maria Ponreka

1 Introduction A social network contemporarily plays a dominant role in spreading the information through word-of-mouth about the new product in the market. Today’s world is mainly ruled by Whatsapp, Facebook, Twitter, Instagram and Linkedin among the adult group of population in the world. By spreading the information through these social media seems to be very economy in the marketing strategy rather than giving advertisement in mass media channels. Many researchers have made an attempt in analyzing the social network model for the new product adoption. As per Mahajan et al. (1990), the diffusion of innovation of new product is mainly based on two types of people, namely innovators and imitators. The person who have first try over the product in the market is innovators and they will be followed by imitators. An opinion given by innovators about the new product makes an essence in the spreading of information and attracts the imitators to try over the product in the market. With this perspective, the researcher applied this social network concept in the adoption of organic food product in the market.

D. Thiyagarajan (B) Department of Business Administration (S.F), Ayya Nadar Janaki Ammal College, Sivakasi, Virudhunagar District, Tamil Nadu, India e-mail: [email protected] M. Ponreka Department of Management Studies, Sri Meenakshi Government College for Women, Madurai, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_16

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2 Related Works In the article, “Predicting Adoption Probabilities in Social Networks” by Xizo Fang, Paul J. Hu, Zhepeng Li and Weiyu Tsai, University of Utah, Salt Lake City, UT have studied the social network has not gained significant attention to identifying the adoption probability of new product. This author has taken some key factors like Social influence, Structural equivalence, entity similarity, and confounding factors. This research had made suggestions that cascade methods primarily using the social influence to predict adoption probabilities and the confounding factors are always critical to predicting the adoption probability. This study is mainly about mobile service providers. The collected data had been measured with the help of AUC and the area under ROC curve to predict the higher probability of adoption of the product in the social network on the basis of the proposed method and benchmarked. It is found that social entities cannot be differentiated by them in terms of adopters (95.78%) and 98.51% as non-adopters as per the benchmark method. This research mainly stresses on the factor of using unobserved confounding factor in predicting the adoption probability. The article namely “Product Adoption Networks and their growth in large mobile phone network” by Pal Roe Sunday, et al. IT University/corporate Development, Markets Telenor ASA, Oslo, Norway has studied the structure of the underlying social network. In this research, the researcher researches the time evolution of adoption networks for different products in the market and also the author measure social influence. This chapter presents an empirical and comparative study of three adoption networks evolving over time in a telecom network. I-phone and video adapter has taken for assessing the spreading of information. Social network monster is playing the main role in the adoption of the new product. The socia1 network monster can grow or break down over time or fail to occur at all. For the product take off, the social network monster acts as the main influence. To test this social network monster, two statistical tools like Graph theory and Kappa test to measure the strength of spreading information over the social network. The article “New Product Adoption in Social Networks: Why direction matters” written by Oliver Hinz, Technische University Darmstadt, Germany, Christian Schulze, Frankfurt School of Finance and Management, Sonnemannstr, Germany, Carsten Takac, Goethe-University Frankfurt, Germany mainly focused on the adoption of new product through the social network. This empirical study has been conducted by collecting data from 300 students. In this research, two main types of networks play an influential factor in influencing the consumers to purchase the product, one is a power of friendship networks (e.g., Facebook) and undirectedadvice networks (e.g., LinkedIn). It came to know from the research that directedfriendship networks are not superior to undirected-friendship networks. This study also reveals the finding of the influence of advisees is three times more than the influence of advisers. Advisee’s early adoption behavior has pressurized the non-adopting advisers to adapt to preserve their social status. The suggestions given in this study is firms have to consider or target their strategies on advisees’, not just advisers.

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The article “Examining the role of cognitive absorption for information sharing in Virtual Worlds” has written by Shalini Chandra, Yin-Leng Theng, May 0’Lwin, and Schubert Foo Shou-Boon, Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore. Businesses have started to use the virtual world for information sharing and that may increase the higher levels of productivity within the company. This chapter investigates the role of cognitive absorption, the state of deep involvement or holistic experience in the adoption and acceptance of virtual world for the sharing of ideas. The variables fetched under cognitive absorption are user trust, familiarity, perceived playfulness, perceived ease, and personal innovativeness. The data analysis has been done with the help of Smart PLS 2.0. The questionnaire has been used as a data collection tool. From 108 respondents, data has been collected. The data have been assessed on the bases of three validities: content validity, convergent validity, and discriminant validity. The convergent validity of all the variables has shown the composite reliability value of 0.7, Average variance extraction value of 0.5 and also it shows the Cronbach’s alpha value of greater than 0.9. Therefore, it shows that the convergent validity exists in this research. The discriminant validity also has shown the value of greater than 0.9. It is also considered as an acceptable level. The findings of the study confirm that cognitive absorption act as a strong correlate of usefulness and ease of use of virtual worlds which leads to adoption intention of the virtual world. The article “How Social Networks and Opinion Leaders affect the adoption of New Products” by Raghuram Iyengar, Christophe Van den Bulte, John Eichert, Bruce West, and Thomas W. Valente published in Marketing Science, Vol. 30 (March/April), pp. 195–212. A mail survey method has been used for data collection. The response rate for this research is only 24% in New York City to 45% in San Francisco. It has found in the research that there is a high correlation between opinion leadership and early adoption and that one has high network leadership (25%) than for self-reported leadership (11%). The findings of the research are the people who are already acted as opinion leaders are not been influenced by peer influence and other people who are searching for information with regard to the product are get influenced. Therefore, it seems that self-reported opinion leadership have more self-confidence and they only occupy the central position in the social network. For this, sociometric techniques have been used to identify the true opinion leaders more effectively than self-reports.

3 Conceptual Framework There is little doubt that the diffusion of various types of information (rumor, gossip, job openings, role performance, etc.,) is somehow influenced by social network structure (Coleman et al.1966; Granovetter 1973; Kerckhoff et. al. 1965). However, remarkably little is known about the manner in which network structure affects information flow. Such social networks today are now facilitated with Facebook, Twitter, e-mail, or mobile phone. Now it has gain importance in today’s world from both academia and industry that explore how to leverage such networks for greater

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business and societal benefits. A salient feature of social networks is the spread of adoption behavior (e.g., adoption of a product, service or opinion) from one social entity to another in a social network (Kleinberg 2008). The social network is playing a predominant role in the adoption of new product in the market. Because, if one person comes forward and trying the new product means, the opinion about product needs to spread from one person to another person. For that compatibility, study was undertaken to know the power distance between the people, how far the people are ready to accept the uncertainty (Uncertainty Avoidance), and masculinity quality to take a risk in trying the new product in the market. The adoption probability needs to be calculated in the social network. Adoption probability means the probability that a social entity will adopt a product, service or opinion in the foreseeable future. Now, many business organizations are having the prediction of adoption of new product through the social network based marketing and also predicting the demand.

4 Objectives of the Study The objectives of the study are to find out the number of days taken by the respondents to purchase the new product in the market, person belongingness to the group, reason to join the group, frequency of participation in the group, information trust by the respondents, Quantity of information passed to the other people, cost spend by the respondents to spread the information, age group and passing the information in the taluk.

5 Area of the Study, Sample Framework and Procedure The researcher conducted this analysis in Virudhunagar district which consists of eight taluks. The data have collected from 384 respondents on the basis of random convenient sampling who are living in eight taluks of Virudhunagar district. Primary data have collected in the four attributes of power distance, uncertainty avoidance, masculinity, and individualism with the help of close-end questionnaire. Five-point Likert scale has been used to estimate the acceptance of contents in that dimensions. The socioeconomic profile collected for this research is shown in Table 1 According to the table, 60.7% of the respondents are male and 39.3% are female; 47.1% of the respondents are private individuals; 26% of the respondents are government employees; 18% of the respondents are self-employed; and 8.9% of the respondents are professionals; 37.8% of the respondents are between the ages of 18 and 30; and 43.5% of the respondents are in the age group of 31–50 and the rest of 18.8% of the respondents are in the group of Above 50. The data related to the social network are collected by the researcher and it is depicted in Table 2.

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Table 1 Demographic profile of the respondents Particulars

Frequency

Percent

Male

233

60.7

Female

151

39.3

Total

384

100

Occupation of the respondents Private

181

47.1

Government

100

26.0

69

18.0

34

8.9

384

100

Self-employed Professionals Total

Age group of the respondents 18–30

145

37.8

31–50

167

43.5

72

18.8

384

100

Above 50 Total Source Primary Data

Table 2 Days taken by the respondents for adoption

S. No

Particulars

1

15 days

99

25.8

2

30 days

154

40.1

3

Two months

32

8.3

4

Three months

42

10.9

5

Four months

10

2.6

6

Five months

25

6.5

7

Never adopt

22

5.7

384

100.0

Total

Frequency

Percent

Source Primary Data

6 Days to Purchase The number of days taken by the respondents to purchase the new organic food product on the market is shown in Table 2. It is clear from Table 2 that 25.8% of the respondents will purchase the new product in the market within 15 days, 40.1% of the customer will purchase the new product within 30 days, 8.3% of the respondents will purchase the new product in the market within 2 months, 10.9% of the respondents have stated that they will purchase the new product in the market within 3 months, 9.1% of the respondents will purchase

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Table 3 Membership in community S. No

Particulars

Frequency

1

Yes, unofficial group

109

28.4

28.4

28.4

2

Yes, official group

136

35.4

35.4

63.8

3

Yes, alumni

75

19.5

19.5

83.3

4

No

64

16.7

16.7

100.0

384

100.0

100.0

Total

Percent

Valid percent

Cumulative percent

Source Primary Data

the new product within 5 months, and the remaining 5.7% of the respondents have never adopted the new product in the market.

7 Members of the Social Group The respondent’s belongingness of social group is also one of the ways for faster adoption of the new product. The communication among the social group leads to the passing of information from one person to another person. Table 3 depicts the data that related to the respondent’s social group. It is clear from Table 3 that 28.4% of the respondents belongs to an unofficial group, 35.4% of the respondents are belong to an official group, 19.5% of the respondents belong to alumni group and meager percentage of 16.6% of the respondents does not belong to any social group.

8 Reason to Join in the Group Every human being would like to join the group to fulfill their social needs. How far the person belongs to the group, it is chance for spreading the opinion about the new product in the market. Therefore, the reason for joining in the group are displayed in Table 4. It is clear from Table 4 that 22.9% of the respondents have joined in the group for the purpose of their group seems to be relevant active and interesting, 32.8% of the respondents have joined in the group for the purpose of updating themselves with the community news, 35.2% of the respondents have joined in the group for the purpose of getting new useful connection, and the remaining 9.1% of the respondents have joined in the group for the purpose of they would like to expose themselves to the group.

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Table 4 Reason to join in the group S. No

Particulars

1

Relavant, active and interesting

88

22.9

22.9

22.9

2

Update with the community news

126

32.8

32.8

55.7

3

Get new useful connections

135

35.2

35.2

90.9

4

Like to expose me in the group

35

9.1

9.1

100.0

384

100.0

100.0

Total

Frequency

Percent

Valid percent

Cumulative percent

Source Primary Data

Table 5 Frequency of participation in the group by the respondents

S. No

Particulars

1

Daily

2

Frequency

Percent

85

22.1

Several times in a week

116

30.2

3

Once in a week

173

45.1

4

4 times Total

10

2.6

384

100.0

Source Primary Data

9 The Frequency of Participation in the Group The frequency of participation in the group may create a chance for product spreading in the market. The person who belongs to the group may often participate in the group means; there is a chance of spreading the new product in the market. The details with regard to the frequency of participation in the group have shown in Table 5. It is evident from Table 5 that 22.1% of the respondents meet the members in the group daily. 30.2% of the respondents have met several times in a week. 45.1% of the respondents have the meeting once in a week with the members of the group.

10 Information Trust by the Respondents The data related to the information trust by the respondents are depicted in Table 6. It is evident from Table 6 that 19.5% of the respondents have to trust their information. 43.2% of the respondents have to trust the information due to they receive the information from their friends and connections. 18.2% of the respondents have to trust the information if they receive from co-official representatives. 18.8% of the

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Table 6 Information trust by the respondents S. No

Particulars

1

Yes

Frequency

2

Yes, comes from my friends/connections

3

Yes, comes from co-offical representatives

4

No, always critical to this information and check other sources Total

Percent

75

19.5

166

43.2

70

18.2

73

19.1

384

100.0

Source Primary Data

respondents do not trust the information and before trusting they used to check the information from other sources. It is interpreted from Table 6 that 43.2% of the respondents have to trust the information if they receive from their friends and connections.

11 Convey Information About New Product The data related to the respondents convey an information about the organic food product after consumption are depicted in Table 7. It is clear from Table 7 that 28.6% of the respondents will pass information to the other people of at least 3 people in the market about the new product, 25.8% of the respondents will pass information to at least 3–5 person about the new product in the market, 20.1% of the respondents will pass information to at least 5–7 person about the new product in the market, 6% of the respondents will pass the information to at least 7–9 persons, 12.8% of the respondents will pass information of above 10 persons about the new product in the market, and the remaining 6.8% of the respondents would not like to pass any information about any new product in the market. Table 7 Convey information about new product

S. No

Particulars

Frequency

1

3 person

110

28.6

2

3–5 person

99

25.8

3

5–7 person

77

20.1

4

7–9 person

23

6.0

5

Above 10

49

12.8

6

No one

26

6.8

384

100.0

Total Source Primary Data

Percent

Social Networks and Time Taken for Adoption of Organic Food Product … Table 8 Cost spend by the respondents for information passing

171

S. No

Particulars

Frequency

1

10–20 Rs

136

35.4

2

20–30 Rs

97

25.3

3

30–40 Rs

89

23.2

4

40–50 Rs

37

9.6

5

Above 50 Rs

25

6.5

384

100.0

Total

Percent

Source Primary Data

12 Cost Spend by the Respondents for Passing the Information The data related to the cost spend by the respondents for passing the information are shown in Table 8. It is clear from Table 8 that 35.4% of the respondents are ready to spend 10–20 Rs to pass the opinion about the new product in the market, 25.3% of the respondents are ready to spend Rs 20–30 to pass the opinion about the new product in the market, 23.2% of the respondents are ready to spend Rs 30–40 to pass the opinion about the new product in the market, 9.6% of the respondents are ready to spend Rs 40–50 to pass the opinion about the new product and the remaining 6.5% of the respondents are ready to spend above Rs 50 to pass the opinion about the new product. It is interpreted from Table 8 that 35.4% of the respondents are ready to spend Rs 10–20 to pass the opinion about the new product in the market.

13 Like to Spread the Information The new product related information has to be diffused among the social groups for faster adoption of the new product in the market. The data related to the way of passing the information about the opinion of a new product in the market are displayed in Table 9. It is clear from Table 9 that 20.8% of the respondents would pass the information through SMS, 9.9% of the respondents would pass the information through E-mail, 41.9% of the respondents have passed the information through Whatsapp, 22.7% of the respondents would pass the information through Facebook, 2.3% of the respondents have passed the information through Twitter, and others like Skype, etc.

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Table 9 Like to spread the information S. No

Particulars

1

SMS

2

E-mail

38

3

Whatsapp

161

4

Facebook

87

5

Twitter

6

Others (Skype) Total

Frequency 80

Percent

Valid percent

20.8

Cumulative percent

20.8

20.8

9.9

9.9

30.7

41.9

41.9

72.7

22.7

22.7

95.3

9

2.3

2.3

97.7

9

2.3

2.3

100.0

384

100.0

100.0

Source Primary Data

14 Quantity of Passing of Information In order to find the information to be passed from one region to another region, the enumerator has fetched the data based on taluk wise. This data will be useful for this research to find out the core area of chances of passing information seems to be high. The summarized quantitative data about the information passing has depicted in Table 10. The researcher has taken the total score of passing information from one region to another region. When looking at this data, it is noted that Sivakasi is a region of high passing information to other regions in the Virudhunagar district. But it is not advisable to compare the Sivakasi region with the other region due to more data have been collected from Sivakasi. So, the comparison could be good if the same data have been converted as average. The researcher has found the average information passing for each region and the calculated data has shown in Table 11.

15 Hypothesis There is no significant difference in the passing of information from one region to another region.

16 Kruskal–Wallis Test To test the hypothesis, Kruskal–Wallis test has applied. It is a nonparametric test and it is also known as one-way ANOVA. It helps to assess the significant differences on a continuous dependent variable by a categorical independent variable. It is used for comparing two or more independent samples of equal or different sample sizes.

5822

391

Thiruchuli

No of information passed

827

Arupukottai

707

357

Sattur

250

287

Rjplym

Kariapatti

1141

Virudhungr

1862

Srivi

Sivakasi

Living place

Sivakasi

Source Computed Data

Information passing region

Region

3607

220

752

289

97

291

184

1239

535

Srivi

2689

211

393

127

138

347

172

832

469

Rajapalayam

Table 10 Quantity of information passing from one taluk to another taluk

1582

59

300

62

126

347

125

264

299

Sattur

2386

40

345

47

1144

344

61

133

272

Arupuko

979

25

171

38

95

279

46

51

274

Thiruchuli

2463

120

492

106

201

342

260

358

584

Vnr

838

34

172

21

89

238

33

60

191

Kariapatti

20,366

959

3332

1081

2717

2545

1168

4078

4486

No of information reached

Social Networks and Time Taken for Adoption of Organic Food Product … 173

827

391

707

250

Thiruchuli

Virudhu

Kariapatti

2.63

7.44

5.12

8.71

3.76 5.45

1.49

5.47

220

3.38

752 11.57

289

97

291 5.38

5.75

211

7.28

393 13.55

127

138

347 11.97

5.93

59

300

62

126

347

125

264

299

344

61

133

272

1.55

0.79

1.63 40

345

47

46

51 2

2.22

38

0.73

25

6.27 171

0.85

95

1.09

7.43

1.65

5.13

6.25 279 12.13

1.11

2.42

11.91

Thiruc Tot/23 Avg

5.95 274

Arupuko Tot/55 Avg

3.32 1144 20.8

9.13

3.29

6.95

7.87

Sattur Tot/38 Avg

120

492

106

201

342

260

358

33

60

1.43

2.61

21

89

2.14

34

8.79 172

1.89

3.59

1.48

7.47

0.91

3.87

6.11 238 10.35

5.64

6.39

8.30

959

3332

1081

2717

2545

1168

4078

4486

Karia No of Tot/23 Avg information reached

584 10.43 191

Vnr Tot/56 Avg

Source Computed Data

No of 5822 61.28 3607 55.49 2689 92.72 1582 41.63 2386 43.38 979 42.57 2463 43.98 838 36.43 20,366 information passed

357

Aruputtai

2.83

172

184

287

3.02

469 16.17 832 28.68

Rjplym Tot/29 A vg

8.23

535

Srivi Tot/65 Avg

1141 12.01 1239 19.06

1862 19.6

Sivakasi Tot/95 Avg

Living place

Sattur

Information Sivakasi passing Srivi region Rjplym

Region

Table 11 Average quantity of information passing to each Taluk

174 D. Thiyagarajan and M. Ponreka

Social Networks and Time Taken for Adoption of Organic Food Product … Table 12 Ranks POI

175

Place

N

Mean rank

Sivakasi

8

38.25

Srivilliputtur

8

35.75

Rajapalayam

8

47.62

Sattur

8

25.75

Arupukottai

8

25.25

Thiruchuli

8

29.00

Virudhunagar

8

33.25

Kariapatti

8

26.12

Total

64

Source Computed Data

The hypothesis set for this analysis is “There is no significant difference in the spreading of information from one region to another region. For that, the information spreading and place has taken as a variable to test this hypothesis. The cumulative information passing of each region has calculated and its mean rank has identified. This furnished information has shown in Table 12. This Table 12 reveals that Rajapalayam has secured the highest mean score of 47.62, following Sivakasi has secured the second highest mean score of 38.25. The area which secured the lowest mean score is Aruppukottai. It shows that the information has the chance of highly spreading in Rajapalayam and lower in the Arupukottai. By using this above data, Kruskal–Wallis test has used to identify the significant difference between these two variables of the place of information and place at 5% level of significance. Its result is depicted in Table 13. As per this test, the significant value is 0.209. This significant value is greater than 0.05. Therefore, the null hypothesis is accepted. It is concluded that there is no significant difference in spreading the information from one region to another region. But based on the mean rank, Rajapalayam region has more chances of spreading more information when compared to another region. Table 13 Test statisticsb

POI Chi-Square

9.657

Df

7

Asymp. Sig.

0.209

a. Kruskal–Wallis Test b. Grouping Variable: PLACE

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17 Living Place and Adoption of New Product in the Market There is no significant relationship between the adoptions of new product in the market among the respondents who are living in different places/region. To test this above set hypothesis, chi-square statistical tools have been used to find the relationship between the two variables. Chi-square test is applied in statistics to test the goodness of fit to verify the distribution of observed data with assumed theoretical distribution. It is a measure to study the divergence of actual and expected frequencies. The chi-square describes the discrepancy between the theory and observation. Therefore, the integrated data on Living place and Adoption of New product in the market among the respondents are shown in Table 14. It is revealed from Table 14 that, the days taken by the respondents 30 in number has gained the highest percentage of 40.1% in the area of Rajapalayam and also from this table, it is known that in the area of Sivakasi the respondents will ready to adopt the product within 30 days, in the area of Srivilliputtur, the respondents will also ready to adopt the product within 30 days, in the area of Rajapalayam, the respondents will also ready to adopt the product within 30 days, in the area of Sattur the respondents will take three months for adoption, in Aruppukottai also, the respondents will ready to adopt the product within 30 days, in the area of Thiruchuli, Virudhunagar and Kariapatti also, the respondents will also ready to adopt the new product in the market within 30 days.

18 Chi-Square Tests In order to test the hypothesis, the chi-square test is used to find the significant relationship between two variables. For the purpose of analysis, 95% level of confidence has been adopted (Table 15). As per this test, the significant value is 0.001. This significant value is lower than 0.05. Therefore, the alternate hypothesis is accepted. It is concluded that there is a significant relationship between the adoptions of new product in the market among the respondents who are living in different places/region. Likelihood ratio (Significant value = 0.000) also shows the high significant relation between the adoption of the new product in the market among the respondents who are residing in various regions.

24 (25.3)

39 (41.1%)

7 (7.4%)

10 (10.5%)

4 (5.2%)

7 (7.4%)

4 (5.2%)

95 (100%)

15 days

30 days

Two mnt

Three mnt

Four mnt

Five mnt

Never adopt

Total

Source Computed data

Sivakasi

Days to purchase

65 (100%)

5 (7.7%)

4 (6.2%)

2 (3.1%)

6 (9.2%)

9 (13.8%)

22 (33.8%)

17 (26.2%)

Srivi

29 (100%)

3 (10.3%)

2 (6.9%)

3 (10.8%)

0 (0%)

4 (13.8%)

13 (45.8%)

4 (7.5)

Rjplym

38 (100%)

0 (0%)

2 (5.3%)

1 (2.6%)

13 (35.2)

3 (7.9%)

10 (15.2%)

9 (9.8%)

Sattur

Table 14 Days taken for passing the information from one taluk to another taluk

55 (100%)

7 (12.7%)

2 (3.6%)

0 (0%)

0 (0%)

3 (5.5%)

21 (38.2%)

22 (15.2%)

Arupukotta

23 (100%)

2 (8.7%)

1 (5.3%)

0 (0%)

2 (8.7%)

1 (5.3%)

13 (9.2%)

4 (5.9%)

Thiruchul

56 (100%)

1 (1.8%)

6 (10.7%

0 (0%)

10 (17.9%)

5 (8.9%)

22 (39.3%)

12 (15.4%)

Vnr

23 (100%

0 (0%)

1 (5.3%)

0 (0%)

1 (5.3%)

0 (0%)

14 (9.2%)

7 (5.9%)

Kariap

384 (100%)

22 (5.7%)

25 (6.5%)

10 (2.6%)

42 (10.9%)

32 (8.3%)

154 (40.1%)

99 (25.8%)

Total

Social Networks and Time Taken for Adoption of Organic Food Product … 177

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D. Thiyagarajan and M. Ponreka

Table 15 Chi-square test Particulars

Value

Df

Asymo. Sig (2-sided)

Pearson chi-square

78.542a

42

0.001

Likelihood ratio

85.408

42

0.000

Linear-by-linear association

1.280

1

0.258

N of valid cases

384

a. 32 cells (57.1%) have expected count less than 5. The minimum expected count is 0.60

Table 16 Cross table of age group and information passing Quantity of information passing Age group Age group

0–25

26–50

51–75

76–100

Above 100

Total

18–30

59

40

26

6

14

145

31–50

77

35

22

18

15

167

Above 50

25

13

20

3

11

72

161

88

68

27

40

384

Total Source Primary Data

19 Age Group and Information Passing The crosstab of Age group and information passing to various regions are depicted in the table in order to find the relationship between the age group and quantity of information passing. The information passing has been grouped in the intervals of 0–25, 26–50, 51–75, 76–100 and above 100 (Table 16).

20 Chi-Square Test In order to test the hypothesis, the chi-square test is used to find the significant relationship between two variables. For the purpose of analysis, 95% level of confidence has been adopted. Its result are depicted in Table 17. Table 17 Chi-square tests Particulars

Value

df

Asymp. Sig. (2-sided)

Pearson chi-square

18.054a

8

0.021

Likelihood ratio

17.433

8

0.026

Linear-by-linear association

2.237

1

0.135

N of valid cases

384

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.06

Social Networks and Time Taken for Adoption of Organic Food Product …

179

As per this test, the significant value is 0.021. This significant value is lower than 0.05. Therefore, the alternate hypothesis is accepted. It is concluded that there is a significant relationship between the Age group and level of information passing from one region to another region. Likelihood ratio (Significant value = 0.026) also shows a high significant relation between the Age group and level of information passing from one region to another region.

21 Exponential Smoothing for Applying Roger’s Model in Identifying the Adoption of Organic Food Product Normally, the SEM model has been created and the equations have been written on the basis of four function Power function Linear function Logarithmic function Exponential Function.

22 Exponential Growth Exponential growth model exhibits the rate of change per instant or unit of time. The value of a mathematical function is proportional to the function’s current value resulting in its value at any time being an exponential function of time. The growth of a system in which the amount being added to the existing system seems to be proportional to the amount already present. The bigger system is the greater the increase. In this research, exponential smoothing has been used to find out the number of days of adoption of organic food product in the market. Exponential growth models can be developed in terms of the time it takes a quantity to double. On the flip side, exponential decay models can be developed in terms of the time it takes for a quantity to be halved or doubled. This calculation is made for the purpose of finding the time in order to know the take-off of the product in the market. By taking this notion, the researcher has framed an equation. f (t) = A(1 + r )t where f (t) A r t

Information passing in time t. The initial value of information passing on time t Rate of passing information time.

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The equation has been calculated and it is shown in Table 18. It is inferred from Table 18 that in the first 10 days the information will spread at the highest rate, later on in between 12 and 15 days, the information spreading shows the low percentage rate of passing the information, after 15 days, the information start to decay that is it shows that the people will never think about the product information to be shared with the other people. The product information will spread more in the first 15 days in the market. From Fig. 1, it is predicted that the information has passed in the market from the initial day of adoption to the fourth day, thereafter, the information starts declined and again it start to raise from the eight-day to 10th day, again there is a fall in the twelfth day and start to rise in the fifteenth day and it shows the maturity trend in the twenty first days till the thirtieth day of the adoption of the organic food product. Table 18 Days taken for quantity of information to be passed S. No

Days (t)

Information to be passed (A)

The rate of passing information (r)

1

1 day

937

907/384 = 2.366

2

2 day

522

1.359

3

3 day

550

1.432

4

4 day

595

1.549

5

5 day

908

2.3645

6

6 day

45

0.117

7

7 days

493

1.2838

8

8 days

112

0.29167

9

9 days

123

0.3203

10

10 days

854

2.2239

11

12 days

16

0.0416

12

13 days

255

0.6641

13

15 days

252

0.6563

14

18 days

72

0.1875

15

21 days

27

0.0703

16

25 days

27

0.0703

17

27 days

16

0.0417

18

30 days

82

0.2135

Source Computed Data

Social Networks and Time Taken for Adoption of Organic Food Product …

181

Fig. 1 Information passed in month

23 Findings and Discussion 40.1% of the respondents will purchase the new product in the market within one month. 35.4% of the respondents belong to the official group. 32.8% of the respondents have joined in the group for the reason of updating with the community news. 45.1% of the respondents have met their group once a week. 43.2% of the respondents have to trust the information if they receive from their friends and connections. 28.6% of the respondents are ready to pass the information of at least 3 persons about the new product in the market after their consumption. 35.4% of the respondents are ready to spend Rs 10–20 to pass the opinion about the new product in the market. 41.9% of the respondents have passed the information through Whatsapp. As per this test, the significant value is 0.209. This significant value is greater than 0.05. Therefore, the null hypothesis is accepted. It is concluded that there is no significant difference in spreading the information from one region to another region. Based on the mean rank, Rajapalayam region has spread more information compared to another region. Majority of the regions will ready to adopt the new product in the market within 30 days except Aruppukottai. As per the Chi-square test conducted between the adoptions of new product in the market among the respondents who are living in different region/places at the significant value of 0.001, the alternate hypothesis is accepted. And also the Chi-square test, the significant value is 0.021. This significant value is lower than 0.05. Therefore, the alternate hypothesis is accepted. It is concluded that there is a significant relationship between the Age group and level of information passing from one region to another region. Likelihood ratio (Significant value = 0.026) also shows a high significant relation between the Age group and level of information passing from one region to another region. 40.1% of the respondents will purchase the new product in the market within 30 days. The risk perception among the respondents restricts them on purchasing the organic food product from the product. The people are highly fond of protecting the environment in the future but they are worried about the product performance, the appearance of the product and their social group would not accept them for adopting the organic food product. The result of this study shows that the information will be passed by the respondents within 18 days and information passing attained its peak during third day, ninth day,

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twelfth day and after 18th day, the information passing starts to decay after eighteenth day. This research mainly focuses on quantity of information passing and it could not draw the chart for social network of information passing from one taluk to another taluk. The future way to carry out the research is to find out the hub point of taluk by using UCI Net software. It aids the researcher to explore the right place to place the product in the market.

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Usage of Generative Adversarial Network to Improve Text to Image Synthesis D. Baswaraj and K. Srinivas

1 Introduction Adversarial neural networks, uses two opposing neural networks to generate new instances of data that can be passed off as real. This is an algorithmic architecture known as “Generative Adversarial Networks (GAN).” The creation of images, videos, and even sounds relies on the employment of them. Ian Goodfellow and his team at the University of Montreal, released the first GAN article [1] in 2014. When Facebook AI research director Yann LeCun talked about adversarial training as “the most exciting topic in the last 10 years in Machine Learning,” he was referring to its widespread popularity. For both good and bad, GAN can replicate almost any distribution of data, that can be trained to generate worlds that are frighteningly like our own in any specific area, such as image, music, speech, literature, or any other type of media. As a result, they can be referred to as “robot artists,” and their work is regarded as powerful and moving. Generative algorithms must be compared to discriminative algorithms to fully comprehend how a Generative Adversarial Network operates. It is important to know about discriminating algorithms, generative algorithms, and models before jump into GAN. A discriminative algorithm’s primary goal is to model a straight answer. Consider a logistic regression method that models a decision border and then goes on to decide the outcome of a judgement depending on how and where it sits in relation to the boundary. That is, a discriminative algorithm looks for patterns in the data and then predicts which label or type each piece of data will fall into. Some of the most prominent algorithms for discrimination are available: D. Baswaraj (B) · K. Srinivas CSE Department, Vasavi College of Engineering, Hyderabad 500031, India e-mail: [email protected] K. Srinivas e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_17

185

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• K-Nearest Neighbors (k-NN) • Logistic Regression • Support Vector Machines (SVMs). A generative algorithm’s primary goal is to discover and attempt to populate the dataset. To put it another way, a generative algorithm adds new data points to an existing dataset. Samples of the model can be used to generate these synthetic data points. Here are a few well-known generative algorithms: • • • •

Naïve Bayes Classifier Hidden Markov Model Gaussian Mixture Model Generative Adversarial Networks.

It takes two neural networks to make a generative adversarial network work. We can create new cases for data to verify its authenticity using generator and discriminator. Suppose we’re trying to come up with something that isn’t just a rip-off of a famous painting. The first step would be to build a collection of handwritten numerals that might be discovered in the MNIST datasets, which are based on realworld examples of handwritten numbers. Now, as we know, the aim of a discriminator is to determine these events are genuine. When it comes to creating new and synthetic images for the discriminator, the generator’s job is to do just that. Because of this, even if they’re fake, it’s hoped that the discriminator will accept them as such. The discriminator’s job is to determine the images coming from the generator are phoney. The normal purpose of generator is to generate handwritten digits as dark as possible for the discriminator using following steps. • Generator generates random integer based on image. • Forwarded to discriminator along with images taken from the dataset. • Discriminator returns the probabilities (1: authenticity, 0: fakery) for authenticity.

2 Literature Survey 2.1 Generative Adversarial Networks As part of their research, Ian Goodfellow and his team came up with an adversarial net framework [1] that claims to train two models simultaneously: a generative model (G) that captures the distribution of the data, and an adversarial model (D) that assesses how likely a sample came from the training data. The goal during G’s training is to maximize the likelihood that will commit an error. If G and D are arbitrary functions, then there is only one solution in which D is always equal to half of G training data distribution. If G and D are described by multilayer perceptron, then the complete system can train through backpropagation. There is no requirement for any Markov chains/unrolled approximate inference networks during training of

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187

samples. The generated samples evaluated both qualitatively and quantitatively to prove the robustness of framework.

2.2 Text to Photo-Realistic Image Synthesis with Stacked GAN In computer vision, producing high quality images from text is a challenging task, but it has numerous practical uses [2]. Text-to-image techniques can approximate the meaning of descriptions, but they lack the necessary details and realistic object parts. An algorithm called Stacked GAN (StackGAN) can generate photo realistic images from text descriptions. Sketch-refinement has been used to break down the complex problem into smaller, easier-to-handle subproblems. StackGAN works in two stages, (1) GAN generates low resolution images by sketching the primitive shape and colors of the object from the given text description and (2) GAN generates high resolution images with photo realistic details from findings and text descriptions from first stage. The refinement procedure can fix flaws by adding intriguing details in results of first stage. By promoting smoothness in the latent conditioning manifold, new conditioning augmentation approaches were applied to broaden the range of synthesized images and stabilize the training of conditional GANs. Generating photo realistic images, the proposed models have been deeply assessed and compared to latest benchmark datasets.

2.3 Image Generation from Scene Graphs The proposed model described in this paper be able to not just recognize images, but also generate them [3]. To this purpose, recent advances in the generation of graphics from natural language descriptions have been fascinating. On narrow domains like descriptions of birds and flowers, these approaches generate stunning results. However, they struggle to correctly replicate complicated sentences for more items and connections. By producing images from scene graphs, we can clearly reason about objects and their relationships, which would otherwise be impossible. To analyze input graphs to predict the boundaries and division masks, convolutional neural networks are used and then transform the scene layout in to an image. The network trained in an adversarial fashion against a pair of discriminators used to provide realistic results. Using Visual Genome and COCO-Stuff, we have been able to show that our methodology can create complex images for more items through qualitative results, ablations, and user surveys.

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2.4 Fine Grained Text to Image Generation with Attentional Generative Adversarial Networks (Attn GAN) In this chapter, the authors proposed a model called Attn GAN, which is an attention driven, multistage refinement model used for generating image from fine grained text [4]. The Attn GAN can synthesize fine grained details in different subregions of the image by paying attention to the important words in the natural language description of the image. For the purpose of instructing the generator, a fine grained image to text matching loss is computed using a deep attentional multimodal similarity model. On the easier CUB dataset, the suggested Attn GAN improves inception scores by 14.14%, and on the more difficult COCO dataset, it improves inception scores by 170.25%. In Attn GAN, attention layers can also be used to undertake a more indepth analysis. First, it illustrates that layered attentional GAN can automatically pick the word level conditions for producing distinct regions of the image. Recently, GAN have been presented as a basis for text to image synthesis. It is general practice to encode the entire text description into a global phrase vector, which act as a precondition for generating image. The GAN trained on a single global phrase vector lacks essential fine grained information and so does not produce high quality images. This difficulty becomes more serious, when producing complicated scenes like those in COCO dataset. Attn GAN is a multistage refining method that is based on attention and enables for finely grained text to image generation to overcome this issue.

2.5 Realistic Image Synthesis with Stacked Generative Adversarial Networks (Stack GAN++) Since GAN suffers to produce high-quality images, despite their impressive results, the Stacked GAN [5] model proposed in this chapter produce high-resolution photo realistic images. Stack GAN version-1 is a two-stage GAN architecture for text to image synthesis. Based on a supplied text description, first stage produces lowresolution images of a scene’ primitive shape and color. After feeding in the results from the first stage and the text description, second stage produces images with photo realistic details. For both conditional and unconditional generative tasks, Stack GAN version-2 offered an advanced multistage generative adversarial network, which is a tree-like arrangement of several generators and numerous discriminators that generates images at various scales that correlate to the same scene. With the use of several distribution approximations, Version-2 has a more consistent training behavior than Version-1. Large-scale trials show that the suggested stacked adversarial networks are better at producing photorealistic images than other methods already in use. Although GAN have been an enormous success, they are notoriously difficult to teach. It’s not uncommon for the training process to be unstable and highly dependent

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on the parameters that are selected. Several papers have proposed that the discontinuous supports of the data distribution and the corresponding model distribution are partly to blame for the instability. It is especially critical when training GAN to generate high resolution images (256 × 256) since the probability of image and model distributions sharing supports in a high dimensional space is much lower than in a lower dimension. A typical GAN training failure is mode collapse, in which many generated samples have the same color or texture pattern. This is a common GAN training failure. There are three main contributions to the proposed Stack GAN. i. Initially Stack GAN version-1 produces high-quality images (256 × 256) from text descriptions. ii. To stabilize the training of conditional GAN and increase the diversity of the generated samples, a new conditioning augmentation approach proposed. iii. To enhance the quality of generated images and stabilizes the GAN training, Stack GAN version-2 used by jointly approximating several distributions.

3 Methodology 3.1 Defining Goal To generate an image that is as semantically coherent as possible with the text description, Text to Image (T2I) synthesis attempts to learn mapping between complex semantic text space and color image. Textual descriptions and pixel level visuals believed to have a heterogeneous gap, which makes it difficult to synthesize realistic, semantically detailed things. Developing a synthesizer capable of bridging the gap across domains becomes imperative.

3.2 Researching Previous Attempts Using several ways based on GANs, it is possible to bridge the gap between domains by using a discriminator to tell apart the synthesized text and image pair [6] from the ground truth pair. While this method has the advantage of modelling semantic coherence between images and text, it also has the disadvantage of not catching errors in synthesis such as those caused by semantic and structural inconsistencies. As a result, the attention mechanism has used to train the generator, which has helped to better match particular visual words with the associated image in recent times. Due to the differences between text and image modalities, the word level attention itself does not guarantee semantic consistency, so Mirror GAN [7] model proposed to address this issue of semantic consistency across several domains by combining both Text to Text (T2T) and Image to Text (I2T) models. When it comes to the inconsistencies in the semantics of heterogeneous data, Mirror GAN I2T job

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does not make it any easier than a homogeneous task like an Image to Image (I2I). For better semantic matching between picture features and text descriptions, current work in attention mechanisms and subsequent advances in Attn GAN have adopted a world level attention mechanism for Attn GAN [4]. Since word feature weights are fixed in this approach, essential words will be overlooked attention is not generated if the attention mechanism does not work appropriate weights in one training loop, resulting in missing image information.

3.3 Defining Approach For the current study, an attention updating method has been used to address this problem of fixed weights. This mechanism enables the attention module to focus on relevant phrases in stages during the picture synthesis process. The above-mentioned adjustments were made because the information gap between heterogeneous gaps is lower in I2I synthesis than it is in heterogeneous generating tasks. To help T2I, textual features should be better encoded and create synthetic images. Using I2I model, we can generate synthesized image which has semantic consistency. This is all about reducing semantic inconsistency as much as possible to get better results.

3.4 Algorithm For the total number of training cycles do for k steps do process the images and text data for forwarding to the discriminator for training end for for m steps do forward the processed random vector input and text data to the generator for training end for for n steps do i. generate output by generator model ii. classify the data from dataset using discriminator model iii. update generator and discriminator model end for

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4 Experiments and Results 4.1 Experiment Setting Implementation Details: Each of the images used in the test has been resized to a dimension of 256 × 256 pixels. During initial stages of preparation, train the I2I, SDM, and T2I tasks. Datasets: We make use of two well-known data sets (1) CUB-Bird dataset [14], which contains 11,788 bird images and each with ten visual description sentences. (2) MS-COCO data set [15–18], which contains 80,000 training and 40,000 testing images of bird, in which each image has five text annotations. Evaluation: Proposed KT GAN and other GAN algorithms are compared to each other using following metrics: i. ii. iii. iv.

Inception Score (IS) Frechet Inception Distance (FID) Comparing the Rank-1 score in text to image retrieval Human Perception Test.

4.2 Effectiveness of New Modules To evaluate the effectiveness of two new components i.e., AATM and SDM, the metrics IS, FIS, and RANK-1 are used. The results are shown in Table 1. i. To replace the attention mechanism in Attn GAN, we introduce AATM (Attn GAN + AATM). For CUB and COCO test datasets, Attn GAN + AATM results in improvement of IS, FID, and Rank-1 metrics. ii. For CUB and COCO datasets, we seen improvements over Attn GAN in IS, FID, and Rank-1 when the SDM is added to Attn GAN. Table 1 IS, FID, and RANK-1 produced by combining different components of the KT GAN on CUB bird and MS-COCO data sets Method

CUB-Bird

MS-COCO

IS

FID

Rank-1 (%)

IS

FID

Rank-1 (%)

Attn GAN

4.36 ± 0.03

23.98

27.9

25.89 ± 0.17

35.49

22.9

Attn GAN + SDM

4.76 ± 0.02

18.21

32.6

29.02 ± 0.17

31.86

24.4

Attn GAN + AATM

4.74 ± 0.05

20.40

29.4

28.54 ± 0.38

32.54

23.7

KT GAN

4.85 ± 0.01

17.32

32.9

31.67 ± 0.36

30.73

24.5

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Table 2 IS, FID, and RANK-1 on CUB-Bird testing data about variants of EQ in word feature update module Method

IS

FID

Rank-1 (%)

Attn GAN

4.36 ± 0.02

23.98

27.9

Attn GAN + AATM B1 )

4.70 ± 0.03

20.76

29.0

Attn GAN + AATM

4.74 ± 0.05

20.40

29.4

Attn GAN + AATM without residual connection

4.49 ± 0.04

22.17

28.3

iii. Table 1 shows the overall the performance of KT GAN has been improved because of contribution of both AATM and SDM components in KT GAN model.

4.3 Component Analysis of AATM We compare the IS, FID, and Rank-1 metrics of several designs in the word feature update module for CUB dataset (Table 2). KT GAN was not tested with more than three blocks due to GPU memory limits. In the final two blocks, the combination of any two AATM versions can be used for blocks B1 and/or B2. i. In Block B1, AATM is used and indicated by AATM (B1). Table 2 shows that Attn GAN + AATM (B1) improves when compared to Attn GAN. ii. Both Blocks B1 and B2 are addressed by combination of Attn GAN and AATM. There are moderate improvements when Attn GAN + AATM compared to Attn GAN + AATM (B1). The performance of generator is significantly improved, when AATMs are added sequentially. iii. To show the impact of residual connection on performance, if we remove the residual connection, then the performance of Attn GAN + AATM degraded.

4.4 Component Analysis of SDM As part of SDM, we conduct ablation research to evaluate the following designs: i. It is possible to improve the encoder with the help of I2I. ii. Begin with I2I generators are a suitable choice. Attn GAN + AATM + GTE taught from scratch using SDL’s Guided Text Encoder, which is then fed back into Attn GAN. In comparison to Attn GAN + AATM without distillation, as demonstrated in Table 3.

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Table 3 IS, FID, and RANK-1 of different variants of SDM on the CUB bird and MS-COCO test data sets Method

CUB-Bird

MS-COCO

IS

FID

Rank-1 (%)

IS

FID

Rank-1 (%)

Attn GAN + AATM

4.74 ± 0.05

20.40

29.4

28.54 ± 0.38

32.54

23.7

Attn GAN + SDM + GTE

4.80 ± 0.07

18.131

32.0

30.03 ± 0.44

31.22

24.2

Attn GAN + AATM8

4.81 ± 0.06

17.45

32.8

29.28 ± 0.00

30.89

24.5

KT GAN

4.85 ± 0.01

17.32

32.9

31.67 ± 0.36

30.73

24.5

4.5 Comparison of KT GAN with Other GAN Models Listed in Table 4, compares the IS scores of proposed KT GAN and other models. KT GAN (4.85) model has the best isometric results on the CUB dataset. Additionally, the KT GAN outperforms most known techniques save for SDGAN on the COCO dataset. In SD GAN, however, the generator must train with various text sentences. With only images with a single phrase description, SD GAN cannot train. This is typical in practical projects like Story Visualization and Text to Video. One statement per image required for KT GAN and Attn GAN, which are both able to be taught conventionally. SD GAN has several Siamese branches, making it more complicated than KT GAN. As a result, SD GAN training requires more powerful hardware. Table 4 IS by KT GAN and other GAN models on CUB BIRD and MS COCO test data sets

Methods

CUB-Bird

MS-COCO

GAN INT CLS [6]

2.88 ± 0.04

7.88 ± 0.07

Stack GAN [8]

3.70 ± 0.04

8.45 ± 0.03

Stack GAN ++ [5]

3.84 ± 0.06

8.30 ± 0.10

HDGAN [9]

4.15 ± 0.05

11.86 ± 0.18

Attn GAN [4]

4.36 ± 0.02

25.89 ± 0.19

Mirror GAN [7]

4.56 ± 0.05

25.47 ± 0.41

Control GAN [10]

4.58 ± 0.09

24.06 ± 0.60

SEGAN [11]

4.67 ± 0.04

27.86 ± 0.31

SD GAN [12]

4.67 ± 0.09

35.69 ± 0.50

DM GAN [13]

4.75 ± 0.07

30.49 ± 0.57

KT GAN (Proposed)

4.85 ± 0.04

31.67 ± 0.36

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4.6 Visualization Figure 1 illustrates examples created by Attn GAN, SE GAN, DM GAN, and KT GAN for qualitative evaluation. Semantic structural ambiguity can be seen in the images generated by Attn GAN and SE GAN (see the left 4 columns of Fig. 1) on CUB dataset. DM GAN produces better looking images than Attn GAN and SE GAN, but not as as KT GAN. On the other hand, proposed KT GAN model, better emphasizes the main object’s contrast with the backdrop. Multi-subject image generation is more difficult, when text descriptions are more sophisticated and contain numerous items, as seen in, for example, the COCO data set (see the right 4 columns of Fig. 1). To effectively capture and organize the most important content, KT GAN can bridge the domain gap between text and image. Overall, an efficient structure is achieved through these steps. In addition, we evaluate the proposed KT sensitivity GAN by making a single change to the input sentence. For example, a big body of water versus a grassy area, affect the visual scene and bird color (“blue” vs “yellow”), as depicted in Fig. 2. In this case, it shows that the proposed KT GAN model is capable of picking up on even the most minute textual alterations and preserving their rich semantic variety and depth.

Fig. 1 Images (256 × 256) are generated by Attn GAN, SEGAN, DM GAN, and KT GAN (left 4 columns are from CUB-Bird and right 4 columns are from MS-COCO test data sets)

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Fig. 2 Examples of KT GAN, catching subtle changes (phrase in red) of text descriptions on CUB-Bird and MS-COCO test data sets

5 Conclusion For Text to Image synthesis, we develop a Knowledge Transfer GAN using a new AATM and SDM models. To better encode text features and generate photographic images, SDM uses Image-to-Image synthesis. It is possible to enhance the details of synthesized images by using the AATM. As a result, the generator can produce high quality images. It is clear through extensive experimentation that Knowledge Transfer GAN is effective and much better than other state of art approaches. Acknowledgements Thanks to the Vasavi college of Engineering for sponsoring and supporting to develop this project successfully.

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References 1. Pradhan AK, Swain S, Rout JK (2022) Role of machine learning and cloud-driven platform in IoT-based smart farming. In: Machine learning and internet of things for societal issues. Springer, Singapore, pp 43–54 2. Christian L et al (2017) Photo realistic single image super-resolution using a GAN. In: CVPR 3. Kumar S, Ansari MD, Gunjan VK, Solanki VK (2020) On classification of BMD images using machine learning (ANN) algorithm. In: ICDSMLA 2019. Springer, Singapore, pp 1590–1599 4. Mani MR, Srikanth T, Satyanarayana C (2022) An integrated approach for medical image classification using potential shape signature and neural network. In: Machine learning and internet of things for societal issues. Springer, Singapore, pp 109–115 5. Shaik AS, Karsh RK, Suresh M, Gunjan VK (2022) LWT-DCT based image hashing for tampering localization via blind geometric correction. In: ICDSMLA 2020. Springer, Singapore, pp 1651–1663 6. Reed S et al (2016) Generative adversarial text to image synthesis. In: ICML 7. Gunjan VK, Shaik F, Venkatesh C, Amarnath M (2017) Artifacts correction in MRI images. In: Computational methods in molecular imaging technologies. Springer, Singapore, pp 9–28 8. Zhang H et al (2017) StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE/ICCV, Oct. 2017, pp 5907–5915 9. Prasad PS, Bethel GNB, Singh N, Gunjan VK, Basir S, Miah S (2022) Blockchain-based privacy access control mechanism and collaborative analysis for medical images. Sec Commun Netw 10. Bowen L et al (2019) Controllable text to image generation. In: NeurIPS, pp 2065–2075 11. Gunjan VK, Shaik F, Venkatesh C, Amarnath M (2017) Visual quality improvement of CT image reconstruction with quantitative measures. In: Computational methods in molecular imaging technologies. Springer, Singapore, pp 45–73 12. Bhardwaj T, Mittal R, Upadhyay H, Lagos L (2022) Applications of swarm intelligent and deep learning algorithms for image-based cancer recognition. In: Garg L, Basterrech S, Banerjee C, Sharma TK (eds) Artificial intelligence in healthcare. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-16-6265-2_9 13. Merugu S, Kumar A, Ghinea G (2023) Hardware, component, description. In: Track and trace management system for dementia and intellectual disabilities. Springer, Singapore, pp 31–48 14. Sinha P, Shaob M (2021) Detection of lung cancer in CT scans via deep learning and Cuckoo search optimization and IOT. Peer Rev Bimonthly Int J 11(5):11–19. Helix-The Scientific Explorer 15. Merugu S, Kumar A, Ghinea G (2023) Geriatric mobility assistive system. In: Track and trace management system for dementia and intellectual disabilities. Springer, Singapore, pp 85–93 16. Rane CV, Patil SR (2020) Data embeddable texture synthesis with fast data extraction. Peer Rev Bimonthly Int J 10(04):83–89. Helix-The Scientific Explorer 17. Kinge S (2019) A multi-class fisher linear discriminant approach for the improvement in the accuracy of complex texture discrimination. Peer Rev Bimonthly Int J 9(04):5108–5121. Helix-The Scientific Explorer 18. Bommagani G, Shaik F (2021) Histogram equalized thresholding method for analysis of diabetic myonecrosis related images. Peer Rev Bimonthly Int J 11(5):32–46. Helix-The Scientific Explorer

Recurrent Neural Network-Based Solar Power Generation Forecasting Model in Comparison with ANN Shashikant, Binod Shaw, and Jyoti Ranjan Nayak

1 Introduction Forecasting plays a very important role in renewable energy-based power systems, it helps to make decision making, schedule, and control. Since the output of renewable energy-based systems such as solar and wind is highly dependent on weather conditions. This nature of dependency data is generally nonlinear which makes it difficult to forecast. A forecasting model is required which can predict non-linear data. This can be done either by Machine Learning (ML) based model or an optimization-based forecasting model. Optimization algorithms and ML can solve the nonlinear problem. Various ML-based forecasting model is reported in the literature such as Artificial Neural Network (ANN) [1, 2], Back Propagation (BP) [3], Support Vector Machine (SVM) [4], Deep Learning [5, 6], Long Short-Term Memory (LSTM) [7], Convolution Neural Network (CNN) [8], etc. All these forecasting models have been applied to forecast the parameter for renewable energy systems such as solar irradiation, solar power generation, wind power generation, etc. To enhance the performance of the forecasting model’s optimization algorithm is introduced in the ML model to optimize the weight and bias. In general, hybridization improves the system performance always, same in case for linear time series data prediction, but for nonlinear forecasting problem it is not true. In contrast with literature [9], it illustrates that the modified optimization algorithm in hybridized with ELM enhances the performance of ELM. Various other hybrid model has been reported in the literature based on optimization are Particle Swarm Optimization based ANN (PSO-ANN) [10], GA-LSTM [11], Genetic algorithm based Back Propagation Neural Network (GA-BPNN) is used to increase the forecasting stability [12]. All these models discussed here are memory less based model except LSTM. In this work, memory-based forecasting model is used, which stores its previous output value to predict the future output. Shashikant (B) · B. Shaw · J. R. Nayak National Institute of Technology, Raipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_18

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These memory-based models are Recurrent Neural Network (RNN), Long ShortTerm Memory (LSTM) and Echo State Network (ESN). Present work represents the development of RNN-based forecasting model in comparison with convention ML model, that is, ANN. The input parameters considereded in this work are time, irradiation, and temperature, whereas power is considered as output parameter. The data consider in this work is collected from Chhattisgarh State Power Transmission Company Limited (CSPTCL) from Feb 2019 to Dec 2019. The work highlights are as follows: • RNN and ANN-based prediction model is developed and predicted for two cases i.e., a month-ahead prediction and two months ahead prediction. • Compared the outcome of recurrent neural networks with ANN and concluded that RNN is superior in terms of MAE, MSE, MAPE, and R2 . • Percentage improvement is shown for RNN concerning ANN. This paper is organized as follows: firstly, an introduction with a literature survey related to forecasting. Then, the methodology is introduced which tells about the development of the forecasting model. Then, statistical parameters are introduced to evaluate the performance of models. Next, the Result and Discussion, which describes the selection of the model based on its performance and results. Finally, Conclusions from this comparison of the model are present and its findings are followed by acknowledgments and references.

2 Methodology This section deals with the development of the model used in the study. Two models are taken into consideration for comparison purposes one is a conventional model, that is, ANN, and the other is a memory-based model, that is, RNN. These two models are explained in detail below:

2.1 Artificial Neural Network (ANN) ANN is a subset of Artificial Intelligence (AI) and is widely used for non-linear function estimation, pattern recognition, clustering, etc. All ML models are black box models that primarily consist of the input layer, output layer, hidden layer, weights, and biases. It works in two phases: training and testing. In the training phase, the input and output data sets are used to tune the weight and bias parameters this tuning is called as training of the ML model, and the output of the training phase is trained weights and bias. Later these trained weights and bias is used in the testing phase for prediction. The error generated in the training phase is propagated backward through the backpropagation algorithm, to minimize the error by modifying weights and bias as expressed in the equation from (1) to (7), followed by the architecture of ANN

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used in work as shown in Fig. 1. The input layer has three neurons which contains time, irradiation and temperature, one hidden layer consists of five neurons and an output layer with single node which represents the power. This simple architecture of ANN is widely used in solar applications [13]. Z t = W21 ∗ I + b

(1)

  O H1t = f Z t

(2)

O t = W32 ∗ O H1t + c

(3)

  yt = f O t

(4)

et = y t − y t

(5)

  T  W32new = W32old − alpha ∗ O H1 ∗ y t 1 − y t ∗ et

(6)

   T W21new = W21old − alpha ∗ I ∗ O H1 (1 − O H1 ) ∗ (W32 )T ∗ y t 1 − y t ∗ et (7) where ‘W 21 ’and ‘W 32 ’ are the weights between the input layer (I) and hidden layer (H 1 ) and weights between the output layer and hidden layer. ‘b’ and ‘c’ bias, ‘Z t ’ is the input to the hidden layer, ‘O t ’ is the input to the output layer. ‘O H1t ’ and ‘y t ’ represents the output of the hidden layer and output layer. ‘y t ’ and ‘y t ’ represents the target value and predicted value. ‘et ’ represents the error and alpha is the learning rate. Fig. 1 Architecture of ANN

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2.2 Recurrent Neural Network (RNN) RNN is a type of NN which is suitable for modeling sequential data. Conventional ANN is a feedforward network with a backpropagation algorithm whereas RNN uses feedback loops to process a sequence of data to get output. This feedback loop stores the output and is used as input to the next sequence of input which is also referred to as memory. This memory is useful for the prediction next sequence output whereas conventional NN is unable to do so. The architecture of RNN is shown in Fig. 2. Where, ‘t’ represents the current state, “t − 1” represents the previous state and “t + 1” represents the future state. The mathematical calculation for the prediction of RNN structure is expressed from Eqs. (8) to (11). RNN structure consists of the input layer, a hidden layer, an output layer, weights, and bias. Here, the current output is dependent on the previously hidden state output which is treated as memory. This makes the RNN to be widely used in speech recognition [14, 15] and natural language processing [16, 17]. a t = b + W h t−1 + U x t

(8)

  h t = tanh a t

(9)

ot = c + V h t

(10)

1 1 + e−ot

(11)

yˆ t =

where “U”, “V ”, and “W ” are the weights between the input to the hidden layer, the hidden layer to the output layer, and weights between the hidden layer from past to present iteration. “b” and “c” are the bias, “at ” is input to the hidden layer, “ht ” is the output of the hidden layer, “ot ” is the input to the output layer. yˆ t is the predicted output.

3 Statistical Measures Statistical measures are an important tool for forecasting. It helps to visualize things, gives insight into problems, analyzing and interprets data. It gives the relation between two or more quantities. In this work, four important statistical measures are used namely MAE, MSE, MAPE, and R2 . The mathematical expression for these parameters is expressed as Eqs. (12)–(15). Its performance can be observed as best when MAE, MSE, and MAPE show minimum value and maximum value for R2 . MAE is used to measure how close predictions are to the outcome. MSE is used to measure the amount of error in the statistical models. MAPE measures the

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Fig. 2 Architecture of RNN

V

V

h (t-1)

O (t+1)

O (t)

O (t-1)

W

X (t-1)

W

h (t)

U

U

V

X (t)

W

h (t+1)

U X (t+1)

accuracy of a forecast system MAE =

MSE =

N 1  |Ai − Pi | N i=1

(12)

N 1  (Ai − Pi )2 N i=1

(13)

 N  1  Ai − Pi  ∗ 100 MAPE = N i=i  Pi 

(14)

N (Ai − Pi )2 R 2 = 1 −  N i=1 2 i=1 (Ai − mean(Pi ))

(15)

where ‘Ai ’ and ‘Pi ’ represents the actual and predicted value.

4 Result and Discussion The RNN-based forecasting model is developed and compared with ANN. These models are trained and tested with real data i.e., solar irradiation, temperature, time, and power, gathered from CSPTCL as shown in Fig. 3. This data set is divided into two groups for training and testing, that is, 70% of data is used for testing and the remaining 30% data is used for testing. For proper

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Fig. 3 SPG per day average data under observation

handling of data, the data are initially normalized with Eq. (16) and the fed to ML model for training and testing, and later the predicted data are deformalized with Eq. (17) to convert them to original values. The developed forecasted model is used for long-term forecasting i.e., for a month ahead and two months ahead prediction, and the results associated with it are shown in Figs. 4 and 5. X normalized =

X i − X min X max − X min

X denormalized = X i ∗ (X max − X min ) + X max Improvement(%) =

|ANNmetrices − RNNmetrices | ∗ 100 ANNmetrices

(16) (17) (18)

The denormalized value obtained from the models is used for statistical measures and the results are tabulated in Tables 1 and 2 corresponding to a month-ahead prediction and two months ahead prediction. From Tables 1 and 2, it is observed that the first three statistical parameters, that is, MAE, MSE, and MAPE for RNN based prediction model shows minimum value in comparison with ANN based prediction model, and maximum value for R2 , which indicates that the performance of RNN is better than ANN for long-term prediction.

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Fig. 4 Comparison of RNN and ANN for a month ahead prediction

Fig. 5 Comparison of RNN and ANN for two months ahead prediction Table 1 Statistical measures for a month ahead prediction Statistical measures

MAE

MSE

MAPE

R2

ANN

11.6576

461.5048

0.4920

0.8119

RNN

10.6619

344.7781

0.4397

0.8595

8.5412

25.2926

10.6301

5.8628

Improvement (%)

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Table 2 Statistical measures for two month ahead prediction Statistical measures

MAE

MSE

ANN

12.4770

556.2309

RNN

10.9059

Improvement (%)

12.5920

MAPE

R2

0.5950

0.7598

409.8455

0.4023

0.8230

26.3174

32.3866

8.3180

In both cases, MAE is between 10.5 and 11 for the RNN model which is of small variation even for two months of data prediction, but as in the case of ANN, it is increasing from 11.6 to 12.5 which shows a large variation in the error which is not reliable for the prediction model. Similarly, the MSE value of RNN is minimum as compared to ANN in both cases, the difference of MSE for RNN considering both cases is 65.0764 whereas for ANN is 94.7261. The difference in MAPE value for both cases for RNN is 0.0374 and for ANN is 0.103. MAPE calculation for RNN is 0.4397 and 0.4023, i.e., the prediction accuracy increases which concludes that the RNN is more reliable for long-term prediction. At last, R2 for RNN is maximum as compared to ANN in both cases. The percentage improvement of RNN over ANN metrics is calculated by Eq. (18) [18], for both cases, and it is observed that for a month ahead prediction the percentage improvement of RNN over ANN is obtained as 8.5412, 25.2926, 10.6301, and 5.8628. The percentage improvement for two months ahead prediction for RNN based on statistical measures is obtained as 12.5920, 26.3174, 32.3866, and 8.3180, which concludes that the overall performance of RNN is better than ANN for long-term ahead prediction.

5 Conclusion Solar Power Generation Forecasting is important for power sector companies because it helps them to plan their scheduling which ultimately leads to profits and minimizes the risk in system reliability. Solar power forecasting is difficult because of nature dependency which introduces non-linearity in the system. To predict long-term MLbased forecasting model is developed (RNN) and compared with the traditional ML model (ANN). The model is predicted for a month ahead generation and two months ahead generation. Four statistical parameters are used for the analysis of models which are MAE, MSE, MAPE, and R2 . The percentage improvement is also calculated for models in terms of their metrics. The improvement for one month ahead prediction is found to be 8.5412, 25.2926, 10.6301, and 5.8628, and for two months ahead prediction is found to be 12.5920, 26.3174, 32.3866, and 8.3180. This concludes that RNN is more suitable for long-term ahead prediction than ANN. It also concludes that the performance of the memory-based ML model excelled over the memoryless based ML model.

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Acknowledgements The Authors would like to thank Chhattisgarh State Power Transmission Company Limited (CSPTCL) for providing the data.

References 1. Amrouche B, Le Pivert X (2014) Artificial neural network based daily local forecasting for global solar radiation. Appl Energ 130(2014):333–341. https://doi.org/10.1016/j.apenergy. 2014.05.055 2. Rahim A, Rifai D, Ali K, Zeesan M, Abdalla AN, Faraj MA (2020) Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): a review of five years research trend. Sci Total Environ 715:136848. https:// doi.org/10.1016/j.scitotenv.2020.136848 3. Lima MAFB, Carvalho PCM, de S. Braga AP, Ramírez LMF, Leite JR (2018) MLP back propagation artificial neural network for solar resource forecasting in equatorial areas. Renew Energ Power Qual J 1(16):175–180. https://doi.org/10.24084/repqj16.253 4. Wang F, Zhen Z, Wang B, Mi Z (2017) Comparative study on KNN and SVM based weather classification models for day ahead short term solar PV power forecasting. Appl Sci 8(1). https://doi.org/10.3390/app8010028 5. Mishra M, Dash PB, Nayak J, Naik B, Swain SK (2020) Deep learning and wavelet transform integrated approach for short-term solar PV power prediction. Meas J Int Meas Confed 166:108250. https://doi.org/10.1016/j.measurement.2020.108250 6. Kumari P, Toshniwal D (2021) Deep learning models for solar irradiance forecasting: a comprehensive review. J Clean Prod 318:128566. https://doi.org/10.1016/j.jclepro.2021. 128566 7. Sun Y (2020) Advanced statistical modeling, forecasting, and fault detection in renewable energy systems 8. Dong N, Chang JF, Wu AG, Gao ZK (2019) A novel convolutional neural network framework based solar irradiance prediction method. Int J Electr Power Energ Syst 114:105411. https:// doi.org/10.1016/j.ijepes.2019.105411 9. Sahu RK, Shaw B, Nayak JR (2021) Short/medium term solar power forecasting of Chhattisgarh state of India using modified TLBO optimized ELM. Eng Sci Technol Int J 24(5):1180–1200. https://doi.org/10.1016/j.jestch.2021.02.016 10. Mohandes MA (2012) Modeling global solar radiation using Particle Swarm Optimization (PSO). Sol Energ 86(11):3137–3145. https://doi.org/10.1016/j.solener.2012.08.005 11. Bouktif S, Fiaz A, Ouni A, Serhani MA (2018) Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7). https://doi.org/10.3390/en11071636 12. Feng Y, Zhang W, Sun D, Zhang L (2011) Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos Environ 45(11):1979–1985. https://doi.org/10.1016/j.atmosenv.2011. 01.022 13. Yadav AK, Chandel SS (2014) Solar radiation prediction using Artificial Neural Network techniques: a review. Renew Sustain Energ Rev 33:772–781. https://doi.org/10.1016/j.rser. 2013.08.055 14. Karita S et al (2019) A comparative study on transformer vs RNN in speech applications. 9(2):449–456. NTT Communication Science Laboratories, 2 Waseda University, 3 Johns Hopkins University, LINE Corporation, 5 Nagoya University, 6 Human Dataware Lab. Co., Ltd., Mitsubishi Electric R. IEEE Xplore 15. Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 international conference on acoustics, speech, and signal processing (6), pp 6645–6649

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16. Morchid M (2018) Parsimonious memory unit for recurrent neural networks with application to natural language processing. Neurocomputing 314:48–64. https://doi.org/10.1016/j.neucom. 2018.05.081 17. Dharaniya R, Indumathi J, Uma GV (2022) Automatic scene generation using sentiment analysis and bidirectional recurrent neural network with multi-head attention. Neural Comput Appl 7. https://doi.org/10.1007/s00521-022-07346-7 18. Pang Z, Niu F, O’Neill Z (2020) Solar radiation prediction using recurrent neural network and artificial neural network: a case study with comparisons. Renew Energ 156:279–289. https:// doi.org/10.1016/j.renene.2020.04.042

Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning M. Sonia, Chaganti B. N. Lakshmi, Shaik Jakeer Hussain, M. Lakshmi Swarupa, and N. Rajeswaran

1 Introduction 1.1 A Subsection Sample Attacks on mobile devices, including smartphones and tablets have been on the rise as their use has grown. Mobile malware is one of the most significant dangers, resulting in a variety of security issues as well as financial losses. During the first quarter of 2017, security professionals detected over 750,000 new Android viruses, according to the G DATA study [1]. A vast variety of mobile malware is likely to continue to be produced and propagated in order to perform different cybercrimes on mobile devices. Because of the prevalence of Android smartphones, it is the most often targeted mobile operating system by mobile malware [2, 3]. Developers of malicious software are encouraged to target Android smartphones by additional factors apart from the sheer number of these devices in circulation. Since the Android M. Sonia Department of CSE, Nalla Narasimha Reddy School of Engineering, Hyderabad, Telangana State, India C. B. N. Lakshmi Department of CSE, TKR College of Engineering and Technology, Hyderabad, Telangana State, India S. J. Hussain Department of CSE, Malla Reddy Engineering College, Hyderabad, Telangana State, India M. L. Swarupa (B) Department of EEE, CVR College of Engineering, Hyderabad, Telangana State, India e-mail: [email protected] N. Rajeswaran Department of EEE, Malla Reddy Institute of Engineering and Technology, Hyderabad, Telangana State, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_19

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working framework permits clients to introduce applications from outsider commercial centers, aggressors might utilize this element to captivate or misdirect Android clients into introducing perilous or dubious applications from their servers [1, 2, 4–6]. Correctly predicting whether a file is malware or not is critical for the public and intelligence agencies, as it aids in detecting whether the provided file includes harmful code or not, preventing a system breach. Many approaches have been used to do this, including IDS, Antivirus, and Firewalls, albeit most of these actions depend on the exemplary technique for distinguishing malware from a document. The first methodology to be utilized for identifying malware and goodware is based on machine learning and profound learning. machine learning was used to appraise the probability of a document containing malware [7–11]. For the first time in the last two decades, Support Vector Machine (SVM) was used to classify malware. Random Forest is another machine learning model that uses ensemble approaches to anticipate this situation. And in deep learning we used DNN and ANN (Artificial Neural Network). Large amount of data is used to detect the malware. Malware poses a major danger to user privacy, financial security, device integrity, and file integrity. In this chapter, we show how we may categorize malware into a small number of behavioral classes based on their activities, each of which conducts a certain set of misbehaviors. Monitoring capabilities pertaining to various Android tiers may be used to identify these misbehaviors [12]. An Android malware detection solution that is based on the host rather than the target detects and stops harmful actions by analyzing and correlating characteristics at four levels: kernel, application, user, and package [13, 14].

2 Proposed Method It is possible to reverse engineer Android apps or APKs to remove highlights, for example, authorizations and how much App Components like Activity and Services. In CSV design, these highlights are utilized as a component vector, with the Malware and Goodware names 0 and 1 individually. The CSV is passed through a Genetic Algorithm to pick the best efficient collection of features, reducing the dimensionality of the feature set. Two machine learning classifiers, Support Vector Machine (SVM) and Neural Network, are trained using the optimum set of features collected. Malware may be detected using cutting-edge machine learning and deep learning techniques. The virus is caught before it has a chance to harm the assets it is trying to harm [15, 16]. There will be no data loss [17, 18].

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2.1 Supervised Classification (Training Dataset) The data has been separated into two portions, with 70:30 ratios for training and testing. Learning algorithms were applied to the training data, Predictions were made on the test data set using the learning.

2.2 Supervised Classification (Test Dataset) About a third of the whole data set is used for testing purposes. Administered learning calculations were utilized on the test information, and the outcomes were then contrasted with this present reality information [19] (Fig. 1).

Fig. 1 Algorithm for proposed system

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2.3 System Design Any development approach or application area may profit from the utilization of programming plan. For any made item or framework, plan is the initial phase during the time spent improvement. A planner’s responsibility is to assemble a model or portrayal of a future substance. In order to build and test software, the first step is to design the system. This process starts once the system requirements have been thoroughly analyzed. “Quality” is a single word that encapsulates the importance. Designers are responsible for creating high-quality software. Configuration gives us visual portrayals of programming that we might use to pass judgment on its quality. To precisely make an interpretation of a client’s vision into a completed programming item or framework, we depend on plan. Because of this, the first step in every software engineering process is to create a working software design. We run the danger of constructing an unstable system without a sturdy design—one that will be challenging to evaluate and whose quality may be known toward the end [20, 21]. All through the plan interaction, information structure, programming structure, and procedural components are refined, evaluated, and recorded. Depending on your perspective, a system’s design might be approached from the specialized or proposed method the board side. Plan according to a specialized perspective comprises of engineering plan, information structure plan, interface plan, and procedural plan [22, 23].

2.4 Use Case Diagram Execution is the most common way of changing a new or refreshed framework plan into a functional one. Implementation may be divided into three categories. A switch from a manual system to a computerized one. There are several challenges to deal with, such as converting files, teaching users, and assuring the integrity of prints. The installation of a new computer system to replace an outdated one. As a general rule, this is a difficult task. There might be a slew of issues if the event isn’t adequately prepared. Using the same computer, implement a redesigned application to replace an existing one. If the files do not change much, this sort of conversion is quite simple to manage. All modules of the Generic tool are implemented: In the first module, users are identified on a personal level. This module determines whether or not a user is authorized get to the data set and furthermore makes a meeting for the client. The use of illegal substances in any form is absolutely prohibited. Tables are formed using user-specified fields in the Table creation module, and the user may build several tables at once. In the development of tables, they might define conditions, limitations, and computations. Throughout the paper, the Generic code keeps track of the user’s needs. Users may edit, remove, or insert new records into the database using the Updating module. In the Generic Code, this is a critical module. The user must enter the field value in the form, and the Generic tool will populate the whole field value for that record. Users may retrieve reports from the database in

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2Dimensional or 3Dimensional views using the Reporting module (shown in Figs. 2 and 3). The report will be prepared for the user when the user selects the table and specifies the criteria. Fig. 2 Case diagram

Fig. 3 Sequence diagram

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3 Testing and Implementation In Fig. 4, since it is the final verification of the specification, design, and code, software testing is an essential component of software quality assurance. For this reason, we conducted extensive testing in order to ensure that our program would function properly and avoid the expenses associated with a software failure. The process of running a program with the goal of identifying an error is known as testing. The plan of tests for programming and other specialized items might be similarly basically as trying as the plan of the actual item. There are two fundamental sorts of testing techniques. Black-Box testing—When a product is designed to accomplish a certain task, it may be necessary to conduct testing to verify that every feature works as intended. White-Box testing—Tests may be carried out to ensure that the product’s internal workings satisfy requirements and that all internal components have been adequately exercised. Various testing approaches, such as white box and black box, were used to evaluate this piece of software. The complete loop designs’ boundary and intermediate conditions have been investigated. In order to ensure that all criteria and logical judgments were satisfied, test data was generated. There is no longer any need to deal with difficulties since exception handlers have been implemented. Importing all necessary packages, such as numpy, pandas, matplotlib, scikit-learn, and machine learning methods. Determining the visualization graph’s dimensions. Importing the dataset and converting it to a data frame. Imputing nulls with relevant information by preprocessing the imported data frame. All of the outputs have been cleaned. It will provide excellent results and visualization plots after using machine learning techniques.

Fig. 4 Deep learning classifier’s output

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4 Results and Discussion In this proposed method, we built one kind of Android malware, called adware. Many modules may be added to it to increase the quality of the end output. It is possible to utilize a dataset with a large number of official connections. Because it is not yet accessible, we investigated utilizing the unb website’s Android malware categorization dataset. As malware categories grow in number, this proposed method may be implemented and explored in the future. Hackers package their malware in such a way that it is not detected by these systems, resulting in zero-day assaults. In conclusion, we can use it to identify a variety of additional categories, and there is still room for development. Execution is the most common way of changing a new or refreshed framework plan into a functional one. Implementation may be divided into three categories. A switch from a manual system to a computerized one. There are several challenges to deal with, such as converting files, teaching users, and assuring the integrity of prints. The installation of a new computer system to replace an outdated one. As a general rule, this is a difficult task. There might be a slew of issues if the event is not adequately prepared. Using the same computer, implement a redesigned application to replace an existing one. If the files do not change much, this sort of conversion is quite simple to manage. All modules of the Generic tool are implemented: In the first module, users are identified on a personal level. This module determines whether or not a user is authorized get to the data set and furthermore makes a meeting for the client. The use of illegal substances in any form is absolutely prohibited. Tables are formed using user-specified fields in the Table creation module, and the user may build several tables at once. In the development of tables, they might define conditions, limitations, and computations. Throughout the paper, the Generic code keeps track of the user’s needs. Users may edit, remove, or insert new records into the database using the Updating module. In the Generic Code, this is a critical module. The user must enter the field value in the form, and the Generic tool will populate the whole field value for that record. Users may retrieve reports from the database in 2Dimensional or 3Dimensional views using the Reporting module. The report will be prepared for the user when the user selects the table and specifies the criteria. In Table 1, the Performance Analysis based on the accuracy, sensitivity, specificity, and precision. The comparison analysis of different methods is presented in Fig. 5.

Table 1 Performance analysis based on the accuracy, sensitivity, specificity, and precision S. No

Parameters (%)

KNN

Decision tree

Random Forest

Deep learning

1

Accuracy

87

88

92

97

2

Sensitivity

92

95

96

98

3

Specificity

88

90

92

95

4

Precision

86

88

93

96

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Fig. 5 Result analysis

5 Conclusion It is feasible to avoid malware feature vectors from including common qualities seen in benign apps using the suggested feature representation. Finally, we employed machine learning methods, which are built to handle a wide range of feature types. The early networks are trained solely with different kinds of features, and the final network is trained using the results of the earlier networks. In order to improve malware detection accuracy, this model’s architecture is ideal. Thus, the proposed design was shown to be successful in the detection of Android malware.

References 1. Li J, Sun L, Yan Q, Li Z, Srisa-An W, Ye H (2018) Significant permission identification for machine-learning-based Android malware detection. IEEE Trans Ind Inform 14(7):3216–3225 2. Arshad S, Shah MA, Wahid A, Mehmood A, Song H, Yu H (2018) SAMADroid: a novel 3-level hybrid malware detection model for Android operating system. IEEE Access 6:4321–4339 3. Kim T, Kang B, Rho M, Sezer S, Im EG (2018) A multimodal deep learning method for android malware detection using various features. 6013(c) 4. Saracino A, Sgandurra D, Dini G, Martinelli F (2018) MADAM: effective and efficient behavior-based Android malware detection and prevention. IEEE Trans Dependable Secur Comput 15(1):83–97 5. Kim T, Kang B, Rho M, Sezer S, Im EG (2018) A multimodal deep learning method for android malware detection using various features. 6013(c)

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6. Firdaus A, Anuar NB, Karim A, Faizal M, Razak A (2018) Discovering optimal features using static analysis and a genetic search-based method for Android malware detection *. 19(6):712–736 7. Chebyshev V (2021) Mobile Malware Evolution 2020, 1 March 2021. Available online: https:// securelist.com/mobile-malware-evolution-2020/101029/. Accessed on 7 May 2021 8. StatCounter. Mobile operating system market share worldwide, May 2021. Available online: https://gs.statcounter.com/os-market-share/mobile/worldwide. Accessed on 10 June 2021 9. Liu K, Xu S, Xu G, Zhang M, Sun D, Liu H (2020) A review of Android malware detection approaches based on machine learning. IEEE Access 8:124579–124607. [Google Scholar] [CrossRef] 10. Wang Z, Liu Q, Chi Y (2020) Review of Android malware detection based on deep learning. IEEE Access 8:181102–181126 11. Rana MS, Gudla C, Sung AH (2018) Evaluating machine learning models for Android malware detection: a comparison study. In: Proceedings of the 2018 VII international conference on network, communication and computing, Taipei City, Taiwan, 14–16 Dec 2018, pp 17–21 12. Mahindru A, Sangal A (2021) MLDroid—framework for Android malware detection using machine learning techniques. Neural Comput Appl 33:5183–5240 13. Sahin ¸ DÖ, Kural OE, Akleylek S, Kılıç E (2021) A novel Android malware detection system: adaption of filter-based feature selection methods. J Ambient Intell Humaniz Comput 15:1–15 14. Firdaus A, Anuar NB, Karim A, Ab Razak MF (2018) Discovering optimal features using static analysis and a genetic search-based method for Android malware detection. Front Inf Technol Electron Eng 19:712–736. [Google Scholar] 15. Fatima A, Maurya R, Dutta MK, Burget R, Masek J (2019) Android malware detection using genetic algorithm based optimized feature selection and machine learning. In: Proceedings of the 2019 42nd international conference on telecommunications and signal processing (TSP), Budapest, Hungary, 1–3 July 2019, pp 220–223 16. Yildiz O, Do˘gru IA (2019) Permission-based android malware detection system using feature selection with genetic algorithm. Int J Softw Eng Knowl Eng 29:245–262 17. Meimandi A, Seyfari Y, Lotfi S (2020) Android malware detection using feature selection with hybrid genetic algorithm and simulated annealing. In: Proceedings of the 2020 IEEE 5th conference on technology in electrical and computer engineering (ETECH 2020) information and communication technology (ICT), Tehran, Iran, 22 Oct 2020 18. Wang J, Jing Q, Gao J, Qiu X (2020) SEdroid: a robust Android malware detector using selective ensemble learning. In: Proceedings of the 2020 IEEE wireless communications and networking conference (WCNC), Seoul, Korea, 25–28 May 2020, pp 1–5 19. Wang L, Gao Y, Gao S, Yong X (2021) A new feature selection method based on a self-variant genetic algorithm applied to Android malware detection. Symmetry 13:1290 20. Yen YS, Sun HM (2019) An Android mutation malware detection based on deep learning using visualization of importance from codes. Microelectron Reliab 93:109–114 21. Lim K, Kim NY, Jeong Y, Cho S, Han S, Park M (2019) Protecting Android applications with multiple DEX files against static reverse engineering attacks. Intell Autom Soft Comput 25:143–154 22. Lee SJ, Moon HJ, Kim DJ, Yoon Y (2019) Genetic algorithm-based feature selection for depression scale prediction. In: Proceedings of the ACM GECCO conference, Prague, Czech Republic, 13–17 July 2019, pp 65–66. [Google Scholar] 23. Lambora A, Gupta K, Chopra K (2019) Genetic algorithm—a literature review. In: Proceedings of the 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon), Faridabad, India, 14–16 Feb 2019, pp 380–384. [Google Scholar]

Mental Health Disorder Predication Using Machine Learning for Online Social Media S. A. Patinge and V. K. Shandilya

1 Introduction All around the world, mental illness has become a major problem. Depressive disorders are predicted to be the second major cause of disability worldwide by 2020 according to the CDC. Substance misuse, violence, and suicide are all possible outcomes of untreated mental illness. Undiagnosed and untreated mental illness affects a large percentage of people in this country. As a result, diagnosing and treating such diseases is a need. It is possible to utilize social media as a diagnostic tool to investigate a person’s psyche. The condition of one’s mind may be seen on social media platforms like Twitter. For social scientific research, Twitter is a great platform since it gives a long term record of people’s attitudes, opinions, and behaviors (every public Tweet ever is in a searchable archive). Twitter’s long-term nature makes it possible to study how speech evolves over time with a level of continuous monitoring that is impossible to attain using other approaches. Twitter’s open network makes it possible to track the people who are shaping the discourse. Since Twitter material is generated by individuals for their own goals, it enables for large-scale monitoring of spontaneously occurring discussion that was previously unavailable. Many human biases introduced in a study context might be avoided by using this content to obtain a more realistic depiction of public discourse [2]. Disparities in offline and online behavior can be introduced by the use of Twitter, and they can lead to a variety of biases. Twitter’s biases can only be discovered by experimenting with the data and doing out research like this. Using Twitter as a data source, this dissertation studies how public policy might influence the conversation on the platform by capturing general sentiment, tracking changes over time, and looking at how online communities evolve. Resilience Month and Real Warriors have their own Twitter accounts, while the other three rely on institutional accounts to spread information S. A. Patinge (B) · V. K. Shandilya Sipna College of Engineering and Technology, Amravati, Maharashtra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_20

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on the social media platform. It’s possible to see if DoD campaigns are working, who they’re influencing, and how they’re engaging different individuals thanks to their internet presence. The purpose of this study was to examine tweets containing this type of content. Keywords linked to mental health, symptoms, and indicators of three distinct mental illnesses were used to extract tweets. Over the course of six months, we collected 10 million tweets using 40 different keywords and used sentiment analysis to sort them into three categories: positive, neutral, and negative. Individuals who express negative emotions and speak negatively more frequently and for longer periods of time are more likely to suffer from various types of mental disorders. Using the Random Forest Classification machine learning model, we were able to achieve classification accuracy of 99.00% during training and 95.00% during testing.

2 Literature Review Analysis of real-time emotions and social media interactions were conducted by Park, Cha, and colleagues. Random tweets from Twitter were evaluated over a two-month period by the authors of this study, and they discovered that persons with mental illness tweet more about their negative emotions and anger than about their good ones. They also looked at how language may be used to analyze sentiment. Nowadays, it’s common practice to post one’s private information on social networking sites, whether it’s good or negative. Those who are clinically depressed also post their thoughts and feelings about their condition online. The inclusion of first person pronouns in the text encourages people to discuss more about themselves. Sharing, self-promotion, opinions, random ideas, myself, inquiries to others, presence, upkeep, anecdotes from me and tales by others, and so on were carefully classified into nine unique divisions. Finally, the LIWC sentiment tool was used to do sentiment analysis on the tweets. Linguistic Inquiry and Word Count (LIWC) is a text analysis program that counts words in psychologically meaningful categories. Each phrase in the LIWC vocabulary is graded on six different dimensions: social, adjective, cognition, perceptual biological processes, and relativity. Subtypes of each kind are allocated a certain number of points. So, the overall score was determined to categorize tweets based on this method. There were minimal drawbacks to this study. Because it does not include emoticons, LIWC was not an effective technique for sentiment analysis in this research, which also included fewer participants. In a study published in 2013, De Choudhury, Gamon et al. found a link between more than 200 distinct variables that might be used to anticipate depressive illnesses. In addition, they broadened the breadth of elements related to mental health that may be gleaned from social networking sites. There is a possibility that the poem conveys a sense of shame, guilt, powerlessness, and self-hatred. The following are the key points of this paper: The depression levels of AMT crowd workers were measured using a CES-D (Center for Epidemiologic Studies Depression Scale) screening test, which was crowd sourced from twitter users who had been diagnosed with clinical

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Major Depressive Disorders (MDD). A number of metrics were utilized to analyze tweets, including user interaction and emotional response, as well as egocentric graphs and linguistic style. Depressive language and antidepressant use were also included as measurements of behavior. According to the findings from their study, people with depression have a lower level of social engagement and more negative feelings, a higher level of self-awareness, an increased interest in relationships and medical issues as well as heightened religious ideas. With LIWC and ANEW [4], they found activation and dominance, which is the degree of physical intensity and control in an emotion, using the ANEW lexicon. These researchers built a classifier that predicts whether or not the user is at risk of developing depression using supervised learning techniques. Identical to Park et al., who scored positive for depression, the outcomes were found to be similar. This system has a 70% accuracy rate and a 0.74'' precision [6]. Wang, Zhang, and others also backed the idea of using social media to identify sad people (2013). Social media data is shown to have a significant impact on both psychology and sociology in this study. Sina Micro blog, a major social networking site in China, is used to identify melancholy people. The following are just a few of the ways it has helped: Data mining is utilized in the study of depression. Chinese micro blogs are included in the Sentiment Analysis approach, which measures the severity of a person’s depression. These researchers presented a word list for depression based on HowNet, as well as guidelines for calculating depressive tendency from Chinese syntax rules, phrase patterns, and rules for computations. They created a model for detecting depression based on the two aforementioned criteria and ten psychological features of depressed users. The three primary phases in this suggested paradigm are as follows: Punctuation is used as a signifier in a micro blog phrase because it is limited to 140 characters. The polarity of each word is determined first, and then the polarity of the entire phrase is calculated using the polarity calculation technique. People’s psychological characteristics were also taken into account, including their use of pronouns, emoticons, and other users’ conduct on social media. Users were then categorized as either depressed or non-depressed based on a variety of categorization methods. This was done using the Waikato Environment for Knowledge Analysis (Weka) application [9]. Three distinct classifiers, including Bayes, Trees, and Rules, were utilized to improve the model’s reliability and validate it [9]. There is about 80% accuracy in the suggested model when using all of the classifiers. Only Chinese micro blogs and Chinese language were allowed to use this, hence it had certain limits. The dataset was limited, making it impossible to assess the model’s reliability when applied to larger datasets, despite the fact that the vocabulary was all based on Chinese. Despite this, the suggested paradigm enabled psychologists identify users who may be at risk for depression, hence enhancing the health of the general population. Depressive emotions of users may be accurately captured via social networks, according to Reavley and Pilkington. The report claims that this research might aid scientists in promoting mental health and raising awareness among the general public. Evidence from the study reveals how and what sort of information Twitter users communicate concerning depression or schizophrenia. The hash tags #depression and #schizophrenia were used to gather data from Twitter over the course of seven days. Nvivo 10, a program for analyzing

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unstructured data, was utilized to do a further content analysis. Tweets were categorized depending on how strongly they suggested an attitude toward sadness or schizophrenia. The content and user information of tweets were used to classify them. There were four content topics that emerged: (1) personal experience with mental illness, (2) awareness promotion, (3) research findings, and (4) resources for consumers. Personal opinion or dyadic contact” is all examples of “(5) advertising”. It was also necessary to categorise different kinds of users. There was a correlation between the number of people tweeting about depression and the number of those who were actually suffering from the condition. There were also a large number of people who tweeted about schizophrenia who were either individuals or groups. In addition, they looked at the sort of tweets, as well as their attitude toward depression and schizophrenia, such as whether they were supportive or critical. It was possible to analyze tweets on mental illness, however there were a few flaws in the study’s methodology. Only a small percentage of tweets were found to be about despair and anxiety. A tiny sample size was due to the fact that only tweets over a seven-day period were gathered, and slang phrases may have been overlooked. According to Martinez-Perez et al., who examined the objectives and functions of Facebook and Twitter groups for various mental diseases, including depression, this paper’s findings might be compared to theirs. According to their classification, groups were categorized into four categories: support, self-help, advocacy, and fundraising. It was found that self-help groups (64%) were the most common, followed by support and advocacy organizations (15%) and awareness and advocacy groups (10%) According to the authors, (MartnezPérez et al. 2014), a study by Schwartz et al. used Facebook as a social networking platform to predict the amount of sadness that a person displays in their social interactions. They developed a regression model to describe depression. This study relied only on the way people communicate their feelings of depression via language. They looked at how people used language to see if they were depressed two-year Facebook dataset was examined, after which the user’s level of depression was established. The lexicon was discovered using LIWC categories. Sentiment analysis and regression model building were completed in the last step of the process. As part of their evaluation of depression, they also looked at seasonal patterns. On a weekly or monthly basis, the author claims, it is possible to measure the amount of depression and its fluctuations.

3 Methodology 3.1 Data Collection Twitter is a rapidly expanding micro blogging platform. “Twitter” Hundreds of thousands of individuals post their ideas online. A tweet, with a maximum character count of 140, is the short form of text. On social media, the hashtag (#) is a common pound (#) sign that is used to describe a specific interest or group. For our research,

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Fig. 1 Data extraction process

we focused on Twitter data. More than ten million tweets were mined throughout a six-month period. REST and Streaming are the two APIs Twitter offers for data extraction. It is possible to access tweets in real time using the Streaming API, while the Rest API is used to get tweets from the past. Tweets were streamed using Streaming API, which allowed us to access them in real time. As a result, Twitter offers OAuth in order to access these APIs. Users must first create a Twitter application, after which they must generate a consumer key, a secret key, an access token, and an access token secret key, which allow others to use the Twitter API on their behalf (Twitter, 2015). The Python programming language was used to create our system. Use Tweet to download tweets from Twitter using an open source program. It makes it possible for Python to access tweets using Twitter APIs. Using Twitter API, the first streaming process may be started with an initial query. With the use of keywords and access credentials, the Twitter streaming API starts the data extraction process. A corpus is just a collection of tweets saved in CSV format (Twitter, 2015). For further analysis, we divided tweets into three categories based on these terms. The following are the subcategories: There are three types of feelings: (1) Positive, (2) Negative, and (3) Neutral.

3.2 Data Collection We removed hashtag # followed by RT@ and @ symbols. Special characters and regular expressions were also eliminated. The content was also stripped of http:/ / and the following web address. After then, all of the tweets were converted to lowercase letters. Specifically, we used blank to substitute the following symbols/ text, key spaces: 1. 2. 3. 4.

Remove hashtag #, @, RT@. Remove Unicode characters/([\ud800-\udbff][\udc00-\udfff])|./g. Remove text which starts from http[^\\s]+. Convert to lowercase and Remove hyperlinks.

We did not eliminate Retweets from our study because they may be quite valuable. Retweet is just retweeting someone else’s tweet when you wish to repost it. As a result, a user may feel the same way. This might have a significant impact on our research.

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Because it eliminates any inconsistencies and unnecessary material from the text that might lead to erroneous findings, preprocessing plays a highly important function. For further processing, pre-processed tweets are matched to relevant wordlists.

3.3 Check Category There are many distinct sorts of mental illnesses, and it would be impossible to research them all. Because of this, we focused our research on three distinct categories: 1. Positive Sentiment Analysis that does not lead to mental illness. 2. Negative Sentiment Analysis as a Cause of mental illness. 3. Third, a person’s neutral conduct is the result of a neutral sentiment analysis.

3.4 Check Wordlist We searched SentiWord Net’s negative bag of words to see whether a term was included. We first examined the wordlist, and if it didn’t work, we went to the NRC wordlist. However, this takes a lot of time. As a result, we devised a new strategy for increasing the system’s temporal complexity. We created an ontology based on relationships between things, as described in the section on wordlist generation. We consolidated all domain-specific terms into a single wordlist. We look to see if a certain phrase or word appears in our personal dictionary. The score is raised by one when a word match is detected. As a result, we run the tweet through these wordlists to get its overall quality score. For example, consider this tweet: Tweet: “insomnia depression and i hate people my life is turning for the worse at too young an age”. Mental Disorder (MD) Score: 5 Matchedwords: insomnia, depression, hate, life and worse.

4 Machine Learning Models After checking the sentiment emotions and converted data into proper supervised category, we had applied various machine learning methods to predict the mental health of the person. Logistic Regression In binary classification jobs, use this strategy to anticipate the value of a variable Y, which can have two possible values of 0 or 1. When there are more than two potential values for Y, this method may be applied to multi-classification issues. The

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following logistic regression equation determines the likelihood that input X belongs in category 1. P(X ) =

exp(β0 + β1 X ) (1 + exp β0 + β1 X )

(1)

Here β 0 is the bias and β 1 is the weight that is multiplied by input X [3]. Support Vector Machine Classification is accomplished by constructing a hyper plane on which all samples belonging to one class will be located on one side, and all samples belonging to another class will be located on the other side. Hyper plane optimization ensures that the distance between classes is maximized. The data points closest to the hyper plane form a support vector [3]. Hyper plane can be created as given in the following equation: H0 : W T + b = 0

(2)

Two more hyperplanes H 1 and H 2 are created in parallel to the constructed hyperplane as given in the following equations: H1 := W T + b

(3)

H2 := W T + b

(4)

Hyperplane should satisfy the constraints given by following equations for each input vector, w I j + b ≥ +1 for I j having calss 1

(5)

w I j + b ≥ −1 for I j having calss 0

(6)

and

Random Forest A bagging technique called Random Forest is used to merge many decision trees to improve prediction accuracy. Individuals are taught on their own in bagging. There are several samples of data created from the original dataset with replacement and each decision tree is trained on a distinct set of data samples in this method. The tree’s features are also chosen at random during the process of building it. It is possible to aggregate the predictions of many trees using a majority vote [4]. Optimizing random forest parameters such as the number of estimators, the minimum size of the node,

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and the number of characteristics used to divide nodes can boost the random forest’s accuracy. XGBoost It is a gradient-boosted decision tree solution optimized for speed and performance. To maximize efficiency, flexibility, and portability, XGBoost was developed as a distributed gradient boosting library. The Gradient Boosting framework is used to develop machine learning algorithms. XGBoost offers rapid and accurate parallel tree boosting (also known as GBDT, GBM) for a variety of data science challenges. XGBoost is a technique for boosting learning in a group. In some cases, relying just on a single machine learning model may not be enough. The predictive capacity of numerous learners may be combined systematically through ensemble learning. In the end, you’ll have a single model that combines the results of multiple other models [5].

5 Result and Discussion The results of our classification methods using machine learning are discussed in this section. The performance was tested on the supervised dataset created after preprocessing steps using Natural Language Processing (NLP) and analyzing the obtained dataset. The Training and testing split on the dataset was 80:20 ratios. Following table summarizes the result of training, testing and validation accuracy. From Table 1 it had been observed that Random Forest Classification model had performed well with 99.91 as training accuracy and 95.18% as testing accuracy. SVC and Logistic regression also performed well and with results obtained we had kept them at subsequent stages. The final model was deployed with best performing RF classification model and through that we had predicted the positive, negative, neutral sentiment analysis which further helped us to predict the mental state disorder of the person. Table 1 Results of machine learning models

Model name

Training

Testing

Validation

Random Forest

99.91

95.81

95.28

Logistic Regression

98.51

94.15

94.16

SVC

97.81

95.00

95.21

XGBoost

94.45

94.33

94.33

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6 Conclusion Our system has presented a novel approach to analyze different mental disorder using Online Social Media (OSN) models via sentiment analysis. The goal was to create a platform that could quickly, accurately, and easily identify people and evaluate language and sentiment trends in their writings. People who have been diagnosed with or show indicators of mental problems that might develop to depression could be identified using our technique. The algorithm identified people by analyzing and categorizing their tweets as good, negative, or neutral. In addition, the suggested system effectively tracked people on Twitter to monitor their actions and language use. People who have been diagnosed with depression have been found to utilize more self-referential terms and have more negative language early on, identifying patterns, and eventually assisting such victims.

References 1. National Alliance of Mental Illness APA (2014) DSM-5 diagnostic classification diagnostic and statistical manual of mental disorders 2. Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Paper presented at the LREC 3. Banitaan S, Daimi K (2014) Using data mining to predict possible future depression cases. Int J Public Health Sci (IJPHS) 3(4):231–240 4. Bradley MM, Lang PJ (1999) Affective norms for English words (ANEW): instruction manual and affective ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida 5. CDC (2013) Mental Health Basics. Retrieved 16 July 2015, from http://www.cdc.gov/mental health/basics.htm 6. De Choudhury M, Counts S, Horvitz E (2013) Social media as a measurement tool of depression in populations. In: Paper presented at the proceedings of the 5th annual ACM web science conference 7. De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via Social Media. In: Paper presented at the ICWSM 8. Desai S (2014) SMART sentiment and emotion analysis (Masters). University of Georgia 9. Han J, Kamber M (2006) Data mining: concepts and techniques 10. Coppersmith G, Dredze M, Harman C (2014) Quantifying mental health signals in twitter. ACL 51

An Application on Sweeping Machines Detection Using YOLOv5 Custom Object Detection K. Balagangadhar, N. N. S. S. S. Adithya, Cheryl Dsouza, and R. Sudha Dharani Reddy

1 Introduction In the area of video analytics, object detection plays a key role and it is very useful in localizing the objects finding it location with the x and y coordinates and it can help to identify where exactly it’s located. Multiple objects can also be detected easily such that we can get the count of objects. The major difference between classification and object detection is that one is used when presence needs to be founded and other is used to find position both are closely related to each other. For our intended application, each video should be able to recognize the in-time and out-time of an object. In our case, using sweeping machines, i1 time and i2 time, we can calculate the time spent on the road. We all know that cleaning is one of the essential tasks in day-to day life, and for a society, it is a major challenge to keep places like roads, railway station, and organizations cleaned and maintained for safety and security purposes. This job is too heavy at the core monitoring and is much more difficult and painstaking process. The proposed system will have a monitoring solution for this problem by custom training–specific objects; in deployed application, it identifies object and as mentioned the time will be captured, the end result will be in an excel sheet with the name of vehicle the in-time, out-time, and the time spent.

K. Balagangadhar · N. N. S. S. S. Adithya · C. Dsouza (B) · R. Sudha Dharani Reddy Department of CSE, CVR College of Engineering, Mangalpalli (V), Ibrahimpatanam (M), India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_21

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2 Related Work As far as training and detection of custom object detection was considered, there are many models which promises very accurate results with very less amount of training data, of them one of the most famous one is yolo (You Only Look Once) model which has been very popular for its promising results with low training data.

2.1 YOLOv5 There are many versions in yolo family and we are going to look at most recent one which is YOLOv5. There is not much change except for the model layer/architecture and several parameters.

2.2 Model Description You Only Look Once (YOLO) series. YOLOv5 was developed by Ultralytics and introduced in May 2020. It aims to provide a balance between accuracy and speed, making it suitable for real-time object detection tasks. Here are some key details about YOLOv5: 1. Architecture: YOLOv5 follows a one-stage object detection approach, where it simultaneously predicts bounding boxes and class probabilities for multiple objects in an image. The architecture consists of a backbone network (usually a modified version of a convolutional neural network like CSPDarknet53 or EfficientNet), neck layers for feature fusion, and detection heads for generating predictions. 2. Object Detection: YOLOv5 uses anchor-based detection, where default anchor boxes of various scales and aspect ratios are used to predict the bounding boxes. It predicts the class probabilities and coordinates of bounding boxes for all anchor boxes at different spatial scales. 3. Training: YOLOv5 can be trained on labeled datasets using a combination of techniques like data augmentation, label smoothing, and focal loss. It typically requires a large, annotated dataset for training, although transfer learning with pretrained models is also common. 4. Model Sizes: YOLOv5 offers different model sizes (S, M, L, X), each with varying complexities and trade-offs in terms of accuracy and speed. The smaller models (Fig. 1). Function: The choice of activation functions is most crucial in any deep neural network. Recently lots of activation functions have been introduced like Leaky ReLU, Mish, swish, etc. YOLOv5 authors decided to go with the Leaky ReLU and Sigmoid

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Fig. 1 Comparison of various networks

activation function. In YOLOv5, the Leaky ReLU activation function is used in middle/hidden layers and the sigmoid activation function is used in the final detection layer. Optimization Function: For optimization function in YOLOv5, we have two options such as 1. SGD 2. Adam. In YOLOv5, the default optimization function for training is SGD. Cost Function or Loss Function: In the YOLO family, there is a compound loss which is calculated based on objectness score, class probability score, and bounding box regression score. Ultralytics have used Binary Cross-Entropy with Logits Loss function from PyTorch for loss calculation of class probability and object score. We also have an option to choose the Focal Loss function to calculate the loss. You can choose to train with Focal Loss by using fl_gamma hyper-parameter.

3 Proposed System 3.1 Dataset We have collected our target data which we want to make a model; here, we are going to make a model for detecting various sweeper machines such as Walk Behind Sweeper, Ride on Sweeper, Truck Mounted Sweeper, City Sweeper and Wall Cleaning, and the images of those machines are as follows (Figs. 2 and 3).

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Fig. 3 Number of classes and their names

Fig. 4 Labeled files

Our first step will be collecting this image; instead of taking images, we downloaded videos from the internet and broke that into images and grabbed every 35th image from that video.

3.2 Manual Labeling In this step, we will label our data manually by using labeling application, yolo uses .txt format for labeling its classes. When we want to design a custom object detection model of our preference objects, we need to highlight them in the image by drawing a rectbox over the image which specifically differentiate it over other things in that image. As we use Roboflow to pre-process and augment data, we are going to label our image in XML format which is mobilized net format for labeling. We need to manually draw rectbox for every image. At the end of this manual labeling, you will find an .xml file beside every image that’s the conformation of successful labeling; any misinterpretations or missing data will be handled in next step (Fig. 4).

3.3 Augmentation As far as our data are considered, augmentation is the most important step as it increases the variance in data, and the developed model can predict accurate results

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as it has seen the different variety of that data, augmentation involves changing brightness, orientation, increasers salt and pepper noise, rotate image, etc. We use Roboflow for this, and it is an online platform which allows to upload our own dataset augment, health check, and export it into various model formats and also handle missing data labels. After this, we will export our dataset into YOLOv5 (PyTorch) format and download it into our computer for training on our GPU; you can also get a sharable link into Google Colab notebook.

4 Training Modifying the Configuration: The final step before starting our training is to configure the model file; YOLOv5 offers various flavors in models which will take time and inference according to the type of flavor. Types of YOLOv5 models are S-small, M-medium, L-large, and X-extra-large and a lite model .cpps. We are going to use S because of minimum time to train and run inference. Open yolov5s .yaml file and change number of class (nc) to total number of classes in your model (Fig. 5). A data template file also required with location of image and class names. Finally, it’s time for training, and we are using NVidia GTX 2070 Super 8 GB graphics card for this training, but it can also be trained on very minimal graphics card or a CPU, in a Ubuntu 20.10 LTS. Training Command python3 train.py --data ./data/template.yaml --img 640 --batch 8 --epochs 50 --cfg ./models/yolov5s.yaml --weights ” --device 0 --data is data template file address --img is image size 640 --batch is batch size 8 --epochs are number of iterations 50 --cfg is the configuration file address

Fig. 5 Configuration

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Fig. 6 Visuals of checkpoint files

Fig. 7 Learning graph representation

--weights are none as it will create weight file --device is the device on which we need to train our model 0, 1,2,3—etc., is for GPU and for CPU

5 Results Inference: After training, we will get best.pt and last.pt which are the weight files of last trained weights which we can be used for predicting or inferencing our model (Figs. 6 and 7).

6 Command Python3 detect.py --source r11.jpg --weights weights/best.pt --conf 0.4 --source is the image

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Fig. 8 Input and output

Fig. 9 GUI program

--weights are the best.pt weight file --conf is the confidence threshold which is 0.4 GUI program to Load/Run a Video File: We have also created a GUI program in PyQt5 which will take a video file as input and run the detection automatically and acquires the CSV sheet and writes detection results into .mp4 format video (Figs. 8 and 9).

7 Conclusion and Future Work In the field of computer vision, object detection is the most prominent and promising area for wide variety of applications. As the complexity comes, you need to customize these according to the desired application. This paper shows how to do training for a specific set of objects, and this is very effective and simple to use when compared with different object detection algorithms such as like RCNN, Mobile Net SSD, EfficientDet, and Detectron. The yolo model is very simple to configure and deploy without any additional libraries. The methodology used can be further be improved by using more fine-tuned data, and the major disadvantage of this methodology is the larger inference time as the suggested algorithm. This is further improved in v5, v6,

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and v7; the base has also been changed from darknet to PyTorch library for simpler to use architecture.

References 1. Fang W, Wang L, Ren P (2019) Tinier-YOLO: a real-time object detection method for constrained environments. IEEE Access 8. https://doi.org/10.1109/ACCESS.2019.2961959 2. Du J (2018) Understanding of object detection based on CNN Family and YOLO. J Phys Conf Ser 1004 012029. IOP Publication, to be published by 2018 3. Shaha M, Pawar M (2018) Transfer learning for image classification. In: IEEE Conference Publication, to be published by doi: 978-1-5386-0965-1 4. Tao J, Wang H, Zhang X, Li X, Yang H (2017) An object detection system based on YOLO in traffic scene. In: Published in 2017 6th international conference on computer science and network technology. https://doi.org/10.1109/ICCSNT.2017.8343709 5. Nguyen DT, Nguyen TN, Kim H, Lee H-J (2017) A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Publication, to be published by doi: https://doi.org/10.1109/TVLSI.2019.2905242

Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review Sirikonda Shwetha and N. Ramana

1 Introduction Over the past 30 years, more individuals have suffered from lung problems worldwide, and the most significant risk factor for a respiratory issue is coming from economically weak countries. Other significant risk factors for a lung ailment include aging, smoking, pollution, and being overweight or obese. Lung disease can affect respiratory function, the ability to breathe, and pulmonary function. Lung problems appear in a variety of ways, and a few of them are caused by bacterial, viral, or fungal infections. Erstwhile lung conditions like asthma, mesothelioma, and lung cancer are caused by ecological conditions. Chronic lower respiratory diseases are a set of conditions that comprise COPD, emphysema, and chronic bronchitis. Because of the high-risk factor of lung disorders, it is critical to detect them early. In recent years, artificial intelligence (AI) has become an emergent technology referred to it as the AI era. The use of appropriate new technology in healthcare [20, 26] is increasing as technology advances daily, and this technology helps to identify diseases [31] as early as possible so that risk factors can be avoided. AI in Health Care The importance of AI in the field of health care can be attributed to two major factors. 1. A high level of accessibility to medical data implementing AI is made considerably simpler with this volume of data. 2. To deal with this enormous amount of medical data, advanced algorithms including machine learning (ML), neural networks (NN), and deep learning (DL) were introduced.

S. Shwetha (B) · N. Ramana Department of Computer Science and Engineering, Kakatiya University, Warangal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_22

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The growth of ML, DL, and NN also significantly contributed to the application of AI in healthcare [32]. By using cognitive technology to manage a massive number of medical records and generate potentially accurate conclusions, AI is helping companies. With the use of DL [22, 23] and NN, AI is transforming the field of image diagnostics in medicine, including MRI scans, X-rays, and CT scans. Classification of Lung Disorders When we breathe, the lungs take oxygen from the air and transmit it to the bloodstream. Our body’s cells require oxygen to function and grow. The average person breathes over 25 k times per day. Asthma, chronic obstructive pulmonary disease (COPD), infections like the flu, pneumonia, tuberculosis (TB), lung cancer, and numerous other breathing issues are all considered forms of illness. Respiratory failure may result from specific respiratory disorders. Skin tests, blood tests, sputum sample tests, examinations of computed tomography (CT) scan, and chest X-ray are the conventional methods for diagnosing lung disorders. COPD (Chronic Obstructive Pulmonary Disease) COPD gets worse over time and makes breathing more difficult. Other symptoms include wheezing and a cough, which is typically accompanied by congestion and chest discomfort. Asthma It is a disorder that causes your airways to enlarge, tighten, and perhaps generate more mucus. This may make it harder to breathe and cause whistling coughing. It can only be managed; there is no cure. Pneumonia The air sacs within the lungs become inflamed due to a typical lung infection. Additionally, they could contain liquid, pus, and damaged cells. Your body has a hard time getting enough oxygen into your blood, which can lead to malfunctioning cells. Black Lung This illness, commonly known as coal workers’ pneumoconiosis or miners’ lungs, is brought on by repeated inhalation of coal mine dust. When people inhale coal dust, the particles settle into the alveoli, causing chronic lung inflammation that can lead to heart failure, TB, and lung cancer. Alveoli are tiny air sacs that help your lungs take in oxygen. Lung tissue makes an effort to fight off and eliminate the particles to reduce inflammation. Tuberculosis The term “TB” is used to refer to tuberculosis. It is an inflammatory condition brought on by the mycobacterium genus of bacteria. It can infiltrate a human windpipe through the air, spread to the lungs, and contaminate the lungs. Macrophages, a type of immune cell, rush to the infected area in an effort to ingest and destroy the bacterial invaders. This response is frequently sufficient to get rid of the bacteria. Mycobacterium tuberculosis [25, 27] will grow inside those macrophages and create colonies in the surrounding lung tissue as they infect more cells, but in persons in the midst of a variety of medical issues, the immune response might not be strong enough to wipe out the invader. The microbes deploy cell-degrading enzymes to kill

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the diseased tissue, causing patients to experience chest pain and cough up blood, and oxygen deprivation results from a pulmonary infection. Lung Cancer In the USA, lung cancer is the most frequent malignancy that kills both men and women. Small cell and non-small cell lung tumors are the two general kind of lung malignancy. Exposure to cigarette smoke, secondhand smoke, radon gas, asbestos fibers, or other toxins can result in lung cancer. Causes of Lung Diseases Lung diseases are caused by a variety of factors, including infection, genetics, and exposure to environmental pollutants. Infection is a leading cause of lung disease, with viruses, bacteria, and fungi all capable of causing respiratory illness. Genetics can also play a role in the development of lung disease, as certain genetic mutations can make an individual more susceptible to developing respiratory problems. Finally, exposure to environmental pollutants such as asbestos or tobacco smoke can also lead to the development of lung disease. Symptoms of Lung Diseases Lung disorders are available in a variety of forms, and each one has a unique range of symptoms. However, there are certain common signs and symptoms of numerous respiratory disorders, such as wheezing, shortness of breath, and coughing chest tightness or discomfort. Fatigue, Loss of weight, Instances of the public dataset [19] are shown in Fig. 1. In the rest part of the paper, we discuss AI techniques and different strategies implemented by authors to identify lung cancer and other lung disorders and also discussed the benefits of using machine and deep learning techniques over traditional methods covered in Sect. 2, Sect. 3 focuses on dataset availability, Sect. 4 illustrates proposed methodology to get good results, whereas Sect. 5 of the paper discusses the conclusion and outlines potential avenues for future research.

Fig. 1 Demonstrates sample images of the diverse lung disease [19]

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2 State-of-Art: Overview There are many AI techniques that can be used to detect lung disease; the most common technique is deep learning, used to detect patterns in data that might indicate the presence of a lung disorder. Kieu et al. [1] analyzed articles related to deep learning techniques to detect lung disorders; the author exhibited various image types such as sputum smear microscopy, CT scan, chest X-ray, and histopathology images and image pre-processing techniques to remove noise, distortion, and image improvement methods to get better quality of the image. The author explored the assortment of deep learning algorithms, the use of ensemble and transfer learning methods. Transfer learning [24] and ensemble are methods used to minimize the time to train the model, enhance the categorization accuracy, and lessen over-fitting. Deep learning algorithms [21] can learn to detect very small abnormalities in images of the lungs that may be indicative of early-stage lung disease. Deep learning algorithms can be trained to recognize patterns in X-rays and CT scans that hold features of various types of lung disorder diseases. Using patient chest X-ray pictures, Goyal et al. presented a novel framework [4] RNN with large short-term memory to detect COVID-19 and pneumonia. To generate better results, Bharati et al. developed VDSNet [3] hybrid deep learning method by merging three techniques such as VGG, data augmentation, and spatial transformer network with convolution neural network to detect a lung disorder. A frequent respiratory disorder is chronic obstructive pulmonary disease (COPD); the author [6] presented a deep convolution neural network and computer vision technique to identify the lung airway trees aberrant appearance on CT scan images. Sri et al. discussed a variety of machine and deep learning algorithms’ advantages and disadvantages as well as a number of lung conditions, such as post-COVID-19, pneumonia, tuberculosis, and COPD. Aykanat et al. [2], conducted a comparison analysis of support vector machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Bayes algorithm using text, and MFCC audio data, and the author categorized lung conditions into 12 classes and concluded that KNN generated highest classification accuracy for audio data, and the most accurate technique for examining audio and text data was SVM, to improve the accuracy of model [5] author suggested to combine LDA-SVM algorithms. A modified version of MobileNetV2 proposed by Souid et al. [14] to classify and predict lung illnesses using chest X-rays images collected from the NIH ChestXray-14 database. To predict the risk factor of Mucormycosis, a black fungus that typically manifests in some people following post-COVID, Abdul et al. [7] deployed a combination of machine learning algorithms, including XGBoost, decision trees, random forests [17], and logistic regression to detect black fungus [16, 18]. Boban et al. explored machine learning methods to diagnose various lung conditions [8]. According to the author’s assessment, the performance of several models on CT scan data, the KNN (K-Nearest Neighbor) classifier is better precise than multilayer perceptron and support vector machine classifiers. In a multi-layer CNN

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strategy deployed by Kumar et al. [9] to identify lung disorders using a cough spectrogram, the author collected samples of eight diseases coughing sounds using an IoT-based device and implemented coughing detection technique in two categories: type I and type II and deployed techniques generated 0.4 accuracy of the test segment. In this article [12], the author analyzed spirometry parameters in underground coal miners, including Forced Vital Capacity (FVC), Forced Expiratory Volume in One Second (FEV1), Peak Expiratory Flow (PEF), and Forced Expiratory Flow (FEF) (FEF25-75). The author came to the conclusion that restrictive respiratory illnesses, including pulmonary fibrosis and coal workers’ pneumoconiosis [15], are more frequent in coal miners. According to the author, 12.5% of underground coal miners had significant FVC deficiencies, while there was no significant FVC deficiency in the general population, and 93% of underground coal miners had FVC abnormalities, compared to 4% in the general population. Dong et al. [13] developed the deep learning algorithm ShufeNet V2-ECA Net to identify and classify the coal workers’ pneumoconiosis (CWP) into three stages. ShufeNet V2 and ECA-Net were successfully integrated to generate a data augmentation model. ShufeNet-Attention delivered the best results among the tests conducted, with an average accuracy of 98%. To help radiologists and medical professionals globally, Dhamija et al. implemented two deep learning-based models—the UST-P and UST-S [11] for the classification of medical images. When segmenting issues like brain tumors, lung nodules [28, 30], skin lesions, and nuclei, these models produced better outcomes. To detect pulmonary tuberculosis using the patient’s CT scans, Xukun et al. proposed a four state-of-the-art 3D CNN [10] model to analyze the lesion area and divide it into five groups such as cavitation, caseous, miliary, infiltrative, and tuberculoma. Challenges to be Addressed • A large percentage of CNN-based methods did not take into consideration the hurdles of X-ray picture quality enhancement. As a result, during automatic CNN features extraction, infected areas of X-ray images were not accurately identified. • The current CNN-based system extracts the features automatically from the whole pulmonary image; however, only the trait of the contaminated pulmonary area is important for diagnosis. CNN is deficient in the ability to extract specific features, which results in the extraction of high-dimensional and irrelevant data for categorization. • The majority of lung diseases, including pneumonia, lung cancer, and black lung, are identified using deep learning; their effectiveness and dependability tested on limited X-ray images, shortage of adequate medical facts in CNN’s pre-trained model leads to untrustworthy feature extraction, and the challenging problem of protracted CNN training times resulting in a computationally ineffective approach for lung disease earliest diagnosis. This study offers a pioneering approach for academics and medical professionals to comprehend the fundamental AI algorithms and their wide range of applications [29]. A comparison of the diverse techniques used to detect and categorize chronic respiratory diseases is shown in Table 1.

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Table 1 Represent various strategies used to detect lung diseases Article

Lung disease categories

Model deployed

[3]

14 classes of lung disorders edema, pneumonia, etc.

Hybrid deep NIH chest X-ray learning—VDSNet images comprise (VGG Data STN 112,120 frontal-view with CNN)

[4]

3 F-RNN LSTM classes-pneumonia, COVID-19, Normal

C19RD-2905 CXIP-5856 Chest-X-ray images

[6]

COPD tuberculosis, Deep CNN and fibrosis

Multi-view snapshots 88.6% accuracy of 3D lung airway tree, CT scan images comprise 190 COPD patients, 90 healthy controls

[7]

Mucormycosis (Black fungus)

Logistic regression, Private data set LR-95%, XGBoost, and comprises 1229 XGBoost-94%, Random forest COVID-19 positive RF-94% patients infected with mucormycosis and 214 inpatients

[8]

Emphysema, lung cancer, etc.

Gray level Co-occurrence Matrix MLP, KNN SVM

CT scan comprises 400 MLP 98% lung disease images KNN 70.5% SVM 99.2%

[9]

Eight pneumonic infections

DCNN + RNN

Private dataset—coughing sound collected comprises of 112 patients

–

[10, 17] Pulmonary tuberculosis

3D convolution neural network

501 CT scan images

–

[11]

Lung nodule infections

UST-P, UST-S

LUNA dataset—comprises 267 CT scan images

–

[13]

Pneumoconiosis (CWP)

ShuffleNet V2-ECA Net

The private dataset comprises 276 chest X-ray

98% accuracy

CNN-based transfer learning

The private dataset comprises 4056 chest X-ray images

Accuracy 82.54%

[15, 16] Pneumoconiosis (CWP)

Data set

Result Average accuracy—73%

Accuracy—94%

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3 Dataset Availability The available dataset is C19RD consists of 2905 chest X-ray images comprise 1583 normal, 219 COVID-19, and 1345 Pneumonia images, CXID dataset comprises 5856 chest X-ray images, NIH chest X-ray sample data set comprises 5606 images and the full dataset comprises 112,210 images, LUNA dataset comprises 267 images, and private dataset [2] that comprises 17,930 lung sound clips from 1630 subjects, each clip with 10 s duration. Figure 2c displays diverse classes of NIH chest X-ray data, whereas Fig. 2a represents available dataset with diverse lung diseases, whereas Fig. 2b illustrate the outcome generated by various model among them GLCM-SVM model outperformed an accuracy of 99.2.

Fig. 2 Shows diverse dataset, lung diseases, and deep learning algorithms

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Fig. 3 Demonstrate the proposed methodology to identify lung disorders

4 Methodology The study revealed that the constructed deep learning model is still a viable area for future investigation on the use of deep learning to classify illnesses using chest X-ray and CT scan data for the diagnosis of respiratory problems. Figure 3 illustrates how we apply nature-inspired algorithms (NIA) to the challenge of designing deep learning architectures to enhance the performances of the existing techniques. It also demonstrates that using this method to learn anomalies in chest X-ray and CT scan images is much more advantageous than using the conventional deep learning method. Additionally, it turns out that the metaheuristic techniques employed to optimize the NIS model’s search strategy are particularly applicable to solving the issue. This study suggests adopting NIA-based technique to increase detection and classification rate, even though most studies that have applied deep learning to the objective of lung ailment detection and classification have shown some good performance.

5 Conclusions and Future Work This study examined the effectiveness of deep learning approaches to identify lung infection from chest X-ray images, CT scan images, voice, and text data, about 100 articles were examined for this paper’s review of the literature on lung disease detection that has been published in well-reputed journal databases. Thirty studies that used computer-aided diagnosis techniques to diagnose lung diseases are included. To diagnose lung disorders including pneumonia, COVID-19, fibrosis, and soon, the majority of these studies used conventional and regular machine learning techniques. Their trustworthiness and efficacy were tested on a limited number of X-ray and CT scan photographs, to improve the performance of the existing model we suggested to integrate nature-inspired algorithms, image enhancement techniques, and ensemble methods into the existing machine learning algorithms.

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Recent research has primarily focused on pre- and post-COVID-19 lung disorders and pneumonia. In addition, research on the most significant occupational diseases reported in coal miners—black lung disease and lung cancer—is lacking. This review aids in a future studies on various types of pneumonia, early detection of the deadliest lung cancer, and the effects of post-COVID-19 on lungs.

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16. Devnath L, Luo S, Summons P, Wang D (2020) Performance comparison of deep learning models for black lung detection on chest X-ray radiographs. In: Proceedings of the 3rd international conference on software engineering and information management (ICSIM’20). Association for Computing Machinery, New York, NY, USA, pp 150–154. https://doi.org/10.1145/ 3378936.3378968 17. Zotin A, Hamad Y, Simonov K, Kurako M (2019) Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks. Procedia Comput Sci 159:1439–1448 18. Wang D et al (2020) Automated pneumoconiosis detection on chest X-rays using cascaded learning with real and synthetic radiographs. In: 2020 Digital image computing: techniques and applications (DICTA), pp 1–6 19. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE CVPR 2017 20. Kumar, Naresh S, Ismail BM (2020) Systematic investigation on multi-class skin cancer categorization using machine learning approach. Mater Today Proc 21. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T et al (2017) CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning 22. Sirikonda S et al (2022) Automatic detection of tomato leaf contamination portion using deep neural network. AIP Conf Proc 2418(1). AIP Publishing LLC 23. Refaai MRA et al (2022) An enhanced drone technology for detecting the human object in the dense areas using a deep learning model. Adv Mater Sci Eng 24. Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou S Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Inform 21(1):76–84 25. Hwang S, Kim H-E, Jeong J, Kim H-J (2016) A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Medical imaging 2016: computer-aided diagnosis, SPIE, Mar 2016 26. Borra SPR et al (2022) Google’s new AI technology detects cardiac issues using retinal scan. Appl Nanosci 27. Jaeger S et al (2013) Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging 33(2):233–245 28. Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. In: International conference on information processing in medical imaging, pp 588–599 29. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88 30. da Nóbrega RVM, Peixoto SA, da Silva SPP, Filho PPR (2018) Lung nodule classification via deep transfer learning in CT lung images. In: 2018 IEEE 31st international symposium on computer-based medical systems. IEEE, pp 244–249 31. Nagavelli R, Rao CVG (2014) Degree of disease possibility (DDP): a mining based statistical measuring approach for disease prediction in health care data mining. In: International conference on recent advances and innovations in engineering (ICRAIE-2014). IEEE 32. Rehman A, Abbas N, Saba T, ur Rahman SI, Mehmood Z, Kolivand H (2018) Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech 81(11)

A Study on Predicting Skilled Employees’ Using Machine Learning Techniques C. Madana Kumar Reddy and J. Krishna

1 Introduction HRM plays a main part in deciding efficiency and execution to further develop consistency. The process of properly managing people in a corporation is known as human resource management (HRM). HR management makes it easier to align employee performance with the organization’s objectives. As a result, HRM’s duties include selecting and training the best candidates for jobs at the proper times, identifying the presence to monitor their performance, and safeguarding employees’ potential skills. Information can be determined utilizing various strategies, one of which is the utilization of DM strategy. Characterization is an estimative DM strategy that utilizations demonstrated outcomes acquired from various data to foresee data values. Classification procedure is a supervised learning strategy in DM and AI, while class or target class is known before. One of the most valuable operations in DM is the development of classification models from an input dataset. Models are regularly evolved by the classification procedures used to anticipate future data patterns. Modeling strategies that use classifiers specifically aim to enable us to estimate the values of unknown parameters based on previously noted interest upsides of multiple factors. In this respect, the key goals of the current study, which were taken from it to help decision-makers in different locations find potential employee skills, are as follows: • Compiling a database of predictive variables. C. Madana Kumar Reddy (B) Department of Computer Applications, Annamacharya Institute of Technology and Sciences (Autonomous), New Boyanapalli, Rajampet, Annamaiah Dt., Andhra Pradesh, India e-mail: [email protected] J. Krishna Department of AI and ML, Annamacharya Institute of Technology and Sciences (Autonomous), Rajampet, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_23

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• Understanding the various factors that influence employee behavior and performance. • Building a prediction model using recommended DM categorization algorithms and determining correlations between the most relevant parameters impacting the model’s overall efficiency. Data classification techniques include SVM, Nave Bayes, and DT classifier, among others. The classification procedure is carried out in this study utilizing the three basic classification techniques stated before. One of the DT families is the C4.5 (J48) technique. It has the ability to create both the decision tree and the rule configurations. Furthermore, it constructs the tree for upgrading the expectation exactness. Apart from it as well, the models formed by using the technique C4.5 (J48) are practically comprehensible in light of the fact that the extricated rules from the strategy have an exceptionally express simple understanding and enjoy the benefit that need not bother with any field learning or boundary setting. Where, on the normal objective, the scientist can without much of a stretch distinguish the main variables. J48 is the best execution for the C4.5 (J48) procedure and will be utilized in this investigation as a variation of the WEKA tool. SVM is viewed as one of the most productive directed procedures of ML with a clear structure and high classification capacity. Likewise, SVM is perceived as the suitable strategy for classification in ML and DM, especially at non-linear and linear decision edges, in which great model accuracy can be achieved. The advantages of SVM include not having a limit on the number of dimensions and relying on the kernel to build the model using knowledge of the kernel change problem. SVM uses the sequential minimal optimization (SMO) technique. This is well as a compelling method of characterization to tackle the optimization issue. In a non-linear SVM, SMO can be deemed the best in class method. To build the prediction model, SVM will prepare the dataset using the SMO technique. One more classification method used to anticipate an objective class is the Naïve Bayes classifier or the Bayesian hypothesis. In addition, it gives a special way to deal with acknowledging different learning algorithm that does not unequivocally utilize probabilities, contingent upon the probabilities in its estimations. The output of the classifier is thus more exact, robust, and attentive to recent data included in the dataset. There are six sections to this study. The first portion provides an introduction, followed by a description of some relevant work on HRM, the classification algorithms used for classification, and DM in HR and prediction in Sect. 2. The method used to create the suggested model is discussed in Sect. 3. Section 4 details the tests that were carried out in order to create the model. Sections 5 and 6 present some findings as well as a comparative analysis. Finally, Sect. 7 concludes with closing remarks and ideas for the future research.

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2 Related Work Many studies in numerous disciplines of science have utilized DM categorization approaches to generate rules and predict specific attitudes. In order to determine the whole set of factors and types of experiences to the regression efficiency of the staff’s ability that have been reviewed, it is important to evaluate and estimate the efficiency of an employee’s performance. This section provides a comprehensive overview of the employee performance regression analysis and the metrics that this model uses based on the following literature review. In this paper, Jantan et al. [13] used the SVM approach in the Employee Achievement Classification procedure. The purpose of this study was to evaluate the effectiveness of the SVM technique in identifying the required data pattern for employee accomplishment classification. Using the SVM technique, the model’s accuracy was judged to be good, but it still has to be enhanced to achieve the higher. In this paper, Kalaivani and Elamparithi [12] used DT approaches to predict employee performance, which was the goal of their study. DT is a common categorization approach that builds a model based on a given data set by creating not only a tree but also a set of rules. CART, ID3, Bagging, C4.5, Random Forest, CHAID, and Rotation Forest are examples of DT algorithms. Bagging, Rotation Forest, and C4.5 algorithms, which are implemented in the WEKA toolkit, are used in this work. Experiments were carried out using data gathered from an institution. In this paper, Desouki and Al-Daher [11] presented a study for applying DM techniques such as decision tree, SVM, and K-Nearest Neighbors (KNN) to the HRM field by examining the results of Performance Appraisal (PA), in order to develop the assessment approach and examine the compatibility of actual implementation with the PA process objectives, which was supported by a multi-discipline academic research organization. Prediction, clustering, and classification are some of the DM tasks that have been used to do this. According to the findings of this study, DM tasks can be beneficial and useful in dealing with human resource activities such as improving performance evaluation techniques. In this paper, Kirimi and Motur [10] focuses on leveraging the user interface to collect data from workers of a government management development institution in Kenya, creating a decision tree based on previous employee data, and determining the link between DT accuracy and employee qualities. They also focused on the possibilities of developing two or more prediction algorithms for projecting employee performance and selecting the most appropriate one for this business. In general, this article is an attempt to analyze DM activities, particularly categorization tasks, in order to assist industry leaders and HR professionals in identifying and researching the key elements affecting their workers’ performance. The research used certain categorization techniques to develop a suggested model for predicting employee performance. The following parts provide a detailed summary of the study, including the methodology, experiments, and results, as well as a discussion of the findings, as well as conclusions and recommendations for future research (Fig. 1).

Step1: Building the Model Model

Step 2: Validating the Model Validating

Input Only

New Data

Input & output

Test Data

Input & output

C. Madana Kumar Reddy and J. Krishna

Training Data

250

Step 3: Applying the Model Output

Fig. 1 Constructing the classification process

3 Classifier Construction For the explanation, which is constructing the classification model, the recommended approach has been embraced exploring a few factors that can impact and gauge the presentation of the employees. To accomplish this objective, a normalized guide is expected to build a DM project lifecycle that incorporates specific advances including problem distinguishing proof and undertaking structure, data assortment and understanding, modeling and experiments, data preparing and pre-processing, testing, and evaluating.

3.1 Objectives and Problem Definition This exploration focuses around how a proposed model supporting decision-makers and HRM can gauge the presentation of MOCA employees and perceive the elements impacting and connecting works with good/poor performance. Likewise, deciding the best DM procedure with the most noteworthy accuracy between the different classification strategies to be utilized.

3.2 Data Collection and Understanding Process There should be a reasonable method for gathering the necessary data. A poll is consequently ready and circulated to MOCA staff, containing the different characteristics that can impact and figure the presentation level (the objective class). The preparation dataset credits mentioned are chosen based on factors, personal factors, and educational factors are the only ones that apply to employee performance (e.g.,

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Table 1 Attributes for predicting the target performance S. No

Variables

Variable symbol

Description

1

JT

A1

Job Title of Employee’s

2

Rank

A2

Rank or Level of Employee’s

3

Job Exp

A3

Working Experience in number of years

4

Duration

A4

At MOCA Service Period (in years)

5

Prev Com

A5

The employee worked for No. of Previous Companies

6

Income

A6

Salary Range of Employee’s

7

WorkCond

A7

(in employee’s perspective) Comfortable conditions in Working. Answer with (Yes–No)

8

Sat Income

A8

(in employee’s perspective) Existing Satisfaction for Salary. Answer with (Yes–No)

9

JobTrian

A9

(in employee’s perspective) Existing trainings for the job. Answer with (Yes–No)

10

Job Satis

A10

(in employee’s Perspective) Existing Satisfaction for the job. Answer with (Yes–No)

11

Age

A11

Age of Employee’s

12

Gender

A12

Gender of Employee’s

13

Mar Status

A13

Marital Status of Employee’s

14

Qualification

A14

Educational Degree of Employee’s

15

Special

A15

Specialization

16

TU

A16

University type

17

Grade

A17

Graduation Grade of Employee’s

18

Performance

A18

This is the target class. Performance of Employee’s either as predicted or informed

work title, rank, age, grade, capabilities, and so on) as shown in Table 1. Such qualities are utilized to determine whether an employee’s performance to be excellent, fair, or good. 145 works from all various areas of MOCA finished the poll with various work names, ages and grades to get a full example.

3.3 Data Preparation and Pre-processing The data handling process is completed, with raw data involving occasions not important. This was because of mistakes and disposing of irregularities. Samples were transferred to spreadsheets to survey and change the sorts of data gathered where specific sorts of traits should be altered to categorical data type from numeric data type; for example, values shown by ranges. According to Table 1, the staff specialization quality (A15) included assets, for example, IT, CS, MIS, which were viewed

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as a solitary worth, IS, and so on. Data speculation is along these lines regularly known to be specific techniques for data conditioning. Just after spreadsheet has been arranged and the processing required. The file was meant an arff design viable with the WEKA DM toolkit utilized in model development. The tool of WEKA is a platform for ML created by scientists of university. Java is the language utilized for execution. It offers a durable pack in a solitary application that permits clients to get to new refreshed innovation in the DM and ML world. Subsequently, the WEKA user can without much of a stretch look at the outcomes and correctness of the executed ML and DM algorithms in adaptable systems for a provided dataset to distinguish the most appropriate algorithm for the given dataset.

4 Modeling and Experiments There have been three characterization strategies that are classifier Naïve Bayes, DT, and SVM. Such classification strategies are utilized and applied to the collection of dataset to build the expectation of performance model of the employees to acquire the most reasonable DM procedure and the most productive factors that can impact and gauge the result of the employees. These factors comprise of (A) Professional data, for example, work title, rank, experience, number of years of service at MOCA, number of previously employed organizations, pay, question about working in agreeable circumstances, get some data about the presence of solace and fulfillment with salary, work, working circumstances, and get some data about training, (B) Personal data, for example, age, (C) Academic data, for example, grade, degree, general classification, and kind of university. This large number of factors used to anticipate being Excellent, Really Good or Good the objective class (MOCA employee performance). As a post-processing step, it is simple to derive these performance measures from the predicted labels and true labels: Accuracy =

No. of(True positves + False positves) No. of(True positves + True negatives + False positives + False negatives)

(E1) Experiment 1: Utilizing the entire factors of the dataset that might influence the presentation (17 factors) (E2) Experiment 2: Utilizing the significant factors coming about because of the utilization of feature determination calculations (10 factors) (E3) Experiment 3: Utilizing the best factors coming about because of the tree created utilizing decision tree strategy (5 factors) (Figs. 2, 3 and 4; Table 2).

A Study on Predicting Skilled Employees’ Using Machine Learning … 85

81.39

253

77.98

80 75

71.13

70 65

SVM

NaiveBayes

C4.5(J48)

75

72.44

73.79

79.33

73.13

80

79.33

82.10

79.33

85

82.76

90

84.124

Fig. 2 Accuracy percentages for prediction algorithms in 1st experiment

70 65 SVM

C4.5 (J48)

Naive Bayes

Fig. 3 Based on feature selection algorithm the percentage of prediction accuracy in 2nd experiment 90

86.93

85

82.10

80 75

SVM

Naïve Bayes

79.33

C4.5 (J48)

Fig. 4 Based on the 5 best factors the percentage of prediction accuracy in 3rd experiment

5 Comparative Analysis and Discussion: Figure 4 Experiment E1 findings show that the SVM approach has the highest accuracy, with an accuracy rate of 81.39%, when all 17 factors from the dataset are used. Additionally, the E1 analysis indicates that each of these factors has some influence on how effectively employees perform. The performance is best affected by the A9 factor. A3, A2, A10, A14, and other factors that participated in the decision tree produced from the C4.5 (J48) had a beneficial impact on performance. The profTrain (A9) factor had the greatest impact on the workers’ output. The results showed that individuals who participated in training and/or courses relevant to their jobs performed much better than those who did not as a result of the factor than those who did not.

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Table 2 The classification rules are generated by using the algorithm C4.5 in E3 to estimate employees’ performance Rule

Extraction of rule

Decision on performance

No. of instances

1

If Job Train = yes & Rank = A & Qualification = D THEN

Excellent

7

2

If Job Train = yes & Rank = A & Qualification = C & #Job Exp = C THEN

V.Good

5

3

If Job Train = yes & Rank = A & Qualification = C& #Job Exp = D THEN

Excellent

1

4

If Job Train = yes & Rank = A & Qualification = C& #Job Exp = A THEN

Good

7

5

If Job Train = yes & Rank = A & Qualification = C& #Job Exp = B THEN

Good

14

6

If Job Train = yes & Rank = A & Qualification = A THEN

Good

1

7

If Job Train = yes & Rank = A & Qualification = B THEN

Good

2

8

If Job Train = yes & Rank = B & Qualification = D THEN

Excellent

2

9

If Job Train = yes & Rank = B & V.Good Qualification = C & #Exp = C &Job Satis = Yes THEN

10

If Job Train = yes & Rank = B & Qualification = C & #Job Exp = C &Job Satis = NO THEN

Good

2

11

If Job Train = yes & Rank = B & Qualification = C & #Job Exp = D THEN

V.Good

3

12

If Job Train = yes & Rank = B & Qualification = C & #Job Exp = E THEN

Excellent

1

13

If Job Train = yes & Rank = B & Qualification = C & #Job Exp = B THEN

Excellent

1

14

If Job Train = yes & Rank = B & Qualification = A THEN

Excellent

3

15

If Job Train = yes & Rank = B & Qualification = E THEN

Excellent

1

16

If Job Train = yes & Rank = C & Job Satis = Yes THEN

Excellent

42

17

If Job Train = yes & Rank = C & Job Satis = No THEN

V.Good

13

3 (continued)

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Table 2 (continued) Rule

Extraction of rule

Decision on performance

18

If Job Train = yes & Rank = D THEN

Excellent

No. of instances 8

19

If Job Train = yes & Rank = E THEN

Excellent

1

20

If Job Train = No & #Job Exp = C THEN

Good

8

21

If Job Train = No & #Job Exp = D THEN

V.Good

2

22

If Job Train = No & #Job Exp = A THEN

Good

4

23

If Job Train = No & #Job Exp = E THEN

V.Good

7

24

If Job Train = No & #Job Exp = B THEN

Good

7

The optimal feature subset for each algorithm was obtained from the entire dataset in the next (E2) experiment using feature selection techniques. ReliefFAttributeEval, CorrelationAttributeEval, and GainRatioAttributeEval were these algorithms. WEKA tool supports each of them. The top 10 factors in this experiment that have a favorable impact on the employees’ performance make up the relevant feature subset. Additionally, the precision of each classification method used on this dataset. Figure 4 E2 findings show that the SVM technique, which uses three separate feature selection algorithms, has the greatest accuracy of the three classification approaches, with accuracy percentages of 82.10, 82.76, and 84.24 in an order. The outcomes of E2 similarly demonstrated that accuracy was greater when fewer correlation coefficients were used for the target class in E2 than when the entire dataset was used in E1. Through the application of diverse feature selection methods, the three classification procedures in E2 had accuracy percentages that were higher than those for the comparable techniques in E1 (Table 3). In this study (E3), the DT method was used as a classification strategy to produce a tree that illustrates the factors that have the most effects on the workers’ performance and ranks them in terms of their influence. The resulting tree showed that the five factors with the most effects on performance were A14, A9, A2, A3, and A10, as shown in Fig. 2. The accuracy of the classifier may be affected by the variables reduction, as determined by this experiment. The E3 results provided an answer to the question of whether or not the classifier’s accuracy will be impacted by the factors’ decrease. The results revealed that the classifier’s accuracy increased with a reduction in the number of factors used. To get the best prediction accuracy, it is crucial to identify the factors that had the most impact on the performance.

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Table 3 Comparative analysis of experiments E1, E2, and E3 to estimate employees’ performance S. No

Experiment

No. of factors used

Algorithm

Accuracy prediction in %

1

E1

17

SVM

81.39

Naïve Bayes

71.13

2

3

E2

E3

10

5

C4.5(J48)

77.98

SVM

84.24

Naïve Bayes

79.33

C4.5(J48)

73.13

SVM

86.93

Naïve Bayes

82.10

C4.5(J48)

79.33

6 Results The reason for this experiment was to distinguish the most appropriate strategy for order for the dataset utilized. Continuation of the above mentioned, model exactness was utilized to characterize the dataset’s most reasonable classification method. The model was made after a 10-overlap approval method was utilized to test the classification system. As displayed in the accompanying (2, 3 and 4) three figures of the experiments referenced over, the strategy of SVM had the most elevated precision in every one of the analyses of the selected procedures. The SVM method has been the most reasonable classifier for the dataset because of the above mentioned. As the last analysis of the exactness of the classification models worked by 3 experiments, it was observed that the precision of the forecast was a lot more prominent in E3 than in E2 and E1 tests for every one of the various strategies utilized with the exception of the C4.5 (J48) method. In E3 and E2 tests, it had a similar accuracy; however, it was significantly more than E1. This could be the less usually involved factors in the process of classification, the more prominent the exactness of the classifier. The E3 results provided an answer to the question of whether or not the classifier’s accuracy will be impacted by the factors’ decrease. The results revealed that the classifier’s accuracy increased with a reduction in the number of factors used. To get the best prediction accuracy, it is crucial to identify the factors that had the most impact on the performance.

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7 Conclusion and Future Scope A significant and critical issue is the execution of DM procedures in the different problem points in the HRM field, particularly in Egypt’s public area, hence, to expand horizons of scholarly and practice work on data mining in HR to enter a highperforming area of government. The SVM philosophy was viewed as a classifier that the most suitable for the development of the predictive model, where it had the most elevated prescient precision through each of the three tests with the most elevated level of 86.93%. For HRM and decision-makers team, this or a better model may be utilized to predict the performance of future expertises that will be advanced, anticipating the performance of late up-and-comer workers where various actions can be made to keep away from any risk related with enrolling substandard performance employees, or so on. In future, to get high precision for the prescient model, this work recommended assisting the pre-owned dataset with setting with a lot of employees. To request to approve these outcomes and assist with picking a more dependable model, the exactness of other classification methods like fuzzy logic, neural network (NN), and numerous others ought to likewise be tested.

References 1. Sadath L (2013) Data mining: a tool for knowledge management in human resource. IJITEE 2(6) 2. Krishna J, Reddy MRK, Kumar MR (2017) Efficient high utility Top-K frequent pattern mining from high dimensional datasets. IJSRCSEIT 2(4):625–631. ISSN 2456-3307 3. AI-Radaideh QA, AI-Shawakfa EM, AI-Najjar MI (2006) Mining student data using decision trees. In: International ACIT’2006, Yarmouk University, Jordan 4. Surjeet KY, Brijesh B, Saurabh P (2011) Data mining applications: a comparative study for predicting student’s performance. IJITCE 1(12):13–19 5. Jantan H, Hamdan AR, Othman ZA (2010) Human talent prediction in HRM using c4.5 classification algorithm. IJCSE 2:2526–2534 6. Reddy CMK (2022) Humidity as well as temperature racking related internet of things with arduinouno. IJHLS IX(8):152–158. ISSN 2348-8301 7. Al-Radaideh QA, Al-Nagi E Using data mining techniques to build a classification model for predicting employees performance. IJACSA 3(2):144–151 8. Yasodha S, Prakash PS (2012) Data mining classification technique for talent management using SVM. In: The ICCEET 9. Hu H, Ye J, Chai C (2009) A talent classification method based on SVM. In: ISIUCE, Chengdu, China, pp 160–163 10. Kirimi JM, Motur CA (2016) Application of data mining classification in employee performance prediction. Int J Comput Appl 146(7) 11. Desouki MS, Al-Daher J (2015) Using data mining tools to improve the performance appraisal procedure, HIAST case. IJAIASM 2(1) 12. Kalaivani V, Elamparithi M (2014) An efficient classification algorithms for employee performance prediction. IJRAT 2(9). E-ISSN 2321-9637

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13. Jantan H, Yusoff NM, Noh MR (2014) Towards applying support vector machine algorithm in employee achievement classification. In: Proceedings of the ICDMICBD, Kuala Lumpur, Malaysia. ISBN 978-1-941968-02-4 ©2014 SDIWC 14. Sadath L (2013) Data mining: a tool for knowledge management in human resource. IJITEE 2 15. Qasem et al (2012) Using data mining techniques to build a classification model for predicting employees performance. IJACSA 3(2)

Forensic Analysis of the Uncompressed Image Data Cluster for Estimating the Image Width K. Srinivas and D. Baswaraj

1 Introduction The use of digital devices is increasing exponentially year by year. The cybercrimes are also increasing. Therefore, the areas of data recovery and digital forensics have gained importance. The area of file carving is a specialization of digital forensics. In the recent past, there are several research papers in this area [3]. The file carving is an important method of recovering a deleted file. In this method, the file table information is not used. The undelete tools use file table information but fail to recover a deleted file in various scenarios [1]. In file carving, the various techniques are used for reassembling the file fragments [1]. In one of the techniques, a graph problem represents the problem of reassembly. Consider the data layout of files named File1.jpg and File2.jpg as shown in Fig. 1. The cluster numbers 108, 109, 110, 125 and 126 have File1.jpg data, and the cluster numbers 111 and 127 have File2.jpg data. In the FAT file system, every file has one entry in a directory table, and every data block has one entry in file allocation table. Each directory entry has the metadata of the corresponding file in various fields. But only two fields namely filename and starting cluster number are shown. The other fields have no importance in the current discussion. The file allocation table entry has the value zero if the corresponding data block is free and has a nonzero value X otherwise. The value X is a special value FFFF, if the corresponding block is the last bock, otherwise it is a successor block number. In NTFS, an additional bit sequence maintains the free/used status of all the data clusters. Every data block has one bit in the bit sequence. A bit zero indicates a free block and a bit one a used block. K. Srinivas (B) · D. Baswaraj Vasavi College of Engineering, Hyderabad, India e-mail: [email protected] D. Baswaraj e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_24

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Fig. 1 The file system tables and data layout of the files

After user deletes the file File1.jpg, the operating system changes the three tables as shown in Fig. 2. The first byte of the filename is changed to ‘_’. This file used the cluster numbers 108, 109, 110, 125 and 126. The OS changes the corresponding locations in file allocation table (FAT) to zeros. The zero in FAT indicates a free cluster. The data in data block section are intact. These data will continue to be available until the operating system allocates the clusters to the other files. From forensic point of view, these fragments are important. The undelete tools can be used for recovering a deleted file. But it has limitations. The undelete tool uses the starting cluster number 108. Then, it refers to the location 108 in FAT. It is zero. It means the OS has not allocated cluster 108 for another file. The undelete tool regards all the consecutive zeros as belonging to the same file. Therefore, the file is recovered as 108, 109, 110. This recovery is incorrect. The

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Fig. 2 The state after deleting the file File1.jpg

limitation of the undelete tools is that it does not address the issue of file fragmentation. Moreover, if the OS allocates the directory entry of the deleted file to another file, then the starting cluster of the deleted file is unavailable. Hence, recovery is not possible for the undelete tool. The file carving is a technology for recovering deleted files. This technology addresses both the drawbacks of the undelete tools. It does not use the file system tables. It reassembles file fragments to recover files. In the research paper [2], there are eight file carving algorithms. These algorithms require that the image header cluster and all the remaining data clusters be available on the storage media. But it is possible that the OS allocates the image header cluster to another file. It is also possible that the sector having the image header is physically damaged.

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Even if all the deleted image data clusters are available except the header cluster, then these algorithms fail to recover the deleted file. These algorithms use the image width attribute for the file recovery. This attribute is obtained from the image header. In this paper, we propose a method to obtain the image width by using one of image data clusters. This helps in recovering a deleted file in the absence of the image header. The image width is an important attribute because it helps in converting one-dimensional data to a two-dimensional image. An image is a two-dimensional figure. The same image is a sequence of bytes on a storage media of a computer, mobile phone, etc. This sequence of bytes is one-dimensional. The operating system divides this sequence of bytes into blocks. A block is also known as a cluster. Due to the file fragmentation phenomenon of the operating system, it scatters the blocks on the storage media. These blocks are also scattered on SSD devices of mobile phones due to wear-leveling algorithms [1]. One of these blocks has a header of the image file. This image header has image metadata like width, height, etc. A file carving algorithm uses this metadata to reassemble a file from its scattered blocks without using file table information. For instance, the research paper [2] has a set of eight such algorithms. These algorithms need image width for carving a file. In the literature, we find the following research work in the area of file carving. In a research paper [4], file carving technique for heavily fragmented JPEG files is presented. Also a new technique, to measure the likelihood of cluster j that follows cluster i in an original image known as coherence of Euclidian distance (CED), is presented. The research paper [7] proposed the solution for the issue of missing JPEG file fragments. The paper [5] presented the design of automated generation of challenge file. The Digital Forensics Research Workshop (DFRW) organizers introduced the concept of challenge file [9]. The paper [6] presented the techniques of using the challenge file generator. This challenge file generates datasets for file carving researcher that act as a virtual disk. The papers [7, 10] present file carving with missing fragments. The paper [8] presented the techniques of reassembling document file fragments. This paper uses the statistical techniques for the reassembly. We have organized the content of this paper in seven sections. In Sect. 2, we briefly explain greedy path algorithms. These file carving algorithms use image width attribute. In Sect. 3, the importance of image width is explained. In Sect. 4, the analysis of image data cluster is presented. The analysis uses the smoothness property of the images to find the image width. In Sect. 5, we present the results of our experiments. Finally, in Sect. 6, we present the conclusion and the future scope of the research work.

2 The Principle and Limitation of Greedy Path Algorithms The greedy path algorithms recover deleted image files by reassembling unordered, mixed image file fragments without using file table information.

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Fig. 3 The image pixels

2.1 The Significance of Image Width The image width is a significant attribute for the recovery process. This attribute is obtained by decoding the image header. For example, a block starting with the bytes ‘BM’ is a block containing a header of an image of type .bmp. Consider the hypothetical situation of image size 7 × 6 pixels, block size of 8 bytes, image header size of 5 bytes, and each pixel requires 1 byte. The total number of bytes for the image is 7 × 6 = 42 bytes. The image file size is 42 + 5 = 47 bytes. So the number of blocks required to save this image file on the storage media is ceil (47/8) = 6. Figure 3 shows the image pixels. Figure 4 shows the distribution of pixel bytes in the blocks. Figure 4 shows the bytes of header and each row of the image in different colors. The tail of a block is defined as the last ‘width’ number of bytes. The head of a block is defined as the starting ‘width’ number of bytes. For example, the tail of the block 2 is p6, p7, p8, p9, p10, p11, and the head of the block 3 is p12, p13, p14, p15, p16, p17. The head and tail of the two consecutive blocks namely block 2 and block 3 are marked with different colors in Fig. 5. The significance of image width is that the head and tail together constitute ‘width’ number of adjacent pixel pairs as shown in Fig. 5. They are (p6, p12), (p7, p13), (p8, p14), (p9, p15), (p10, p16) and (p11, p17). This significance is true even for every real uncompressed image.

2.2 The Smoothness Property of Images The images are smooth. That is, the probability that the change in color from one pixel to its adjacent pixel is very low.

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Fig. 4 The sequence of bytes of the image pixel bytes in blocks Fig. 5 The head and tail of consecutive blocks

2.3 The Drawback of Greedy Path Algorithms The greedy path algorithms use the facts explained in the above two subsections. These algorithms reassemble the scattered image fragments to reconstruct an image file. Greedy path algorithms compare head of one block with a tail of another block to identify whether the two blocks contain the adjacent image fragments in an original image. If the image header is missing, then the image width cannot be found. Hence,

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head and tail for a block cannot be obtained. Hence, the image recovery is not possible even though all the blocks except the header block are present. In Sect. 5, we present a method to find the image width by using one of the blocks of the image. This image width helps to identify whether the two blocks contain the data of the adjacent image fragments in an original image.

3 Importance of Width Attribute An image is a two-dimensional figure. The same image is saved as a sequence of bytes on a storage media. This sequence of bytes is a one-dimensional. The mapping from one-dimensional data to a two-dimensional figure (i.e., the corresponding image) uses the image width.

3.1 The Image Width is Important in Image Recovery A forensic analyst may encounter a cluster of uncompressed image data. His concern would be to know the corresponding image fragment to a human eye. A computer program can display the image fragment of the corresponding data if the width of the image is available. The image header has image attributes. If the header is not available, then it is a challenging task for the analyst to realize the corresponding image fragment. The corresponding image fragment of a cluster of image data may be an important clue to crack a criminal case. The storage media may have the number of deleted images with the same width but with missing headers. The width attribute leads to the successful recovery of all the deleted images with the same width [2]. These algorithms construct a graph out of the unallocated clusters of the storage media. These algorithms require image width for calculating weights for the graph.

3.2 The Problem Statement The cluster data is one-dimensional. Let the data be available in an array A. The cluster size is 4096 bytes. So the array size is 4096. These 4096 bytes represent 4096 pixels of an image fragment as shown in Fig. 6. Now, the problem statement is given below. Problem Statement Given an array A of size 4096 bytes containing an image fragment of 4096 consecutive pixels, find the corresponding image width.

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Fig. 6 Mapping from cluster data to image fragment

4 Analysis of Disk Block Data for Finding Width The images are smooth. In most cases, in an image, the difference between the color values of any two adjacent pixels is low. The algorithm for finding the width uses this inherent characteristic of the images. In an image of width w, consider any 2 * w number of consecutive pixels. This set of pixels contains w number of adjacent pixel pairs. For example, in Fig. 5, the consecutive pixels p6 to p17 have six adjacent pixel pairs. They are (p6, p12), (p7, p13), (p8, p14), (p9, p15), (p10, p16) and (p11, p17). Given an array A and the image width w, any 2 * w consecutive values constitute w adjacent pairs. The average of sum of differences (AvgSoD) of these w adjacent pairs is low due to smoothness property of images. Let w /= w' . Given an array A, any 2 * w' consecutive values do not constitute w' adjacent pairs. The average of the sum of differences AvgSoD of these w' adjacent pairs is not as low as AvgSoD due to smoothness property of images. Given an array A alone, we can find the image width. We calculate AvgSoD for various values of widths. The correct width is a value corresponding to the lowest AvgSoD. Let this operation be named as width test operation and the outcome as width test outcome. For example, when the array A contains the values from p1 to p42 of Fig. 5, then the width test outcome is 6. In the array A of size 4096, if the width test is performed at various regions and if the width test outcome is the same, then we can conclude that the image width is the width test outcome. Equation (1) is the mathematical expression for finding the sum of differences of w adjacent pixels. And Eq. (2) finds the average SoD. SoD(w) =

w ∑ i=1

(|A[i] − A[i + w]|)

(1)

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SoD(w) w

(2)

AvgSoD(w) =

We do not know the correct width. We find the correct width by using Eq. (2) as follows. Equation (3) is a generalization of Eq. (2). We compute AvgSoD values for various values of n using Eq. (3). The smallest AvgSoD is found, and the corresponding n value is the correct width. AvgSoD(n) =

SoD(n) n

AvgSoD(w) < AvgSoD(n) ∀ n /= w

(3) (4)

Equation (4) is the mathematical expression of the width test operation. The width test operation is successful when the images are smooth. The above method for finding the width may fail if the adjacent pixel pairs belong to an edge in the image. Along the edge in an image, two adjacent pixels have high color difference. Figure 7 shows that Eq. (3) is true when n > w. Here, n = 7and w = 6. In this case, all the pairs namely (p12, p19), (p13, p20), (p14, p21), (p15, p22), (p16, p23), (p17, p24) and (p18, p25) are considered as adjacent pixel pairs. But in an actual image fragment, all are not adjacent pixel pairs. We note that among these pairs, the pair (p12, p19) is not a pair of adjacent pixels. Figure 8 shows that Eq. (3) is true when n < w. Here, n = 5 and w = 6. In this case, all the pairs namely (p12, p17), (p13, p18), (p14, p19), (p15, p20) and (p16, p21) are considered as adjacent pixel pairs. But in an actual image fragment, all are not adjacent pixel pairs. We note that among these pairs, the pair (p13, p18) is not a pair of adjacent pixels. Figure 9 shows that Eq. (3) is true when n < w. Here, n = 3 and w = 6. In this case, all the pairs namely (p12, p17), (p13, p18) and (p14, p19) Fig. 7 Equation (3) considers (p12, p19) and (p18, p25) as adjacent pairs for n > w (i.e., n = 7, w = 6) resulting in higher AvgSoD

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are considered as adjacent pixel pairs. But in an actual image fragment, all are not adjacent pixel pairs. We note that among these pairs, none is a pair of adjacent pixels. The algorithm A1 finds the width using the image data in a disk cluster. This algorithm takes two parameters. The first parameter A contains the sequence of bytes of the disk cluster. We assume that the cluster size is 4096 bytes. The second parameter specifies the starting offset for the analysis in the array A. This algorithm returns the width of the image fragment. This algorithm assumes that one byte represents a pixel color value. So the array A has 4096 pixels’ color values. This algorithm can also be used to verify whether the input bytes in an array A belongs to an image fragment. This algorithm has offset ‘os’ parameter. For various regions of the image fragment, the width is the same. If the array A actually has data bytes of an image fragment, then the algorithm returns the same width for different values of the parameter ‘os’. The statements from lines 5–9 compute the SoD using Eq. (1). The statement at line 10 computes the average SoD using Eq. (2). We assume that the actual width Fig. 8 Equation (3) considers (p13, p18) as adjacent pairs for n < w (i.e., n = 5, w = 6) resulting in higher AvgSoD (i.e., n = 7, w = 6) resulting in higher AvgSoD

Fig. 9 Equation (3) considers (p12, p15), (p13, p16) and (p14, p17) as adjacent pairs for n < w (i.e., n = 3, w = 6) resulting in higher AvgSoD

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of an image fragment is in between 50 and 1000. For each value, in this range, the average SoD is computed. The value corresponding to the ‘smallest average SoD’ is the actual width. The statement at line 13 finds this value. This algorithm has a limitation. Let X is the number of bytes required for width number of pixels. If the disk cluster size is less than the size 2 * X, then this algorithm does not work.

5 Experiments and Results The algorithm A1 given in the previous section is coded in C language. This program finds the width of the uncompressed image fragment of 4096 bytes. We have used the MS Paint application to transform the input JPEG files to 24-bit BMP files. The transformed files are uncompressed files. A JPEG file shown in Fig. 10 is an input file for our experiments. A block of 4096 bytes starting at an offset 4096 is read into the input array A. The second parameter value is set to zero. The parameters minWidth and maxWidth are set to 40 and 500, respectively. A JPEG file shown in Fig. 10 is an input file for our experiments. A block of 4096 bytes starting at an offset 4096 is read into the input array A. The second parameter value is set to zero. The parameters minWidth and maxWidth are set to 40 and 500, respectively. The graph in Fig. 11 shows the average SoD values for the assumed widths from 40 to 500 pixels. For more clarity, the graph in Fig. 12 shows the average SoD values for the assumed width from 40 to 100. As shown in these graphs, the smallest average SoD occurred at the assumed width 60 which is the correct width of the image.

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Fig. 10 An input image of width 60

Fig. 11 The average SoD for assumed widths from 40 to 500 for the image of Fig. 10

Fig. 12 The average SoD for assumed widths from 40 to 100 for the image of Fig. 10

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6 Conclusion In this paper, we have presented an algorithm to find the width of the image using the raw data present in an unallocated disk block. The experimental results show that the proposed algorithm finds width of the image. This width can be used to display the image fragment of the corresponding unallocated disk block. The width can also be used, in conjunction with a suitable file carving algorithm, to carve as much portion of the file as possible. In our future research, we would like to investigate whether this algorithm can be applied to MCUs in JPEG files.

References 1. Pal A, Memon N (2009) The evolution of file carving: the benefits and problems of forensics recovery. IEEE Sig Process Mag 26(2) 2. Memon N, Pal A (2006) Automated reassembly of file fragmented images using Greedy algorithms. IEEE Trans Image Process 15(2) 3. Poisel R, Tjoa S (2013) A comprehensive literature review of file carving. In: International conference on availability, reliability and security 4. Tang Y, Fang J, Chow KP, Yiu SM, Xu J, Feng B, Li Q, Han Q (2016) Recovery of heavily fragmented JPEG files. Dig Invest 5. Srinivas K, Venugopal T (2017) Automated generation of a natural challenge file for file carving algorithms. In: International conference on applied sciences, engineering, technology and management-2017 at DRK Institute of Science and Technology, Hyderabad, Telangana, India 6. Srinivas K, Venugopal T (In press) Testing a file carving tool using realistic datasets generated with openness. Int J Data Anal Tech Strat 7. Sencar HT, Memon N (2009) Identification and recovery of JPEG files with missing fragments. Dig Invest 8. Shanmugasundaram K, Memon N (2003) Automatic reassembly of document fragments via context based statistical models. In: ACSAC’03 proceedings of the 19th annual computer security applications conference. IEEE Computer Society Washington, DC, USA 9. https://www.dfrws.org 10. Durmus E, Mohanty M, Taspinar S, Uzun E, Memon N (2017) Carving with missing headers and missing fragments. In: IEEE workshop on information forensics and security (WIFS), Rennes, France

Performance Comparison of Various Supervised Learning Algorithms for Credit Card Fraud Detection Chandana Gouri Tekkali, Karthika Natarajan, and Thota Guruteja Reddy

1 Introduction Credit card fraud is one kind of identity theft where criminals take place to make transactions or obtain cash in advance using a credit card account assigned to them. This can be happened to your existing accounts, either through the theft of credit cards physically, hacking account numbers and PINs, and also new credit card accounts being opened without holder’s knowledge. The effective identification of credit card fraud is understanding the various technologies, algorithms, methodologies, and their types. Now, the implementation of effective fraud detection algorithms is key to reducing these losses. ML techniques [2–5] like LR, DT, ANN, and GB were used. The experts evaluate these complaints based on the actions performed by cardholders to know whether the transaction is legitimate or fraudulent. The inquirer of this department gives data to the automated system which is utilized and trained well and upgrades the algorithm over time to increase the fraud detection rate. The author Ali´c et al. [1] implemented ANN and NB algorithms for classifying cardiovascular and diabetes. The method ANN gave good results of 97.92%. Alarfaj [6] applied many approaches in credit card fraud detection such as extreme learning method. ELM, DT, random forest (RF), support vector machine (SVM), LR, XGBoost and convolutional neural networks (CNNs) baseline model are used. The neural network given best results. Itoo et al. [7] took highly skewed credit card dataset, applied resampling techniques, and distributed their dataset into training and testing parts with different ratios like 50:50, 34:66, and 25:75. They applied their preprocessed datasets to three machine learning techniques LR, KNN, and NB.

C. G. Tekkali (B) · K. Natarajan · T. Guruteja Reddy VIT-AP University, Amaravati, Andhra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_25

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Khare and Sait [8] examined the presentation of DT, RF, SVM, and LR as ML algorithms. These classifiers were used on the raw European dataset. The experimental analysis of the outcome has been finished up LR. It has result in correctness of 97.7%, SVM of 97.5%, and DT demonstrates accuracy of 95.5%. Among all utilized ML techniques, the best outcomes are acquired by RF with higher precision and accuracy of 98.6%. The author Sailusha et al. [9] describe about machine learning algorithms namely RF and AdaBoost. They compared the performance of models by performance metrics called accuracy, precision, recall, and f1 score and also depicted the ROC curve from the confusion matrix. Some other author Gracia [10] utilized two machine learning algorithms RF and LR on credit card dataset. The main aim of the author is to improve trending fraud detection approaches by better prediction of illegal transactions and accounts. So, a model RF outperforms than LR. Moumeni et al. [11] compared the classification performances of three different machine learning techniques like principal component analysis (PCA), LR, and MLP (multilayer perceptron). For that, American Bank dataset was utilized which is about accounts in the telecommunication network. Sharma et al. [12] has applied some machine learning techniques. The results demonstrated that KNN proved as efficient when compared with other methods. The machine and deep learning algorithms were also used in various applications like economics research [13], tweets related to COVID-19 [14], drug discovery [15], supply chain collaboration [16], etc. This work explains about clean monitoring of data with respect to data preprocessing models, training, and test, and applied structured test data to classifiers which are as follows in Fig. 1.

Fig. 1 Framework of proposed work

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2 Methodology 2.1 Dataset To do experiments on fraud detection, I accessed a dataset from the Kaggle site (data analysis Web site) which provides datasets. The dataset contains columns named v1, v2, v3, …, v28, time, amount, and class as target variables. And the fraud target class has represented as 1, and the normal transaction has a declared value of 0.

2.2 Data Preprocessing Data preprocessing is an essential step in the data analysis pipeline. It involves transforming raw data into a format suitable for analysis and also for machine learning algorithms. Preprocessing tasks aim to improve data quality, remove inconsistencies, and make the data more amenable to modeling. This stage is important to carefully analyze and preprocess the data to ensure accurate and reliable results in subsequent analyses or machine learning applications.

2.3 Data Cleaning (DC) In data cleaning, we removed incorrect and duplicate data in a dataset by using the data cleaning process. This is the process of corrupted, incomplete data, fixing/removing incorrect, duplicate, or incorrectly formatted, in the dataset. For data cleaning, used mutate_if (the mutate function is used to create a new variable from a dataset) and str_trim (to trim the white space) functions from dplyr and stringr packages.

2.4 Data Exploration (DE) Data exploration is used to expose view and visualize data in uncover insights from the identified areas to drive into more. Print data using head(), tail(), var, names, summary and also find column mean, median, sd.

2.5 Data Manipulation (DM) This is the task of rearranging the data for easier understanding. In this step, we will scale our data using the function scale (). The part of this task is to modify the data to

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become read easier and to more organized. Then, manipulate the data for the purpose of data analysis and visualization of data.

2.6 Data Modeling (DMD) This is an abstract technique that orders the data elements, standardizes, and tells how they interact with one another in a real-world entity. In data modeling, split our dataset into a training set and a test set with a ratio of 0.70. This means that 70% of instances will be examined in the train data, whereas 30% will be examined as the test data. Then, found the dimensions using the dim () function. The dim () function is used to represent the dimension of the matrix, an array, or a specified data frame.

3 Classifiers 3.1 Logistic Regression The LR model shows the relationship between the dependent and independent variables. An LR classifier is used to interpret the dependent variable (Z) from the independent variable (Y ) values. It can be used in positions where consistent quality is required. Regression analysis entails drawing a line across a set of data points that best fits the data’s overall outline shape. The LR model representation is as follows: Z = b0 + b1 Y + e

(1)

Equation (1) notes that Y is an independent variable, and e-means an error.

3.2 Decision Tree DT is a classifier to take over both types of complications such as classification and regression. DT is visualized as a tree which is used to solve or derive the problem of classification. The nodes in the tree are expressed attributes. The middle nodes or internal nodes are used as secondary attributes, and the leaf node is considered the target label. This kind of process gives better prediction in the classification problem. The DT uses variables called entropy and information gain. In general, entropy is the degree of randomness of the data while being processed. Information gain improves the importance of content in the nodes. It is calculated by comparing the entropy of a dataset before and after a transformation. The definition of entropy is as follows:

Performance Comparison of Various Supervised Learning Algorithms …

Entropy(SM) = −Z + log 2 Z + −Z − log 2 Z −

277

(2)

Equation (2) tells entropy definition where SM indicates total samples, ‘Z+’ proportion of positive examples, whereas ‘Z−’ proportion of negative examples.

3.3 Artificial Neural Networks An ANN is contemplated as a mathematical representation that emulates the function of the human brain and can complete non-linear actions. An ANN model is built by various processing units called neurons where input and output take place. The ANN model uses neuralnet() function to help us to establish a neural network for our data. In the part of the implementation, set a threshold at 0.5 for ANN, i.e., values greater than 0.5 will correspond to 1 and the rest will be 0. We got an accuracy of 99.61%.

3.4 Gradient Boosting Machine learning has one of the most powerful algorithms named gradient boosting algorithm. Boosting algorithms iteratively combine weak learners (learners who perform less in the process) into strong learners. In general, the errors in machine learning algorithms can be classified into two types: bias and variance errors. Mostly, this GB is used to reduce the model’s bias error.

4 Experimental Results This section deals with the results which are obtained during experiments. Tables 1 and 2 given are functions used and accuracy comparisons, respectively. The comparison results of all the specified supervised classifiers on credit card fraud data are shown in the figures. The essential parameters considered for model differentiation are accuracy and area under curve (AUC). The performance of LR was observed from logistic line plotting as shown in Fig. 2 of 99.93%. The AUC of LR is also represented in Fig. 3. The decision tree model trains the data by information gain and entropy values. So, this approach works better for our credit card dataset. The DT model has the highest accuracy value of 99.93%. The results obtained show it is clear that in all comparisons, DT dominates with higher accuracy of 99.93%. ANN has good at classifying data fraud and non-fraud. It got an accuracy of 99.61%. The GB insists on a parallel tree boosting approach which is used to solve data science-related problems in an efficient, fast, and accurate way.

278 Table 1 Functions used for classifiers

Table 2 Accuracy of different classifiers

Fig. 2 Logistic plotting

Fig. 3 AUC of LR

C. G. Tekkali et al.

Classifiers

Function used

LR

glm()

DT

rpart()

ANN

neuralnet()

GB

Bias and variance errors

Classifiers

Accuracy

LR

99

DT

99.93

ANN

99.61

GB

96

Performance Comparison of Various Supervised Learning Algorithms …

279

Fig. 4 Convergence curve for GB

Fig. 5 AUC for GB

And, it achieves 96% accuracy and produced a convergence curve in Fig. 4 to estimate accuracy and bias error. The AUC drawn by values of sensitivity and specificity is also represented in Fig. 5. The bar graph represents the performances of different supervised algorithms as shown in Fig. 6.

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Fig. 6 Comparison of accuracies between supervised learning algorithms

5 Conclusion Detection of fraud is a vital task, and it can be utilized in various fields. This work has used some machine learning algorithms like LR, DT, ANN, and GB. Among all models, the DT got a higher accuracy rate of 99.93%. The other model’s ANN, LR, and GB also achieved more than 95% of accuracies such as 99.61%, 99%, and 96%, respectively. The examined technique was more precise when compared to other current techniques. In future extension, compare these supervised learning algorithms with other deep and reinforcement learning algorithms.

References 1. Ali´c B, Gurbeta L, Badnjevi´c A (2017) Machine learning techniques for classification of diabetes and cardiovascular diseases. In: 2017 6th mediterranean conference on embedded computing (MECO), pp 1–4. https://doi.org/10.1109/MECO.2017.7977152 2. Bonaccorso G (2017) Machine learning algorithms. Packt Publishing Ltd. 3. Singh A, Singh P, Tiwari AK (2021) A comprehensive survey on machine learning. J Manage Serv Sci 1(1):3 4. Punia SK, Kumar M, Stephan T, Deverajan GG, Patan R (2021) Performance analysis of machine learning algorithms for big data classification: Ml and AI-based algorithms for big data analysis. Int J E-Health Med Commun (IJEHMC) 12(4):60–75 5. Tekkali CG, Vijaya J (2021, August) A survey: methodologies used for fraud detection in digital transactions. In: 2021 second international conference on electronics and sustainable communication systems (ICESC). IEEE, pp 1758–1765 6. Alarfaj FK, Malik I, Khan HU, Almusallam N, Ramzan M, Ahmed M (2022) Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access 10:39700–39715. https://doi.org/10.1109/ACCESS.2022.3166891 7. Itoo F, Meenakshi, Singh S (2021) Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. Int J Inf Tecnol 13:1503–1511. https://doi.org/10.1007/s41870-020-00430-y 8. Khare N, Sait SY (2018) Credit card fraud detection using machine learning models and collating machine learning models. Int J Pure Appl Math 118(20):825–838 9. Sailusha R, Gnaneswar V, Ramesh R, Rao GR (2020) Credit card fraud detection using machine learning. In: 2020 4th international conference on intelligent computing and control systems (ICICCS), pp 1264–1270. https://doi.org/10.1109/ICICCS48265.2020.9121114

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10. Gracia SVJB, Ponsam JG, Preetha S, Subhiksha J (2021) Payment fraud detection using machine learning techniques. In: 2021 4th international conference on computing and communications technologies (ICCCT), pp 623–626. https://doi.org/10.1109/ICCCT53315.2021.971 1887 11. Moumeni L, Saber M, Slimani I, Elfarissi I, Bougroun Z (2022) Machine learning for credit card fraud detection. In: Bennani S, Lakhrissi Y, Khaissidi G, Mansouri A, Khamlichi Y (eds) WITS 2020. Lecture notes in electrical engineering, vol 745. Springer, Singapore. https://doi. org/10.1007/978-981-33-6893-4_20 12. Sharma S, Kataria A, Sandhu JK, Ramkumar KR (2022) Credit card fraud detection using machine and deep learning techniques. In: 2022 3rd international conference for emerging technology (INCET), pp 1–7. https://doi.org/10.1109/INCET54531.2022.9824065 13. Babenko V, Panchyshyn A, Zomchak L, Nehrey M, Artym-Drohomyretska Z, Lahotskyi T (2021) Classical machine learning methods in economics research: macro and micro level example. WSEAS Trans Bus Econ 18:209–217 14. Gulati K, Kumar SS, Boddu RSK, Sarvakar K, Sharma DK, Nomani MZM (2022) Comparative analysis of machine learning-based classification models using sentiment classification of tweets related to COVID-19 pandemic. Mater Today Proc 51:38–41 15. Elbadawi M, Gaisford S, Basit AW (2021) Advanced machine-learning techniques in drug discovery. Drug Discov Today 26(3):769–777 16. Ghazal TM, Alzoubi HM (2022) Fusion-based supply chain collaboration using machine learning techniques. Intell Autom Soft Comput 31(3):1671–1687

Simple-X a General-Purpose Image Classification Model Through Investigative Approach Rella Usha Rani and N. N. S. S. S. Adithya

1 Introduction In human life, the most performed task is identifying an item or items; it is very fascinating to know how a 6-year old can identify things and remember them for a lifetime such as differentiate between an apple an orange, rose and a hibiscus flowers, etc.; this is called classification; our human brains create a memory pattern for every object we see as a unique pattern. This task is called classification; this looks much simple for us human beings. But when a machine needs to perform, this thing gets more interested and fascinating as they only see the object in 2D format as image and machine does not have other senses that we have. This is performed using conventional neural networks (CNN)—the most used approach in artificial intelligence. CNN performs this task by identifying the features which distinguish a specific object from others, which include shape of the object, color intensity, texture of the object, etc., and generates a filter pattern which can be used to recognize it in new samples (Fig. 1).

2 Related Work Deep learning uses architectures made up of several nonlinear transformations to try to model the high-level abstractions of visual input [1]. Deep convolutional neural networks have shown exceptional performance in image classification and event R. U. Rani (B) Department of CSE (AI & ML), CVR College of Engineering, Ibrahimpatnam, India e-mail: [email protected] N. N. S. S. S. Adithya Department of CSE, CVR College of Engineering, Ibrahimpatnam, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_26

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Fig. 1 Filter of CNN

detection on single-label datasets (e.g., CIFAR-10/100 and ImageNet). CNN architectures have lately been used to solve multi-label problems. For the multi-label annotation problem based on a network topology, Gong et al. investigated and analyzed numerous multi-label loss functions. However, because there are so many parameters to learn for [1] CNN, a good model necessitates a huge number of training examples. Therefore, training a task-specific convolutional neural network is not applicable on datasets with limited numbers of training samples. Some research has shown that CNN models pre-trained on big datasets with diverse data, such as ImageNet, can be used to extract CNN features for other image datasets with insufficient training data, and a CNN feature-SVM pipeline for multi-label classification has been proposed. To get CNN activations as off-the-shelf features for classification, global pictures from a multi-label dataset are directly fed into a CNN that has been pre-trained on ImageNet. For a multi-label classification problem, Chatfield investigated the effect of CNN representations based on different CNN architectures. Based on two very deep convolutional networks, Simonyan collected and aggregated visual descriptors over a wide range of scales, achieving state-of-the-art performance on the Pascal VOC datasets with SVM classifier. Initially, AlexNet was originally offered by Alex as a solution to the object recognition problem [2]. It was the first attempt to learn network parameters for a recognition job using a very big database. AlexNet is made up of twenty-six layers, the last two of which are SoftMax and output layers. There are three parts to network architecture. The network’s first section is divided into two units, each of which includes a convolution, RELU, normalizing, and pooling layer. The network’s second section is made up of four units, each of which contains a convolution and pooling layer. The nonlinear activation unit, which corresponds to the fully connected (FC), RELU, and drop-out layers, is the final part of the network. The drop-out layer prevents the data from being over-fitted during training. This network’s repeating structure adapts data attributes and extracts robust features. The accuracy of the CNN [3] design is mostly determined by three factors: a large size database, a high-end computing unit, and the network depth. The second issue can be solved with a GPU unit. However, the last parameter is uncertain because there is no such metric that may determine a network depth limit. More complicated and robust features are extracted as you go deeper into the network. For the object detection job, Simonyan proposed the [4] VGG16 architecture.

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3 Proposed System See Fig. 2.

Fig. 2 Simple-X architecture

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3.1 Dataset In the task of classification data is very key aspect, the most volume, variance and bias the data it’s going to yield an effective and confident your results will be. In the ImageNet competition, which is most popular among the researchers, the CIFAR10 dataset consists of ten classes of different objects; this is for identifying different categories of objects, and this is to test the adaptability. The traffic dataset is for the autonomous vehicle to recognize various sign boards; this is intended to test if model meets the need of adjusting to a simple task. The L-class (leaf classification) is custom dataset. It is for identifying different plant leaves; this is to test if model can differentiate between similar-looking objects or not. These datasets are being augmented and split into training, testing, and validation partitions for different models according to their specific input task [5].

3.2 Augmentation When preparing the dataset, the vital and most essential thing to be remembered is that, as we have different types of senses and viewpoints, it is very easy for humans to easily to visualize object. For a machine, it is very difficult to capture the variance and biases of these types. Augmentation is a technique in which we take of this thing and we rotate images with 10 degrees to right every time and generate 10 sets of images. This improves the performance and efficiency of the model. There are various techniques like adding noise to image, flipping image, and many more; this must be used according to the application.

4 Training The primary motive of the proposed model is to yield moderate to effective results with minimum iteration and in minimum data volumes as possible. Because we are here optimizing for time consumption and the computing power with an acceptable efficiency. The training procedure is straight forwarded; the entire dataset is split into training, testing, and a validation sets. The models we are competing with have different input shapes; according to their input shapes, we make the pickle files consisting of the x with data and y with labels which is a numeric array (Table 1). The training of three datasets with four different models on which AlexNet, Simple-X are fully trained on a native system with NVidia GTX 1650 4 GB graphical processing unit, MobileNet uses an approach called transfer learning with ImageNet weights. VGG16 uses Google Collaboratory as it is high resource demanding. The time required for training depends on the volume of the dataset, and efficiency or

Dataset name

Vehicles

Traffic signs

Cifar10

S. No

1

2

3

Table 1 Datasets allocation

32, 32, 3

227, 227, 3 32, 32, 3

224, 224, 3

224, 224, 3

MobileNet

32, 32, 3

224, 224, 3

224, 224, 3

Simple-X

32, 32, 1

100, 100,1

100, 100, 1 50 (104S)

100 (4S)

100 (4S)

100 (101S)

100 (104S)

200 (34S)

VGG16

AlexNet

VGG16

AlexNet

224, 224, 3

Epochs and time

Input shape

50 (54S)

50 (38S)

50 (38S)

MobileNet

50 (30S)

20 (6S)

20 (6S)

Simple-X

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Fig. 3 Training graphs of competent networks

confidence also varies according to the epochs and time excluding the factors like optimizer, loss function, and model structure (Fig. 3). The approximate training time is 1 h for VGG16, near to 40 min for Alex and VGG16 and 25 min around for Simple-X.

5 Testing After the estimated training process and model evaluating the results with given test data. We need to validate is the model performance is adequate or not. This validation is done through a custom program which compares the expected result versus predicted results, when the models practice it correctly it revises one point, and this will be done over a set of images and the key findings here are inference time, probability, and average. By observing this key parameter, we can conclude that our model will yield moderate to efficient results in minimum time and with limited computing power (Table 2).

6 Visuals See Figs. 4 and 5.

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Table 2 Validation results S. No

Network

Original

Predicted

Confidence

Time

1

AlexNet

dathura14.jpg

dathura

1.0

2.89

2

L-class

dathura14.jpg

dathura

1.0

0.201

3

MobileNet

dathura14.jpg

dathura

1.0

0.354

S. No

Score

Network

Original

Predicted

Prob

Time

AVG

1

1

Simple-X

Datacluster Truck (168)

Truck

1

1.429

100

–

1

AlexNet

Datacluster Truck( 168)

Truck

0.880

0.420

100

–

1

VGG16

Datacluster Truck (168)

Truck

0.560

0.807

100

–

0

MobileNet

Datacluster Truck (168)

Auto

0.870

1.356

0

2

2

Simple-X

Truck

Truck

1

0.068

100

–

2

AlexNet

Truck

Truck

1

0.130

100

–

2

VGG16

Truck

Truck

0.560

0.556

100

–

0

MobileNet

Truck

Auto

0.560

0.104

0

7 Conclusions and Future Scope Among the all-competent networks that we took for comparison, AlexNet and VGG16 perform effectively when adequate time, volume of data, and computing power other resources are given. They will struggle when these lack; MobileNet uses transfer learning technique, as this works effectively; it cannot be adapted to multiple variety of concepts. As an example, fixing motorbike and fixing rocket are not two similar tasks. So when adapting to a wide range of learning, these abovementioned neural networks fail in one or in other accepts. The proposed model on other hand can be adapted to a wide variety of tasks, because the core structure is light and can be rearranged according to the usage. This can be further improved by hyperparameter tuning and configuring to custom to job-specific task.

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Fig. 4 Comparative image predictions

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Fig. 4 (continued)

Fig. 5 Model comparison on datasets

References 1. Wei Y, Xia W, Lin M, Huang J, Ni B, Dong J, Zhao Y (2016) HCP: a flexible CNN framework for multi-label image classification. IEEE Publication. https://doi.org/10.1109/TPAMI.2015.249 1929 2. Rabano SL, Cabatuan MK, Sybingco E, Dadios EP, Calilung EJ Common garbage classification using MobileNet. IEEE Publication. 978-1-5386-7767-4/18

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3. Shaha M, Pawar M Transfer learning for image classification. IEEE Conference Publication. 978-1-5386-0965-1 4. Karaman S, Anders A, Boulet M, Connor J, Gregson K, Guerra W, Guldner O, Mohamoud M, Plancher B, Shin R, Vivilecchia J Project-based, collaborative, algorithmic robotics for high school students: programming self-driving race cars at MIT. IEEE Publication. 978-1-50905379-7/17 5. Bresson G, Alsayed Z, Yu L, Glaser S Simultaneous localization and mapping: a survey of current trends in autonomous driving. IEEE Publication. https://doi.org/10.1109/TIV.2017.274918

Blockchain-Based Source Tracing System Using Deep Learning: A Review Hemlata Kosare and Amol Zade

1 Introduction The perspective for deep learning to improve nearly every sector of the industry has been recognized. During the current epidemic, which was caused by the increase in novel coronavirus infection (COVID-19), deep learning methods were applied to forecast the disease transmission amount in a specific place and aid authorities in handling the pandemic by utilizing the anticipated findings. Using biometric protection and expression of gratitude capabilities, deep learning technology can support law enforcement agencies in identifying potential physical threats in the present. Deep learning can be used to achieve this. The effectiveness and productivity of a deep learning system are directly proportional to the data’s level of quality that is utilized during the phase of model education. The bulk of deep learning algorithms has centralized storage and processing in mind when it comes to training the model. This raises the possibility of a single point of failure as well as the possibility of data being tampered with by adversaries. If the data that are utilized for deep learning operations are changed in any manner, the training model may become corrupted. The decentralized blockchain technology successfully manages the integrity of data as well as its security and confidentiality. Blockchain technology and deep learning integration may have several beneficial effects, such as improved model development for prediction, model sharing, effective data market management, automated and reliable decision-making, and deep learning-based systems’ greater resilience.

H. Kosare (B) · A. Zade G. H. Raisoni University, Amravati, India e-mail: [email protected] A. Zade e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_27

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2 Literature Review Babar et al. identify in the pandemic situation how forged news is harmful to civilization. Although we cannot forget about the advantages of social media, we honestly cannot supervise the misuse of those to unfold rumors and fake articles. The author tries to find out the solution to this problem and explains the blockchain technology for innovative solutions in food, fashion, supply chain, and banking; by using this technology, the system can be transparent and overseen with the aid of using the smart contract. Because it employs blockchain technology, the system has all of its benefits, such as transparency, immutability, and security [1]. da Cruz1 et al. identify the primary three factors of sustainability, i.e., environmental, social, and profitable, of present-day society, begin, and among those the lesser interest to the environmental and social bones on the expenditure of the frugality. This exploration via way of means of the writer shows the usage of blockchain generation to use traceability withinside the pressure chain. Its effectiveness in phrases of offers is in keeping with the second. New blockchains are being elaborated with effectiveness withinside the brain, which makes us given that blockchains could be the destiny of traceability structures for sustainability [2]. Abdul Hussein et al. select interpreted facts that are incontrovertible, like what happens in centralized networks. Preventing the issue of statistics and understanding of the product’s origin and growth of the transparency of the product to the consumer originating from a sequence of the pressure chain from being exploited or improved, this exploration indicates erecting a community to hint the product. The consequences are predicted to be crucial in phrases of extended meals safety and extended demanding situations among distributors [3]. Tanwar et al. illustrate that the advent of the blockchain era (BT) in the present has brought about a distinctive, highly disruptive, and mobile age. The decentralized database in BT places a strong emphasis on sequestration and statistics security. The settlement system also guarantees the reliability and security of the statistics. Still, it increases new protection troubles much like adulthood assaults and doublespending. To control those abovementioned troubles, statistics analytics is wanted on blockchain-grounded stable statistics. The analysis of those figures highlights the significance of the emerging machine learning (ML) era. ML uses a reasonable number of statistics to produce accurate conclusions. In ML, data sharing and data tractability are unquestionably crucial to reducing the fragility of outcomes. The aggregate of those technologies (ML and BT) can supply in large part targeted results. The writer offers an in-depth observation on ML relinquishment for making BTgrounded clever operations greater bendy in opposition to assaults. For example, to look into attacks on a network powered by blockchain convolutional neural networks (CNN) and long short-term memory (LSTM), deep learning (DL) techniques like clustering, bagging, and support vector machines (SVM) may be used [4]. Casino explores the continuing state of the blockchain era and its programs and highlights how specific traits of this disruptive era can revise “business-as-usual” practices. They have hooked up a complete class of blockchain-enabled programs

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throughout exceptional sections comparable as pressure chain, business, healthcare, IoT, sequestration, and records management, and we set up serious refrains, trends, and bobbing up areas for exploration. We additionally purpose to the defects connected withinside the related works, specifically the constraints the blockchain era gives and the way those obstacles generate throughout exceptional sections and diligence. They have connected colorful exploration gaps and unborn exploratory instructions which are awaited to be of expressive price each for academics and interpreters [5]. Xiong et al. offer blockchain as a tally of accounts and offer which might be written and saved with the aid of using all parties. It guarantees a reliable supply of verity approximately the country of granges, supplies, and bonds in husbandry, in which the library of comparable records is often surprisingly expensive. Blockchain era can chase the provenance of meals and consequently enables produce steady meal pressure chains and make religion among administrators and consumers. As a dependent on the manner of maintaining records, it enables the usage of records-pushed technology to make husbandry smarter. This exploration with the aid of using the writer and platoon examines the operations of the blockchain era in meal pressure chains, agrarian insurance, and clever husbandry and offers agrarian merchandise from each theoretical and doable viewpoint [6]. Salah et al. illustrate the developing range of problems associated with food protection and infection dangers has hooked up a giant want for a powerful traceability answer withinside the farming supply chain. Blockchain is a troublesome generation that can offer a modern reply for producing traceability in cultivation and meal supply chains. The proposed answer through the writer and his group makes a specialty of the usage of clever contracts to control and manipulate all interactions and transactions among all of the members involved withinside the supply chain ecosystem [7]. Wang et al. illustrate countries where traditional blockchain-based supply dogging operations are largely constructed on the concept that the underdone data gathered through each IoT knot are credible and harmonious, which may not frequently be the case. The author and platoon employ the multi-dimensional gadgets of origin (MCO) gadget to remove the improbable records until all of the records uploaded to the chain are credible in order to solve this problem. They devise the multidimensional information cross-verification (MICV) and multi-source data matching computation (MDMC) methods to do this. Large-scale tests show that the proposed plan ensures the veracity of the data and is off the chain with a well-known outflow [8]. Kang and Li make clear blockchain generation is an arising technology withinside the area of facts technology. We receive a fair switch and distribution of statistics thanks to its decentralized nature, allotted storage, and sensitive statistics revision. To ruin the colorful being traceability structures problem, this exploration designs a brand new traceability machine grounded on blockchain generation and implements a machine prototype to corroborate the feasibility of the network. In this exploration, the writer and platoon deal with failings of the classical agrarian product traceability network, much like low security, unreliability, and issue in the facts library; a statistictaking part traceability machine grounded on blockchain generation is offered [9].

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Yang et al. display that the classical track gadget has troubles with centralized operation, ambiguous information, untrustworthy information, and smooth generation of facts islets. To destroy the under troubles, this exploration with the aid of using the author and platoon designs a traceability gadget grounded on blockchain generation for the storehouse and questions product facts withinside the pressure chain of agrarian products. They supply interpretation evaluation and workable operation; the effects display that our community improves the question effectiveness and the safety of exclusive facts, ensures the authenticity and belief ability of information in pressure chain operation, and meets authentic operation conditions [10]. Agrawal et al. explain that the fabric and apparel enterprise is one such instance that calls for traceability implementation to deal with triumphing issues of records asymmetry and low visibility. Customers find it difficult to get product information that can promote ethical purchasing behavior or ensure product authenticity. In this context, the writer and crew check out and advise a blockchain primarily based on traceability framework for traceability withinside the multitier fabric and apparel delivery chain. All partners may have a rare opportunity, flexibility, and authority thanks to the blockchain-based traceability tool to point back to their distribution network and establish a clear and sustainable supply chain [11]. Martin Westerkamp et al. tell power chain traceability is one of the best ways to exploit blockchain features like decentralization, invariability, and transparency since it eliminates the need for prior trust relationships between realities. The writer and platoon have encouraged a device that lets in for the traceability of produced goods, containing their factors. Products have characterized the usage of non-fungible virtual commemoratives which might be created on a blockchain for every block of cultivated products. Assessing the translation of the network, they have proven that prototypical perpetration for the ethereum virtual machine (EVM) scales linearly with the quantum of consumption and commodities chased [12]. Westerlund et al. explain approximately traceability refers to chasing meals from the customer returned to the ranch and vice versa for nice manipulation and operation. Still, severe traceability consequences have damaged right down to seize the necessities of force chain stakeholders. Blockchain is a brand-new distributed database era that might destroy a few issues of classical traceability systems, much like the value of relinquishment and susceptibility to hacking and records tampering. The creator and platoon’s findings recommend that upon perpetration and donation through all pressure chain actors, blockchain-grounded traceability can supply value savings, degraded reaction time to meal dishonors and meals-borne illness outbreaks, bettered security, and delicacy, and higher compliance with authorities bylaws, and consequently extend customer trust [13]. Tracing and Tracking Tracking and tracing control is a machine that calls for the recording of productassociated statistics related to product motion, shipping, and transition among places till the product reaches its very last destination. This statistic is stored till the product reaches its very last destination. Within the context of this control, traceability is a vital factor that needs to be met through operational procedures. The monitoring

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and tracing of a product are vital for quite a few reasons, starting with the instant the product starts its order procedure and persevering via its preparation, shipping, and motion from one shipping stakeholder to some other shipping stakeholder till it reaches its very last destination. Since its inception, blockchain generation has been instrumental withinside the improvement of a large number of programs throughout lots of industries [14], in particular, the ones wherein protection and belief are critical additives of the enterprise process. Neeraj and his crew have attempted to carry out a fact take a look at the numerous useful programs of blockchain generation throughout lots of industries to reshape the economic system as an entire and the supply chain and logistics device in particular. The studies additionally lend credence to the pleasure surrounding blockchain’s potential, i.e., the declaration that it may revolutionize each enterprise and society via way of means of the digital impact its miles generating withinside the hard environments of growing countries. In addition, the muse of these paintings changed into evolved the use of an observational technique to mirror critics in addition to supporters of the adoption of generation-led via way of means of blockchain [15]. Neha and her group carried out an in-intensity look to offer a complete literature evaluation that investigates the usage of blockchain as a base era for securing monetary in addition to non-monetary applications. This turned into achieved via the usage of an in-depth look. The purpose of this enterprise is to decide whether or not or now the longer blockchain era can turn in the preferred ranges of protection answers in plenty of applications. Previous studies are checked out and analyzed to decide their benefits, difficulties, and proposed answers. The survey inquired approximately plenty of protection concerns, such as the ones approximately confidentiality, availability, authenticity, accountability, and dependability, among others. According to the findings of the look, there may be capacity for the usage of the blockchain era in each monetary and non-monetary offering due to its capacity to offer solutions to the giant majority of protection concerns [16]. In current years, many nations had been faced with intractable troubles in a way to cope with the speedy growth withinside the number of various illnesses. These illnesses are often the result of the surroundings and the meals this is fed daily; as a result, ingesting meals that is wholesome and smooth is continually a problem for nearly every consumer. The statistics this is offered on the product’s packaging is generally what affects the picks that end customers make. In this paper, we promote a unique technique that makes use of blockchain and IoT technology to assist the starting place traceability of agrarian merchandise on farms. This will assist us to get around the difficulty that we are presently facing. All of the statistics and facts wanted for starting place traceability can be recorded and stored withinside the blockchain device withinside the shape of logs in a ledger via clever contracts. This can be executed with the aid of using the usage of sensors and computational codes to study parameters that have an effect on farming methods which includes temperature, humidity, solute concentration, and pH. These parameters consist of temperature, humidity, and solute concentration. The proposed device has been constructed and positioned via rigorous testing, revealing extraordinarily encouraging results [17]. Significant disruptions are being due to blockchain technology that is affecting the management of supply chains. This looks at via way of means of Nguyen and

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their group aimed to boost studies concerning blockchain primarily based on deliver chain traceability via way of means of figuring out the possibilities and barriers that accompany the adoption of public blockchains. This changed into performed so that the look at ought to make contributions to the development of the field. The authors supplied a precis of the applicable literature on the standards of deliver chain traceability, conceptualized key factors which can be one of a kind to the general public blockchain, and took an interest in possibilities and barriers withinside the procedure of imposing traceability on usage of blockchains [18]. In phrases of privateness, useful resource control, and records administration, Parimala and the group provided a complete assessment of a way to combine FL with IIoT. The traits of IIoT and the basics of distributive and FL are first mentioned in this phase of the survey. The cause of this text is to offer a synopsis of the purpose at the back of integrating FL and IIoT to obtain records privacy protection and on-tool learning. The subsequent step withinside the system is for them to speak about the capacity of the use of strategies consisting of system learning, deep learning, and blockchain for FL in stable IIoT environments. In the subsequent step, they examine and offer a precis of, the distinct approaches to control the significant quantities of records. Next, a complete historical past at the control of records and assets is provided and that is accompanied by the aid of using displays of programs of IIoT with FL withinside the healthcare enterprise and the automobile enterprise. Last however now no longer least, the authors shed mild on challenges, a few feasible solutions, and capacity directions for future research [19]. There has been the integration of numerous articles which have proven the various opportunities of making reliable synthetic intelligence fashions in e-health via way of means of using blockchain that is an open community for the permission of information sharing. These articles had been proven to have several applications. AI uses a huge form of proposed decision-making capacity and algorithms, similar to big portions of data. Healthcare experts could have to get entry to the blockchain, to show the patient’s clinical records. Therefore, via way of means of incorporating the maximum latest improvements of that technology into the clinical system, carrier performance can be improved, expenses can be reduced, and healthcare becomes greater accessible. The blockchain era makes it viable to shop cryptographic records, which are critical for AI [20]. A framework has been proposed through a few that accumulate a small quantity of information from several sources (numerous hospitals), then trains an international deep studying version of the usage of blockchain primarily based on federated studying. The information is validated through blockchain technology, and federated studying educates the version on an international scale, all even preserving the confidentiality of the organization [21]. Shinde, on the opposite hand, presents a bibliometric and literature evaluation of ways blockchain can act as a protection internet for AI primarily based on structures. Scopus and Web of Science are examples of famous studies databases that have been investigated for this analytical take look at and review. According to the findings of the studies, a big contribution is made through concepts shown at meetings and sure articles posted in journals. Shinde and their crew recommend the concept of blockchain primarily based on AI structures;

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however, there may be nonetheless a big quantity of studies paintings to be completed earlier than those structures can be implemented in a real-world setting [22]. There has been an upward thrust in interest withinside the use of synthetic intelligence (AI) in a whole lot of contexts to offer perception for decision-making and to make predictive analytics extra accessible. In current times, there have additionally been trying to apply blockchain, which is a peer-to-peer dispensed system, to facilitate synthetic intelligence packages. These packages include, for example, stable statistics sharing (for version training), keeping statistics privacy, and assisting relied on AI choices and decentralized AI, supply chain management (BC-SCM), and transparent network modeling [23–27].

3 Types of Blockchains This parameter categorizes the existing research into three distinct subfields according to the blockchain platforms that are supported by the various existing deep learning frameworks for blockchain. Based on their structure, functionality, and policies, contemporary deep learning frameworks may divide blockchain platforms into public, private, consortium, or federated categories. The next paragraphs will elaborate on these categories. (1) Public Blockchain: Users and device studying devices have permissionless or limitless get right of entry to the allotted ledger via the general public blockchain platform this is utilized by the present blockchain-assisted deep studying frameworks. This gets right of entry is made feasible through a blockchain, and this is open to the general public. Users can execute transactions by getting access to a copy of the allotted ledger this is shared among all nodes withinside the public blockchain network. The confidentiality of transaction information is ensured through the use of public blockchains, which is characteristic of decentralized statistics processing and storage. In addition, due to the fact public blockchain structures are proof against extensive sort of safety flaws, they help deep studying models in generating accurate and reliable results [28]. (2) Private Blockchain: The blockchain-assisted deep learning frameworks make use of private blockchains, which are governed and controlled by a single enterprise. (3) Consortium or Federated Blockchain: The current frameworks for blockchainassisted deep learning make use of consortium blockchain platforms, which include characteristics of both platforms for both public and private blockchain. A consortium blockchain operates as a permission network and allows several varied parties to have the authorization function [29].

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4 Deep Learning Models To assist with decision-making, a deep learning model analyzes the data that have been obtained and identifies patterns that may be utilized across a variety of case studies. Depending on how the neural community layers are set up, the deep studying models which are utilized to make decisions in a variety of application sectors can be broken down into five primary categories. Below is a quick summary of several deep learning models that have found patterns and taken actions based on information found in blockchains. (1) Convolutional Neural Network: The convolution neural network (CNN), which is also referred to as ConvNet in some instances, examines a picture to identify the objects in the picture, assigns weights to the objects, and categorizes the objects according to the context. In addition to this, it enables the possibility of locating instances of objects inside the processed picture [30]. The deep learning frameworks that have been built on blockchain have made use of CNN to categorize photographs, identify objects, and segment instances for a range of different use cases. Because it employed flexible filters to identify the elements of the image, utilizing CNN in blockchain-based research has the advantage of just requiring the algorithm to spend a small amount of time preparing the image. This is one of the benefits of using CNN. (2) Recurrent Neural Network: The performance of a CNN model is improved when it is given input in the form of visual data. On the other hand, a recurrent neural network, often known as an RNN, may produce patterns from sequential or timeseries data [31]. Some of the most well-known applications of RNNs encompass voice or speech recognition, speech-to-textual content conversion, voice search, and herbal language processing. Blockchain-based systems can do all of these things (NLP). In addition, the data that are fed into CNN models are not connected in any way, whereas the data fed into RNN models are connected and affect the final output. Gated recurrent units (GRU) and long short-term memory (LSTM) [32, 33] are upgraded variants of RNN that are widely employed for precise forecasting to overcome the weaknesses of RNN. This is because GRU and LSTM both can store more information than RNN. (3) Generative Adversarial Networks (GAN): The generative model has a cap that can original data and discovers the patterns without being supervised. To be more specific, it is a model of the deep learning variety that makes use of convolutional neural networks. Both a generator network and a discriminator network are used in the construction of the GAN model. While the discriminator acquires the ability to classify the input as genuine or fabricated, the generator is responsible for producing new examples [34]. (4) Deep Reinforcement Learning (DRL): DRL is a technique that helps expert systems interpret the data more precisely by taking its cues from philosophies of human performance that are founded on an interactive ecosystem. This allows DRL to draw on a wealth of knowledge about human behavior. The DRL models that make up the environment are what allow intelligent agents to behave and

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learn in that environment. In addition, agents receive overt praise or criticism depending on the results of their deeds. Models that are based on reinforced learning are those that provide positive reinforcement for actions that lead to the achievement of the desired outcome [35]. (5) Geometric Deep Learning: This variant of deep learning emphasizes constructing neural networks from data that do not conform to Euclidean geometry [36]. A graph is an example of a certain kind of data that are not of the Euclidean variety. It may be possible to accomplish data modeling with less time and effort when working with graph-based data. Instead of feeding data in their typical format to generic neural networks, geometric deep learning models take their information in the form of graphs. In its most basic version, geometric deep learning possesses the ability to mine the input for information that is both more minute and more specific. Problem Statement: • Traditional blockchain-based source tracing systems typically rely on the unreliable and inconsistent raw data collected by each IoT node, which is not always the case. • The unstable and inconsistent raw data gathered by each IoT node are often the basis for traditional blockchain-based source tracing systems, albeit this is not always the case. • We offer the multi-dimensional certificates of origin (MCO) approach to filter out the potentially amazing data to address this issue. • Once MCO is done, we optimize the decision ability using deep learning. The research methodology used in the proposed system is as follows: 1. The literature will be collected from various sources like library IITs, NITs, and other web sources of the journal. 2. Solidity environment to create the smart contract. 3. Blockchain environment framework will be designed in MATLAB. 4. Blockchain environment framework will be developed in Python. 5. MICV and MDMC will be developed in a Python environment. 6. If necessary, a cloud service provider like Google or AWS will be used.

5 Conclusion The ability to make judgments in extremely complicated problem statements and across many different fields of study has been improved by deep learning. This deep learning system requires openness in its operations and has security dependability for data annotation for a centralized system. These requirements, however, are insufficient to deal with high-end mechanisms. The chain of neural processes works to train the neural network whenever a single centralized system runs the detailed deep learning model. The blockchain environment offers a very high level of security,

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Fig. 1 Proposed system

which provides a significant amount of compatibility with the deep learning mechanism. We have conducted an extensive literature review for a variety of applications involving the combination of blockchain technology and deep learning. We have outlined several distinct types of blockchains and deep learning models, each of which is highly compatible with one another and can eventually be incorporated. We have also examined the performance of several different blockchain frameworks based on deep learning in a variety of applications. In conclusion, we have reached some conclusions on the reliability of various source tracing mechanisms for any possible optimizations that can be completed through the use of deep learning.

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Geospatial Data Visualization of an Energy Landscape and Geographical Mapping of a Power Transmission Line Distribution Network J. Dhanalakshmi

and N. Ayyanathan

1 Introduction GIS (geographical information systems) is a group of software tools and methods used for integrating and evaluating geographical-based data from a variety of sources. Using tools from the field of spatial statistics, these data can be visualized and their spatial relationships are analysed [6]. Using a geographical data framework to design the executive framework is important for rural electrification planning. Different energy planning systems use geographical information system-related studies involving multi-criteria decision-making and geographical information systems for different perspectives [1]. Energy planning utilizes geographical information systems. Different methods of energy planning have been examined in the literature. Contributing to the existing literature, this paper offers more energy resource options nearby the community [4]. Basic information and GIS improve rural electrification planning. A final map of electrification options is produced by multiplying the weighted and reclassified values [1]. Due to their continuity and reliability of supply, power utilities have recently become an interesting option for rural areas. However, the power grid operations have not reached 100% as per the census in Tamil Nadu. The author identified the southern part of the region, namely Kodaikanal in the Dindigul district of Tamil Nadu [2]. The introduction section describes the background study on the geographical information system for rural electrification demand planning in Tamil Nadu [17]. The main idea is to compare existing GIS techniques for energy-related initiatives. The proposed system IPO cycle (input–process–output cycle) comprises geospatial data with respect to the proposed energy landscape and environmental fields. The J. Dhanalakshmi (B) · N. Ayyanathan B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_28

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feature selection and engineering would be scientifically done to build the decision support system [18]. The organization of the remaining sections is as follows: Sect. 1.1 describes the review summary; Sect. 2 describes the research design process metrics; Sect. 2.2 provides the data description of energy landscapes; and Sects. 3.1 and 4 present the implementation process metrics and discuss the experimental results. Finally, Sect. 5 concludes with a future enhancement of the work.

1.1 Related Works The academic researchers reviewed many articles on geographical information system for rural electrification planning in Tamil Nadu. Bahaj et al. proposed the framework on rural development of geographical mapping in Kenya. Household data have been collected for this rural electrification. Based on minigrid tariff, the data are observed with national grid tariff. The minigrid of solar PV is used in urban areas for local community. Based on the work, higher tariff is best to work in national rate [1]. Taye et al. described the lowest electrification rates in Ethiopia. The study proposes the renewable energy systems of geospatial technologies in south gander zone. The data are taken from various sources, namely forest, wind speed, land use–land cover, and are analysed using optimal electrification in four district zones [15]. Finally, the study covers the towns and villages for developing the electrification in rural areas [2]. Xiao Yuan et al. explained the geographical origin in the Academic Success Centre. The data are collected from the year 2015–2017. The maps are visualized using home address and zip code. The GIS software tool is used to analyse the educational research based on the graphical data representation. GIS maps are useful for displaying the relationship between spatial data and database attributes [3]. Ran et al. discussed about the rural planning in slope analysis. Based on the land slope coverage, latitude and longitude have been taken for efficient mapping analysis. The resources and environment humidity are to check the slope coverage in towns and villages. Based on the humidity and temperature, village areas are covered with rural planning and given the topographic map to visualize the slope area of villages [4]. Alajangi et al. described the web maps and client–server architecture. Remote sensing and ArcGIS have been used for analysing the planning and monitoring the rural areas [12]. A geographical information system is analysed in Ranga Reddy District, Telangana, and Moinabad Mandal [10]. The slope is calculated using its pattern and texture. From the map analysis easily, the areas have been monitored using remote sensing and ArcGIS map [5]. Sarkar proposed the geographical analysis in mountain villages. Based on 2011 census, the 68.84% population are in villages. The present article is to provide a spatial data base for rural development. According to Environmental Space Research Institute, the data are collected and the GIS database is to provide the rural electrification in mountain villages [4].

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Holguin et al. described renewable model that produces the 100% rural electrification in Peru. The optimal model is used to select the best geographical technology to produce electricity in energy resources in rural areas. Comparative analysis is done using decision support model for high equipment energy sources [7]. Leonard et al. said about the clustering and network design methods for rural planning in Africa. A minimum spanning tree model is used for grid design model. Based on household design, the individual solar PV is set for the individual homes. These techniques are illustrated using clustering model. A best fit has been deployed with network distribution of user input–output process [8]. Mentis et al. focus on GIS-based methodology to perform electrification in Ethiopia. The two aspects of energy planning of optimal electrification are done, and another is minigrid and off-grid systems. These minigrids help to find some remote areas with low density population. Off-grid shows the energy availability in the lowest rural electrification [11]. Dinesh Kumar et al. presented a digital India through the rural electrification in villages. A single framework design is modelled using village-level geospatial framework with revenue maps and imagery data [13]. Lopez-Gonzalez et al. presented a novel conceptual framework for achieving sustainability in rural electrification using a new conceptual framework to access throughout the world. This work aims to contribute to the improvement of rural electrification programmes in other developing countries with renewable energy through its conceptual framework [14].

2 Research Design The research design of the proposed decision support system comprises of five phases, namely (1) data collection pertaining to the above problem requirement; (2) energy supply chain construction with start node and destination mountain villages; (3) feature selection and feature engineering; (4) experimental setup and the selection of research tool; (5) geospatial data visualization.

2.1 Energy Supply Chain Network As part of the design process analysis, this study followed several procedures, which can be seen in Fig. 1. In the design process, a research work is carried out by collecting the data pertaining to the southern region of Tamil Nadu for the rural electrification demand planning. A hill station of Kodaikanal is chosen for the data collection of design process work. The IPO cycle (input–process–output cycle) of the system is designed with the identified input variables of energy landscapes. The geospatial latitude and longitude values of the Kodaikanal mountain villages are the input variables for the proposed supply chain network data visualization. The geographical mapping done through

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Fig. 1 Research design process

the online dynamic maps of energy landscape is shown, and patterns of them are discussed extensively.

2.2 Data Description The data set is collected from the official web sources of Tamil Nadu State Government, India [16]. The web sources provide the details of southern region of Kodaikanal mountain villages. From the data sources, 15 villages are chosen as destination centres of proposed power transmission line. The following are the attributes of above comprises of village name, village code, country, pin code, longitude and latitude coordinates.

3 Methodology Kodaikanal, a popular summer resort in the Western Ghats, is referred to as the “Princess of Hill Stations”. Based on census of 2011, the Kodaikanal has two towns and 15 villages. Rural areas of 15 villages have been deployed in this work for analysing the renewable energy sources of demand planning of power distribution network [9]. The study utilizes the hill stations data from southern region of Kodaikanal, Tamil Nadu. In that particular data set, 15 villages are chosen for the feature selection and are visualized using Power Business Intelligence. From this geographical mapping data visualization, the demand planning of power distribution network is analysed and visually represented the rural electrification planning in mountain villages.

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3.1 Experimental Setup and Implementation Process Metrics The input variables are imported into the Microsoft Power Business Intelligence research tool for building the decision support system. Implementation process metrics are carried out as per the research design. The experimental results are obtained and interpreted for decision-making. Step 1: Open the Microsoft Power Business Intelligence for accessing the geospatial data visualization tasks. Step 2: Import 15 village’s data set of the variables state, city, village name, village code, pin code, latitude and longitude coordinates and classify using the filled map and select the feature selection for mapping the network. Step 3: Once the filled map is enabled for feature selection, select the attribute with latitude and longitude coordinates. Step 4: Now from the feature selection, the IPO cycle will be processed with distance kilometre for each and every villages and shows the distance of individual villages from Dindigul district. Step 5: Finally, the selected route map can be visualized and geospatial data visualization of energy landscapes is shown with the classified instances of power transmission distribution network. The result gives the perfect route map with geographical mapping for rural electrification demand planning for mountain villages.

4 Results and Discussion The mountain villages of Adukkam, Kamanur, Kilakkuchettipatti, Kodaikanal, Kookal, Mannavanur, Pachalur, Periyur, Poolathur, Poombarai, Poondi, Thandigudi, Vadagounchi, Vellagavi and Vilpatti are visualized based on village code and pin code of southern region of Kodaikanal, Tamil Nadu. Figure 2 shows geographical mapping and distribution network of power transmission—Kodaikanal mountain villages as shown below.

Fig. 2 Geographical mapping and distribution network of power transmission—Kodaikanal mountain villages

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Figure 3 shows the clustering of mountain villages which can be visualized based on the average of latitude and longitude. The mountain villages, namely Adukkam, Poolathur, Vellagavi, Vilpatti, Vadagounchi and Thandigudi, form the first cluster and shown in the blue colour, whereas Kookal, Mannavanur, Poombarai, Poondi and Kodaikanal which are grouped as the second cluster and shown in dark orange colour. The Kilakkuchettipatti, Periyur and Pachalur are shown in the light orange colour. The Kamanur alone is separated, and it is shown in the colour of dark blue. The distance map is calculated and visualized the power transmission lines of supply chain network to reach Kodaikanal via Batlagundu as shown in Fig. 4. The paths have been identified and show the clear viewpoint measures of power pathway lines to transmit the power facilities from Dindigul district. Figure 5 shows the supply chain nodes of power transmission lines to reach Kodaikanal. The power pathway starts from Dindigul district and follows the transmission lines of Natham, Sendurai, Sirugudi, Chinnalapatti, Kattakamanpatti, Ayyampalayam, Ammainaickanur, Nilakottai and Batlagundu. Finally, the power transmission pathway reaches the destination point of Kodaikanal. From

Fig. 3 Clustering of mountain villages—based on latitude and longitude

Fig. 4 Power transmission line supply chain network—distance in km (Kilometre)

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Fig. 5 Supply chain nodes of power transmission lines (Dindigul to Kodaikanal)

the Kodaikanal, 15 villages of rural electrification areas are covered and visualized through geographical mapping using Power Business Intelligence tool. Now, from this visualization, demand planning of rural electrification can be carried out for the renewable energy sources for these mountain villages. The solar energy is the source for all the rural electrification areas of these 15 mountain villages. But using of solar energy can be used only for 11 h per day due to temperature and humidity of climatic conditions in hill stations. The Adukkam, Poolathur, Vellagavi, Vilpatti, Vadagounchi and Thandigudi are the nearest mountain villages and will be having maximum temperature that is 33 °C and the minimum temperature that is 23 °C. The average hours of sun per day are only 11 h. After 11 h, maybe the climatic conditions will change so the possibilities of solar energy cannot be used in rural areas for 24 h. The next nearby villages are Kookal, Mannavanur, Poombarai, Poondi and Kodaikanal in which the energy production is taken from Dindigul district via Batlagundu, which gives energy production to Kodaikanal. From the Kodaikanal, the nearest mountain villages are getting solar and wind energy. Based on the latitude and longitude calculation, the wind mill is good in these rural areas. Finally, for the Kilakkuchettipatti, Periyur and Pachalur, the solar energy is provided. From the Kodaikanal, the solar energy production is carried out in these three villages. The Kamanur alone is a separated far distance village from these 15 villages. The rural areas are having the highest population than the urban areas in Kodaikanal. The number of households in rural areas are 18,836, whereas the total population is 70,018. So, the energy needs are high in rural areas. Based on the climatic conditions in the hill stations, the energy production is low. The solar energy is only possible for 11 h per day. From this research analysis in the surface area, wind energy is good and whilst the climatic condition changes, there are possibilities to transfer wind energy to the particular villages that they can use wind mill after 11 h of solar energy. In the rainy season, there is a possibility to have hydropower from the reservoirs dam of power generation in these mountain villages. Hence, from the above geospatial data visualization of the undertaken power transmission line energy landscape, the resulting route map, distance map and the

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respective population density of the supply chain network node villages provide clarity to the demand planners of the rural electrification demand planning.

5 Conclusion and Future Enhancement The computational intelligence model for proposed geospatial data visualization for the rural electrification demand planning of the power transmission distribution network of Kodaikanal mountain villages is designed and implemented successfully. The mountain villages are perfectly suited for generating alternate power from the natural resources like solar, wind, etc. The suitable collaborative big data analytics framework comprising of both renewable energy and non-renewable energy for providing the energy analytics of the destination mountain villages is the future enhancement of this research work.

References 1. Bahaj A, Blunden L (2019) The impact of an electrical mini-grid on the development of a rural community in Kenya. Energies 12:778 2. Taye BZ, Workineh TG, Nebey AH, Kefale HA (2020) Rural electrification planning using geographic information system. Cogent Eng 7(1):1836730 3. Yuan X (2020) The application of geographic information system (GIS) in academic success center (ASC) of a medium-sized liberal art university. Educ Res Theory Pract 31(3):94–100 4. Ran LH, Bo Z (2013) Application of GIS in rural planning. Int J Model Optim 3(2):202–205 5. Alajangi S, Pyla KR, Eadara A, Prasad NSR (2013) Web GIS based information system for rural development. Int J Sci Res 5(5):2469–2475 6. Sarkar S (2018) Application of spatial database for rural development and planning in Indian context—a theoretical overview. Int J Res Anal Rev 5(3):131–135 7. Holguín ES, Chacón RF, Gamarra PS (2019) Sustainable and renewable business model to achieve 100% rural electrification in Perú by 2021. IEEE: 978-1-5386-8218-0/19 8. Leonard A, Wheeler S, McCulloch M (2020) Geospatial clustering and network design for rural electrification in Africa. IEEE PES/IAS Power Africa: 978-1-7281-6746-6/20 9. Adkins JE, Modi V, Sherpa S, Han R (2017) A geospatial framework for electrification planning in developing countries. IEEE: 978-1-5090-6046-7/17 10. Bissiri M, Moura P, Figueiredo NC, da Silva PP (2019) A geospatial approach towards defining cost-optimal electrification pathways in West Africa. Energy 11. Mentis D, Andersson M, Howells M, Rogner H, Siyal S, Broad O, Korkovelos A, Bazilian M (2016) The benefits of geospatial planning in energy access—a case study on Ethiopia. Applied Geography Elsevier 72(1):1–13 12. Korkovelos A, Khavari B (2019) The role of open access data in geospatial electrification planning and the achievement of SDG7. An OnSSET-based case study for Malawi. Energies 12:1395 13. Azad DK, Singh AK (2021) Development of village level geospatial framework for “digital India.” Int J Adv Remote Sens GIS 10(1):3415–3424 14. López-González A, Ferrer-Martía L, Domenechd B (2019) Sustainable rural electrification planning in developing countries: a proposal for electrification of isolated communities of Venezuela. Energy Policy 129(1):327–338

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15. Perçukua A, Minkovskab D, Stoyanovac L (2018) Big data and time series use in short term load forecasting in power transmission system. Proc Comput Sci 141(1):167–174 16. Kodaikannal modified master plan of Mountain Villages (Southern Region of Tamilnadu) 17. Dhanalakshmi J, Ayyanathan N (2019) An implementation of sustainable energy model using multilayer perceptron and EM algorithm. In: National conference on recent trends in computer science and mathematics 18. Dhanalakshmi J, Ayyanathan N, Pandian NS (2019) Energy analytics and comparative performance analysis of machine learning classifiers on power boiler data set. IEEE

Sign Language Identification Using Deep Learning Methods Ishaan C. Saxena, Rohan Anand, and M. Monica Subashini

1 Introduction Hearing impaired persons communicate via hand gestures, so regular people have difficulty understanding their language. Due to this, technologies that recognize various gestures and communicate the information to ordinary people are required. Even after a lot of studies and publishing of extensive research papers on ASL, Indian sign language differs significantly from American sign language. ISL takes the help of two hands to communicate, whereas ASL uses a single hand. Details are typically hidden due to hand overlapping, while using both hands. Furthermore, a lack of datasets, along with regional diversity in sign language, has hampered ISL gesture detection efforts. Our research intends to take the first step toward overcoming the communication gap between hearing- and hearing-impaired persons.

2 Literature Survey A good literature survey forms the core of any study that is carried out. Considering the relevance to the present study, a literature survey is carried out and some past papers were gone through, and the data is provided below. “Sign Language Recognition using 3D convolutional neural networks” was a paper written by Huang et al. This paper was published in 2015 [1]. In order to characterize sign language motions, current SLR approaches develop their own features, which are subsequently used to build classification models. However, using this I. C. Saxena (B) · M. M. Subashini School of Electrical Engineering, Vellore Institute of Technology, Vellore, India e-mail: [email protected]; [email protected] R. Anand School of Computer Engineering, Vellore Institute of Technology, Vellore, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_29

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method makes it difficult to develop features that are dependable and can support the vast range of hand movements. An accuracy of 94.2% is reported on the given dataset. Further research was done by El Badawy et al. and it was published in 2017. The paper was titled “Arabic sign language recognition with 3D convolutional neural networks” [2]. Arabic Sign Language Recognition has gained popularity due to its complexity and wealth of information. On static and dynamic data, most studies used various input sensors, feature extractors, and classifiers. These various approaches were adapted and used in our earlier work in the field of Arabic Sign Language Recognition. The algorithm obtained 98% accuracy for observed data and an average accuracy of 85% for fresh data. The results might be improved by including additional data from more distinct signers. Following this further research was done by Anantha Rao et al. and it was published in 2018. The paper was titled “Deep convolutional neural networks for sign language recognition” [3]. This research proposes the identification of Indian sign language motions using Convolutional Neural Networks. In this study, a hearingimpaired person could freely utilize the smartphone app, since continuous selfie mode sign language video was chosen as the capture technique. When it was juxtaposed with classifier models published on the same dataset, a recognition rate of 92.88% was achieved. In the same year, another paper was published by Bantupalli and Xie, which followed a different method than the above one [4]. Recent advances in deep learning (DL) and computer vision have led to substantial success in the domains of visual communication utilizing DL and computer vision-based methodologies. The model tested on the sample had an accuracy of 98.5%, which is regarded as good. In 2019, Research was conducted by Moklesur Rahman et al. The paper was titled “A New Benchmark on American Sign Language Recognition using Convolutional Neural Network” [5]. ASL is one of the most widely used sign languages in the world, and due to its significance, methods for recognizing ASL with low accuracy already exist. The study employed four publicly accessible ASL datasets of the alphabet and numerals. The proposed CNN model improves the recognition accuracy of ASL by 9% as reported by some existing well-known methods. The following year, a paper was published by Kadhim and Khamees on “A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets” [6]. A real-time ASL identification system was constructed utilizing a ConvNet algorithm using real-coloring photos. The experimental findings had a towering accuracy of around 98.53% for training and 98.84% for validation. The system demonstrated good accuracy for all datasets even when all of the extra test data had not been used. In the same year, research was further done in Deep Learning by Wadhawan and Kumar and their paper was titled “Deep learning-based sign language recognition system for static signs”. The aim of this work is to investigate the pragmatic modeling of static signals in the context of sign language identification using deep learning-based CNNs. In this study, around thirtyfive thousand sign photos of approximately a hundred static signs were collected from various customers. The findings are also analyzed using several optimizers, and the suggested technique attained the best training accuracy of 99.72% and 99.90% on colorful and uncolored pictures, respectively.

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2.1 Novelty After comparing all the methods, the difference in our model was we used CNN. It is designed to process Pixel Data. These are capable AI and image processing systems that make use of deep learning to carry out important and useful tasks. It uses a system that is like the Multilayer Perceptron which has the function of reducing processing requirements. Moreover, due to this, we have an accuracy of more than 98% always, and because of this in our model, we get more accurate results. The accuracy can be further scaled up depending on the Neural Net and the efficiency of it.

3 Methodology The process flow is depicted below (see Fig. 1). The Convolution Neural Network performs the process of feature extraction and prediction of sign languages.

3.1 Data Pre-processing A distinct data set was created separately for both training and test cases (see Fig. 2). For purpose of getting higher accuracy, around 12,845 images were collected for training. The images were segregated into 27 classes—‘A’ to ‘Z’ and an extra ‘0’ class was assigned, a blank image. The images had different dimensions and colors, so for ease of use, they all were converted into 310 × 310 Pixels. Furthermore, using OpenCV, they were converted into Black and White and now a Gaussian Blur along with Binary Inversion Layer was applied for ease of interpretation. Algorithm: Step 1: Import NumPy, CV2 libraries and set Minimum Value. Step 2: Define function path and set color of the frame, give Gaussian Blur, and create an Adaptive threshold with Gaussian, Binary, Inversion framework. Step 3: See the results and use the Return function. Fig. 1 Methodology

Collection of Data

Data PreProcessing

Training and Testing Samples

Prediction of Sign

Feature Extraction

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Fig. 2 Sample data

3.2 CNN Architecture The CNN architecture is divided into two parts: • A Convolution Tool is used to isolate and recognize the image’s various characteristics for evaluation in a process known as “Feature Extraction” [7]. • A Fully-Connected Layer predicts the image’s class using the convolution method’s output and the previously obtained information. Convolution Layers The CNN design is made up of Convolutional layers, Pooling layers, and Fullyconnected (FC) layers [8]. In addition to these layers, there are 2 more important attributes namely—The dropout layer and the Activation function. (1) Convolution Layer a. The first layer elicits the various qualities from the input photographs. In this layer, an arithmetic operation of the Convolution layer and a filter of size M X M is done. The filter is placed over the input image, the Filter’s Dot Product, and the Part of the Images over the Filter. b. This is subsequently used as an input to further layers, allowing humans to comprehend a variety of distinct qualities [1, 9]. (2) Pooling Layer a. This is the layer that succeeds the Convolution layer. Its main purpose is to reduce the size of the Convolved Feature Map, which in turn helps us to lessen the computational costs. This entails removing the linkages between layers and runs automatically on each feature map. There are several pooling approaches that may be utilized, depending on the strategy. Sum Pooling calculates the components’ combined sum in the specified segment [1, 2]. (3) Fully-Connected Layer (FC)

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a. This layer consists of weights and biases accompanied by neurons. The input picture for the FC layer is flattened and sent to the previous layers. The arithmetic functional operations are then applied to the compressed vector after it has passed through a few further stages. The modus operandi begins at this point [1, 9]. (4) Activation Function a. They are one of the foremost parameters of the CNN architecture. Its basic function is to decide which information must be fired in the forward direction and which should not. b. It provides a way to put non-Linearity in the network. The ReLU, which is utilized here, is one of numerous frequently used activation functions. The Sigmoid and SoftMax functions are chosen for binate models, while SoftMax is commonly employed for various class classification [1, 2]. (5) Dropout Layer a. Overfitting is caused when the features are coupled in a good way. In this phenomenon, the Model works too well on the given Training Dataset, but fails to obtain the same accuracy when placed in a new Dataset [1, 9].

4 Implementation The project is divided mainly into 2 parts: 1. Creating and training the model 2. Deployment.

4.1 Creating and Training the Model Processing Data At the start, we import all the required libraries (as seen in Fig. 3).

Fig. 3 Importing libraries [10, 11]

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The process starts by processing the Image and keeping the Model Data ready for use. This is done by getting access to the folder, where the Training and Testing Data are kept separately and imported using the OS module. The dataset is now ready for the model, but we’ll supplement it with new and varied data using ImageDataGenerator from Keras (TensorFlow), so that the model can train and test itself. Creating the Model The sequential approach was chosen, because it works best with single input and single output models. Moreover, it’s the simplest to comprehend and apply. The Conv2D class will then be used to create a basic Convolutional Neural Network on the photos. The parameters used in Conv2D are filters, kernel size, input size, strides which is by default 1, and padding which is also by default True. Max pooling is then used to lessen the spatial dimensions of the yielded size. Once this process is completed, it is required that the Neurons take input and give the output. For this purpose, a Dense Layer is added, which has a dropout of 0.2 which helps avoid Over-Fitting. The Activation function used here is ReLU. At last, the Soft-Max activation function is used to equalize a network’s output to a probability allocation across the end product. Finally, the model is built using Adam as the optimizer and categorical log loss. After this, Dataset is loaded and now the Model is ready for Testing.

4.2 Deployment The Model is now deployed, and Flask API is taken up to create a local Web App to run this model. The Required Libraries are imported at first and then the process is continued. The Flask App is defined, and appropriate weights and the Model are loaded. After this, an Interface is created, so that the uploaded Images are taken and with the help of our Model, it gives the predictions. The Image is processed and then the Function-model_predict is defined. Finally, a command to run on the local server is given.

5 Results and Discussion Data Fitting and Data Training are performed on 10 Epochs, the accuracy, and losses of each were found out. We now try to fit the data. After that, Epochs are carried out and the Accuracy and Val_Accuracy is calculated for each Epoch. After evaluating the trained model with the Test Images, it was found out that 99.18% accuracy was achieved.

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Fig. 4 Class-wise classification report

5.1 Class-Wise Classification Report The precision, recall, f1-score, and support are calculated. The accuracy is then predicted, and Micro–Macro averages are also calculated (as seen in Fig. 4).

5.2 Class-Wise Heat Map The Class-wise division is done using the heat map and each division is assigned a color depending on the class from top to bottom (as seen in Fig. 5).

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Fig. 5 Class-wise heat map

5.3 Loss and Val Loss Per Epochs After calculating and dividing the Class-wise Heat Map, the Loss and Val_loss are calculated per epoch. The data is shown for each epoch and the same is plotted (as seen in Fig. 6). Fig. 6 Loss and Val loss versus epochs

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Fig. 7 Accuracy and Val accuracy versus epochs

5.4 Accuracy and Val Accuracy Per Epochs After calculating and dividing the Class-wise Heat Map, now the Accuracy and Val_ Accuracy is calculated per epoch. The data is shown for each epoch and the same is plotted (as seen in Fig. 7).

6 Deployment on Web App Using Flask The Model is first deployed using Flask, after that the image is uploaded from our dataset. After the uploading is done, the Image is further selected (as seen in Figs. 8 and 9) for predicting the outcome using our Model.

Fig. 8 Image selected for prediction

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Fig. 9 Result

7 Conclusion We have developed a model, which predicts hand signs of English alphabets using CNN with a sequential model. We start by collecting images and converting them to 310 × 310 pixels. Further, by leveraging OpenCV, the images are converted into black and white images. These black and white images are fused with a gaussian blur filter and binary inversion, which aids the model to become easier for the interpretation of hand signs. Furthermore, we use CNN layers on our model to train and test the dataset. Two different sequential models are created, while the first one shows overfitting and less accuracy to avoid this, we adopt our second model which produces significantly improved results by using dropout and an accuracy of 99.18% without any overfitting. The next step is to plot graphs of loss and accuracy versus epochs to check the overfitting. Lastly, we use the Adam optimizer algorithm with categorical cross-entropy as the loss function to compile the model. The saved model is then deployed on a web app using Flask along with bootstrap used for the frontend development. Finally, we were able to predict the alphabets accurately. Our CNN model was quite efficient and successful based on testing. The model was deployed, as a web application making it user-friendly, providing a seamless experience, and easily accessible. Acknowledgements The authors are grateful to the VIT Management for the encouragement and the resources provided for completing this project. They helped us providing a platform and an environment to go ahead with the work.

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References 1. Huang J, Zhou W, Li H, Li W Sign language recognition using 3D convolutional neural networks. https://ieeexplore.ieee.org/abstract/document/7177428 2. El Badawy M, Elons AS, Shedeed HA, Tolba MFArabic sign language recognition with 3D convolutional neural networks. https://ieeexplore.ieee.org/abstract/document/8260028 3. Anantha Rao G, Syamala K, Kishore PVV, Sastry ASCS Deep convolutional neural networks for sign language recognition. https://ieeexplore.ieee.org/abstract/document/8316344 4. Bantupalli K, Xie Y American sign language recognition using deep learning and computer vision. https://ieeexplore.ieee.org/abstract/document/8622141 5. Moklesur Rahman Md, Shafiqul Islam Md, Hafizur Rahman Md, Sassi R, Rivolta MW, Aktaruzzaman Md New benchmark on American sign language recognition using convolutional neural network. https://ieeexplore.ieee.org/abstract/document/9067974/authors#authors 6. Kadhim RA, Khamees M Real-time American sign language recognition system using convolutional neural network for real datasets. https://pdfs.semanticscholar.org/d062/c6528ac21eba 5ede54208072a63e7a782225.pdf 7. Basic codes for deep learning models. https://github.com/fchollet/deep-learning-models 8. Naik K Deploying of deep learning models using flask. https://www.youtube.com/watch?v= CSEmUmkfb8Q&t=323s 9. Vasudev R Understanding and calculating no. of parameters in CNN. https://towardsdatascie nce.com/understanding-and-calculating-the-number-of-parameters-in-convolution-neural-net works-cnns-fc88790d530d 10. Sequential Class using Keras. https://keras.io/api/models/sequential/ 11. CNN using tensor flow. https://www.tensorflow.org/tutorials/images/cnn 12. Parekh M A brief guide to convolution neural networks. https://medium.com/nybles/a-briefguide-to-convolutional-neural-network-cnn-642f47e88ed4

Development of LoRa-Based Weather Monitoring and Lightning Alert System in Rural Areas Using IoT Ome Nerella and Syed Musthak Ahmed

1 Introduction Emergency alert systems are quite essential to reduce losses during natural disasters, which might be caused due to storms, lightning and bad weather conditions. According to NCRB Annual Report, more than 2500 people in India are killed due to thunderstorm and lightning. The rate of deaths are increasing year by year in various districts of our country mainly due to climatic changes. In May 2018, strong dust storms, thunderstorms and lightning wreaked havoc on Telangana, Punjab, Rajasthan, Uttar Pradesh and Uttarakhand resulting in thousands of injuries and fatalities. Because of the presence of large trees and water bodies, rural and forested regions are particularly vulnerable. People labouring in fields in rural regions account for the bulk of lightning casualties. Lightning is a common cause of power outages and forest fires. It may also wreak havoc on computer systems and communication systems as well as cause planes to crash. The Lightning Detection Network on the earth can detect lightning in live time. A high-density network is required in places where lightning strikes are frequent.

2 Literature Review In [1], the authors presented the risk due to lightning: their challenges in disaster compensation. They presented a survey of lightning associated fatalities to set up a disaster management system in India. Thunderstorms and lightning are two of nature’s most powerful occurrences. The capacity of emergency personnel to locate the exact location of a disaster is crucial to improve their performance by providing O. Nerella · S. M. Ahmed (B) School of Engineering, SR University, Warangal (Urban), Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_30

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comprehensive coverage of the impacted area. Lightning detection capabilities are built into the IoT devices, which are synced with the sensor network’s other devices. They are all part of a network that uses a trilateration method to locate different events in a huge data ecosystem [2]. According to single-site data of meteorological factors, such as Humidity, Pressure, Temperature, Wind direction and wind speed, Amirhossein Mostajabi et al. [3] developed machine learning algorithms to correctly anticipate close and distant lightning risks. The generated alerts are compared to datasets from lightning detection systems and the results show that the model can predict lead periods of up to 30 min with statistical significance. During some weather dangers, it will be difficult in some areas to verify and monitor key weather parameters using wires and analogue devices. Roopa et al. [4] used NodeMCU and Lab View to create an IoT-based Real-Time Weather Prediction System to overcome this challenge. This method is an excellent choice for monitoring meteorological data in a specific location and making the data accessible from anywhere on the planet. Anandharajan et al. [5] built and analysed an intelligent weather prediction module based on the highest temperature, lowest temperature and rainfall during a good sample period of a few days. Machine learning algorithms were used to make a smart prediction based on the given data. The analysis and forecast are based on linear regression, which predicts the climate for the next day accurately. Based on the dataset, an accuracy of greater than 90% is established. Machine learning techniques outperformed traditional statistical methods in recent investigations. Gopinath et al. [6] suggested a method for forecasting weather patterns and predicting rainfall using Machine Learning. Weather forecasting and prediction are based on previous dataset that have been collected and compared to present value. To forecast rainfall, the user doesn’t need a large data backup. Instead, a machine learning method was used to achieve the same results. The goal of the system was to develop and evaluate the application of machine learning to anticipate and make decisions about future weather conditions in a weather station. LoRa is a low-data-rate, long-range wireless technology that offers intriguing solutions to challenges in Embedded and IoT applications. It used low power, allows information from a variety of sensors to be gathered from far-flung sites [7]. In [8], an experiment on using LoRa communication was carried out to study the effect of environmental factors, such as sun radiation, rain, temperature and humidity. In their work, Strong RSSI signals were recorded with when the ambient temperature is between 30 and 40 °C and onboard temperature is between 40 and 50 °C. They found that there is no discernible difference in RSSI signals, when rainfall rates range between 12 and 180 mm per hour. In [9], the authors presented their work to support agricultural activities, such as plant irrigation, rain and drought monitoring using IoT and ML platform. In [10], the authors developed announcement system that converts text information into speech by incorporating devices like Microcontroller, amplifier, speaker and Talkie library in their system.

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Lightning strikes are influenced by a range of environmental conditions. Ghosh et al. [11], in their work, predicted the lightning strike on some of the most relevant characteristics, such as electric field strength, electric field grade and air pressure, their work is mainly based on lightning strikes’ influence on various environmental conditions. In [12], the researchers developed a system that gives advance warnings to people about lightning strikes via a website and alert SMS. They extended their work beyond the Vajrapaat mobile application by considering the climate data in contrast to lightning data provided by existing App. Yan et al. [13] created an ARM-based intelligent lightning monitoring system that not only counts storms but also monitors the strength and current of storm waves. They incorporated optical isolation circuit protection, a data transmission device, and the system to maintain linearity between input and output coils to manage the required current. Lopez et al. [14] proposed a method to alert about thunderstorm based on location of lightning and electric field mill sensor. It is used to create alert criterion for people and delicate equipment in order to avert risks and losses in which by strengthening the alert activation criterion, the observation of an ambient electric field was added as an additional parameter to boost the number of useful alerts issued for the site. In [15], the authors developed a protection system to save destruction of machine and system due to lightning. The objective of their research was to prevent disasters by lowering the likelihood of harm to society or wildlife. Nerella et al. [16], in their work, presented the various advance warning and alert system to detect lightning and reduce disasters due to lightning using AIoT platform. Intention of the work is to develop cost-effective lightning alert system for the people staying in rural areas.

3 Hardware Requirements 3.1 ESP 32 and Lightning Detector Figure 1a shows the ESP 32-based IoT development board. It is a low cost MCU with on-chip Wi-Fi and Bluetooth facility. It is a 30 pin breakout board, which supports various peripherals like SPI, I2C, UART, ADC and DAC etc. ESP 32 is the best choice to develop IoT and LoRa-based applications. Figure 1b depicts the Lightning detector module. The AS3935 is a fully integrated, re configurable lightning detector chip, which detects the presence and vicinity of potentially harmful lightning activity in the area, as well as the distance to the storm’s head. A lightning sensor identifies cloudto-ground and intra-cloud traffic and informs the user within a 40-km radius. This AS3935 lightning detector uses SPI/I2C protocol to communicate with Processing Unit.

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Fig. 1 a ESP 32 module. b Lightning detector

Fig. 2 a DHT11 module. b BMP 180 module

3.2 DHT11 and BMP180 The picture of the DHT11 module is shown in Fig. 2a. It consists of resistive-based humidity sensor, NTC-based temperature sensor and 8 bit MCU to measure the humidity and temperature of a atmosphere and to produce 40 bit digital output signal. The front and backend view of a Pressure sensor module is shown in Fig. 2b. The BMP 180 is really a high performance barometric digital pressure module with an accuracy of ± 0.12 hpa. It uses I2C protocol to communicate with the Microcontroller Unit.

3.3 Rain Sensor and LM 386 Audio Amplifier The Fig. 3a shows the Rain sensor module. It is used to measure the rain intensity level in a particular area. This module provides two different forms of output (Analog and Digital). The picture of LM 386 audio amplifier is shown in Fig. 3b, which is used to amplify the DAC signal. ESP 32 DAC GPIO pin is connected to audio amplifier for audible announcement on the speaker.

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Fig. 3 a Rain sensor module. b LM 386 audio amplifier

Fig. 4 a Front and back end view of LoRa module. b Speaker

3.4 LoRa Ra 02 Module and Speaker The picture of LoRa module is shown in Fig. 4a. LoRa (Long Range) is a wireless communication protocol based on the spread spectrum modulation technique, which is used for low power, low-data and Long-Range applications. LoRa Ra-02 module operates with 433 MHz and it communicates with Microcontroller unit for developing Embedded and IoT applications using SPI Protocol. Figure 4b shows the 8 Ω speaker module, it takes signal from LM386 audio amplifier. Here, Talkie library is used in the programming to produce weather and lightning alert announcements on the speaker.

4 Software Requirements 4.1 Arduino IDE Arduino IDE is an Embedded Software for developing Microcontroller-based applications, which is available freely and suitable for Arduino-based development boards. Easy to add the board packages, supporting a huge number of libraries, Community support etc., are the benefits of Arduino IDE.

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4.2 ThingSpeak and IFTTT Platform ThingSpeak is an open source cloud platform, which is used to store, analyse and react on the sensor data. IFTTT (If This Then That) is a web-based automation application that allows user to connect applications and services without writing code.

5 Proposed System Design The LoRa-based weather monitoring and lightning alert system is shown in Fig. 5. The system includes weather stations, Gateway module, cloud platforms, client system and alert system. The development of LoRa-based weather station is shown in Fig. 6. It is built using the devices ESP32, lightning detector (AS3935), DHT11, BMP 180, Rain sensor, GPS module, solar panel, LM386 audio amplifier, 8 Ω speaker, LCD and LoRa RA02 module. The ESP 32 is a main controlling unit, which processes the various sensor data and sends the data to the gateway in the form of packets. Here, LoRa module with ESP 32 acts as a Gateway (LoRa to Wi-Fi or Wi-Fi to LoRa). The Gateway module is shown in Fig. 7, this module receives LoRa packet (weather and lightning data) from LoRabased weather stations, processes it and sends weather and lightning information to ThingSpeak platform and IFTTT platform. IFTTT platform then makes a voice call alert to the Gram Panchayat officials about abnormal weather conditions. The alert signal is also cautioning and alerting is also sent to rural population by means of ways as mentioned.

Fig. 5 LoRa-based weather monitoring and lightning alert system

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Fig. 6 LoRa-based weather station

Fig. 7 Gateway module

6 Results and Discussion The component assembly for Prototype testing is shown in Fig. 8. Figure 8a depicts the LoRa-based weather station. It sends weather data to gateway in the form of packets. Here, the frame structure of LoRa packet is “SHD0 D1TD2D3PD4D5D6RD7LTD8D9E” in which S, H, T, P, R, LT, E indicates start of frame, humidity, temperature, pressure, rain sensor readings, lightning type and end of frame, respectively and D0D1, D2D3, D4D5D6, D7, D8 indicates humidity, temperature, pressure, rain sensor values, lightning type value and approximate lightning strike distance, respectively. Figure 8b shows component assembly Gateway module. It receives data packet from weather stations and processes the LoRa packet for sensor readings. The received packet and sensor readings are displayed on PC as shown in the Fig. 9.

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Fig. 8 a Prototype of weather station. b Prototype of gateway module

Depending on the strength of the lightning signal, distance of strike is predicted. Then, MCU sends an alert to the rural people by way of announcement. All the sensor readings are stored in Excel file using PLX-DAQ tool for further processing of weather and lightning information. Experiment is carried out under different weather conditions and the results are recorded in ThingSpeak platform. The various parameters considered are temperature, humidity, pressure, rain and lighting. The Sensor Reading Recorded is shown from Figs. 10, 11, 12, 13 and 14 and which are represented in a graphical way

Fig. 9 Weather and lightning data on PC at the gateway

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on ThingSpeak platform. Temperature readings recorded on ThingSpeak platform during normal and cloudy day are shown in Fig. 10. Humidity and pressure readings are recorded, it is observed that humidity and pressure readings are high during cloudy weather compared to normal weather conditions, which are shown in Figs. 11 and 12. The recorded Rain sensor readings are shown in Fig. 13. The observation is made on the set up on two days, 16th April 2022 (hot day) and cloudy day (4th May 2022). The rain sensor gives a low value on a rainy day and high value otherwise. The Lightning signal recorded on ThingSpeak platform is shown in Fig. 14. The reading is HIGH value when lightning occurs, else it is LOW value. Artificially, lightning signal was created on 26th March 2022, 16th April 2022 and 4th May 2022.

Fig. 10 Temperature readings on ThingSpeak platform

Fig. 11 Humidity readings on ThingSpeak platform

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Fig. 12 Pressure readings on ThingSpeak platform

Fig. 13 Rain sensor readings on Thingspeak platform

Depending on the lightning intensity created, the detector estimated the lightning range (lightning distance) as 10 km on 26th March 2022, 1 km on 16th April 2022 and 1 km on 4th May 2022 in ThingSpeak platform, which are shown in Fig. 14.

7 Conclusion and Future Scope IoT and LoRa-based system deployment for weather monitoring and lightning alert system is essential in rural areas to warn people about disasters due to bad weather and lightning conditions. Expensive lightning alert systems are deployed in few states, where the alerting is done through SMS, Mobile Apps and via Television channels with the assistance of State Disaster Management Authority (SDMA) and END (Earth network data). In spite of these efforts to alert the rural people, there is still deficiency in reaching them due to lack of literacy, poor network facility and/or unawareness of lightning-related Apps. The proposed system is one added

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Fig. 14 Estimated lightning range readings on ThingSpeak platform

solution to overcome these limitations. Here, low cost, easy deployable model is developed to alert about weather and lightning. ESP 32 is used as a processing unit at weather station and Gateway, in which Arduino programming is used to perform these functionalities. The present work is a model developed to alert rural people to save from natural disaster. This work can be extended further by adding intelligence to the system. 1. Lightning Emulator may be added to randomly create the lightning system. 2. Apply Machine Learning to study weather conditions and predict the lightning occurrence. Acknowledgements I wish to thank the Government of India for providing opportunity to do the Ph.D and awarding the NFOBC Fellowship Award (Ref no. 190510183560) based on UGC NET2019 Merit. I would like to thank my supervisor Dr. Syed Musthak Ahmed for all his help and advice with this Research work.

References 1. Illiya F, Mohan KM, Mani SK, Pradeepkumar AP (2014) Lightning risk in India challenges in disaster compensation. Econ Polit Weekly 23:23–27 2. Kotal SD, Roy SS, Sharma RS, Mohapatra M (2019) Development of a LowCost IoT system for lightning strike detection and location. J Electr 8:1–14. https://doi.org/10.3390/electroni cs8121512

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3. Shaik F, Sharma AK, Ahmed SM, Gunjan VK, Naik C (2016) An improved model for analysis of diabetic retinopathy related imagery. Indian J Sci Technol 9:44 4. Khandelwal K, Chahar B, Chahar PS (2022) Problems and challenges of social entrepreneurship from an entrepreneur’s perception. In: Choudhury A, Singh TP, Biswas A, Anand M (eds) Evolution of digitized societies through advanced technologies. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-19-2984-7_6 5. Ahmed SM, Kovela B, Gunjan VK (2021) Solar-powered smart agriculture and irrigation monitoring/control system over cloud—an efficient and eco-friendly method for effective crop production by farmers in Rural India. In: Proceedings of international conference on recent trends in machine learning, IoT, smart cities and applications. Springer, Singapore, pp 279–290 6. Gopinath N, Vinodh S, Prashanth P, Jayasuriya A, Deasione S (2020) Weather prediction using machine learning and IOT. Int J Eng Adv Technol 9:1–5 7. Kaushik A (2022) The usage of technology by the senior citizens: opportunities and challenges. In: Choudhury A, Singh TP, Biswas A, Anand M (eds) Evolution of digitized societies through advanced technologies. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-19-2984-7_7 8. Elijah O, Rahim SKA, Sittakul V, Al-Samman AM, Cheffena M, Din JB, Tharek AR (2021) Effect of weather condition on LoRa IoT communication technology in a tropical region: Malaysia. In: IEEE access, vol. 9, pp 72835–72843. https://doi.org/10.1109/ACCESS.2021. 3080317 9. Ahmed SM, Kovela B, Gunjan VK (2020) IoT based automatic plant watering system through soil moisture sensing—a technique to support farmers’ cultivation in rural India. In: Advances in cybernetics, cognition, and machine learning for communication technologies. Springer, Singapore, pp 259–268 10. Kumar L, Kamthe M, Mohakud N, Sharma P, Yadav KS (2021) Text to speech using Arduino. Int J Eng Sci Comput 11:27559–27562 11. Bouazzi I, Zaidi M, Usman M, Shamim MZM, Gunjan VK, Singh N (2022) Future trends for healthcare monitoring system in smart cities using LoRaWAN-based WBAN. Mob Inf Syst 2022:1526021 12. Ahmed SM, Sheshikala M, Maurya A, Gunjan VK (2020) Sensory-motor deterioration in older drivers and their amelioration through various training strategies: a study. In: Gunjan V, Senatore S, Kumar A, Gao XZ, Merugu S (eds) Advances in cybernetics, cognition, and machine learning for communication technologies. Lecture notes in electrical engineering, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-15-3125-5_31 13. Merugu S, Kumar A, Ghinea G (2023). Dementia and intellectual disability management system: a conceptual study. In: Track and trace management system for dementia and intellectual disabilities. advanced technologies and societal change. Springer, Singapore. https://doi. org/10.1007/978-981-19-1264-1_2 14. Merugu S, Kumar A, Ghinea G (2023) A review of some assistive tools and their limitations. In: Track and trace management system for dementia and intellectual disabilities. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-191264-1_3 15. Patel K (2013) Effect of lightning on building and its protection measures. Int J Eng Adv Technol (IJEAT) 02:182–185 16. Nerella O, Ahmed SM (2022) Advance warning and alert system for detecting lightning risk to reduce human disaster using AIoT platform—a proposed model to support rural India. In: 5th international conference on communications and cyber-physical engineering (ICCCE2022). CMR Engineering College, Hyderabad

Automatic Text Recognition from Image Dataset Using Optical Character Recognition and Deep Learning Techniques Ishan Rao, Prathmesh Shirgire, Sanket Sanganwar, Kedar Vyawhare, and S. R. Vispute

1 Introduction Text recognition is very useful to extract data from images. Medical records, bank transactions, transportation details that are available in the form of images can be stored and utilized for different purposes like analysis and research. The data from these images can be used when we are able to get the data from images. This can be done using text recognition. In this paper, we discuss the analysis performed on popular image datasets that one can use to infer and get started with text recognition from images. We described in detail which algorithms have been used most commonly and have provided the highest accuracy. We also have provided a basic idea of SVM and CNN, which are used often in text recognition for images. We further describe the methodology followed when solving a machine learning problem. The paper also shows our analysis on handwritten digits dataset using a variety of algorithms and comparative analysis of their accuracy. Finally, we have used tesseract engine to read data from a form and store it in a database for future use. Major challenge that we faced during our research was to find collection of analysis performed on the described datasets. Extraction or generation of text data from images that contain text has become possible because of Optical Character Recognition (OCR). It is also used to detect text simultaneously when someone is writing, which is also called pen computing. Computers cannot directly process the textual data in the images or from online writing. So, OCR systems help extract data and make it available for processing to I. Rao (B) · P. Shirgire · S. Sanganwar · K. Vyawhare Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India e-mail: [email protected] S. R. Vispute Computer Department, Pimpri Chinchwad College of Engineering, Pune, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_31

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the computer systems in a format that can be further used for a variety of purposes. OCR has applications in several sectors like medical, banking, transport, and many more. It can be used for recognizing doctors’ prescriptions for dispensing medicines directly. It can be used to identify text written on bank cheques and also on insurance documents. It can be used for identifying a vehicle’s number plate in case of speeding or an accident. It can be used for many data entry-related applications. It is also being used in retrieving text from historical documents [1]. Research on OCR systems began even before the invention of computers [2]. However, devices used for OCR were not efficient. The history of OCR systems is well stated in [2, 3]. The research on OCR systems, models, and tools has grown profoundly in the past couple of decades. Paper [3] shows a list of papers that have done research on OCR and the number of times they have been cited. Several challenges occur when we perform OCR. Printed text is simpler to extract as they are uniform and consistent. However, handwriting recognition is quite difficult. Every person has different handwriting. The handwriting of a person is not very consistent. It changes from time to time, sometimes within the same document. Cursive writing brings some more difficulties as it is difficult to break the words into letters. This has been discussed in [4]. The use of different writing materials and different writing surfaces can also affect the accuracy of OCR systems. Printed Character Recognition and Handwritten Character Recognition are two types of OCR [5]. PCR is the recognition of machine-printed text. It is relatively easier than HCR. HCR is further classified into Online and Offline systems [5]. Online HCR provides better accuracy than Offline systems, because they capture time-based information of strokes like speed and direction of the stroke [2]. Offline HCR is difficult, because most of the time, a lot of preprocessing is required before actually being able to extract the text from the document. In our paper, we look at the research done in OCR over the past few years. We represent a basic model that is often used when performing OCR. We discuss the models that have been used several times and introduce datasets that one can use to do analysis when starting with OCR. We look at a popular tool for OCR called Tesseract. Finally, we represent our analysis for handwritten digit dataset and also results of using Tesseract for analyzing a form and storing results in a database.

2 Literature Review Classifiers have been proposed as a method for performing Intelligent Character Recognition (ICR) in paper [6] and the results are then transferred to excel spreadsheets. An accuracy closer to 95% was achieved for special characters and alphanumerics when the outcomes of these methods were demonstrated. Here, the author has used a dataset of around 200 samples, with an additional 100 for special characters. Features help to determine class labels and for this, supervised learning is needed. Hence, it is used in their proposed system.

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In paper [7], a hybrid model has been created for handwritten digit recognition from datasets that combines an SVM with a strong convolutional neural network. The proposed hybrid model in this paper incorporates the core properties of both classifiers. CNN acts as an automated function extractor, and SVM acts as a binary classifier in the proposed hybrid model. For training and evaluating the algorithm used in the proposed model, the MNIST dataset of handwritten digits is used. The paper’s hybrid model achieves 99.8% identification precision on the MNIST handwritten digit dataset, demonstrating its effectiveness. Paper [8] introduces Handwritten Word Recognition (HWR) and its importance in the preservation of important documents and textual data. It describes types of Handwriting Recognition (HR) as Offline and Online Handwriting Recognition. It represents analysis on the MNIST dataset using algorithms like K-Nearest Neighbors (KNN), SVM specifically LinearSVC (Support Vector Classifier), and Neural networks. The neural networks make use of Backpropagation. Distilling Gated Recurrent Unit (GRU) was used for Unconstrained Handwritten Text Recognition (HTR). It solved the manuscript horizontal text problem. At last, they present the advantages and disadvantages of the traditional techniques of HR like Character Extraction and Character Recognition along with the advantages and disadvantages of the different algorithms used for analysis in the general sense. They also discussed the merits and demerits of Backpropagation and GRU. The author in the paper [9] demonstrates the use of TensorFlow in analyzing the MNIST dataset. A simple neural network is created for this purpose and Softmax Regression (SR) is used to classify expression features using probability. The mathematical formulation for the same has also been discussed. They obtained an accuracy of around 92% using neural networks. Paper [10] discusses and summarizes various algorithms that are tested against the MNIST dataset and EMNIST dataset, one of which is CNN. Some more deep learning techniques have also been used for the same. MNIST is a repository that has a lot of digit images that are used to train ML and DL models used for processing images. EMNIST is a dataset that contains a combination of handwritten digits as well as letters. It is structured quite similarly to the MNIST database. The best result obtained for the MNIST dataset was obtained when we used CNN with a couple of convolutional layers, a dense layer, and DropConnect regularization. In the case of the EMNIST dataset, combining Markov models and CNN gave an accuracy of 95.44%. The authors of the paper [5] analyzed numerous approaches or methods used by different researchers for HCR. The authors have mentioned a method that could be programmed to recognize various handwriting styles as well as some valuable data, such as EMNIST and IAM. This paper examines handwriting character recognition in depth. This study/review paper would discuss the different methodologies that have been used in this area to date, as well as their benefits, drawbacks, and accuracy rate. In paper [11], a preprocessing strategy including convolutions with specific kernels assisted in boosting the accuracy of the Tesseract 4.0 OCR engine. A reinforcement learning model was used to guide the image preprocessing technique that

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was also adaptive. The goal was to keep the edit distance and actual value of the recognized text as small as possible. It was found that it increases the F1 score to 0.729 from 0.163, while character level accuracy can increase from 0.134 to 0.616.

3 Algorithmic Survey 3.1 SVM SVM is an algorithm that belongs to the supervised machine learning category of machine learning techniques. Supervised Learning is a methodology that utilizes a labeled dataset to train the algorithm. We map an input to output through a certain function. This function is the model that we try to build by training with known samples of data. SVM is a more reliable classification method as it can handle both continuous and categorical variables [12]. The SVM algorithm is used in character recognition as an optimal classifier as there can be uncertain conditions. When a single character is written in various fonts, handwriting, or text size, uncertain conditions can arise. SVM can be used effectively as a classifier in these situations for character recognition [13]. Each dataset item in SVM is represented by a plot in an N-dimensional space, where N is the number of features included in the data. Next, select the ideal hyperplane for data separation. A hyperplane is a flat, n-1-dimensional subset of an ndimensional Euclidean space that separates it into two parts. A kernel is used to implement the SVM algorithm, which converts an input data space into the appropriate format. A low-dimensional input space is transformed into a higher-dimensional space using the SVM kernel. Kernel, in its simplest form, adds additional dimensions to issues that cannot be separated to make them separable. SVM employs three types of kernels: polynomial kernels, linear kernels, and radial basis function kernels. In terms of function expression, popularization, and learning efficiency, it outperforms the classic artificial neural network. The theoretical and mathematical foundations of the SVM are tight, avoiding the empirical components in neural network implementation. VM is built on SRM (structural risk minimization) principle and has a high generalization capacity.

3.2 Convolutional Neural Network (CNN) CNN is a Deep Learning algorithm in which an image is used as input, and weights and biases are then assigned to various objects of images to differentiate them from one another. It is majorly used for image recognition. Figure 1. represents CNN and its layers. CNN consists of three layers:

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Fig. 1 Structure of convolutional neural networks

1. Convolution: This layer is used to calculate the output volume by taking the dot product of all filters and the picture patch. 2. Pooling: This layer is added into the covnets regularly. Its main goal is to reduce the volume, which speeds up the calculation, saves memory, and reduces overfitting. Pooling layers are classified into two types: maximum pooling and average pooling. 3. Fully Connected: This layer of the neural network is a conventional one that receives input from the layer above, computes class scores, and outputs a onedimensional array with the same number of classes as the layer above.

4 Datasets 4.1 MNIST It is one of the most popular datasets for those who are just getting started with image processing. It comprises photos of handwritten digits. It is made possible by the American Census Bureau and high school students in the United States. A total of 70,000 photos are included in the dataset; 60,000 are used for training; and the remaining 10,000 are used to test the model that was developed after training. Each image is a 28*28 pixels image. The pixel values range from 0 to 255. It is described in detail in [10]. A sample image is shown in Fig. 2.

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Fig. 2 MNIST dataset example

4.2 EMNIST Extended MNIST is a variant of the complete NIST dataset [14]. It is made out of handwritten figures and letters. As mentioned in [10], EMNIST is further subdivided since there are several datasets accessible within this core dataset. The amount of classes and data provided varies per subtype. It includes capital and tiny characters, as well as numerals ranging from 0 to 9. In Fig. 3, an example image is displayed.

Fig. 3 EMNIST dataset example

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4.3 IAM It is a collection of handwritten texts. Version 3.0 of the dataset contains 1539 pages of scanned text from 657 authors. The text is written in a continuous form. It is composed of words and uses cursive characters. They are written in paragraphs. The database comprises unrestricted handwritten text [15]. A sample image is shown in Fig. 4.

5 Methodology The basic workflow, described in Fig. 5, is described in a stepwise manner as follows: Step 1: Define the problem of machine learning. It could be a classification problem, a clustering problem, or something else. Define the resources from which the data will be accessed in order to build the model.

Fig. 4 A sample image from IAM dataset [15]

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Fig. 5 Basic workflow

Step 2: There may be multiple sources of the information needed to solve the issue. It is necessary to collect all of this information, so that it can be used for model training and additional processing. Different sources produce data in various formats. According to the requirements, this data is transformed into a standard format. Step 3: It’s possible that the data collected isn’t always in the right format for us to use it immediately for model training. Missing or undefinable values in the data must be handled properly in order to prevent it from impairing the performance of the model we are developing. Bias can result from different values in various columns having different scales. Therefore, methods like normalization and scaling are used. Step 4: In order to better comprehend and evaluate data, we can select the most important aspects among all of the features by using visualization. Finding relationships in our dataset is helpful. Histograms and scatter plots are helpful techniques.

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Step 5: It is necessary to choose features that are unrelated to one another. A stronger link between the feature and the target property is also required. A certain feature may occasionally be changed to produce a more beneficial feature. It is referred to as feature engineering. Step 6: Training and testing data are randomly divided from the processed data. To develop the rules for predicting accurate values for the test data, the chosen model is trained on training data. Step 7: To see if the trained model can accurately predict values for previously unobserved data, the trained model is put to the test using testing data. To verify that the model is valid, many metrics including recall, accuracy, and precision are used. Step 8: The deployment of the model is complete. It can now be put to use for its intended function.

6 Accuracy Measured Used 6.1 Confusion Matrix It is a two-by-two matrix that evaluates the performance of any classification model. It describes the set of positive and negative samples that were correctly or wrongly categorized. Metrics like accuracy, recall, precision, AUC-ROC, and other popular accuracy metrics for machine learning algorithms can be computed using the confusion matrix. Recall =

TP TP + FN

(1)

Precision =

TP TP + FP

(2)

Accuracy =

TP + TN P+N

(3)

where TP TN FN FP P N

True Positive, when the actual value is yes and the predicted value is also yes. True Negative, when the actual value is no and the predicted value is also no. False Negative, when the actual value is yes but the predicted value is no. False Positive, when the actual value is no but the predicted value is yes. Positive, it is the set of all records whose value is yes. Negative, it is the set of all records whose value is no.

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7 Analysis of Accuracy of Algorithms In Table 1, we represent the analysis of algorithms on different datasets. First, we analyzed the handwritten digits dataset and performed preprocessing on the data. Then we applied the above-mentioned algorithms to obtain the accuracy of each algorithm on that dataset. It contains 8*8 pixel images. A sample image of the dataset is shown below in Fig. 6. Among the different algorithms used, SVM had the best accuracy. It’s comparison to other algorithms used is represented in the graph below in Fig. 7. The accuracy of the neural network on the MNIST dataset is indicated in the second entry in Table 1. The MNIST dataset is included in the TensorFlow package. It has a total of 60,000 photos for training and 10,000 photos for testing. The algorithm’s accuracy is displayed below in Fig. 8. Figures 9 and 10 depict the use of the tesseract OCR engine on a form. Figure 9 depicts the input to the tesseract, whereas Fig. 10 depicts the output from the tesseract. Each word detected by the engine is displayed as a red rectangle encircling the word, with extracted text above the word. The form’s text is extracted and saved in a database. This is one of the most popular use cases of the OCR tool tesseract.

Table 1 Analysis of algorithms on different datasets Sr.No

Dataset

Algorithms used

Best algorithm

Accuracy of the best algorithm

1

Handwritten digits dataset

Logistic regression, Decision tree, KNN, XGBoost, Random forests, SVM

SVM

99.32

2

MNIST

Neural network

Neural network

96.71

Fig. 6 Sample image of the dataset available in sklearn

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Fig. 7 Comparison of algorithms on handwritten digits dataset

Fig. 8 Accuracy of the neural network on MNIST

Fig. 9 Input to tesseract OCR

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Fig. 10 The output of tesseract OCR

8 Conclusion In this paper, we looked at OCR’s foundations and its research. We concentrated on the answers offered by many authors on common handwritten datasets and investigated some of the prominent models used for this purpose. We’ve also included several datasets that might be useful when getting started with OCR. OCR’s general methodology has also been questioned. Finally, we examined several datasets using various machine learning and deep learning techniques. On the Handwritten digits dataset, SVM has the highest accuracy of 99.62%. The sole technique utilized to evaluate the MNIST dataset was a neural network, which provided an accuracy of 96.71%. We also demonstrated the usage of an OCR tool on a form. We concluded that OCR has numerous potential uses and that further study is potential in this sector.

References 1. Vamvakas G, Gatos B, Stamatopoulos N, Perantonis SJ (2008) A complete optical character recognition methodology for historical documents. In: The Eighth IAPR international workshop on document analysis systems, pp 525–532. https://doi.org/10.1109/DAS.2008.73 2. Islam N, Noor IZ (2016) A survey on optical character recognition system. ITB J Inf Commun Technol 3. Jamshed M, Maira S, Rizwan K, Mueen U (2020) Handwritten optical character recognition (OCR): a comprehensive systematic literature review (SLR). IEEE Access 8:142642–142668 4. Utkarsh D, Pranjal R, Manish S (2017) Cursive handwriting recognition system using feature extraction and artificial neural network. In: IRJET 2017, vol 04, no 03, pp 2202–2206, e-ISSN: 2395–0056

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5. Vinkit B, Mohit B, Sujit K, Chalak G (2020) A review on handwritten character recognition methods and techniques. In: 2020 international conference on communication and signal processing (ICCSP) 6. Renuka K, Soubhik D, Paritosh M (2017) Supervised machine learning in intelligent character recognition of handwritten and printed nameplate. In: International conference on advances in computing, communication and control (ICAC3), pp 1–5 7. Savita A, Amit C (2020) Hybrid CNN-SVM classifier for handwritten digit recognition. Int Conf Comput Intell Data Sci 167:2554–2560 8. Prem V, Anmol P, Asish T (2020) A comparative study of handwriting recognition techniques. In: International conference on computation, automation and knowledge management (ICCAKM), pp 456–461 9. Hao Z (2020) An off-line handwriting recognition employing tensorflow. In: International conference on big data, artificial intelligence and internet of things engineering (ICBAIE), pp 158–161 10. Alejandro B, Yago S, Pedro I (2019) A survey of handwritten character recognition with MNIST and EMNIST. Appl Sci. https://doi.org/10.3390/app9153169 11. Dan S, Elena C, Costin-Anton B (2020) Improving the accuracy of tesseract 4.0 OCR engine using convolution-based preprocessing. Symmetry. https://doi.org/10.3390/sym12050715 12. Pranit P, Bhupinder K (2020) Handwritten digit recognition using various machine learning algorithms and models. In: IJIRCST, vol 8, no 4, pp 337–340, ISSN: 2347–5552. https://doi. org/10.21276/ijircst.2020.8.4.16 13. Drewnik M, Pasternak-Winiarski Z (2017) SVM Kernel configuration and optimization for the handwritten digit recognition. In: Saeed K, Homenda W, Chaki R (eds) Computer information systems and industrial management. CISIM, pp 87–98 14. Cohen G, Afshar S, Tapson J, Van S (2017) EMNIST: an extension of MNIST to handwritten letters. arXiv 15. Marti UV, Bunke H (2020) An English sentence database for off-line handwriting recognition. Int J Doc Anal Recogn. https://doi.org/10.1007/s100320200071

Securing IoT Networks Using Machine Learning, Deep Learning Solutions: A Review Vivek Nikam and S. Renuka Devi

1 Introduction The Internet of Things (IoT) has been a buzz these days and its impact can be seen in everything from the way people shop and travel to how businesses track inventory. Simply put, the Internet of Things is a concept in which any device (with an on/off button) is connected to the internet and other online devices [1]. The IoT serves as a vast network of connected people or objects to share and collect data. It consists of a huge number of objects of different sizes and shapes, i.e. from smart ovens to cook food automatically for a given time length, to self-driving vehicles that can detect people or objects on the way, and wearable gadgets to track how many steps you took in a day and monitor your heart rate and suggest diet and workout plan accordingly. These devices have in-built sensors connected to the IoT platform, where data is collected from various devices and analytics are applied to share the most important details to meet unique needs. These powerful platforms can easily find important information and ignore the one which is not needed. They can use such data to make suggestions, detect patterns, and possible problems before they take place. For example, if an automobile company needs to know about the bestselling components like alloy wheels or leather seats, companies can analyse and— • Detect the most popular areas in a showroom, where customers spend the most time using sensors, • Get into the sales data available to know the bestselling components, • Align sales information with supply to avoid situations like “out of stock”. This way, automobile companies can make smart decisions by using connected devices on the basis of real-time data about the bestselling components. It can help V. Nikam (B) · S. R. Devi Vellore Institute of Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_32

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them to save money and time. It makes the process even more efficient with the details offered with cutting-edge analytics. You can easily automate some tasks with smart systems and objects, especially when these are time-consuming, repetitive, or even risky. An IoT network gathers various interconnected devices working with other devices without human intervention, such as smart appliances, wearable tech, and self-driving cars. IoT network is associated with most network infrastructures like 4G LTE and 5G to meet the IoT resource needs. IoT is the most common application of 5G networks as the existing mobile networks are still trying hard to continue being stable in all areas. A temperature-controlled room, automatic light switch, and other connected devices in a school cafeteria or an office setting are vulnerable to hacking attacks once they get internet connectivity [35]. The IoT becomes a breeding ground for attackers, if users find more new ways to exploit them in the living room or in businesses. The IoT network has now become the main concern for security. The devices are connected to the network and all the workloads and information are accessed by the network. This is how hackers can compromise the data and systems on the network. It is important for security teams and professionals to adapt to this emerging reality when it comes to protecting sensitive information and enterprise networks. The ever-rising connected IoT devices have both convenience and challenges in equal amounts [36]. • Whenever people see an open network in enterprise environments, they start connecting their devices without asking any IT professional. • There is also a lack of proper monitoring of temperature control systems in a conference room. Most enterprises may not be able to notice if anything goes wrong when devices are working fine. • There is still a lack of security and visibility on the devices in a distributed IT environment. The devices are usually deployed within the network. Some are deployed externally on online or public cloud systems. Privacy leakage, and physical, network, and software attacks are the common threats to IoT networks and services. Here are some of the common security threats. • DoS Attacks—The target server is flooded with requests to keep IoT devices from using services. Sometimes, DDoS attackers request IoT services using plenty of IP addresses and the server gets confused in choosing valid IoT devices. It is one of the most dangerous attacks of its kind. • Jamming—In this process, fake signals are sent to affect the radio transmissions of devices and deplete the energy, bandwidth, and memory resources of IoT sensors or devices, and CPUs, during the failed attempts. • Man-in-the-Middle—In this type of attack, spoofing and jamming signals are transmitted to secretly alter, monitor, and do eavesdropping on private communication through the devices.

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• Spoofing—A legal IoT device is imitated through a spoofing node like an RFID tag and medium access control address to have unauthorized access to the IoT network and launch attacks like man-in-the-middle and DoS. • Software Attacks—These attacks are very common in which mobile malware like worms, Trojans, and viruses cause financial loss, privacy leakage, degradation of network performance, and power depletion in IoT systems. • Privacy Leakage—It becomes important for IoT systems to protect user data whilst exchanging and caching data. Some caching owners sell out IoT privacy data out of curiosity about the stored content on their devices. This type of attack usually affects the wearable devices, which gather personal information of the user, such as health and location information. There will be over 75 billion IoT devices by 2030. The increasing penetration of the market introduces network administrators to new challenges. Over 60,000 devices have been infected globally by the Mirai botnet. DDoS attacks are mostly backed by IoT botnets. Traditional network monitoring approaches are not enough to cover the whole IoT network. With such a great diversity of smart devices like smart bulbs, cameras, thermostats, etc., it is important to recognize the device type. It will help apply different filtering rules to enforce security. This way, an effective machine learning model can easily detect intrusion.

2 Literature Survey To deal with IoT security issues, Craven [1] proposed a novel DL-based “Intrusion Detection System (DL-IDS)” to detect security vulnerabilities in IoT. Though it is not the first IDS, many of them lack appropriate features and dataset management and it affects precision in detection. They combined the “stacked-deep polynomial network (SDPN)” and “spider monkey optimization (SMO)” algorithms to attain proper detection. SMO chooses the right dataset features and data is classified as anomalies or normal by SDPN. DL-IDS detected anomalies like “user-to-root (U2R)” attack, “denial of service (DoS)”, “remote-to-local (R2L)” attack, and “probe attack”. This method achieved better performance in extensive analysis in terms of precision, accuracy, F-score, and recall. Shi et al. [35] presented in-depth analyses of IoT security challenges and needs, explored the major role of deep learning, and reviewed advanced research in IoT with deep learning approaches. They also compared deep learning algorithms like LSTM, RNN, DBN, CNN, and AE. They also discovered research challenges in existing investigations and defined future research directions of DM in the security of IoT devices. Authors in [6] conducted a survey on the development of Industrial IoT (IIoT) security systems deployed on blockchain and 5G. They provided a comprehensive review of cutting-edge security implementation and further explored the product lifecycle of IoT. They presented several faults and virtues in deep learning and machine

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learning algorithms on “fog architecture” for security. The security algorithms they have discovered can be helpful in several IoT security challenges and paved the way to implement technologies like edge computing, fog computing, blockchain and 5G and presented their applications for smart environments. A lot of security systems have been introduced to deal with resource limitations and scalability issues in IoT, for example, intrusion detection and firewalls. Authors in [36] conducted a survey on literature related to the “Intrusion Detection System (IDS)” from 2015 to 2019 for IoT security. They explored several analysis and placement strategies of IDS in IoT infrastructure. They found several attacks in IoT as well as DL and ML techniques to detect IoT attacks. One major issue with deep learning is that it cannot be deployed in resourceconstrained IoT devices as it needs more power, storage, and computation capabilities. Authors in [24] proposed a Fog-based framework for IoT attack detection with a high, robust, and distribution detection rate using deep Learning. This framework uses an “attack detector” on “fog nodes” due to its high computation, closeness to edge devices, and distributed nature. They compared 6 DL models to choose the top-performing DL model. They used 5 datasets to evaluate all DL models and each dataset consists of several attacks. It is observed that the “long short-term memory model” performed better than the rest of the DL models. It is best in detection accuracy and response time and achieved a detection accuracy of 99.96% and detection rate of 99.97% in “binary classification” and a detection accuracy of 99.65% in “multi-class classification” to detect various cyber-attacks.

3 Discussions 3.1 The Role of Machine Learning in the Security of Internet of Things Devices The commercial, economic, as well as societal implications of the Internet of Things will be far. Participating networks in IoT networks are typically very resource-hungry, making them convenient targets for criminals. As a result, several initiatives have been undertaken to address security and privacy problems in the IoT, particularly with the use of cryptographic techniques. Smarter IoT devices and networks are possible with the help of Deep Learning and Algorithms. Authors in [14] carried out an in-depth analysis of IoT network potential attacks, security requirements, as well as available security solutions. They illustrate the requirement for Deep Learning and Machine Learning solutions to contemporary security breaches. They also discuss the current DL and ML methods for addressing the various IoT network security vulnerabilities. Using ML and DL, this paper proposes a number of avenues for additional research into safeguarding the IoT.

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How Do IoT Devices Use AI to Enhance Security? An IoT device with limited memory, computation, battery, and radio bandwidth may not be able to execute latency-vulnerable and power-hungry security tasks, especially when there is a heavy flow of data. Authors in [18] investigated IoT systems and review attack models of security solutions on the basis of ML techniques like reinforcement learning, supervised learning, and unsupervised learning. MLbased access control, IoT authentication, malware detection, and secure offloading schemes are discussed in this study to secure the data. They have also discussed issues to address and implement such security systems in real-life IoT applications. The light security approaches are usually not robust against computer attacks. Hence, the authors investigate malware detection, IoT authentication, secure offloading, and access control in this study. Current security expects each learning agent to be aware of the right state and determine the quick reward for each event. Additionally, the agent ends up with poor strategies, especially when starting the learning process. A lot of current Machine Learning-based schemes have high communication and computation expenses and need a lot of training information and complex processes. This way, it is important to investigate DFW ML techniques with low communication and computation expenses, especially in cases where there is no edge computing and cloud servers. Machine and Deep Learning Methods for the Internet of Things (IoT) Security IoT platforms have become a universal phenomenon, which plays a vital role in the daily lives of people. With significant demand and accessibility for smart networks and devices, there has been a great security challenge for IoT devices. Several security measures have been applied to secure IoT. Authors in [18] conducted a survey on solutions based on ML for IoT security. Conventional techniques are not so capable with the severity of advanced attacks and technological advances. So, there is a need for a strong and up-to-date security solution for modern IoT systems. Machine Learning has witnessed great technological advancement and opened several windows for future research to deal with current and next-gen IoT challenges. To detect and recognize unusual behaviour and attacks on IoT networks and smart devices, machine learning has been very effective. The authors discuss the IoT architecture whilst reviewing machine learning approaches. This paper presents a comprehensive and modern literature review on the ML security of IoT, including its architecture. It is a detailed study of various types of attacks, categories of ML algorithms, attack surfaces with effects, and ML-based security. Additionally, the authors discuss research challenges. They cover all the papers on ML security and IoT by comparing several review papers up to 2019. There was a great rise in research on IoT security. The literature review focuses on the algorithms for IoT security to provide various insights about IoT attacks and their effects on a surface level [17].

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Securing the Internet of Things and Wireless Sensor Networks via Machine Learning The Internet of Things connects sensors, physical devices, appliances, and other objects together without any human intervention. WSNs serve as the key building blocks of the Internet of Things. Both WSNs and IoT are used as important and nonimportant applications that cover almost every part of modern life. In addition, the devices are also subject to resource limitations in such networks and they make the problem worse. Authors in [7] conducted a survey on various threats to both Wireless Sensor Networks (WSN) and IoT. Machine Learning is one of the most effective and recent approaches to deal with those challenges [3]. ML brings a lot of solutions to ensure the security of WSNs and IoT. The authors survey various next-generation ML techniques to deal with those problems. Machine learning uses various learning algorithms as a kind of AI to train devices without strict programming. Machine Learning is best for both IoT and WSNs for its simple mathematical models in their complicated environments. In addition, ML can align with the unexpected behaviour and changing dynamics of IoT and WSNs [18]. Artificial Intelligence Enhancing Internet of Things Security Internet of Things uses communication, computing, and authentication as development routes to provide various innovative solutions for various use cases. Each IoT architecture layer has various security threats because of its extensive, open-source, and resource-limiting nature. Authors in [15] surveyed AI’s capability to boost the security of IoT. They review the complexity and individuality of IoT protection and find out how AI approaches like DL and ML provide innovative capabilities to meet the security needs of IoT. They analyse the technical capability of AI to solve various security issues and come up with an overall process for IoT security. They suggest the best AI solutions deal with four common threats to IoT, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), malware attacks, intrusion, and authentication [2]. There are always new adverse effects and challenges come forward in terms of architecture, algorithm, and data, even with new security solutions for IoT. Deep Learning for Intelligent IoT The IoT is playing a vital role in shaping technological adaptation for human beings in daily lives. IoT applications range from important use cases like healthcare, and smart cities to industrial purposes and they are very diverse. Authors in [9] discuss challenges and opportunities in deep learning to make IoT devices smarter. It is important to make modern wireless networks self-sufficient and robust. IoT networks can be more autonomous and network efficient with machine learning techniques [4]. Deep learning is very costly and complex in terms of computation. Aligning deep learning with IoT is a challenge to improve overall efficiency. It is very important to keep a balance between efficiency and computational cost for the future of IoT networks. Demands for seamless integration with IoT and ML overhaul the main communication stack from physical to application layers. This research covers nine papers about modern research trends in deep learning for IoT. They discuss different

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topics like healthcare, climate change, network security, and modern network technologies apart from important challenges whilst proposing novel ideas to deal with 255 issues of its kind. Deep Learning and Big Data Technologies for IoT Security With the evolution of IoT, technology has become one of the most important parts of daily life. It has gone way beyond human intervention with interaction and communication between devices. However, security breaches pose a huge threat to IoT networks. Getting a foolproof solution is the need of the hour by combining current technologies with emerging ones to meet security issues. Shahid et al. [4] conducted a complete survey on cutting-edge IoT security, deep learning, and big data technologies [15]. A branch of machine learning, deep learning has come up with great results to detect security breaches in earlier studies. In addition, huge veracity, volumes, and variety of data are generated by IoT devices. Better data handling and greater performance can be made possible by leveraging big data technologies. In addition, they have discussed a relationship between IoT security, deep learning, and big data technologies in a comparative analysis. In addition, they came up with a thematic taxonomy with a comparison of all of the above concepts [16]. Comparing Deep Learning and Machine Learning Approaches Data-driven learning has never been so important, thanks to pervasive sensors that constantly collect huge amounts of data. Learning algorithms are based on developing techniques which advance over time. DL and ML techniques have been widely accepted in various use cases, such as financial, medical, and automotive, and they are recently entering the manufacturing segment, given the rising production demand and need for more efficient plants [19]. Machine learning algorithms are basically categorized into “supervised”, “semisupervised”, “unsupervised”, “reinforcement”, and “active” learning. Supervised learning models are important to learn a function mapping input into the output on the basis of various I/O pairs called “training data”. Supervised Learning techniques are ideal to solve regression and classification issues, whilst the output variable can be either a real value or category (i.e. “no threat” or “threat”). Unsupervised Learning models the distribution of data or a given structure to learn about data without any output variables [9]. Unsupervised learning is further categorized into association and clustering. In clustering, the inherent groups of data are discovered. In association, rules which set huge portions of data are found, such as learning temporal specifications for electric systems to differentiate between normal control operators, cyber-attacks, and disturbances accurately. Active learning focuses on limited training samples on the basis of users’ experiences, which are “omniscient” to label the given data. It is natural to deal with designing cyber-security systems, because labelling is either impossible or time-consuming in cases that were never affected by intrusion [8]. Active learning empowers machine learning with domain expert knowledge and reduces labelling efforts and improves the reliability of supervised learning at the same time for intrusion detection. A software agent is used in reinforcement learning

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who learns the right action plan over the given environment. As per the action, an agent accepts the result and maximizes its returns over time by choosing the right action [12]. On the other side, deep learning has emerged from machine learning. It learns data points and is based on “artificial neural networks”. It relies on the cascade of several layers of non-linear units to extract innovative features [25–26]. The output from the earlier layer is used as input in successive layers [27]. It is also possible to classify deep learning approaches into supervised and unsupervised learning and they are mainly able to learn several deep and machine levels corresponding to various abstraction levels [5].

4 Suggestions Major challenge for IoT cyber-security is that most of the machine learning and deep learning techniques proposed here rely on first-class data for the most part. Several associated devices are connected in IoT frameworks and high-level data streaming also increases the risk of noisy and corrupt data from the networks. Hence, efficient machine learning and deep learning models are required to deal with noisy data and achieve top-quality training data [10]. With enhanced connectivity amongst more and more smart devices and machines, security threats have also been enhanced gradually [20]. IoT devices have more security threats than mobile devices and computers. The diversity, complexity, and great mobility of these devices have made them more vulnerable to security issues [21]. IoT has come a long way over the years, but it is yet to be matured. Only a few studies have discussed the mobility of data and the openness of networks and the privacy issues behind them [22–23]. The collection and exchange of information with IoT have become a matter of concern over the internet. Due to the limited capabilities of traditional approaches and the complexity of IoT security, it is very important to develop the latest security measures [11]. This way, Artificial Intelligence (AI) has come up with different approaches as the new research path and modern technology. This way, machine learning (ML) is the centre of focus in this study as part of AI. Its methods and algorithms have been proven to solve various complicated tasks in engineering use cases [28]. For example, traditional network monitoring approaches can consider intrusion as misuse and it may lead to unknown attacks like Zero Day [29]. Machine Learning consists of smart approaches to boost performance criteria by considering past performance or example data with learning [30, 31]. Simply put, machine learning algorithms create behavioural models with mathematical equations on giant data sets. It is not important to program ML explicitly to learn on its own. Machine Learning models can predict vulnerabilities on the basis of new data input [32]. Machine Learning is rooted in several disciplines of engineering and science, such as optimization theory, AI, cognitive science, and information theory [33]. Machine learning is especially useful when human expertise is not possible to use

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or not possible (tracking a hostile situation where humans cannot intervene, such as speech recognition, robotics, etc.) and in environments where solutions are subject to change over time to certain problems (such as, tracking malicious code in an application or routing in a network) [34]. In addition, there are some real-life use cases of Machine Learning. For example, Google analyses threats against Android applications and mobile endpoints through Machine Learning. In addition, it also detects and kills malware from infected devices. Similarly, Amazon’s patented Macie service uses machine learning to sort stored data and classify it in cloud service. Similarly, there are many use cases of machine learning. But, there are risks of true negatives and false positives [13]. If machine learning makes wrong predictions, there is room for proper modification and guidance. This way, Deep Learning is the new breed of Machine Learning, which can predict accuracy on its own. It can be more suitable for prediction and classification roles in IoT solutions.

5 Conclusion Machine Learning can be helpful for smart devices and machines to make the most of their learning from programmable data provided by human developers or from the device. It is also the smart device’s thinking ability that can automate or vary the behaviour or situation on the basis of information, which is gathered as the vital aspect for IoT services. There are several use cases of Machine Learning, such as regression, classification, and estimation of density. Fraud detection, computer vision, biometrics, authentication, malware detection, and speech recognition are some of the applications, where machine learning techniques and algorithms are needed. This way, machine learning can provide smart services by integrating with IoT. In this paper, we have discussed Machine Learning applications in privacy and security solutions to make IoT more robust and prepared to deal with real-world threats and help make lives easier.

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Smart Warehouse Management System S. V. Aswin Kumer, Nirbhay Jha, Karishma Begum, and Kodali Brahmani

1 Introduction Cereal grain wastage is one of the serious problems in India that needs to be addressed as soon as possible and as per the Human Development report of development, more than 30% of the food gets wasted which is produced in India. The majority of us are not aware that food waste has such a detrimental influence on a country’s economy. When food grains are thrown away, it wastes the resources and work that went into their production. During the handling of food grains, a significant amount of electric force is also wasted. It also causes deforestation. This research describes a systematically framework that detects the atmospheric nature of food grains and continuously informs the user about their current state. Actual characteristics such as moisture, temperature, humidity, and alkali gas are considered while evaluating the nature of the cereal grains. The outline and writing investigation of various efforts done to control food grain wastage are presented in Sect. 2. Part III explains the systematic structure of the system that operates the warehouse management system, and also the final section summarizes the experimental results of the proposed framework. S. V. A. Kumer (B) · N. Jha · K. Begum · K. Brahmani Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India e-mail: [email protected] N. Jha e-mail: [email protected] K. Begum e-mail: [email protected] K. Brahmani e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_33

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A. Existing System Vinay Sambhaji and Mahesh S. Kumbhar developed an amazing crop storage monitoring and regulation system. To maintain the standard of the food grains, the system uses a moisture and temperature sensor. The sensors detect moisture and temperature and transmit data to the administrator via local area networks or wireless devices. After that, the administrator takes the relevant actions depending on the data interpretation. The mechanism regulates grain temperature and relative humidity, which once over the threshold value, causes the grain to decay. The technique allows for flexibility while also ensuring that the grain remains stable. Every system created should be cost-effective and flexible enough to deliver accurate results. B Proposed System All three sensors were attached to an Arduino board and operated at frequencies of 0–4 MHz 5.5 V, 4.5–20 MHz, and 0–10 MHz 2.7–5.5 V, respectively. Therefore, IEEE decided to standardize electronics around three communication protocols to assure device compatibility with the Arduino communication protocol. All three sensors were attached to an Arduino board and operated at frequencies of 0–4 MHz 5.5 V, 4.5–20 MHz, and 0–10 MHz 2.7–5.5 V, respectively. The Institute of Electrical and Electronics Engineers (IEEE) decided to standardize electronics around three communication protocols to assure device compatibility with the Arduino communication protocol. The sensors compare the value of properties like humidity, temperature, and gas to the threshold value. If the value is outside the threshold range, the information is transmitted to the receiver side of the board through the AT Mega 328 microcontroller, which is shown in the serial monitor and 16 × 2 LCD for triggering the alarm and taking necessary action to avoid crops damage (Fig. 1). Fig. 1 Block diagram

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2 Literature Survey Since food grains are the country’s primary source of revenue, they must be stored until the next yield [1]. Creepy crawlies and organisms might contaminate the grains. Agricultural production is a severe concern in Indian agriculture, because of a weak foundation and a lack of an effective inventory network across the board. India is one of the world’s largest producers of grain crops. Rural goods account for around 8.5% of the country’s total [2]. On the other hand, Indonesia is one of the countries experiencing severe food shortages. Theophilus Wellem’s article looks at two store network the board models for smoothing out Indonesia’s manufacturing network [3]. Agbo David Odu is the major model. “Removable (Plug-In) Electronic PasswordBased Door Lock at a Low Cost” [4]. Ranchers are constantly updated with Agbo David Odu’s information on promote costs (all around the world), grain interest, and so on [5]. The information was gathered from a variety of sources, including the Ministry of Agriculture, State Institutes, the Indonesian Geophysical Dept, Input Industries, and merchants. Ranchers can also benefit from climatic data when it comes to harvesting. E-Choupal is currently in use in more than 38,000 municipalities in Indonesia, with 6600 booths in various states [6]. Walmart is another retailer with a store network. Ranchers and city handling centers provide data to a new inventory network structure. Assortment Centers serve as a gathering point for food grains and to pique people’s attention in general via large stores. The focuses assign a value zone to food grains, allowing ranchers to maximize their profits. The food grains must be protected from common contaminating factors, such as stickiness and warmth. In order to preserve quality, the basis of the capacity area is critical. The two concepts stated above offer a better foundation for keeping up with changing food product specifications.

3 Working A. Signal-side Framework Several sensors make up the signal side of the proposed smart warehouse management system. The sensors collect data on physical variables that aid in determining the quality of grain. After then, the sensor data is relayed to the system. The device then determines the grain quality and sounds an alert if the available air conditions are not favorable (Figs. 2 and 3). B Features of Arduino Board The Atmega328 is used in the Arduino Uno, which is a microcontroller board. It has a Micro USB, a force jack, an in-circuit blueprint programming (ICSP) header, a 16 MHz resonator, and a reset button, as well as 20 advanced data pins (of that which six are being used as pulse width modulation yields and six are being used as basic information sources), a USB connection, a force jack, an ICSP header, a 16 MHz

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Fig. 2 Framework

Fig. 3 Flow chart

resonator, and a reset button. To begin, this includes anything necessary to aid the MCU; simply connect computer with a USB cable through USB port, power adapter or battery with DC supply [7, 8]. It has a modified Atmega16U2 that acts as a to chronic from USB converter. Advanced users can reassemble this aid MCU, because it comes with its own SATA bootloader (Fig. 4).

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Fig. 4 Arduino UNO

C Temperature Sensor A temperature and moisture sensor complex with an aligned computerized signal yield is included in the DHT11 Temperature and Humidity Sensor. It ensures exceptional dependability and dazzling long-haul solidity by employing a choose computerized signal-obtaining process as well as temperature and stickiness detecting technology [9, 10]. This sensor combines a series resistance moisture estimation component and a temperature sensor estimation component, as well as a superior exhibition 8-digit microcontroller, to provide high-quality service, quick response, pro capability, and cost-effectiveness. Thus, every component of the DHT11 is fine-tuned in the lab to provide extraordinarily precise moisture alignment. The adjustment coefficients are saved in the OTP storage as projects, which the sensor’s inward sign differentiating mechanism uses (Fig. 5). D LCD (16 × 2) The term LCD stands for liquid crystal display. It’s a form of digital screen module, which is also found in a variety of circuits and devices, including phones, calculators, computers, and television sets. The most typical uses for these displays are multisegment led diodes and 7 segments. The low cost, ease of programming, animations, and the fact that there are no constraints on displaying unique characters, special and even graphics, are the main benefits of using this module (Fig. 6). E Gas Sensor Gases such as formulation elements, oxygen, ethyl alcohol, sweet-scented mixes, sulphides, and smokes can be detected by this gas sensor. PT1301 is the high device of the chip MQ-3 gas sensor. This gas sensor’s working voltage ranges from 5 to 5.0 V. Lower conduction is used in the MQ-3 gas sensor to cleanse the air as a gas police job material. We will detect harmful gasses in the environment; however, the conduction of the gas sensor will increase as the convergence of soilure working gas [11, 12]. The Sensor module MQ135 may be unable to distinguish between

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Fig. 5 DHT11-temperature and humidity sensor

Fig. 6 LCD pin diagram

smoke, formaldehyde, water vapor, and other harmful gases. It will almost certainly be able to discriminate among a variety of harmful gases [13, 14]. The MQ-135 gas sensor might be a pricey item to purchase (Fig. 7). Fig. 7 MQ135—gas sensor

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Fig. 8 Potentiometer

F Potentiometer A potentiometer is basically a voltage divider for voltage-controlled operation, and it limits the current supplied to the circuit, which is proposed in this article. The resistance can be varied by changing the position of the knob, based on that, the voltage taken from middle pin of the potentiometer is varies also the current can be controlled. A potentiometer has the 2 terminals of the data supply fastened to the furthest limit of the electrical device. To vary the yield voltage, the rotating contact gets emotional on the electrical device on the yield aspect. Lρ/A = K : V = KL : E = Lρx/A = Kx where, x: Potentiometer wire length, E: Lower EMF cell, K: constant (Fig. 8). G 4 × 4 Keypad The intrinsic pushbutton contacts on this 4 × 4 lattice keypad are related with line and segment lines, and there are 16 of them. These lines may be checked for a pressed button by a microcontroller. All section lines are set to information inside the keypad library, and all column lines are set to include. So, it chooses a column and raises it to the top of the screen. It then verifies each section line one by one from that point onward. The button on the column hasn’t been pushed, if the section association stays low. If it hits high, the microcontroller recognizes the line (the one that was set to high), which segment it is on (the one that was identified high) (Fig. 9). H Servo Motor A servo engine is a kind of engine that can pivot with extraordinary accuracy. Ordinarily, this sort of engine comprises a control circuit that gives criticism on the current situation of the engine shaft, this input permits the servo engines to turn with extraordinary accuracy [15, 16]. If you need to rotate an object at specific points or over a certain distance, you’ll need a servo engine. It is basically made up of a simple engine that is controlled by a servo system. If the engine is operated with DC, then it is justified as DC servo engine and if the engine is operated with AC, then it is

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Fig. 9 4 × 4 Keypad

Fig. 10 Servo motor

called AC servo engine. According to this proposed method, the DC servo engine is used for providing good efficiency also, the controlling of that engine is very easy (Fig. 10).

4 Experimental Results The implementation of the above proposal is completed to measure the performance of the proposed work. All the necessary parameters have been observed and measured. The Figs. 11 and 12 shows the hardware model of the proposed work which produces the results as expected.

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Fig. 11 Case 1

Fig. 12 Case 2

5 Conclusion We have planned to create a client model as well as a signal side suggested model. In the proposed work, the temperature, humidity, and the gas present in the ware house have been measured to protect the goods kept in that ware house from the atmospheric changes. To demonstrate the actual capability of the projected food grain capacity of the board framework, the framework’s execution must be performed. Acknowledgements We want to express gratitude toward Dr. SV. Aswin Kumer sir for overseeing all exercises and for loaning his full participation, in spite of his bustling timetable, in the fruitful execution of this project. We’re likewise thankful to Dr. M Kasi Prasad sir for his direction, help, and backing in the execution.

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References 1. Ding Libo School of Mechanical Engineering’ Nanjing University of Science & Technology Nanjing, China. e-mail: [email protected] 2. Proceeding of International Conference on Electrical Engineering, Computer Science and Informatics (EECSI 2014), Yogyakarta, Indonesia, 20–21 August 2014 3. Theophilus Wellem Department of Information Systems Satya Wacana Christian University Salatiga, Indonesia, 50711 International Journal of Computer Applications (0975-8887) 4. Odu AD (2017) Low cost removable (plug-in) electronic password—based door lock. Am J Eng Res 6.7:146–151 5. Jolhe BD, Potdukhe PA, Gawai NS (2013) Jawaharlal Darda Institute of Engineering and Technology. Int J Eng Res Technol 2(4). ISSN: 2278-0181 6. Vinay SS, Kumbhar M (2014) Computer science. Int J Innov Res Sci Eng Technol 7. https://circuitdigest.com/article/servo-motor-working-and-basics 8. https://learn.parallax.com/tutorials/language/propeller-c/propeller-c-simple-devices/read4x4-matrix-keypad 9. Aswin Kumer SV, Sairam Nadipalli LSP, Kanakaraja P, Sarat Kumar K, Sri Kavya KCh (2021) Controlling the autonomous vehicle using computer vision and cloud server. Mater Today Proc 37(Part 2):2982–2985. ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2020.08.712 10. Santhosh C, Aswin Kumer SV, Gopi Krishna J, Vaishnavi M, Sairam P, Kasulu P (2021) IoT based smart energy meter using GSM. Mater Today Proc 46(Part 9):4122–4124. ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2021.02.641 11. Aswin Kumer SV, Kanakaraja P, Punya Teja A, Harini Sree T, Tejaswni T (2021) Smart home automation using IFTTT and google assistant. Mater Today Proc 46(Part 9):4070–4076. ISSN 2214–7853. https://doi.org/10.1016/j.matpr.2021.02.610 12. Aswin Kumer SV, Kanakaraja P, Areez S, Patnaik Y, Kumar PT (2021) An implementation of virtual white board using open CV for virtual classes. Mater Today Proc 46(Part 9):4031–4034. ISSN 2214–7853. https://doi.org/10.1016/j.matpr.2021.02.544 13. Kanakaraja P, Aswin Kumer SV, Jaya Krishna B, Sri Hari K, Mani Krishna V (2021) Communication through black spot area using LoRa technology and IOT. Mater Today Proc 46(Part 9):3882–3887. ISSN 2214–7853. https://doi.org/10.1016/j.matpr.2021.02.339 14. Kanakaraja P, Aswin Kumer SV, Kotamraju SK, Jhansi Lakshmi M, Irfan Sk, Chandra Lekha U (2021) Environment quality monitoring system based on cloud computing analysis. Mater Today Proc 46(Part 9):3864–3870. ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2021. 02.332 15. Kanakaraja P, Sairam Nadipalli LSP, Aswin Kumer SV, Sarat Kumar K, Sri Kavya KCh (2021) An implementation of advanced IoT in the car parking system. Mater Today Proc 37(Part 2):3143–3147. ISSN 2214-7853. https://doi.org/10.1016/j.matpr.2020.09.042 16. Suryawanshi VS, Kumbhar MS (2014) Real time monitoring & controlling system for food grain storage. Int J Innov Res Sci Eng Technol 3(3). http://www.ijirset.com/upload/2014/iciet/ ece/15_8.pdf

IoT Enabled LoRa-Based Patrolling Robot Miriyala Sridhar, P. Kanakaraja, L. Yaswanth, Sk. Yakub Pasha, and P. Sailesh Chowdary

1 Introduction In the field of robotics and automation, technology has brought in a dynamic and huge transition that today includes a various range of applications. Surveillance is just the process of keeping a close check on someone, a group, or anything else, especially if they are in jail or suspected of doing anything illegal. Border crossings, public spaces, offices, and manufacturing facilities are all places where surveillance is essential. However, it is mostly used to keep track of activities. People or built frameworks, such as robots and other mechanization technologies, can observe indoors and outside. A robot is simply a programmable electrical unit capable of performing specific exercises, therefore obviating the need for human labor, providing exceedingly precise results, and circumventing human constraints. One of the greatest accomplishments in mechanical autonomy is the replacement of people in reconnaissance fields. The Arduino Uno microcontroller, which is the robot’s brain, is provided. DC motors, a wheeled chassis, a battery, a LoRa SX1276, and a variety of sensors are all included in this robot [1]. The robot can be controlled manually or automatically. The user interface communicates with the robot using the Internet of Things. The Low-power Wide Area Networking (LoRa) technology is a wireless system that uses very little power (LPWAN). It has been suggested as a suitable wireless connection mechanism for installing the Internet of Things because of its great range, little complexity, and low energy consumption (IoT). Several studies on real-world coverage performance have been carried out. This work aims to present a novel test methodology for evaluating LoRaWAN coverage in a smart campus in an experimental setting. The design of tools, methods, and procedures is part of the new method. In addition, the architecture of the mobile node is described. A mapping M. Sridhar · P. Kanakaraja (B) · L. Yaswanth · Sk. Y. Pasha · P. S. Chowdary Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, AP 522502, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_34

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step is required to highlight the LoRaWAN coverage in the test area as part of the survey. Finally, the essay includes a use case meant to examine the applicability of the suggested technique rather than a specific implementation. The use case results demonstrate the survey methodology’s usefulness in a genuine, smart campus.

2 Empirical Survey Portugal et al. have proposed the concept of “multi-root patrolling in generic graphs to find the multi-path robot algorithms”. They proposed a tradeoff in performance team travel cost and coordination [2]. Bhatnagar et al. are newly implemented “target platform following robot using LoRa Transceivers”. They are proposed to find out the environment locations in different platforms with accurate analytics using LoRa. They are proven when compared to ZigBee wireless range LoRa is excellent and power consumption, cost of the types of equipment are cheaper [3]. Jin et al. l are started research on the “Multi-robot Optimization method based on Hybrid LoRa.” The proposed technology to visualize various environments through Mobile Robot using Hybrid (Combination of BLE and LoRa) Techniques. They achieve accurate results to find out the location of outdoor environments [4]. Vasu et al. they proposed method for patrolling jobs for security gaurds now days very critical for that reason they are proposed robot for patroling without human efforts, they design very low price components with better accuracy. All the survilliance information is monitor on IoT Dashboard instantly [5]. Manasa et al. Introduces Night Vision Patrol Robot it survilliance particular root they are given root path in multiple directions. They also use Sound sensor and IR Based Night Vision Camera to findout enemies easily with capturing sounds and images. They also design based on the sound levels automatially its take direction in the given path and capture the images using Night vision Camera [6].

3 System Design The system is divided into two key sections: user-controlled and robot section. The user-controlled portion contains a laptop or a smartphone to communicate with the robot end. The user component of the system can therefore be movable by using a laptop or mobile device, unlike users of a typical stationary computer system. Furthermore, the LoRa module can be used to communicate via wireless technologies, thanks to the assistance of the Internet of Things (IoT). We employ an Arduino UNO, which is a fundamental component of the robotic vehicle’s body or chassis, on the robot end. Each of the 30 rpm DC motors connects the wheels to the chassis. Power for a motor is required at 12 V, which is supplied by an external battery supply. A relay driver connects the Arduino to the motors. Four relay drivers control two

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motors that are used for amplification. IDE software is used to programme the microcontroller, which controls how the robot should move. The action associated with it in manual mode is this.

4 Methodology The Fig. 1 shows the Layout diagram of IoT Enabled Patrolling Robot. LoRa is a long-range spread spectrum modulation technique. The technology’s capacity to carry data over exceptionally long distances is called LoRa [7]. In rural locations, a single LoRa-based port can collect and transmit signals across a distance of over 10 miles (15 km). Even in heavily populated places, messages can travel up to three miles (5 km). The energy required to deliver a data packet is insignificant in battery life, assuming that data packets are brief and only sent a few times each day. Furthermore, when end devices are sleeping, power usage is assessed in milliwatts (mW), allowing the battery to last longer [8]. Figure 1 shows the combination of both transceiver and receiver of the IoT-based patrolling robot. The function of the transceiver is to send signals to the receiver, which is placed on the robot. The ESP32 CAM will capture the pictures and send them to the Blynk app-contained device through the LoRa module. In this project, two motor drivers are used—one for camera rotation and the other for robot motion. Push buttons are used to control the robot’s motion and rotate the CAM module in 360 degrees. There is a total of six pushbuttons used: button 1 is for forward, button 2 is for the right side, button 3 for backward, button four is to move left side, button 5 is for the rotation of the ESp32 CAM module in the clockwise direction, and the last button is for anti-clockwise rotation [9].

Fig. 1 Proposed methodology layout diagram

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Fig. 2 Proposed transmitter section

4.1 Transmitter Section This figure shows that a potential divider is used to avoid the short circuit for the switching network. Switches are used to control the robot’s direct path—the instruction given by the Arduino nano board to the esp32 cam module. Then the cam module will capture the pictures, which are tied up to the Arduino nano board. And ESP32 contains Wi-Fi and Bluetooth modules inbuilt by itself. It can be utilized in Internet of Things (IoT) applications for smart home gadgets, industrial wireless control, wireless monitoring, and more [10]. A DIP container is used to package this module. As demonstrated in Fig. 2, it can be instantly integrated into the backplane, enabling for rapid product development while also giving customers a high bandwidth alternative that can be used in different IoT hardware terminals.

4.2 Receiver Section A motor driver is used to operating two motors at a time, which is helpful for the robot’s motion. Then, another motor driver is used to control the ESP 32 CAM module. Finally, the output is displayed on the monitor or smartphone with the help of the Blynk app. Blynk is an app for iOS and Android that may be used to control Raspberry Pi, Arduino, and other web-connected devices. We can drag and drop elements to design a project’s graphical user interface on a virtual dashboard. You can easily put everything together and start exploring right away [11]. No board or shield can bind Blynk. Instead, it’s about allowing you the freedom to use whichever equipment you like. Whether your Arduino or Raspberry Pi is connected to the internet by Wi-Fi, Ethernet, or this new Esp32 chip, as shown in Fig. 3, Blynk will get you online and prepared for the Internet of Things.

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Fig. 3 Proposed receiver section

4.3 LoRa SX1276 Transceiver Long Range is the name of the LoRa technology. A wireless radio frequency technology was developed by the SimTech company. With very small power consumption, LoRa systems can transmit and receive bidirectional data over very long distances [12]. As seen in Fig. 4, this capability is helpful for remote sensing approaches that require to relay data but only have a little battery. Lora can run for years and can frequently go 15–20 km on a single charge (more on this later). Keep in mind that the names LoRa, LoRa WAN, and LPWAN are all distinct and should not be used synonymously. Later on in this essay, we shall briefly discuss each of them. In any typical IoT solution for warehouse management or field monitoring, hundreds of sensor nodes will be put in the field, monitoring critical data and sending them to the cloud platform. For those sensors to be portable, they must be wireless and run on a little battery. While BLE can communicate data over very short distances with very little power, RF wireless technologies can send data over great distances, but use more power, making them non-display powered [13]. The application of LoRa technology is required for this. The restrictions of Wi-Fi and BLE communication can be overcome with LoRa, which allows for long-distance communication without using a lot of power.

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Fig. 4 Structure of LoRa SX1276 transceiver

4.4 ESP32 Camera (Ai-Thinker Module) The ESP32 CAM is a small, low-power camera module built on the ESP32 microprocessor. It has a TF card slot and an OV2640 camera. The ESP32 CAM can be used for Wi-Fi picture upload, QR identification, and other clever IoT applications in addition to wireless video surveillance. Sadly, the ESP32 CAM module is also without a USB port. An FTDI adaptor is required to programme this device. This provides connectivity for embedded devices via Wi-Fi (and, in some versions, dual-mode Bluetooth) [14]. The manufacturer frequently refers to modules and development boards that employ this device as “ESP32”, even though ESP32 is merely a chip, as seen in Fig. 5.

4.5 Interfacing Blynk Application (IoT Dashboard) Blynk is a type of application editor. It can be applied to single or multiple projects. Each project may include visual components like digital LEDs, switches, value indicators, and even textual interfaces and the ability to communicate through one or many hardware devices. For example, you can control Arduino or ESP32 pins straight from your phone using the Blynk program without writing any code. Additionally, a project can be shared with friends or clients, who can view it on connected devices but cannot edit it. As seen in Fig. 6, one idea is to create a smartphone application that allows users to remotely manage the lighting, window treatments, and room temperature.

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Fig. 5 Structure of ESP32 camera module

Fig. 6 Creation of Blynk IoT dashboard

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Fig. 7 Implementation algorithm for IoT patrolling robot

5 Flow Chart The in-depth workflow for my essay and the Patrolling Robot’s use of the Blynk IoT Server. As depicted in the flowchart in Fig. 7, the robot control mechanism uses SX1276 LoRa Wireless Transceiver modules and the ESP32 camera to visualize real-time environments using Blynk server.

6 Results and Discussions With the aid of the LoRa and the distinctive ID of camera modules, the Blynk platform enables us to view the current situation or location in the display. Figure 8 displays a thorough explanation of the procedure and real-time implementation photographs. Figure 9 describes the location of real-time environments on the Blynk IoT platform.

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Fig. 8 Real-time implementation images

The images mentioned above were captured by a robot using an ESP32 CAM attached to a patrolling robot, and they will be sent through LoRa SX1276 and ESP32 module to a Blynk server. These images can be viewed on mobile phones and tablets that have the Blynk app installed. The ESP32 module will be used to connect the Blynk app to the robot. These pictures are taken inside the college where the place is restricted for the students.

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Fig. 9 Visualize the location of a person or object on Ubidots IoT platform

7 Conclusion We explored an IoT-based patrolling robot that uses LoRa technology in this study. It is a more efficient technology that uses less power and covers a longer range of internet connectivity than Bluetooth and Wi-Fi modules. Furthermore, the Arduino nano microcontroller controls all the sensors and motors connected to them. Finally, we can conclude that we can track the restricted zones by utilizing an ESP32 camera and LoRa technology.

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References 1. Hoshino S, Ugajin S, Ishiwata T (2015) Patrolling robot based on Bayesian learning for multiple intruders. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 603–609 2. Portugal D, Pippin C, Rocha RP, Christensen H (2014) Finding optimal routes for multi-robot patrolling in generic graphs. In: 2014 IEEE/RSJ international conference on intelligent robots and systems, pp 363–369 3. Bhatnagar O, Surendran N, Alam MM (2020) Development of an algorithm for a target platform-following robot using LoRa signals. In: 2020 11th international conference on computing, communication and networking technologies (ICCCNT), pp 1–5 4. Jin Z, Zeng X (2020) Research on a multi-robot routing optimization method based on hybrid lora location. In: 2020 5th international conference on control, robotics and cybernetics (CRC), pp 221–225 5. Vasu P, Ghazali KWM, Zakaria NA, Nahar H, Roslan I, Ramly M (2021) IoT patrolling robot, vol 2021, p 112 6. Manasa P, Harsha KS, Deepak DM (2020) Night vision patrolling robot. J Xi’an Univ Archit Technol 8(5):172–187 7. Lopez-Lora A, Sanchez-Cuevas PJ, Suárez A, Garofano-Soldado A, Ollero A, Heredia G (2020) MHYRO: modular hybrid robot for contact inspection and maintenance in oil & gas plants. In: 2020 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1268–1275 8. Manuel MP, Daimi K (2021) Implementing cryptography in LoRa based communication devices for unmanned ground vehicle applications. SN Appl Sci 3(4):1–14 9. Maneekittichote T, Chanthasopeephan T (2020) Mobile robot swarm navigation and communication using LoRaWan. In: 2020 6th international conference on mechatronics and robotics engineering (ICMRE), pp. 22–25 10. Brecher DB (2020) Use of a robotic cat to treat terminal restlessness: a case study. J Palliat Med 23(3):432–434 11. Scilimati V et al (2017) Industrial internet of things at work: a case study analysis in roboticaided environmental monitoring. IET Wirel Sens Syst 7(5):155–162 12. Gujjani LNS, Arikatla VSK, Chilakaluri S, Sireesha B, Lakshminarayana G Women safety night patrolling IoT robot 13. Amrutha JN, Rekha KR (2020) Night vision security patrolling robot using raspberry Pi. Int J Res Eng Sci Manag 3(8):432–436 14. Cudjoe E, Pawliszyn J (2009) A new approach to the application of solid phase extraction disks with LC–MS/MS for the analysis of drugs on a 96-well plate format. J Pharm Biomed Anal 50(4):556–562

Single Image Dehazing Using CNN Samarth Bhadane, Ranjeet Vasant Bidwe , and Bhushan Zope

1 Introduction Haze is a phenomenon in which particulate matter like smoke, fog, and dust obscure the visibility of a scene. Sources of Haze include combustion, smoke from vehicles, mist, fog, steam, and volcanic ash. The presence of these particles in the air leads to light scattering, causing visibility degradation. Scattering of Light is the phenomenon in which the molecules present in the atmosphere (like dust, and water vapor) absorb light and emit it in all directions. As can be seen in Fig. 1, the hazy images affect the contrast and colors of the picture. The depth of haze in the image has an inverse relationship with a point’s brightness in the picture. Thus the presence of haze in an image degrades the quality of the picture. Various computer vision applications, such as object detection and object recognition [5], are undermined by this problem. Various haze-removing methods have been proposed using the “Atmospheric Scattering Model” to evaluate the “Transmission Matrix and Global Atmospheric Lightening.” Some of the methods are based on priors and learning models. “Dark Channel Prior” [24], is a powerful Prior-based method that assumes that a haze-free scene is comprised of a pixel with low intensity (dark channel). However, this method fails when the region has complex edges and structures and where the light is similar to Atmospheric Light (no dark channel). Some other prior-based methods include [8, 23, 29]. These prior-based methods work well on a S. Bhadane (B) Pune Institute of Computer Technology, Pune, Maharashtra, India e-mail: [email protected] R. V. Bidwe · B. Zope Symbiosis Institute of Technology, Pune (SIT), Symbiosis International (Deemed) University (SIU), Lavale, Pune, Maharashtra, India e-mail: [email protected] B. Zope e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_35

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Fig. 1 Sample outputs from the proposed model

normal image but fail when the images have more complex scenery or variable haze distribution. Many DNN approaches have been proposed in recent years, and they can also be combined in many ways [30]. Hence, some other solutions use “convolutional neural networks” (CNN) [2] and “generative adversarial networks” (GAN) [11]. Dehazenet [6] proposed by Cai et al. is one such end-to-end CNN network that can be used in the estimation of transmission matrix. Another such model is proposed by Ren et al. [20]. It uses its coarse-scale net to predict the transmission map and its f.'' e-scale net to improve the outcomes. This paper discusses a CNN model for dehazing images. The model calculates the Transmission Matrix and Atmospheric Lighting to dehaze images. A hazy image is entered into this network and outputs a clean image. The model is tested on both real and fabricated photos after being trained on a database of generated hazy photographs. The improvements in image quality can be seen visually and through PSNR and SSIM metrics [4]. “Peak signal to noise ratio,” or PSNR, is the ratio between an image’s maximum power and the power of noise in the image. A higher PSNR value indicates better image quality [31]. “Structural Similarity Index” (SSIM) [25] is another metric that is employed to measure similarity between two photos based on the Luminescence [16], Contrast, and Structure of the pictures [15]. We go over numerous dehazing trends in the next section. The remainder of the paper discusses the network design and presents the model’s findings before concluding.

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2 Related Work There are several challenges for dehazing images, like the preservation of edges and textures. Recent research advances in dehazing images include the use of the Atmospheric Scattering Model (ASM), Image Enhancement techniques, and Learning model based methods.

2.1 Atmospheric Scattering Model (ASM): ASM [17] explains the hazy image formation as: H (x) = I (x) · t (x) + A(1 − t (x))

(1)

where H (x) is the Hazy Image, I (x) is the Ground Truth or Image without Haze, t (x) is the Transmission matrix and A is the Global Atmospheric Lighting. The Transmission Matrix t (x) is given by, t (x) = e−βd(x)

(2)

where, β is the Scattering coefficient and d(x) is the scene depth. Equation 1 can be reformulated as, I (x) =

H (x) − A +A t (x)

(3)

From Eq. 3, it can be observed that having knowledge of A and t (x) can help us in obtaining the Dehazed Image I (x) from the Hazy Image (H (x)). Many studies have been proposed where the estimation of transmission matrix or atmospheric lighting or both have helped to dehaze images. Most of these studies assume homogeneous haze distribution, resulting in inaccurate results.

2.2 Image Enhancement Based Methods Many techniques for Image Enhancement exist where the hazy images are processed based on Computer Vision Applications for estimating the transmission map. The Linear Transformation method [10] is used to calculate the transmission map using Linear Transformation, and the dehazed image is obtained. Another method is based on Structural Preserving [18] in which the structure of the image is preserved, and the image’s radiance and depth are restored by using filters and solving the atmospheric attenuation model. Another technique is dehazing using color-lines [9], in which

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the absence or lack of inconsistency of a dominant color-line helps in dehazing the images. The Dark Channel Prior (DCP) [24] is a popular algorithm for dehazing images. The basis for DCP is the finding that, in the majority of non-sky patches, at least one RGB channel has the lowest intensity at some pixel; as a result, choosing these minimal-intensity pixels from all three channels would provide a dark image (or channel). The transmission map is estimated and refined based on this dark image, and the dehazed image is obtained.

2.3 Methods Based on Learning Models There are various models based on CNN that are proposed for dehazing images. The Dehaze Net [6] is a Model which uses the CNN model to learn the transmission matrix. The AOD-network [12] is another Model implementing CNN for dehazing images, which generates the dehazed image directly through lightweight CNN. A hazy image is fed into the Dehaze Net, which then outputs the transmission matrix. The “Densely Connected Pyramid Dehazing Network” (DCPDN) [27] is a model that employs ASM to recover the dehazed image after learning the transmission map and atmospheric light. Suarez et al. [22] and NB Raj et al. [3] proposed dehazing images using GAN, employing GAN for each color channel independently to remove haze from images. The Feature Fusion Attention Network [19] is another model which uses CNN. The Feature Attention (FA) module considers the haze’s uneven distribution across several image pixels. The Proximal Dehaze Net [26], combines the deep learning model with the DCP to obtain the dehazed image. Shrivastava et al. [21] compares some of these techniques and discusses their performance based on PSNR and SSIM metrics. [7, 14, 28] explains how hazy images are handled for remote sensing applications in UAV and USV using an end-to-end image dehazing approach based on CNN.

3 Proposed Model For dehazing images, we have a CNN model trained with a dataset containing natural and synthesized hazy images. The model contains two modules. The first module contains the six convolution layers. Figure 2 shows the architecture of this convolution layer. This layer’s primary function is calculating the photos’ depth and haze level. The second module of the model is the Image generation module; this module is responsible for recovering the image by using a multiplication layer and additional layers. The image is recovered by using the ASM. The Eq. 1 can be modified to Eq. 4 [12]. I (x) = K (x)H (x) − K (x) + b

(4)

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Fig. 2 Architecture of the proposed model

where, K (x) =

1 (H (x) t (x)

− A) + (A − b)

H (x) − 1

(5)

The K (x) is estimated by the first module. The model consists of six convolution layers and a series of concatenate layers between convolution layers. The estimated K (x) is used to recover I (x) from Eq. 4. The CNN is used to estimate the value of K (x) and then the Image is recovered using Eq. 4. Figure 2 shows the model proposed which estimates the t (x) and A indirectly by estimating K (x).

4 Results This section contains an analysis of the results of the proposed model. The metrics used for evaluation are PSNR and SSIM. For the model’s training and evaluation, we combined real-world hazy photos with the D-HAZY [1] and SOTS [13] datasets. The datasets include both artificial and natural hazy images. 13,000 blurry photos make up the dataset as a whole; 90% of these images are used to train the model, while 10% are used to test it. Figure 3 displays the model’s output on a few of the images. Table 1 shows the PSNR values of images from Fig. 3. On average, the PSNR value has increased by 12.31 db on the dataset and the SSIM has increased by 0.183. The results of the model are similar to state-of-the-art methods used for dehazing images.

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Table 1 PSNR and SSIM Values for images Sr. No

Hazy image

Dehazed image

PSNR

SSIM

PSNR

SSIM

1

63.741

0.808

71.336

0.927

2

62.331

0.753

74

0.898

3

63.21

0.781

73.528

0.941

4

58.583

0.632

66.634

0.79

5

61.944

0.785

74.921

0.948

6

60.042

0.629

69.235

0.883

7

Their9

0.854

74.515

0.955

8

63.897

Explain

76.71

0.951

9

61.771

0.7

71.225

0.899

10

58.49

0.658

64.357

0.833

5 Conclusion In this paper, we have proposed dehazing model based on “Convolutional Neural Networks.” This model indirectly estimates the transmission matrix and atmospheric lighting from the ASM using the CNN, then is used to recover the Image using the modified equation for Atmospheric Scattering Model. The outcomes show that the model provides state-of-the-art outcomes and excels at creating synthetic and real-world pictures.

References 1. Ancuti C, Ancuti CO, De Vleeschouwer C (2016) D-hazy: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE international conference on image processing (ICIP), pp 2226–2230. https://doi.org/10.1109/ICIP.2016.7532754 2. Bengio Y, Lecun Y (1997) Convolutional networks for images, speech, and time-series 3. Bharath Raj N, Venketeswaran N (2018) Single image haze removal using a generative adversarial network. arXiv e-prints arXiv:1810.09479 4. Bidwe RV, Mishra S, Patil S, Shaw K, Vora DR, Kotecha K, Zope B (2022) Deep learning approaches for video compression: a bibliometric analysis. Big Data Cogn Comput 6(2). https:// doi.org/10.3390/bdcc6020044, https://www.mdpi.com/2504-2289/6/2/44 5. Bidwe S, Kale G, Bidwe R (2022) Traffic monitoring system for smart city based on traffic density estimation. Indian J Computer Sci Engg 13(5):1388–1400 6. Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25. https://doi.org/10.1109/TIP.2016.2598681 7. Cai T, Zhang S, Tan B (2021) Aee-net: an efficient end-to-end dehazing network in UAV imaging system. In: 2021 13th international conference on machine learning and computing. ICMLC 2021, Association for Computing Machinery, New York, NY, pp 397–403. https://doi. org/10.1145/3457682.3457739 8. Cozman F, Krotkov E (2023) Depth from scattering (Jan 1997). In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 801–806

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9. Fattal R (2015) Dehazing using color-lines. ACM Trans Graph 34(1). https://doi.org/10.1145/ 2651362, https://doi.org/10.1145/2651362 10. Ge G, Wei Z, Zhao J (2015) Fast single-image dehazing using linear transformation. Optik 126(21):3245–3252. https://doi.org/10.1016/j.ijleo.2015.07.138. https://www.ciencedir ect.com/science/article 11. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8 f06494c97b1afccf3-Paper.pdf 12. Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: all-in-one dehazing network. In: 2017 IEEE international conference on computer vision (ICCV), pp 4780–4788. https://doi.org/10. 1109/ICCV.2017.511 13. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2019) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505. https://doi.org/10.1109/TIP. 2018.2867951 14. Li Y, Ren J, Huang Y (2020) An end-to-end system for unmanned aerial vehicle high-resolution remote sensing image haze removal algorithm using convolution neural network. IEEE Access 8:158787–158797. https://doi.org/10.1109/ACCESS.2020.3020359 15. Mane D, Bidwe R, Zope B, Ranjan N (2022) Traffic density classification for multiclass vehicles using customized convolutional neural network for smart city. In: Sharma H, Shrivastava V, Kumari Bharti K, Wang L (eds) Communication and intelligent systems. Springer Nature Singapore, Singapore, pp 1015–1030 16. Mane D, Shah K, Solapure R, Bidwe R, Shah S (2023) Image-based plant seedling classification using ensemble learning. In: Pati B, Panigrahi CR, Mohapatra P, Li KC (eds) Proceedings of the 6th international conference on advance computing and intelligent engineering. Springer Nature Singapore, Singapore, pp 433–447 17. Mccartney EJ, Hall FF (1976) Optics of the atmosphere: scattering by molecules and particles. Phys Today 30:76–77 18. Qi M, Hao Q, Guan Q, Kong J, Zhang Y (2015) Image dehazing based on structure preserving. Optik 126(22):3400–3406 19. Qin X, Wang Z, Bai Y, Xie X, Jia H (2020) Ffa-net: feature fusion attention net- work for single image dehazing. Proc AAAI Conf Artif Intell 34:11908–11915. https://doi.org/10.1609/aaai. v34i07.6865 20. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single image dehazing via multiscale convolutional neural networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision—ECCV 2016. Springer International Publishing, Cham, pp 154–169 21. Shrivastava P, Gupta R, Moghe AA, Arya R (2021) A comparative study on single image dehazing using convolutional neural network. In: Satapathy SC, Bhateja V, Favorskaya MN, Adilakshmi T (eds) Smart computing techniques and applications. Springer Singapore, Singapore, pp 383–394 22. Suárez PL, Sappa AD, Vintimilla BX, Hammoud RI (2018) Deep learning based single image dehazing. In: 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), . pp 1250–12507. https://doi.org/10.1109/CVPRW.2018.00162 23. Tan RT (2008) Visibility in bad weather from a single image. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8 24. Wang JB, He N, Zhang LL, Lu K (2015) Single image dehazing with a physical model and dark channel prior. Neurocomputing 149:718–728. https://doi.org/10.1016/j.neucom.2014.08.005, https://www.sciencedirect.com/science/article/pii/S0925231214010157 25. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10. 1109/TIP.2003.819861 26. Yang D, Sun J (2018) Proximal dehaze-net: a prior learning-based deep network for single image dehazing. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision—ECCV 2018. Springer International Publishing, Cham, pp 729–746

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Artificial Intelligence in Healthcare and Medicine Aakriti Sethi, Tushar Gupta, Ruchi Ranjan, Varun Srivastava, and G. V. Bhole

1 Introduction Artificial intelligence also popularly known as AI today existed in the world for long now but it is only recently that it has set its position in various industries. Artificial Intelligence is a computer technology that enables machines to work and behave like humans, which indirectly refers to a machine that can think. AI has gained much popularity and has various applications in several industries. It has various applications in several fields making the lives of the people a lot easier than it ever was. Also, there are many fields where we desperately need artificial intelligence for better productivity and results. There have been many debates with the start of AI itself about it replacing the humans in the various industries, because of its capability to behave like humans; after all, it can think. Numerous studies have already shown that AI is capable of doing important healthcare jobs including disease diagnosis. Today, algorithms already surpass radiologists in identifying cancerous tumors and advising researchers on how to create cohorts for expensive clinical trials. However, it will definitely be a long time before AI completely replaces humans in large medical process domains for a variety of reasons [1]. Artificial intelligence (AI) is now transforming the field of medicine in great ways and is also a viable tool for aiding healthcare administration. Today, many medical professionals like doctors and hospitals use AI algorithms to analyze enormous data sets that are available to them with some lifesaving information. AI is also vastly being used to improve patient transfer within hospitals and patient flow to hospitals. A. Sethi (B) · T. Gupta · R. Ranjan · V. Srivastava · G. V. Bhole Department of Information Technology, Bharati Vidyapeeth (Deemed to Be University) College of Engineering, Pune, Maharashtra, India e-mail: [email protected] G. V. Bhole e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_36

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2 Literature Survey Artificial Intelligence is a powerful tool to support healthcare administration and enable the diagnosis and treatment of some diseases. Artificial Intelligence is based on the concepts of learning and reasoning [2]. The machine learning algorithm is further divided into various types which again help in healthcare administration or treatment and diagnosis of the diseases (Fig. 1). Looking at the success of AI in healthcare and medicine, it is quite expected that AI will be utilized extensively in the delivery of healthcare, and there is enormous potential for cost savings as well as quality improvement of services due to the rapid advancements in AI research [3]. AI can be used in healthcare for various reasons, like improving operational efficiency, patient monitoring, predicting patient flow, etc. [2]. The researches that have been done on the use of AI in healthcare and medicine are related to either the AI based algorithms used or its applications and challenges faced. Neither of the researches reviewed here talk about the solution of those challenges, which is an important factor to be studied. This paper deals with the various applications of AI in healthcare and the common challenges faced during its use. It also discusses about the possible solutions or alternatives to those challenges.

Fig. 1 Concepts of artificial intelligence [2]

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3 Uses of AI Out of all the applications of AI, medicine and healthcare are one of the oldest recognized fields where it is and can be vastly used. Numerous clinical decision support systems have been proposed and created by researchers since the middle of the twentieth century. The 1970s witnessed significant achievements for rule-based techniques, which have been demonstrated to read ECGs, diagnose diseases, select effective medications, provide interpretations of clinical reasoning, and help doctors come up with diagnostic hypotheses in challenging patient cases [4].

3.1 Diagnosis and Treatment Artificial Intelligence, with its vast set of applications in the medicine and healthcare, is a powerful tool for the diagnosis and treatment of the diseases. It enables the doctors or medical staff to detect factors which cannot be detected otherwise by humans. AI helps these factors to be detected through some algorithms and therefore, helps in easy detection of diseases. AI also helps in accessing data from huge databases which is next to impossible when done manually [5]. The ease and speed that AI provides enable doctors and medical professionals to provide quality treatment to the patients.

3.1.1

COVID-19

COVID-19 has been the biggest pandemic that the world has seen in decades. The spread of COVID-19 had become so difficult to regulate for the countries. It was believed to be extremely dangerous because it had no defined treatment for it and the diagnosis took time because the symptoms were very similar to common cold. Therefore, there was a sudden and significant need of some way to diagnose and treat the disease. There are two AI based approaches that have been in use recently, known as Machine Learning and Deep Learning. The introduction of ML and DL approaches in the COVID-19 battle within a short time following the onset of COVID-19, research in the business, medical, technological, and military sectors has made significant strides. For instance, in medical image analysis, ML and DL support COVID-19 diagnosis and offer non-invasive detection methods to prevent medical staff from catching diseases. Additionally, the patient’s severity score is provided for use in subsequent treatment [6].

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Liver Diseases

There are several liver diseases diagnosed today, like viral hepatitis, NAFLD, ACLF, liver transplantation, etc. All of these can be diagnosed and treated through AI algorithms and approaches. Machine Learning is usually used for fetching data from huge data sets and making it easier to diagnose the disease. Even Natural Processing Language is also used to fetch and analyze the data. Image detection through AI helps the medical professionals in successful diagnosis of the liver diseases. There are most of the liver diseases that have been studied for the successful diagnosis and treatment with AI. There are still some that have not been researched about much and still need to be studied [7].

3.1.3

Colorectal Cancer

Colorectal cancer is a disease which had no firm and sure treatment for the longest time. Recently, deep learning tools have been used to gather information, diagnose, and treat colorectal cancer. It has become really feasible for the patients as well as the doctors and medical experts through AI based technology. The ability of AI to prescribe treatments for colorectal cancer shows great potential in the clinical and translational field of oncology, meaning better and more individualized treatments for people in need [8].

3.2 Management For the hospital management system, the flow of data is an important factor. It helps to determine the patients, their treatment history, and the in and out patients in the hospital or a clinic. AI based system capture all of this information and use Machine Learning for the analysis to provide not only a quality treatment to the patients but also determine ways to improve the administration of the hospital. AI based systems in healthcare institutions enable the management to regulate and manage the scheduling of the patients. The decisions of scheduling the patients are made on the basis of various factors like health history, patient’s response to the treatment, medical staff available, and much more. AI largely impacts the decision-making process in healthcare and medicine. Machine Learning algorithms enable the prediction of events and decision support systems in healthcare that are used to detect the anomalies in images generated by diagnosis. AI has the power to effectively support the management in the healthcare industry and enable the medical professionals to take decisions that are more accurate than ever [9].

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4 Challenges and Solutions As mentioned before in the paper, there are various reasons why replacement of humans with AI is not going to happen anytime soon. There are some serious problems or challenges that are associated with using AI in healthcare. Healthcare and medicine are fields that need the most concern because they deal with human life. Therefore, these problems either need an alternative or a solution so that AI can be trusted within the field of medicine and healthcare.

4.1 Privacy AI based systems deal with massive data sets containing information about the patients, their disease, and the treatment. Privacy is more of a challenge for the system developers when dealing with patient records because they contain extremely sensitive data and thus become difficult to be regulated through large databases [10]. When it comes to healthcare, understanding the broader mindset and changes toward privacy is essential since trust becomes of utmost importance. The most personal information about a person is their health information, which is also the most important resource for curing diseases, enhancing wellness, and helping the elderly, those with disabilities, and those in social care [11].

4.2 Accountability Accountability is the biggest issue with most of the technologies, specially those that are AI based. When a Tesla Model S autonomous car killed a person, the main question was who would be held accountable for the malfunctioning of the vehicle? Similarly, if any such malfunctioning or accident happens in healthcare, who would be accountable for it? AI based applications work on certain algorithms created by humans. The implementation of the system is done by the medical staff and management; therefore, this results in the conviction of the accountable person for any accident [10]. Therefore, it needs to be addressed carefully and answered to resolve the ethical issues. There are certain mechanisms that have been defined by some researchers. These mechanisms make sure that accountability for AI systems has been defined. These mechanisms have a few drawbacks but they define some definitions of safety and fairness, related to the accidents that happen with AI based systems [12]. The problem of accountability will be solved by these mechanisms only up to a certain extent but gradually and eventually, new mechanisms could be introduced according to the situation and need.

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4.3 Unemployment Since the use of AI is increasing rapidly in healthcare industry these days, the human healthcare workers are needed in small numbers. Also, there are very few people who are actually trained to use these AI based systems, and therefore, not everyone can sustain themselves in the work field with little or no knowledge of AI based technologies. Therefore, it is necessary for the hospital management to arrange for the required training of their medical staff in order to use the AI based technologies and sustain their jobs.

5 Conclusion AI is the most rapidly growing technology in almost all the fields. Healthcare and medicine are the most significant ones among the other fields to AI spread its application. Healthcare and medicine are fields that need the most accurate results and AI has been continuously providing it with its requirements. AI based systems and its approaches are widely being used in the fields of healthcare and medicine, for various purposes. There have been certain serious challenges that have been faced by the medical professionals while using AI based systems for healthcare management and medicine. The solutions or alternatives to those challenges have been researched by various professionals and the others with no solutions till now are being studied for providing the best quality treatment to the patients and management services to the medical staff.

References 1. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthcare J 6(2):94 2. Ellahham S (2020) Use of artificial intelligence for improving patient flow and healthcare delivery. Middle East Medical Portal 3. Reddy S, Fox J, Purohit MP (2018) Artificial intelligence-enabled healthcare delivery. J R Soc Med 112(1):22–28 4. Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2(10):719–731 5. Holloway L, Bezak E, Baldock C (2021) Artificial intelligence (AI) will enable improved diagnosis and treatment outcomes. Phys Eng Sci Med 44(3):603–606 6. Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S (2021) Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions. Int J Environ Res Public Health 18(3):1117 7. Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH (2021) Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology 73(6):2546–2563 8. Yu C, Helwig EJ (2021) The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artif Intell Rev 55(1):323–343

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9. Stanfill MH, Marc DT (2019) Health information management: implications of artificial intelligence on healthcare data and information management. Yearb Med Inform 28(01):056–064 10. Lee DH, Yoon SN (2021) Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Intl J Environ Res Public Health 18(1):271 11. Bartoletti I (2019) AI in healthcare: ethical and privacy challenges. Artif Intell Med 2019:7–10 12. Kim B, Doshi-Velez F (2021) Machine learning techniques for accountability. AI Mag 42(1):47–52 13. Kashid MM, Karande KJ, Mulani AO (2022) IoT-based environmental parameter monitoring using machine learning approach. In: Kumar A, Ghinea G, Merugu S, Hashimoto T (eds) Proceedings of the international conference on cognitive and intelligent computing. Cognitive Science and Technology. Springer, Singapore. 14. Mulani AO, Jadhav MM, Seth M (2022) Painless non-invasive blood glucose concentration level estimation using PCA and machine learning. In: CRC Book entitled Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications 15. Jadhav HM, Mulani A, Jadhav MM (2022) Design and development of Chatbot based on reinforcement learning. In: Wiley-IEEE book entitled natural language processing using machine and deep learning

Share Market Application Using Machine Learning Shraddha S. Tanawade and S. V. Pingale

1 Introduction Sentimental analysis is becoming more and more common in information technology as a way to ascertain sentiments, emotions, and views. It offers details about public opinions of that specific business. An increase in social networking sites has led to a tremendous increase in user reviews, comments, and opinions. Intelligent systems are being developed to assess these details. They categorize the reviews and comments into predetermined sentiment categories like neutral, positive, or negative opinions. Text mining or categorization is an important method for processing textual data. Models have generally classified emotions as either positive or negative, good or bad, joyful or sad. I will take advantage of the Parallel Dots Machine Learning API to put this concept into practice. Since the stock market is a non-parametric, non-linear system, it is very challenging to represent it accurately. Investors have been looking for a technique to identify the best stocks to purchase or sell, as well as the best times to do so. In the stock market, a return on investment is anticipated. As a result, the stock price may be accurately and intelligently forecast. Most people think that using basic analysis is a useful strategy only in the long run. However, fundamental analysis is not appropriate for medium- and short-term speculations. Artificial intelligence and machine learning approaches have recently been used in this field.

S. S. Tanawade (B) · S. V. Pingale Department of Computer Science and Engineering, SKN Sinhgad College of Engineering Korti, Pandharpur 413304, India e-mail: [email protected] Punyashlok Ahilyadevi Holkar, Solapur University, Solapur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_37

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2 Literature Survey According to authors presented in article [1], stock market forecasting has grown in importance over recent years. One technique is technical analysis, however, these techniques do not always produce reliable findings. Therefore, it is crucial to create techniques for a more precise forecast. Investments are frequently made utilizing forecasts derived from stock price by taking into account all potential influences. Regression analysis was the method used in this case. A significant quantity of data must be analyzed before a forecast can be produced due to the massive volumes of data that the stock markets generate at any given moment [2]. Each method under “regression habits” has benefits and drawbacks compared to its competitors. Linear regression was one of the significant methods that was highlighted. The way that linear regression models work is that they may be fitted in a variety of ways, such as by reducing the “lack of fit” in another norm or by reducing a modified form of the least squares loss function. On the other hand, non-linear models may be fitted using the least squares method. According to Loke Kar Seng [3], there is a growing tendency toward the use of artificial intelligence and machine learning to forecast stock values. Every day, more and more academics devote effort to developing methods that will enable them to raise the stock prediction model’s level of accuracy [4]. There are many different approaches to predicting the price of a stock since there are so many possibilities available, but they do not all function in the same way. Despite using the same data set for each approach, the results vary. The stock price prediction in the given study was done using the random forest method, which forecasts the stock price based on financial statistics from the preceding quarter. This is simply one way to address the issue using a predictive model, employing the random forest to extrapolate the stock’s future price from past data. However, there are always more elements that affect the price of the stock, such as investor attitudes, public perceptions of the firm, news from different sites, and even occasions that generate fluctuations throughout the whole stock market. The accuracy of the stock price prediction model may be improved by utilizing a model that can accurately evaluate feelings together with the financial ratio. Siddhartha Vadlamudis [5], according to him, the stock market is the location where buyers and sellers go in order to purchase and sell shares of a corporation. More and more individuals are becoming interested in the stock market as time goes on. This subject is more crucial for the research because of the growing interest of the public. Different prediction methods are offered by machine learning, which might be quite useful in this field [6, 7]. Machine learning is being used by several industries to enhance. With the use of machine learning, computers can learn independently of any outside software [8]. These machine learning methods are initially taught using a data set. The training data set is what is known as. The algorithm then generates the forecasts in accordance with the guidelines provided by the training data set [9].

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3 Objective 1. The objective of the project is to provide a clear and accurate prediction to the user about buying or selling the stocks. 2. It assists a company to comprehend how the public feels about its branding, goods, and services.

4 Methodology A conceptual model known as a system architecture describes the behavior, structure, and numerous viewpoints of a system. A formal description and representation of a system that facilitates inferences about its behavior and structure is called an architectural description (Fig. 1). This is the project’s approach, which consists of created components and subsystems that work together to execute the system.

5 Proposed Algorithm Sentimental analysis, to put it simply, determines if the text under consideration is positive, negative, or neutral. To recognize and extract opinion from the text, a system integrates machine learning and natural language processing algorithms. Modern technology is used by Parallel Dots Sentiment Analysis API to deliver a precise analysis of the overall sentiment of the textual data combined from many sources, including comments, surveys, reviews, etc. It divides a text’s sentiment into three categories: positive, neutral, and negative using Long Short Term Memory (LSTM) algorithms.

5.1 Data Flow Diagram The project’s operation is explained by the data flow diagram (Fig. 2). The information was obtained from Moneycontrol.com. Following the feature extraction, the dataset needed for training is produced. Preprocessing is carried out on this data. The model is prepared by using the Jsoup Libraries. The two sets of data are then integrated. Prediction is made using this dataset to produce reliable results. The model is applied to test data, and predictions are made.

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Fig. 1 System architecture

Fig. 2 Data flow diagram

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Fig. 3 Create account page

6 Implementation 6.1 Create Account Page This is the create account page where user requires registration. For registration, Full Name, EmailID, and Strong Password are required. Once registration is completed then by using EmailID and Password user can log in to this app (Fig. 3).

6.2 Home Page This is the home page of our sentiment based share marketing app. You have to login to the app using EmailID and Password. If you have not created the account, then click on Create new Account, enter details, and register (Fig. 4).

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Fig. 4 Home page

6.3 Company Search Page After login to homepage, we get redirected to this page. Here, we can search for the shares of different companies which we want. After clicking on search button, it shows the list of various companies. According to company, share user can invest their money in good company (Fig. 5).

6.4 Company List Figure 6 shows all companies which are added in the app. Users can see different companies here. Users can click on any company and get the information about that companies.

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Fig. 5 Company search page

6.5 Sentimental Analysis Sentimental Analysis using Machine Learning. Percentage shows that investors should buy or not the share of that company (Fig. 7).

6.6 News Update News Update page was also added so investors can see updates in the App only (Fig. 8).

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Fig. 8 News update

7 Conclusion I discovered that the random forest algorithm is the most effective algorithm for predicting the stock price of a firm based on historical data and testing the accuracy of the various algorithms. However, I will be using Parallel Dots, a machine learning API, in my project. Long Short Term Memory (LSTM) algorithms, a recurrent neural network (RNN) method to deep learning, are used in Parallel Dots. This project provides an analysis of user comments that are presented as text and expressed as percentages, such as fear, sadness, anger, happiness, and excitement.

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PollX—Polling Assistant Neel Shah, Suhas G. Sapate, Ajit Pradnyavant, Vishal Kamble, and Anis Fatima Mulla

1 Introduction India is a democratic republic that is sovereign, socialist, and secular. Democracy is a form of government in which citizens exercise power by voting. Elections are a necessary component of any democratic system. Every year, numerous types of elections are held for various purposes. Elections allow the people, or citizens of the country to choose their representatives. Voting is a method for efficiently resolving this issue. People can elect a new government every few years due to elections. Polling is the act of voting in an election. Polling station is a place where citizens cast their votes for their choice of candidate. Polling station consists of 10 to 11 polling booths, managed by multiple presiding officers. Presiding officer takes control and charge of polling station. He is responsible to maintain integrity and secrecy of polls. He has knowledge about voting procedures. He has to inform the male, female, and total count of votes to their respective zonal officers at frequent intervals of time. Zonal officers have multiple presiding officers under him. He is responsible to carry out the smooth operation of the election process and to manage polling stations under polling locations. He has to keep a proper track of vote count at every time intervals. As mentioned in the paper [1], polling is done using Electronic Voting Machines (EVM) or ballot paper. Using ballot paper is not an environment friendly solution and a slow process. The EVM comprises two units, one for control by the polling staff and the other for the use of voters. After voting each poll booth receives a count of voters. The presiding officer informs the male, female, and total count of voters N. Shah · A. Pradnyavant · V. Kamble · A. F. Mulla Department of Computer Science and Engineering, Annasaheb Dange College of Engineering and Technology, Ashta, Maharashtra 416301, India S. G. Sapate (B) Department of Computer Science and Engineering, Sanjeevan Engineering and Technology Institute, Panhala, Maharashtra 416201, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_38

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to their respective zonal officer through a phone call at a frequent interval of time [2]. The event that several presiding officers make a call to the zonal officer at the same time leads to a busy call situation which creates hindrance in communication. Therefore, to tackle this issue, we have decided to make a cross platform application for the officers which notifies the male, female, total count, and few more details of the particular booth [3]. With the help of this application, officers can easily send the count at various intervals of time, export data, and have a proper graphical analysis of the data. PollX Assistant is a cross platform mobile application developed for instantaneous communication for displaying vote count, having graphical analysis of data, and generating various reports. The main purpose of this proposed system is to have a faster and secure communication between officers and have a proper track of vote counts.

2 Literature Survey Following articles, manuals, and drafts are studied during the literature survey phase of this work.

2.1 Analysis of Electronic Voting Systems This section covers the survey of some existing systems so that the gap can be identified and accordingly, a better solution can be developed. A few important articles and considered for this study as below.

2.2 The Indian Electoral Process and Negative Voting [4] According to this paper, with a population of over 1 billion people, India is the world’s largest democracy. It has a population of over 668 million people and 543 parliamentary constituencies. The existing voting method has numerous security flaws, and proving even basic security properties regarding them is challenging. Electronic systems are used by governments for a variety of purposes, including increasing election activity and lowering election costs. There is still some need for improvement in electronic voting systems because there is no method for the system to determine if a user is genuine or not, and there is no mechanism to protect electronic voting machines from criminals.

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2.3 On What Basis Indian People Vote [5] This article describes the election commission of India. The Election Commission of India is an independent constitutional body tasked with overseeing India’s federal and state election systems. The organization oversees elections for the Lok Sabha, Rajya Sabha, State Legislative Assemblies, and the President and Vice President of India.

2.4 Handbook for Presiding Officers [6] As mentioned in this handbook, the presiding officer plays a significant part in the polling process. Presiding officers are polling station officials who are responsible for the conduct of their polling booth and must be well-versed in voting processes. The term “zonal officer” refers to a corporate branch/field that operates under the direction of the managing director and has a specific jurisdiction over industrial parks. There are 5–10 presiding officers under each zonal officer.

2.5 Election Material and Electronic Voting in India [7, 8] According to this article, depending on their position on the committee, members of the election committee are responsible for different aspects of the election process. The election official/election administrator verifies voter eligibility, maintains the electoral roll, and communicates with voters. The election official/election administrator is the process’s top supervisory authority and is responsible for ensuring that the election process is legally sound. They are also in charge of declaring election results. The secretary is in charge of verifying who is eligible to vote. He or she also keeps track of the voting process to guarantee that no one votes twice. He also keeps track of the vote count and compiles the final vote totals. The existing election is carried out using ballot paper which leads to the use of more amount of papers, cutting down of trees, and requiring more laborers, and this process is comparatively slow. Along with this, there are few systems which support online voting, but these applications have few drawbacks and lack of features. To overcome this situation, we have planned to build an application which can make faster and efficient communication among officers without using papers. The main purpose of this proposed system is to have a faster and secure communication between officers and have a proper track of vote counts. As officers have numerous other jobs at polling stations, they need ease in their work for maximum productivity with minimum effort. PollX Assistant is a fast, secure, and reliable alternative to existing systems. So to solve this problem we are implementing algorithms which will automate various tasks and generate reports.

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3 Problem Statement and Objectives 3.1 Problem Statement After reviewing the literature available in the fields of elections and voting, we have defined our problem statement as below. To design and develop a cross platform application for collecting and analyzing polling data of elections under district collector.

3.2 Specific Objectives The objectives of this project are as below: • To design and implement a secure database for efficient storage, access, and updating of the records in the fastest way possible. • To design a secure, authenticated, and authorized login system for different levels of access. • To develop a proper and user-friendly interface for iOS and Android devices to smooth out the process of exchanging data between the officers. • To implement an algorithm for automating various tasks and generating various reports. • To analyze polling statistics and present it graphically with different dimensions.

4 Methodology 4.1 System Architecture The system architecture of the proposed work is depicted in Fig. 1.

4.2 Modular Diagram The modular diagram of the modules involved in this methodology is depicted in Fig. 2. The proposed solution is an Android and iOS application which helps officers in election procedures. According to the necessity of the project, we have five important modules depicted in Fig. 2. Authentication Module: It deals with verifying identity when the user is trying to access resources. This module is further divided into a login and registration

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Fig. 1 System architecture of proposed methodology

Fig. 2 Modular diagram of proposed methodology

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module. Admin, zonal officer, and presiding officer have to register before using the application. Once the user registers, he will need to verify his/her details using OTP. After verifying using OTP, the application will be submitted to Admin for verifying the identity of valid officers. Once the application is verified, the user will be able to login into the application. Voting Module: It deals with the overall voting process. Admin has the right to register all the candidates who are standing for election. The Zonal officer has to register all the details of the booth under his area. Presiding officer has to enter the details of the voter and verify it. After successful verification, a voting panel will be visible, where a voter can cast his/her vote. Data Exchange Module: Data exchange module deals with communication and exchange of data between the officers. After logging into the application, presiding officer has to enter the male count and female count of voters at a particular interval of time and submit it. This data is then submitted to their respective zonal officers. Graphical Analysis Module: The data is graphically represented in the graphical analysis module. Once all of the data from the booths has been collected, the zonal officer can perform a graphical analysis of the data using pie charts and bar graphs.

4.3 Flow Charts and Working of Each Process A flowchart is a visual representation of a process, system, or computer algorithm. It is a picture of boxes that indicates the process flow in a sequential manner. It helps us to clarify complex processes. The flowcharts are prepared but not shown here due to space limitations. Flow chart for registration process: The user must first register to access the application. If the user’s profile is validated, he or she can log into the application otherwise, and his or her data will be sent to a zonal officer or admin for verification. Flowchart for login process: When a user wants to log into the application, his or her profile is checked to see if it is verified. If his/her profile is not verified, he/she will be taken back to the login page and a message is given to wait until it is verified. The user’s position is checked after the profile is verified and the user is routed to the relevant section of the application based on their position. Flowchart for admin: The details of the candidate standing for election must be registered first, followed by the details of the booth. After that, the Admin will be able to accept the registration of a zonal officer from the registered booth. After all of this is completed, the Admin will be able to do a thorough graphical analysis of the entire voting process for all of the booths. This is depicted in Fig. 3 for sample. Similarly, flow charts for zonal officers and presiding officers are prepared.

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Fig. 3 Flowchart for admin process

5 Results, Analysis, and Discussion 5.1 Results Application performance refers to how well an app functions and responds to end users, as well as how well it operates on a mobile device under different loads and situations. To measure the performance of the application, Firebase performance monitoring tool is being used. Firebase Performance Monitoring is a free tool that allows you to analyze the performance of your application. This tool collects the performance data from the app, and then examines and analyzes it in the Firebase console. The graphics stats for this application are depicted in Fig. 4.

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Fig. 4 Graphics stats

The graphics performance report includes data on a number of essential graphics metrics including: Missed Vsync: The total number of Vsync events is missed divided by the total number of frames that took more than 16 ms to render. High input latency: The set of input events that took more than 24 ms is divided by the number of frames which took more than 16 ms to render to arrive at this number. Slow UI thread: The number of frames that took more than 16 ms to render was divided by the number of times the UI thread took more than 8 ms to complete. Slow draw commands: The number of frames that required more than 16 ms to render was divided by the number of times it took more than 12 ms to submit draw orders to the GPU. Slow bitmap uploads: It is computed by multiplying the number of frames rendered in more than 16 ms by the number of times the bitmap was uploaded to the GPU in more than 3.2 ms. Render time: The render time distribution for each frame in the test run. Your UI will noticeably slow down if the render time exceeds 32 ms. Frames that take more than 700 ms to render are said to be frozen. The crawl stats report displays information about Google’s crawling activity on your application. For example, how many requests were made and when, what your server response was, and whether or not there were any availability issues. The results of the crawl stats report for the proposed methodology are depicted in Fig. 5. The performance overtime report of the proposed methodology is depicted in Fig. 6.

Fig. 5 Crawl stats

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Fig. 6 Performance

The application’s performance, graphics, and memory consumption are shown in Fig. 6. The application’s CPU usage is depicted in the first graph. As the amount of CPU used decreases, the application’s performance improves. This application consumes roughly 10% of the CPU. The second graph depicts the application’s graphics performance. It is calculated by multiplying the frames per second (fps). The application’s memory usage is depicted in the third graph. It uses about 25 megabytes of RAM. The application’s network consumption is depicted in the fourth graph. While the processor is important for device speed, it is the Random Access Memory (RAM) that matters the most when it comes to overall usage experience. Less the memory that app consumes while in use, the more efficiently the user’s phone operates. In order to avoid excessive memory utilization, a number of factors that affect memory usage, such as push notifications and memory leaks, are addressed. Primary memory usually refers to RAM, while secondary storage refers to internal or external storage of the mobile phone.

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5.2 Analysis and Discussion The results obtained from the implementation of the proposed methodology are further analyzed to get better insights about the proposed methodology. The factors affecting performance of the application are described as below. Network bandwidth: Network bandwidth plays an important role in the performance of the application because the database is stored in the cloud. So to access the data, having an internet connection is a must. The minimum bandwidth requirement is 30KBps for smooth operations. If the bandwidth is limited, there may be some latency in the data being displayed on the screen. Traffic: Currently the application has been configured for 100 concurrent users. So if the number of concurrent users crosses 100, there is more traffic and the user may not be able to access the data until the traffic decreases below 100. Later, this can be extended up to 200 k users as per the need. RAM: Devices having less free memory may cause the application to run slowly. So users need to free up their RAM by closing unwanted applications to get faster performance. Unavailable servers: Sometimes the database server may be down so the user will not be able to access the data from the database. To handle this issue, multiple database servers need to be maintained so that if any server is down other servers can process the required data. This application was tested among 220 different users with various devices and setups in order to analyze the application based on the user experience. Fig. 7 depicts the ratings and analysis of these users. This application is a cross platform application developed using flutter, which creates flexibility for the officers to access the app on multiple operating systems. Speed defines how fast the screen loads. Screens that load quickly are essential. No one likes waiting, particularly when all they have to do is look at a screen-loading symbol. In order to achieve this, the database is designed in such a way that the data can be accessed in an efficient manner. Data is stored in the form of a key-value pair, so that the data can be retrieved with nearly constant complexity. The user interface of an application is everything that the user can see and interact with. Responsive design simply refers to the use of a single code set that adapts to variations in device layout. To create a responsive user interface that adapts to different screen sizes, certain design thinking principles are taken into account.

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Fig. 7 Analysis of proposed system

6 Conclusion This article has presented a prototype for a cross platform mobile application secure database for efficient storage, access, and updating of the records in the fastest way possible. A secure, authenticated, and authorized login system designed for different levels of access is developed for this prototype model. An efficient database to smooth out the process of exchanging data between the officers with a proper userfriendly interface for iOS and Android devices is designed. An algorithm designed helps for automating various tasks and generating various reports. Polling statistics are analyzed and presented graphically with different dimensions. Hopefully, the proposed system will help the election commission to think it as an alternative to ballet paper based elections [9]. The authors are also working on incorporating the Aadhar card of Indian voters into the proposed system as a part of future scope [10]. Similarly, Google Assistant based devices [11] or IoT based techniques [12] also can be thought of in our future enhancements in the proposed system.

References 1. Kumar S, Walia E (2011) Analysis of electronic voting system in various countries. Intl J Comput Sci Eng (IJCSE) 3(5):1825–1830

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2. EG Poll Counting (2019) Dreamstep Software Innovations Pvt Ltd. https://play.google.com/ store/apps/details?id=com.election.dreamstep 3. Poll Manager (2019) National Informatics Centre 4. Khorakiwala R (2014) The Indian Electoral Process and Negative Voting. Law Rev 8. https:// ssrn.com/abstract=3227842 5. Choudhari L, Savla N, Pathak V (2015) On what basis indian people vote. Intl J Human Social Sci Invent 4(5):43–48 6. Handbook for Presiding Officers (2018) https://eci.gov.in/files/file/8993-handbook-for-presid ing-officers-october-2018/ 7. Election Material. https://www.polyas.com/election-material/election-committee 8. Electronic Voting in India. https://en.wikipedia.org/wiki/Electronic_voting_in_India 9. Buckley F, Reidy T (2015) Ballot paper design: evidence from an experimental study at the 2009 local elections 10. Agarwal H, Pandey GN (2014) Online voting system for India based on AADHAAR ID. In: 2013 Eleventh International Conference on ICT and Knowledge Engineering 11. Kamble AOM (2022) Google assistant based device control. Int J Aquat Sci 13(1):550–555 12. Kashid MM, Karande KJ, Mulani AO (2022) IoT-based environmental parameter monitoring using machine learning approach. In: Kumar A, Ghinea G, Merugu S, Hashimoto T (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore

A Security Framework Design for Generating Abnormal Activities Report of Bring Your Own Devices (BYODs) Gaikwad Sarita Sushil, Rajesh K. Deshmukh, and Aparna A. Junnarkar

1 Introduction With the growing use of digital application, this is evident: ‘BYOD’ never and will not be overlooked. New businesses are relying on BYOD in building its system of network infrastructure. An increasing amount of businesses were permitting their workforce in bringing its personal gadgets to work and utilise them. Smartphones and tablets have given users unparalleled flexibility, resulting in IT consumerization. As a result, when employees buy their personal gadgets, the firm may save a lot of money on IT and hardware. For businesses that execute a well-defined ‘BYOD’ plan, ‘BYOD’ offers greater productivity for workers and comfort for employers. A person sitting in front of their computer can detect their personal mails on their gadgets, update their account of Twitter, can also conduct a video conferencing call. It also has number of advantages on organisations, such as allowing employees to spend less time offline, allowing project work to progress at a faster pace, and allowing critical business decisions to be made outside of working hours of workday. Meanwhile, companies and their staff still have a considerable workload to perform with ‘BYOD’ portion, digital gadgets such as PCs, smartphones, tablets, laptops, PCs, and other devices which co-workers move and work with are here to stay. Individuals’ professional and personal lives have become increasingly intertwined in today’s competitive environment. While working, one may handle their family livings and its converse. An employee’s load of work grows, and he or she is typically bound by time limit even though post working timeline. Condition like these makes employee’s work profile and family life balance more difficult, resulting in unhappiness. Few of the workforce love to perform their professional tasks in privacy or in personal living spaces. Age old arrangements of working, on the other hand, do not allow for such flexibility, generating employee dissatisfaction. One solution G. S. Sushil (B) · R. K. Deshmukh · A. A. Junnarkar Kalinga University, Raipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_39

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to this problem is the BYOD schemes, which proves to be a means of connecting staff’s electronic gadgets with organisation’s computer network on everyday jobs [1–6]. In other words, firm permits their employees in equipping them with devices that are linked to their employment. This concept was initially presented in the early twentieth century, and it has been shown to be beneficial to employees and businesses in a variety of industries. The BYOD method has been used in a variety of fields, including medicine [7–9], education of higher standards [10, 11], and medical care. Advantages that such innovations provide promote the adoption of new technologies or philosophies. As a result, this concept offers the most influential resolution for both administrators and users. Lesser cost of procurement, higher job satisfaction, spacious working place flexibility, improved productivity, a more empowered working environment, and lesser hours of work are some of the recognised benefits in this strategy. Nonetheless, the method has numerous drawbacks, including non-work-related activities, core skill loss, privacy concerns, and rapid technology change. These advantages and disadvantages may be found in many areas and vary depending on the field. Adoption of new technology or concepts frequently has advantages and disadvantages. When correct standards are followed, technology may be useful. Because the use of bring-your-own-device (BYOD) is on the rise, there is a dearth of study into its security. As a result, in this research, we will look into the security problems surrounding BYOD methodology and its disadvantages.

2 Literature Review Many research [12–14] looked into the notion of bringing your own device in the workplace. According to the findings of the study, a company’s revenues rose by 2 million dollars if one thousand staff adopted BYOD scheme. Unnecessary work difficulties, such as network traffic, hardware issues, operating systems, and learning latest technologies, may all be minimised if and when a working person utilises their personal gadgets, according to the research. BYOD and cloud computing play a key role in delivering and regulating information technology security nowadays, according to a comparable research [15–17]. According to Kristine and Judith [18], allowing workers to bring their own devices to work enhances their flexibility, adaptability, productivity, and dedication. These advantages demonstrate the widespread use of BYOD across sectors. The writers also claimed that BYOD might have a detrimental impact on work operations on occasion. Employees may exhaust invaluable time with unproductive tasks like Facebook, WhatsApp, etc. if they use their own devices. As a result, this method is not appropriate for all sectors of employment. Many research [19–21] looked into the threads and security problems around BYOD. Many researchers and writers looked at the physical and immaterial strands related to BYOD, with previous dealing as per theft or device loss and latter related to protection of digital data concerns including malware and virus assaults [22–25]. The pervasiveness of data created by smart devices was underlined by Mahesh et al. [26].

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Because this data contains critical data of user, losing them because of cyber-attack or theft has disastrous implications. In their first series, Mobile Iron [24] proposed around eight parts which would aid with acceptance of a safe and measurable BYOD programme. Sustainability, Device selection, user experience and policy, liability, trust model, economics, internal marketing, and app governance and design are the ones to consider. The growing tablet uses and smart cell phones—which had seized a large part of customer business—have made ‘BYOD’ appealing. According to Wipro [27], the primary cause for the increase with ‘BYOD’ is propagation of smartcell phones and the costumerisation of IT. Businesses are now adopting BYOD because of numerous advantages it provides, including enhanced user experience, higher productivity, and the flexibility to access data and apps from anywhere at any time, as well as lower hardware and administration costs. When it comes to accessing business data and systems, information security has always been a top priority. Because of numerous recent assaults and limits on the resources of these gadgets, it is unbelievable that gadgets like these by allowing admission to these entities with respect to critically proprietary linked information. Ghosh et al. [28] listed mobile security methods such as establishing duties and jobs for protecting, handling devices, checking applications, and mobile gadgets registering that will be applied in such gadgets, upgrading protection settings, and teaching staff on security problems. As a result, ‘BYOD’ knowledge is becoming more crucial as the use of digital technology grows. While some companies have implemented BYOD, many others are still debating whether or not to do so. According to a poll conducted by Schulze [25], 60% of business establishments have not yet embraced BYOD, but are considering it. About a few portions like 10% of non-acceptors were choosing out of BYOD, while 24% are working on infrastructure, processes, and regulations. The primary cause for non-adoption of ‘BYOD’ is concerns of security and protection. In accordance with digital revolution, future business will be on understanding ‘BYOD’ and incorporating it into company policy. In today’s dynamic environment, Cognizant Co. [29] said in their study that all businesses must adopt the BYOD idea in order to survive. They encountered several problems, data protection, including support systems, cost for BYOD, and security.

3 System Model Its goal is to detect anomalous service consumption behaviours that might occur after connecting to the internal network normally (service). When a user accesses the groupware service via his or her own device, the data collected is evaluated to select and gather behavioural aspects linked to the access/use behaviour. The user’s behaviour is patterned using the gathered data, and then it is compared to previous patterned data to determine if the present behaviour is normal or not. The system was built utilising an agent-less technique, which means that not a single entity, like MDM, was implanted in order for collecting user behaviour data.

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Fig. 1 BYOD environment security system

Meanwhile, proposed technique employed Browser Fingerprinting technology for authentication of groupware system at Captive Portal page in capturing context information of accesses of client. Specific information like client’s data of devices, access location, access time, authentication time span, and so on was converted into features in producing data of pattern every time the client visits the groupware. Production of Network traffic occurs if visiting services of groupware are also recorded to obtain usage context information. The use context information preserves all information created by the groupware, like user’s access address, use URL, session ID, use service, prior URL, downloaded data, and so on. Work culture system of organisation and environment of network are created as shown in Fig. 1 to test the suggested technique. Context information was gathered by replication of traffic created if a client contacts from either outside or inside of service. Three systems are primarily intended in protecting BYOD atmosphere. Primary tasks are a collecting system that gathers traffic to the services of company in order for identifying every entity and create data that is appropriate for the scenario. The second system is the detection system, which uses the context information provided by the collecting system to pattern the user’s behaviour and then determines if it is working fine. Detecting schemes saves data like user’s behaviour, prior patterned profiles, and so on. System third is system of control, which works in conjunction with security devices previously in enabling regular user’s accesses to the network whereas restricting devices/individuals deemed anomalous by the detection system. Control is imposed here according to a set of rules. The data created by visiting the groupware service were provided importance such as user’s activity and executed into context information under circumstances with many gadgets and surroundings of user. To represent user’s activity when using the groupware service, the context information was separated into three kinds: finish, usage, and access. Access context information was applied in explaining user’s access conditions, like “In the evening, a user inside the organisation accessed a groupware service using a Chrome Browser on an Android smartphone,” for example (Fig. 2).

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Fig. 2 Kinds of contextual information and classifications

Table 1 Example of access context information Type

Main concept

User information

User ID, Group (Department)

Device information

Type of device, OS, browser, etc

Access information

Access period, access type (Internal/External), access location (inside the company, outside the company, GPS), access network (mobile, wired network, internet, intranet), access time (dawn, morning, afternoon, night)

3.1 Access Context Information User data, Device information, accessing information, and so on makeup access context information. The data is gathered from network packets and portal of captive, was authentication processes occur if Groupware is accessed by a user (Table 1).

3.2 Usage Context Information The HTTP packet is created while usage of every individual page of services of groupware is mined for use context information. The behaviour numbers assigned to

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Table 2 Example of usage context information Type

Main concept

Information on using the web service Access address, session ID, requested URL, behaviour number, name of service used, time for request/response, information on downloaded file Information on using the DB

DB query request, time for request/response

every node URL of “groupware page structure graph” established prior to crawling of groupware’s page are the same as the behaviour numbers assigned to Every URL Node in “groupware page structure graph” generated by crawling prior to pages of groupware (Table 2).

3.3 Finish Context Information Post to logging out users of service or session’s end timespan has passed; the collecting system generates finished context information. Usage and access context information recorded up to particular time were incorporated in user operation once the final context information is created. In a BYOD scenario, the user connects to the network of company network and utilises services of groupware using his or her own device. Throughout such an operation time, the above-mentioned context information is created on every phase of applying groupware service. Access context information may create data of pattern with user’s typical feature based on user’s device type, time of access, type of access, and so on. This article gathers all use behaviour created following a user’s access as a single unit, as well as previous data pattern applied in comparison of abnormality; it merely chooses to use context information produced in identical scenario of access. Following its configuration, a groupware service offers whole applicant having similar environment of work (email, notice, approval, schedule management, and so on). The time when groupware service is applied by user, a usage behaviour outline is created that is user typical and identical in access behaviour pattern. Such scenarios are known as process mining, and it tries in extracting relevant data out of company’s repository of work processing. Groupware services are examined and advanced structuralized utilising a crawler of webpage for use context and behaviour patterning. To provide importance to the behaviour and associated service name, every page of structuralized service is linked to the user demanded URL. For example, activities on a bulletin board may be separated into glancing at a list, post creation, post reading, and so on. Each URL’s behaviour number, service name, and other information are gathered in context information created if user accesses groupware service. Two patterning approaches are required to detect the aberrant behaviour specified in III. The first is a list of previous transactions created in the same access circumstance, as well as the behaviour data associated with those transactions. The user that accesses the same work environment is likely to use a comparable ratio of

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work services. The second set of data pertains to the user’s service usage sequence in the same access circumstance. In the early stages of accessing, the user develops a sequence of service usage patterns with a high frequency ratio. They may be utilised to detect aberrant usage behaviour after aggregating the two use patterns mentioned above. All use context information created when using the groupware service is gathered to assess user behaviour. Data on sequence of services utilised directly post visiting groupware service may be compared, or all behaviours created by the user’s transaction can be turned into statistics. The variation among examined information and previous data created in similar access scenario was utilised in assessing and detecting user’s aberrant conduct. • Analysis on Detection of Abnormalities and Access Transaction Use Behaviour In conjunction with behaviour information, the abnormality detection system saves data of URL within whole usage traffic created by utilising groupware service. Data on quantity about behaviour created out of usage transaction in behaviour is derived from the accumulated data. The likelihood of each behaviour occurring is calculated by use of forms in behaviour which could be created out of total groupware and quantity of every behaviour produced with transaction currently (Fig. 3). Transaction having access pattern similar to present accessing scenario is sought between previous patterned information about users created within III.C to assess abnormal behaviour regarding users via entire usage behaviour of access transaction. Occurrence probability of the same behaviour is examined after aggregating the recurrence number of whole behaviours contained in transaction sought. When recurrence probabilities regarding past and current behaviours are collected, the error value is calculated by comparing amount of change with whole behaviour recurrence possibilities and recurrence probability of every behaviour, as shown in Fig 4.

Fig. 3 Observation of user behaviour during the access transaction

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Fig. 4 Analysis of usage behaviour for the detection of aberrant conduct

Error Value = (present#1 − past#1)2 + . . . + (present #n − past #n)2

(1)

To check if error value produced is found to be beyond usual range, it is matched to probability of occurrence of past behaviour data. Because it is possible to make a mistake when assessing aberrant behaviours based just on change measure of overall behaviour, a second match is conducted in checking if change measure of every behaviour falls outside of the usual range. • Analysis on Abnormalities Detection and Sequence of Occurrence of Initial Use Behaviour Approach of sensing anomalies with first usage behaviour examines the sequence and frequency of utilising the groupware service immediately after the user logs in. Although information on groupware service behaviour was gathered as minimal piece, different behaviours that occur in common services can be combined into a single service. Following the grouping of use behaviour as high level element, conduction of comparison with data pattern about original usage behaviour sequence in past when service was used extra compared to a certain amount of times. Algorithm of LCS (Longest Common Sequence) algorithm, an assessing technique similar to strings, is used to compare the first usage behaviour sequence in present and past. The similarities between current information and individual information from the past are measured. Finally, the occurrence probability of the present usage behaviour is computed utilising all of the detected similarities. When the amount is less than threshold, conduct is considered unexpectedly abnormal (Fig. 5).

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Fig. 5 Abnormal behaviour sensing using initial usage sequence

4 Results For testing and assessing suggested technique, a network and groupware service was set up as shown in Fig. 1 for simulating the company’s work environment. The behaviour data was then gathered in order to develop user service use patterns. Threatening actions were also performed repeatedly to assess the results in order to discover the weak spots identified in III. Having different patterns of work Users were grouped based on kind of task they do, such as official employees who do same work primarily on daily basis, marketing personal responsible of using service frequently outside an organisation, researchers that utilised various services intensely, and so on.

4.1 Analysis on Use Behaviour of Overall Transaction Data of office employees that perform fixed works was utilised to examine the usage behaviour that occurred in the entire transaction. The test examined all previous patterns of users doing fixed tasks, as well as patterns that happened in the same access scenario over different lengths of time. Only the most commonly used behaviour components were chosen and compared to make comparisons easier (Fig. 6). Ensuing same access scenario choice due to data that happened to deal with urgent activities, even people performing fixed labour were not successful in generating a specific pattern. As a result, it would be preferable to choose and analyse just data from the same access scenario in order to detect changes in user behaviour. This would minimise the chance of making a mistake while detecting aberrant behaviour.

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Fig. 6 Occurrence probability variation of transaction behaviour

4.2 Occurrence Sequence Analysis of Initial Use Behaviour If a person enters internetwork, she or he views the chosen websites or menus in a sequential manner. After user has accessed work service, she or he uses regularity of tasks which takes place sequencly after user has accessed work service he or she utilises daily, testing like one in 4.1 is used in abnormalities sensing. In the same access condition, the options owing to the lack of data adequate for analysis while collecting beginning usage behaviour in whole transactions, because of information created among services utilised within urgent activities, outcome put forth that no specific patterns were identified. When likelihood of occurrence is compared to situation like these in identifying abnormality, possibility is lesser comparatively to cut-off readings, rising risk in judgement being abnormal by mistake. Based on the findings of the two experiments above, it can be concluded that picking data from the same situation can improve precision of identifying abnormality during analysis of data patterned about users (Fig. 7).

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Fig. 7 Pattern variation in early use behaviour sequence

5 Conclusion Previous security solutions that solely managed the point of access to the service had limitations in identifying aberrant user activity in a BYOD context. Previous security systems also struggled to identify security issues such as unrecognised device loss or theft, stolen accounts, malicious information leaking by ordinary users, and so on. This article outlines anomalous behaviours that might arise as a result of security flaws in the BYOD environment and proposes a method for patterning and analysing user data through different contextual factors in order to detect such flaws. In addition, the article demonstrated by testing the technique of identifying abnormal usage behaviour by utilising the analysis result, as well as impact which analysing a transaction of user in similar situation impacted in improving outcome. Behaviours will be examined and fragmented into additional categories by evaluating diversely the users’ saved behaviours. In the future, the patterning approach for such analysis and fragmentation will be researched in order to develop ways for detecting aberrant behaviours by studying the users’ activities.

References 1. Hynes P, Younie S (2018) Bring your own device?. In: Debates in Computing and ICT Education 2. Bennett L, Tucker H (2012) Bring your own device. ITNOW. https://doi.org/10.1093/itnow/ bws010

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3. Van Wingerden C, Lidz A, Barse A, DeMark J, Hamiter D (2017) Bring your own device (BYOD). In: Information and Technology Literacy 4. Miller S, Welsh KE (2017) Bring Your Own Device (BYOD) in higher education: opportunities and challenges. In: Mobile Learning: Students’ Perspectives, Applications and Challenges 5. Hayes B, Kotwica K (2013) Bring your own device (BYOD) to work 6. Disterer G, Kleiner C (2013) BYOD bring your own device. Proc Technol. https://doi.org/10. 1016/j.protcy.2013.12.005 7. Keyes J (2014) BYOD for healthcare 8. Portela F, Moreira da Veiga A, Santos MF (2017) Benefits of bring your own device in healthcare 9. Moore PY (2018) Factors influencing the adoption of bring your own device policies in the United States healthcare industry. ProQuest Diss. Theses 10. Difilipo S (20133) The policy of BYOD: considerations for higher education. Educause Rev 11. Sangani K (2013) BYOD to the classroom [bring your own device]. Eng Technol. https://doi. org/10.1049/et.2013.0304 12. Rose C (2013) BYOD: an examination of bring your own device in business. Rev Bus Inf Syst 17(2):65–70. https://doi.org/10.19030/rbis.v17i2.7846 13. Downer K, Bhattacharya M (2015) BYOD security: a new business challenge. In: Proceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015. https://doi.org/10. 1109/SmartCity.2015.221 14. Ghosh A, Gajar PK, Rai S (2013) Bring your own device (BYOD): security risks and mitigating strategies. J Glob Res Comput Sci 15. Gartner (2012) Gartner IT Glossary. https://www.gartner.com/en/information-technology/ glossary/consumerization. Accessed 20 Jan 2018 16. Semenikhina E, Drushlyak M, Bondarenko Y, Kondratiuk S, Dehtiarova N (2019) Cloud-based service geogebra and its use in the educational process: The BYOD-approach. TEM J. https:// doi.org/10.18421/TEM81-08 17. PS SreeLaxmi, KK Bharathi, CHM Pushpa (2015) Role of mobile cloud applications and challenges in BYOD. Int J Comput Tech 18. Dery K, MacCormick J (2012) Managing mobile technology: the shift from mobility to connectivity. MIS Q Exec 19. Markelj B, Bernik I (2012) Mobile devices and corporate data security. Int J Educ Inf Technol 20. Morrow B (2012) BYOD security challenges: control and protect your most sensitive data. Netw Secur. https://doi.org/10.1016/S1353-4858(12)70111-3 21. Alotaibi B, Almagwashi H (2018) A review of BYOD security challenges, solutions and policy best practices. In: 1st International Conference on Computer Applications and Information Security, ICCAIS 2018.https://doi.org/10.1109/CAIS.2018.8441967 22. Mahinderjit Singh M, Sin Siang S, Ying San O, HassainMalim NHA, MohdShariff AR (2014) Security attacks taxonomy on bring your own devices (BYOD) model. Int J Mob Netw Commun Telemat. https://doi.org/10.5121/ijmnct.2014.4501 23. Li F, Huang CT, Huang J, Peng W (2014) Feedback-based smartphone strategic sampling for BYOD security. In: Proceedings—International Conference on Computer Communications and Networks, ICCCN. https://doi.org/10.1109/ICCCN.2014.6911814 24. Zain ZM, Othman SH, Kadir R (2017) Security based BYOD risk assessment metamodelling approach. In: Proceedings ot the 21st Pacific Asia Conference on Information Systems: “‘Societal Transformation Through IS/IT’”, PACIS 2017 25. Ali MI, Kaur S (2019) BYOD secured solution framework. Int J Eng Adv Technol. https://doi. org/10.35940/ijeat.F8202.088619 26. Mahesh S, Hooter A (2013) Managing and securing business networks in the smartphone era. In: Fifth Annual General Business Conference Proceedings at Sam Houston State University, Huntsville, TX

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27. MobileIron (2011) BYOD strategies chapter 1. http://www.webtorials.com/main/resource/pap ers/mobileiron/paper1/byod_part_1.pdf. Accessed 12 Feb 2016 28. Ghosh A, Gajar PK, Rai S (2013) Bring your own device (BYOD): security risks and mitigating strategies. J Glob Res Comput Sci 4(4):62–70 29. Cognizant (2014) Making BYOD work for your organization. Whitepaper, Banglore

Fake News Detection Using Machine Learning N. Pavitha, Anuja Dargode, Amit Jaisinghani, Jayesh Deshmukh, Madhuri Jadhav, and Aditya Nimbalkar

1 Introduction Everyone in today’s culture relies on a variety of online news sources because the internet is so widely used. The effects of fake news are far-reaching, and they include the development of prejudiced beliefs that can be used to sway elections in favor of particular politicians. Additionally, spammers use alluring news headlines to generate revenue via clickbait advertisements. We undertake binary classification of various news articles available online using AI, NLP, and ML approaches in this study [1]. According to new research, the age of social media began in the twentieth century [2]. The quantity of postings and articles on the internet is expanding, as is the number of people using it. To detect fake news, they employed a variety of techniques and tools. According to an article, focusing on detecting fake news. They’ve been working on it for about a year and are now at the alpha stage. In 2017, Nguyen Vo, a student at Cambodia’s investigated and executed Fake News identification. In his Fake News detection research, he combined a bidirectional GRU with an attention mechanism described by Yang et al. There are three types of fake news: cyborg users, trolls, and social bots. The social bot is able to independently produce content. Second, trolls are real individuals who “attempt to disrupt online communities” in an effort to stir up sentiment on social media. The other is the cyborg. In order to do tasks on social media, humans create accounts and use programmers, resulting in cyborg users that combine “automated N. Pavitha Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, India A. Dargode (B) · A. Jaisinghani · J. Deshmukh · M. Jadhav · A. Nimbalkar Department of Information Technology and MCA, Vishwakarma Institute of Technology, Pune, Maharashtra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_40

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actions with human input.” Network analysis techniques and linguistic signals are two examples of approaches for spotting false information. The most often used algorithms for this kind of work are the Naive Bayes Classifier and Support Vector Machines (SVM). This paper involves various algorithms used to detect news as spam or ham. Further, the paper has literature review that has been developed in this domain, and then it focuses on methodology and materials included to build this project which includes information about dataset followed by algorithms. Lastly, the paper concludes with successfully developing fake news detection with high accuracy.

2 Literature Review Gilda [3] has illustrated how NLP is crucial to rely upon fictitious material. They have made use of the probabilistic context-free grammar (PCFG) placement and the TFIDR of bi-grams. To determine the best model, they looked into their dataset using multiple lesson calculations. They find that the 77.2% accuracy of the TF-IDF of bi-grams, which is simply transformed into a Stochastic Angle Plummet, does not accurately discriminate solid assets. Granik and Mesyura [1] proposed a procedure for identifying Fake News employing a Gullible Bayes classifier. They utilized BuzzFeed News to memorize almost everything and attempted Naive Bayes classifier to test it. The dataset was from the news distributed by Facebook and accomplished up to seventy-four percent precision within the test set. A plan for the robotized placement of fake news on Twitter was developed by Cody Buntian. They linked Twitter to this tactic [2]. Additionally, using amateur swarm sources rather than authors could be a helpful and significantly less expensive technique to quickly categorize true and false memes. Della [4] has displayed a work that lets us see how social systems and contraption studies (ML). They utilized a novel strategy to distinguish Fake News (ML) and connected this in a Courier chatbot and compared it to a real-world application, accomplishing a discovery exactness of eighty-one percent. Kaushal performed three calculations for familiarization, to be specific Credulous Bayes, Clustering, and Choice bushes on a few highlights like tweet degree and buyer level like supporters, URLs, spam words, answers, and hashtags. Enhancement in spontaneous mail discovery is measured [5] by and by large precision, spammer location exactness, and non-spammer discovery accuracy. Krishnan employs a prevalent system for fake data substance location. To begin with, substance fabric capabilities and client highlights have been extricated through Twitter API [6]. This paper [7] proposes to apply a machine learning gathering technique for robotized news article classification in this paper. This inquiry looks into distinctive printed qualities that can be utilized to tell the contrast between untrue and genuine substance.

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In this investigation [8], they utilize a machine learning gathering approach to recommend an arrangement to the fake news recognizable proof issue. This inquiry looks into distinctive literary qualities that can be utilized to tell the contrast between wrong and genuine substance. They prepare a combination of a few machine learning calculations using numerous outfit approaches abusing those properties, which are not well explored within the display literature. Using the LIWC authenticity score [9] and our sample of for-sure phony articles from Rick Pearson and the SEC, they build a likelihood function for detecting fraudulent content. They establish that articles on these platforms matter and that investors pay attention to them before moving on to the indirect impacts of manipulation and trust on markets, and they do not analyze the direct impact of the fraudulent articles on markets. This thought centers on the word “data,” which is basic in any catastrophe that is not unmistakable. An assortment of particular information sorts was inspected. The hot look list on Sine Weibo (a Chinese social media stage) is one of the essential information sources (in which a catchphrase has been gotten to each day for how numerous times as well as how numerous hours). This paper presents a nitty gritty review of recognizing wrong news, checking fake news characterizations based [10] on brain investigation and social presumptions, existing data mining calculations, appraisal estimates, and operator datasets in this audit. They moreover see pertinent questions around zones, open concerns, and future request points for identifying fake news on social media.

3 Materials and Methods 3.1 Datasets The datasets used are publicly accessible on the internet. Data from multiple sites contains both bogus and true news pieces. The factual news articles produced contain accurate descriptions of actual events, while the website with bogus news feature statements are not backed up by facts. In this investigation, we employed two separate datasets. The articles from the two datasets are integrated in a single dataset (hereafter referred to as True and Fake). These two final datasets are joined to form a single huge dataset known as data.

3.2 Methodology This research employs a variety of strategies, such as combining ensemble techniques with a variety of groupings of linguistic features categorizing stories from the news in various categories as true or false. The innovative aspect of our suggested strategy

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Fig. 1 Flow of fake news detection

is the employment of ensemble techniques. There are a slew of reputable websites that publish reliable news content that can be used for fact-checking (Fig. 1). However, for research, we combined two datasets that comprised news from a number of fields, as well as a mix of real and fake articles, into one massive dataset. The datasets were obtained from Kaggle and are accessible over the internet. The purpose of this study is to assess the performance of fake, true news detection classifiers. Some preprocessing is done on data sets like merging both fake and true datasets and dropping columns like title and date and also removed stop words.

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Fig. 2 Bayes theorem formula

3.3 Algorithms 3.3.1

Naïve Bayes

This theorem makes good decisions by using historical data. The assumption implemented by Bayes theorem, the category is carried out by acquiring the most posterior, that is the maximal P(C i |X). Through simply counting the class distribution, this assumption significantly minimizes the computational value. Regardless of the fact that the belief is not always legitimate in maximum instances due to the fact the features are based, Naive Bayes has been capable of carrying it out properly (Fig. 2).

3.3.2

Logistic Regression

It is an S-shaped curve which could convert any actual-valued integer to a cost among 0 and 1, however in no way precisely (1 + e-value)/1. A good way to forecast an output cost, enter records (x) are linearly combined with weights or coefficient values (abbreviated as Beta in Greek) (y).

3.3.3

Decision Tree Learning

It is a kind of supervised machine learning wherein facts are separated on an everyday basis based on a parameter (you provide an explanation for what the input is and what the associated output is inside the training records). Two entities, selection nodes and leaves, can be used to give an explanation for the tree. The selections or final outcomes are represented through the leaves. The records are separated on the decision nodes.

3.4 Metrics of Performance 3.4.1

Accuracy

Accuracy is a widely used statistic that measures the extent of accurately expected genuine or false observations. To compute the accuracy of a model’s performance, apply the equation (Fig. 3).

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Fig. 3 Accuracy formula

Fig. 4 Recall formula

Fig. 5 F1-Score formula

It can have negative effects if it was truly untrue (false positive), if an article was forecasted as false when it included factual information, trust concerns can develop. As a result, it employed three other metrics, precision, F1-score, and recall to account for the wrongly classified observation.

3.4.2

Recall

Recall is the number of affirmative classifications outside of the true class. It represents the expected percentage of true articles (Fig. 4).

3.4.3

F1-Score

The F1-score may be a compromise between exactness and review. The consonant cruel of each pair of numbers is calculated. As a consequence, both wrong positive and wrong negative perceptions are taken into account. The F1-score may be computed as (Fig. 5).

4 Results and Discussion The greatest accuracy attained on Decision Tree is evidently 99.91%. The next highest accuracy is achieved on Support Vector Machine (SVM) which is 99.52%. The next highest accuracy is achieved on Random Forest of 99.22%. The next highest accuracy is achieved on Logistic Regression which is 98.91%. The least accuracy is achieved on Naïve Bayes which is 94.91%. Table 1 represents the name of the classifier and accuracy achieved by the classifier.

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Classifier

Accuracy (%)

Naive Bayes

94.91

Logistic regression

98.91

Decision tree

99.91

Fig. 6 Confusion matrix

4.1 Confusion Matrix and Classification Report In the paper, different indicators were used to evaluate the algorithm performance. In most of them, the confusion matrix is used (Fig. 6).

4.2 Naïve Bayes See Figs. 7 and 8. Fig. 7 Confusion matrix

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Fig. 9 Confusion matrix

Fig. 10 Classifier measures

4.3 Logistic Regression See Figs. 9 and 10.

4.4 Decision Tree See Figs. 11 and 12.

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Fig. 11 Confusion matrix

Fig. 12 Classifier measures

5 Conclusion Manually classifying communications necessitates a thorough understanding of the domain as well as the ability to spot anomalies in the text. The information we used in our research came from the Internet and consisted of news stories from various domains that covered the majority of the news and did not particularly identify political news. The study’s main purpose is to find textual patterns that distinguish false news pieces from legitimate news. Using a LIWC tool, we retrieved various text features from the articles and sent them into the models. To obtain best accuracy, the learning models were trained and optimized on the parameters. Some models performed better than others in terms of accuracy. To compare the outcomes of each method, we used a variety of performance indicators and a group of people. There are numerous unanswered questions in this area that need to be addressed by researchers. For example, it is critical to determine the major factors that influence news dissemination. Machine learning techniques can be used to point out the main sources involved in the spread of fake news. Real-time fake news in videos may become a popular trend in the future. The fact that the data is erratic is one of the

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negatives of the issue, as any model could contain anomalies and be inaccurate. Word2vec, subject modeling, and POS tagging are a few ideas that could be applied to enhance the system in the future. The model will have much more depth after feature extraction and improved categorization.

References 1. Granik M, Mesyura V (2017) Fake news detection using naive Bayes classifier. In: IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON) 2. Buntain C, Golbeck J (2017) Automatically identifying fake news in popular Twitter threads. In: IEEE International Conference on Smart Cloud 3. Gilda S (2017) Evaluating machine learning algorithms for fake news detection. In: IEEE 15th Student Conference on Research and Development (SCOReD) 4. Vedova MLD, Tacchini E, Moret S, Ballarin G, DiPierro M, de Alfaro L (2017) Automated online fake news detection using content and social signals. ISSN 2305–7254 5. Gupta A, Kaushal R (2015) Improving spam detection in online social networks. IEEE, New York 6. Krishanan S, Chen M (2018) Identifying tweets with fake news. In: 2018 IEEE International Conference on Information Reuse and Integration for Data Science 7. Wong J (2016) Almost all the traffic to fake news sites is from Facebook, new data show 8. Lazer DMJ, Baum MA, Benkler Y et al (2018) The science of fake news. Science 359(6380):1094–1096 9. Kogan S, Moskowitz TJ, Niessner M (2019) Fake news: evidence from financial markets. https://ssrn.com/abstract=3237763 10. Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media. ACM SIGKDD Explor Newsl 19(1):22–36 11. García SA, García GG, Prieto MS, Guerrero AJM, Jiménez CR (2020) The impact of the term fake news on the scientific community’s scientific performance and mapping in the web of science. Social Sci 9(5):73 12. Holan AD (2016) Lie of the year: fake news. Politifact, Washington, DC 13. Robb A (2017) Anatomy of a fake news scandal. Rolling Stone 1301:28–33 14. Conroy NK, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inform Sci Technol 52(1):1–4 15. Hua J, Shaw R (2020) Coronavirus (covid-19) “infodemic” and emerging issues through a data lens: the case of China. Int J Environ Res Public Health 17(7):2309

Advanced Intelligent Tutoring Systems: Featuring the Learning Management Systems of the Future Trishna Paul

and Mukesh Kumar Rohil

1 Introduction Artificial intelligence along with the techniques-based thereon have been intertwined with the domain of education and learning since the 1970s in response to the challenges faced by the academicians to create pedagogically valuable learning systems and their artefacts which could possibly replace a human being as an instructor or prove to be a significant tool for him/her to convey knowledge with ease. Such systems, over time, have taken various forms like Computer-Aided Instruction (CAI) media, Learning Management Systems, Intelligent Tutoring Systems, etc. [1]. Computer-based Intelligent Tutoring Systems are an excellent choice for helping students to prepare for high-stakes exams. Recent advancements in Intelligent Tutoring Systems have demonstrated that users of tutoring systems may progress quickly and dramatically in specific areas and abilities. From early e-learning systems to today’s ITS, there have been significant progress, with technology which could deliver a vast range of multimedia-based instructions so as to assist learning along with a range of analytic methodologies that serve students with optimum incentive and evaluation in order to optimise the learning curve for them [1]. An Intelligent Tutoring System (ITS) is basically a software trying to offer learners with rapid and customised instruction and feedback without having to consult a human trainer. The purpose of ITSs is to use various computational approaches to make learning more vivid and efficient. ITSs have demonstrated their ability in both formal and professional settings. Effective tutoring, cognitive-developmental theories, and layout have a significant relation. An ITS is designed to address the issue T. Paul (B) · M. K. Rohil Birla Institute of Technology and Science, Palani, Rajasthan 333031, India e-mail: [email protected] M. K. Rohil e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_41

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of trainees’ over-reliance on trainers in superiority education. It aims to provide every trainee with a high-quality education while strengthening the educational system as a whole [2].

2 What is an ITS? Intelligent Tutoring Systems (ITSs) are computational models that attempt to give users with tailored teaching and feedback, frequently using AI technology and without the necessity of a human teacher. Because of their potential to give a one-on-one curriculum, ITSs have gotten a lot of press. Deep learning algorithms enable systems to recommend specific studying tactics to people. Intelligent Tutoring Systems (ITS) are large, integrated software systems that employ the principles and techniques of intelligent tutoring. Artificial intelligence (AI) solutions to challenges as well as the demands of teaching and learning They make it possible. Models of student knowledge and application are being sought. Learning tactics to improve or amend students’ grades knowledge. They’re based on research and development. artificial intelligence approaches and strategies artificial intelligence (AI), and the content and design of the website are based on that. The way of teaching topic presentations can be adjusted. Students’ specific abilities are taken into account. They’re supposed to encourage and enhance the teaching and learning process in while honouring the selected subject of knowledge The learner’s uniqueness.

3 Architecture of ITS The four fundamental components of a conventional ITS [3] (as depicted in Fig. 1) are: Student Domain Model

User Interface Model

Pedagogical Model Student Student Model Model

Fig. 1 Architecture of an intelligent tutoring system

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Knowledge Domain. Student Model. Pedagogical Model/Teaching Strategies. User Interface Model.

3.1 The Knowledge Domain Domain knowledge is the knowledge of a specific, specialised discipline or field which use different terminologies and have different processes and metrics and operational mechanisms. In contrast to general knowledge, it is very specific knowledge that can be applied in a particular context. Operations on domain knowledge are performed by meta-knowledge. Design Pattern (recurring solution to common problems) is just a way used to describe a knowledge model. It provides information pertaining to the real teaching materials (for example, mathematics, computer science, physics, etc.). The domain knowledge and how an expert operates in the realm of knowledge are represented by the domain model [1, 4]. However, special care must be taken to improve the comprehension [5].

3.2 The Student Model Learner’s or student’s cognitive and affective understanding is represented using the Student Model. Its goal is to give information that may be utilised to identify when feedback should be adjusted. Other instructor modules rely on it for data. The Student Model’s main purpose is to guarantee that the system has reliable information on each student so that it can respond appropriately, stimulate students’ interests, and encourage learning [6]. There exist three ways for illustrating student’s misunderstandings. • The overlay model: The approach attempts to compare the given student’s conduct to that of an expert. The disparity between those two states might be viewed as the student’s lack of abilities and knowledge. • The perturbation model: This model extends the overlay model by including a bug library. It aims to model the learner not just in terms of proper knowledge, but also in terms of recognised faults and misunderstandings in the area. • Learner-based modelling: This is another sort of student modelling. Because misunderstandings are formed throughout the process of knowledge acquisition, learner-based modelling focuses on that process. Machine learning techniques may be used to construct problem-solving rules that describe the stages performed until the learner creates a misperception [1].

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3.3 The Pedagogical Model/Teaching Strategies It is also known as the expert model or the teaching model, and it offers the knowledge foundation for selecting and planning instructional materials based on the Student Model. To respond to the student’s involvement with the system, it chooses the appropriate action (for example, delivering feedback or offering a suggestion). The Pedagogical Model functions by the system’s teaching approach, taking into account the student’s response time and profile [3]. The expert model’s major responsibilities are outlined below: Choose the content that the communication model displays. Decide on a tutorial technique to be used, based on learning procedure. Choose and create questions to assess learning ability. Choose and produce honest criticism. To address knowledge gaps in learners and give support and more resources. Take steps to ensure that students are motivated throughout the class [1].

3.4 The User Interface Model Commonly known as the communication model, it facilitates user’s interactions with the system. There are several methods of communication between the user and the system. Most of these are as follows [1]: • Graphics-based communication can take the form of one or more of the following: – Pedagogical agents that have been animated. They are clever computerised characters who assist people in navigating a given area. – Humans that have been synthesised. They’re educational AI bots who have been reimagined as real-life human characters. – Virtual reality is a term used to describe a (fully synthesised) situation where in students are immersed in a visual environment that contains a pedagogical agent [3]. • Emotional and social connection are two aspects of social intelligence with following considerations. – It is accomplished by verbal analysis (for example, problem-solving time, errors, and pleas for assistance). – Facial emotion recognition and eye movement understanding are examples of visual systems. – Indicators of metabolism: Non-invasive physiological devices (or the devices which do not penetrate skin or enter bodily cavity) which assess change in heart rate, body/eye movements, vocal inflections, and detect students’ emotional moods.

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– Recognition of speech cues: Speech cues can be used to extract negative, neutral, and positive emotions. Both acoustic prosodic (variations in pitch, stress, etc., while pronunciation) and other forms of linguistic characteristics are used in the top-performing feature set [7]. • Component Interfaces: These interfaces analyse student input (understand equations, vectors, and formulae) or assess discipline-specific symbols (for example, chemistry and molecular biology). For example, Crowley and Medvedeva [8] developed an ITS to learn to solve the problems related to visual classification. • Based on the communication in Natural Language (NL), the teachers are divided into four categories: – Mixed-Initiative Dialogue: Either the instructor or the students start the conversation and lead it. – Dialogue with a Single Initiator Tutor: Takes into account the student’s past and subsequent statements, but the actual initiative is only taken by the tutor. – Directed Dialogue: The tutor maintains control of the conversation and asks pupils for specific information. Tutor deciphers brief student responses and generates NL explanations. – Fine-tuned Discourse: Dialogue is imitated using menus, logical forms, or semantic grammars [1, 9].

4 Literature Review of Some Existing Intelligent Tutoring Systems • An Intelligent Tutoring System on World Wide Web: This study examines the challenges of creating World Wide Web-accessible ITS, particularly the issue of migrating current ITS to the World Wide Web platform. Kele¸s et al. [7] introduce the ELM-ART system, which is a World Wide Web-based ITS for learning programming in LISP. ELM-ART explains how to use various well-known ITS technologies in a World Wide Web setting. • An Intelligent Tutoring System for Deaf Learners of Written English: The journey towards that prototype system of a device aimed at improving fluency in deaf youths who are native (or near native) signers of American Sign Language is described in the study (ASL) done by Abd El-Sattar [10] who imagine a system that takes a piece of text produced by a deaf student, analyses it for grammatical problems, and engages the student in an instructional dialogue, allowing him or her to make necessary text repairs. A major purpose of the project by Abd ElSattar is to set up a system that adjusts this procedure to the patient’s knowledge score and learning capabilities, as well as the ability to participate in multi-modal, multi-lingual tutorial instruction in both English and the user’s native language. • An Intelligent Tutoring Spoken Dialogue System: The Why2-Atlas text-based tutoring system serves as the “backend” for ITSPOKE, which is a spoken dialogue system. A student begins by typing a Natural Language response to a qualitative

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physics question. The learner is subsequently engaged in a spoken dialogue in which ITSPOKE provides feedback, corrects mistakes, and elicits more detailed explanations. Nye utilises ITSPOKE to develop an empirical knowledge of the consequences of the addition of spoken language capabilities to text-based dialogue teachers [1]. • An Intelligent Tutoring System for Mathematics: ActiveMath is a web-based smart mathematics teaching solution. The technological and pedagogical objectives of Active Math, as well as its design and architecture concepts, information retrieval, and learning ability, are presented by Nye [1] by focusing on AI-based characteristics in particular. • An Intelligent Tutoring System for Visual Classification Problem-solving: Visual categorisation issues can be solved with an intelligent teaching system. Crowley and Medvedeva [8] outline the creation of a broad Intelligent Tutoring System for solving visual categorisation problems. They advocate that methodologies and materials, cognition theory, recent empirical work on diagnostic problem—solving competence, the evolution of pathological competence all influence an effective methodology. Within the paradigm of the integrated problem-solving technique descriptive languages component, the providing a complete elements of cognition access to appropriate and experience and understanding system design. The expert’s model’s abstraction problem-solving methods produce a dynamical solution which it on domain ontology, task ontology, and example data. Responding to the present state of the Student Model, student interactions with the solution graph are filtered through an instructive layer that is provided by a second set of abstract problem-solving procedures and pedagogic ontologies.

5 Advanced Artificial Intelligence Tutors in Education What function does AI play in education and learning? Actually, it is fantastic. First and foremost, AI-enabled solutions aid in internal work optimisation. Pretty soon, there will also be a necessity to integrate people’s hard work with smart gadgets. It is clear that technology has fundamentally altered the way people learn and educate. Listed below are the top five Ai Technology in Teaching Cases for Efficiency Improvement: iTalk2Learn The ITS named iTalk2Learn [11] is a primary school-focused online learning platform. Machine learning must be utilised to construct individual teaching strategies, according to the system developers, which is a cooperative European endeavour. The curriculum concentrates on factions, which the creators claim continue to be a barrier to a disproportionate percentage of children’s mathematical growth. The curriculum offers a mix of scheduled exercises and collaborative laboratory exercises, thanks to the employment of seven modalities. Voice recognition is one of the characteristics

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of the intervention programme. At the moment, deep learning technologies are only optimised for users who talk English. The extracts from all the files containing abovementioned data are analysed by the programmers to “train” the system on how to detect typical inquiries and voice pictures. iTalk2Learn’s German voice recognition is also effective. Third Space Learning In the United States, Thirdspace Learning [12] is one of the major online math platforms. AI has been incorporated since 2016 and its goal is to keep track of the progress of the students and also to improve their services. Additionally, the business plans to use AI to deliver real-time tutoring responses to its online teachers in the near future. Thirdspace wants artificial intelligence to detect issues and advise the teacher before they become worse if the student misunderstands the work or if the teacher overlooks anything critical. Thirdspace Learning’s founder, Tom Hopper, noted that the organisation keeps track of every training session, which totals hundreds of hours. More than 400 math specialists have been identified and trained, according to the company’s website, and more than 100,000 h of mathematical instruction have been accomplished to date. Thirdspace is presumably looking to expand its database in order to lay a solid basis for its AI operations. This would be very useful for determining student learning styles and personalising mathematics learning. Duolingo Chatbot Duolingo announced the launching of iPhone-compatible chatbots for learning French, Italian, and Spanish fluency in 2016. Users interact with the programme via suggestions in their native language, like with traditional bots. Despite this, the business claims that its chatbot is unique because it can “receive and reply to a few thousands of distinct replies” rather than a limited number of clues. The “help me reply” feature, which suggests various responses if the user is having difficulty answering, is one of the benefits. During discussions, unfamiliar and new terms can be translated or evaluated for pronunciation in real time. The firm now claims to have a user base of more than 150 million people. According to research done on the usefulness of Duolingo for learning Spanish, it is a very successful tool [13]. Thinkster Math Thinkster [14] is a mathematics training platform that uses AI and machine learning to assist math teachers keep track of their students’ progress. Thinkster, according to his website, employs artificial intelligence to show a student’s thought process while completing a mathematical problem. The student’s work is recorded, and the steps taken to answer the mathematical issue are tracked by the training platform. As a consequence, mathematics trainers will be able to identify problem areas and report on solutions to boost student productivity. To attain common competence in this area, the platform tracks the student’s ability to complete assignments that contain interruptions.

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EdTech Foundry EdTech Foundry, a Norwegian organisation, released Differ, a chatbot that may assist students in their educational pursuits [15]. The method was designed to deliver rapid responses to students’ inquiries, which are typically asked several times throughout the semester. These inquiries range from broad to more specific questions concerning the syllabi and the course expectations. Furthermore, in addition to answering inquiries, the chatbot occasionally recommends academic papers and reading to students who are interested in topics related to their studies. A chatbot also provides explicit opportunities for a student to contribute to the lesson, for as through forum posting. To function, the chatbot must collect and work on questions and student–teacher interaction all of the time in order to increase its capacity to provide appropriate replies and suggestions. The findings of the business’s pilot programme revealed a five-fold increase in student involvement in the communications. Learning Management Systems: A lMS is an internet tool for creating, managing, and delivering courses. Teachers’ work is organised and streamlined using course management software. The built-in tracking tools generate thorough reports and help you keep track of your progress on the course. In such a sense, it is just one shop for all things intellectual.

6 ITS Versus LMS—Future of Education A comparison between the ITS and LMS has been outlined in Table 1:

7 Conclusion AI research approaches and Intelligent Tutoring Systems (ITSs) are viewed as the future’s training framework, with numerous tests completed using these. ITSs are quite fruitful, and despite having instructors’ positions, they face the challenge of supporting understudies. Understudies’ differences aren’t taken into account in traditional presentation and instruction delivery conditions. We reviewed existing Intelligent Tutoring Systems and explored some advanced Intelligent Tutoring Systems for teaching. Further, a comparative study of LMS and ITS has been tabulated. Intelligent software agent technology has been proposed as a possible option for extending Intelligent Tutoring Systems to meet the demand for social context for learning. In future, each and every component of Intelligent Tutoring Systems needs to be effective and more usable by utilising the in-built AI with each of these modules. We observe that the major challenges are: (1) Integration of the four modules of ITS, (2) Improving and Evaluation of user interfaces of ITS, and (3) Acquisition, representation, and manipulation of the domain knowledge.

An ITS carries the potential to play a substantial role in the future of education by means of addressing numerous issues which are now plaguing the industry. One of the most important issues in young people’s education, and one that applies to anyone of any age, is that humans are complex creatures who require tailored learning approaches for succeeding. This goes against the grain of several of our present educational systems that are constructed on standardised testing and a one-size-fits-all approach. Individuals have been left behind as a consequence of the existing approach that fails to emphasise on distinct skill-sets within learners. AI and ITS-like systems take on this obstacle’s front, creating an environment for learning centred around individualised curriculum and showcasing individual skills/interests. Several experts believe that this is the most effective method of teaching, and numerous countries are adopting it

a. b. c. d. e. f. g.

By Problems faced

By Needs

Assume the role of a production set to represent student competency Explain the problem-solving objective framework Instruct students in the framework of problem-solving Encourage a conceptual grasp of problem-solving skills Working memory load should be kept to a bare minimum As you learn, change the grain size of your lesson Ensure that consecutive approximations to the target skill are easier

ITS

Criterion

Table 1 Comparison between the ITS and LMS

a. b. c. d.

It lessens the workload of teachers Increases the effectiveness of learning Course development is an important step in the educational process Analysis is ongoing

(continued)

Classroom-based teaching-and-learning has long been on priority in India’s educational system, as has adherence to traditional teaching practises. Classroom-based learning, on the other hand, is unable to meet the demands of twenty-first-century education, such as easy access to knowledge, chances for digital learning, the capacity to receive quick feedback, and increased motivation to learn. The K—12 sector is now ready to bridge this learning gap and provide comprehensive options to its pupils and teachers, thanks to the expansion of digital devices, tools, and platforms. While e-books and tablets are some of the digital tools available to schools for their students, they do not provide a comprehensive learning solution. Institutions are under continuous pressure to perform with technological advancements. To improve education, schools require digital platforms that support formal teaching–learning activities as well as community building. To this goal, Learning Management Systems (LMS) are available

LMS

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Limitations

Human contact is disappearing Educators are out of work Financial difficulties Emotional Awareness is lacking Addiction to Ai Technology Data Issues in Ai Technology Telecommunication Disruption Learners’ ability to think is diminished i. Difficulties with maintenance j. The learners’ indolence

a. b. c. d. e. f. g. h.

m. n. o.

j. k.

a. b. c. d. e. f. g. h.

Advantages

Information that is well-organised Personalised Education Better for Special Education Students Immersive education Tutoring Systems that are Intelligent Facilitation of Adaptive Group Formation by Example Moderation with Intelligence Virtual Reality Education i. Software for grading essays Assessment of Problem-solving in Real Time Dynamic Scheduling and Predictive Analysis to Improve Course Quality l. Intelligent Game-based Learning Environments for Virtual Humans Automated Translation Disabled People’s Empowerment (Differently-Abled) Human Error is Reduced

ITS

Criterion

Table 1 (continued)

a. LMSs have traditionally been course-centric rather than student-centric. The availability of tools can be leveraged to meet the demand for teachers to be re-skilled. Currently, an LMS does not support all teaching methods or provide resources for specialised educational practises, such as audio discussion boards b. Management and maintenance of an institution-wide Learning Management System necessitates a level of technical competence which most classroom teachers lack. Consequently, individuals who decide what technology to buy and how to install it are frequently detached from the classroom and only develop a partial understanding of the pedagogical implications of their final selections c. Some teachers lack the computer and information literacy skills, as well as the information management skills required to effectively use a Learning Management System to support their teaching. These teachers must not only learn how to work in such spaces, but also develop a critical viewpoint on their use of LMS in a variety of styles of instruction d. Designing and organising a combination of learning activities that are suited to student requirements, instructor abilities and style, and institutional technical capacity is a struggle for many teachers

a. It comes simple-to-use and is incredibly effective b. The session list includes each server module’s description along with accessibility c. Management personnel, educators, and students get to save considerable time by securing internet access to accounts that allows them to work anywhere in the world d. It makes the management of users, roles, courses, teachers, and facilities as well as generation of reports a breeze e. End users get to receive system reminders regarding delivery dates, test dates and answering the questions, amongst other stuff f. Several sorts of communication tools exist in a Learning Management System g. The system will run its own web forum, mail server, and chat application h. Using a Learning Management System furnishes guarantee for each student’s easy access to these tools without having to install additional software, and for the fact that all students are using the same resources

LMS

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References 1. Nye BD (2015) Intelligent tutoring systems by and for the developing world: a review of trends and approaches for educational technology in a global context. Int J Artif Intell Educ 25(2):177–203 2. Albatish I, Mosa MJ, Abu-Naser SS (2018) ARDUINO tutor: an intelligent tutoring system for training on ARDUINO. Int J Eng Inf Syst 2(1):236–245 3. Devedzic V, Debenham J (1998) An intelligent tutoring system for teaching formal languages. In: Goettl BP, Halff HM, Redfield CL, Shute VJ (eds) ITS 1998, LNCS, vol 1452. Springer, Heidelberg, pp 514–523 4. Sharma S, Kumar U (2015) Review of transform domain techniques for image steganography. Int J Sci Res 4(5):194–197 5. Sychev O, Penskoy N, Anikin A, Denisov ME, Prokudin A (2021) Improving comprehension: intelligent tutoring system explaining the domain rules when students break them. Educ Sci 11(11):719–743 6. Mahdi AO, Alhabbash MI, Naser SSA (2016) An intelligent tutoring system for teaching advanced topics in information security. World Wide J Multi Res Dev 2(12):1–9 7. Kele¸s A, Ocak R, Kele¸s A, Gülcü A (2009) ZOSMAT: web-based intelligent tutoring system for teaching–learning process. Expert Syst Appl 36(2):1229–1239 8. Crowley RS, Medvedeva O (2006) An intelligent tutoring system for visual classification problem solving. Artif Intell Med 36(1):85–117 9. Granic A, Stankov S, Glavinic V (2000) User interface aspects of an intelligent tutoring system. In: Proceedings of the 22nd international conference on information technology interfaces (Cat. No.00EX411). IEEE, Pula, Croatia, pp 157–164 10. Abd El-Sattar HK H (2008) An intelligent tutoring system for improving application accessibility of disabled learners. In: Proceedings of the 2008 fifth international conference on computer graphics, imaging and visualisation. IEEE, Penang, Malaysia, pp 286–290 11. Muangprathub J, Boonjing V, Chamnongthai K (2020) Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon 6(10):e05227 12. Cukurova M, Mavrikis M, Luckin R (2017) Interaction analysis in online maths human tutoring: the case of third space learning. In: André E, Baker R, Hu X, Rodrigo M, du Boulay B (eds) AIED 2017, LNCS, vol 10331. Springer, Cham, pp 636–643 13. Purcaru D, Purcaru A (2016) Experimental systems for acquiring technical knowledge and practical skills on electronic measurements. Ann Univ Craiova, Ser Autom Comput Electron Mechatron 13(40):5–14 14. Chen X, Xie H, Hwang G-J (2020) A multi-perspective study on artificial intelligence in education: grants, conferences. J Softw Tools Institutions Researchers. Comput Educ Artif Intell 1:100005 15. Horacek H (2008) A high-level categorization of explanations a case study with a tutoring system. In: Proceedings of the third international conference on explanation-aware computing. ACM, Aachen, Germany, pp 84–95

Stock Prediction Using Machine Learning and Sentiment Analysis Preeti Bailke, Onkar Kunte, Sayali Bitke, and Pulkit Karwa

1 Introduction Predicting rightly the value of the stock opens the door to massive profits for both the buyer and the seller. In general, it is assumed that the forecast is more scientific than random, which means that it can be forecasted by thoroughly examining all stock market records. Machine learning (ML) is a useful tool for performing such procedures. It forecasts market current prices of stocks that are close to the carrying amount, improving accuracy. Because of the efficient and precise measurements, the establishment of ML in the stock market has impressed numerous researchers [1]. The dataset used is an important part of machine learning. The data set based on Yahoo finance includes the following five variables: close, low, open, volume, and high. Close, open, high, and low are stock bid values at different times with nearly the same name. Closing values are used to make predictions of future value. The values are extracted from the data set and then they are tested with the help of algorithms. ARIMA and LSTM are involved in this concept. Reversal includes error reduction, for which LSTM helps with data recovery and long-term results. LSTM is capable of retrieving data from previous stages and can be utilized for further predictions. ARIMA model is a time series algorithm that provides a view of future stocks.|

P. Bailke · O. Kunte (B) · S. Bitke · P. Karwa Deparment of Information Technology, Vishwakarma Institute of Information Technology, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_42

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2 Literature Survey Stock market prediction is a tedious task since there are many changes happening at a given instance of time. Currently, various research experts have given methods and techniques in the stock market for selecting a trade. A survey involves study and analysis of stock markets and the factors affecting stock prices for future value predictions. Iyer and Mehra [2] have presented a detailed survey of twelve different techniques which include State Vector Machine Regression (SVMR), Hidden Markov Model (HMM), Back Propagation Neural Networks (BPNN), Stacked LSTM, Fusion models, etc., used in the predictions of various different stock markets. Authors have presented the results along with pros and cons of the techniques in tabular form which gives a brief outline about them. Vats and Samdani [3] presented a similar study in which they analyzed the machine learning techniques like Neural Networks, Reinforcement Learning, Genetic logarithms, Support Vector Machines, Expert Weighting, Decision Trees, and Boosting. It describes predictions of the Tokyo Stock Exchange using neural networks. The neural network utilized here included 15 input variables, one output variable, and two hidden layers to anticipate stock price patterns in Japan. Despite the fact that the prediction results are 19% more accurate than other existing models, certain drawbacks are presented. Finally, in the prediction of non-linear stock market data, neural network have the following limitations: 1. Predictions using the neural network’s are computationally costly. 2. When working with small sample sizes, they are prone to overanalyzing data and falling into local minima. 3. NN’s generalization skills are lacking. This work focuses mostly on Support Vector Machine (SVM), which claims to be one of the most appropriate time series prediction algorithms known. SVMs benefit from two ideas: margin maximization and kernel techniques. Using the largest margin, SVMs create an ideal separating hyperplane. The input feature space is transferred into a higher dimension so that a linear model may separate it in the higher dimensional space (hard margin). Linear, radial basis function and sigmoidal kernels can all be utilized for this purpose. Support vectors are input vectors that describe the maximum margin’s width into a csv document. Jia [4] has predicted Google’s daily stock from 2005 to 2015 using LSTM. The root mean square value of LSTM is 0.0265. Yu and Li [5] have done stock market predictions on NASDAQ using numerous models. In this work, Shallow LSTM, Deep LSTM, and 1-D CNN techniques have achieved testing accuracy of 55.2%, 55.3%, and 55.6%, respectively. After reviewing relevant papers, comparison of complexity vs. accuracy is shown in Table 1.

Stock Prediction Using Machine Learning and Sentiment Analysis Table 1 Comparison of complexity versus accuracy

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Model

Complexity

Accuracy

Linear regression

Low

Medium

LSTM

Medium

Medium–high

ARIMA

High

High

3 Methodology Sections 3.1 and 3.2 describe the dataset used. Sections 3.3, 3.4, 3.5 and 3.6 describe techniques used. Section 3.7 explains RMSE.

3.1 Dataset The data is collected online from ‘Yahoo Finance’ website. The data set includes the following values: Open, High, Low, Volume, Adj_Low, and Close. For prediction, the model uses only Date and Close columns of the dataset. Close column contains the price of the stock at closing time of that trading day. In order to have better results, the Close column is filtered for further processing. In order to ensure the reliability of the results, the filtered Close values are checked for null values. Further, the scaled down values are converted into an array. If the dataset is not reliable, an Alpha vantage API key is taken. For twitter sentiment analysis real-time twitter API is taken and data is extracted from it [6]. To obtain historical and real-time data for several markets Alpha vantage is used.

3.2 Fetching Data Yahoo Finance is available as a library in python which is imported as yfinance. Here two inbuilt functions namely render_template and request_form (nm) are used. Function render_template is used to initiate output from a template file that is available in the application’s templates folder. Function request_form (nm) is used where the server receives data through the POST method, and the value of the “nm” variable derived from the form data is attained by user = request.form[‘nm’]. A function is defined as get_historical (quote). Here the quote value is the company name which is posted in request_form (nm). Function get_historical (quote) is described below: The function includes two variables as start and end date. The timeline between the start and end dates is of the previous two years. This real-time data is downloaded and then converted into a csv file and due to technical reasons if this is not possible then alpha vantage API key is used. The Alpha vantage API is a method to obtain historical and real-time data for a number of other markets. The data can be accessed

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directly in Python or any other library and stored for later use. The next step is to use the get_data function where preprocessing is done. In preprocessing, the file is converted to csv and is read. All the empty and null values are removed. Flask is used as a framework in the application to show data. Flask has extensions that can add extra app features. Extensions are available for object-related map editors, form verification, upload management, various open verification technologies, and several tools related to the standard framework.

3.3 LSTM LSTM is a type of RNN capable of achieving long-term reliability. LSTM has the ability to remember information for a long time and avoid long-term dependence. LSTM has a chain structure and internally operates using gates and layers of sensory networks as alternatives to RNN. The LSTM structure is constructed in the form of a cell which runs across LSTM, for which the value is being changed by active gateways by allowing or not allowing data to be added to the cell state. Figure 1 represents a diagrammatic expression of LSTM where each line represents a vector, i.e., an output from a particular node will act as the input to the next node. The first step here is the forget gate. Basically the network decides what part of previous information is to be remembered and what part needs to be forgotten. The previous information and the new information are transformed into a vector [0, 1]. It transforms to zero if information is irrelevant and to 1 if it needs to be processed later. It is done by sigmoid function activation [7]. Output is multiplied with previous state and irrelevant information is multiplied by 0 so that it vanishes. The next step involves using the latest network which is

Fig. 1 Repeating module in LSTM

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really a tanh generated neural network. The network assists in a way that the earlier state which was hidden and new data which was given as an input creates a new memory vector. This vector consists of the information from the latest input data which gives the information from the earliest state which was hidden. This vector helps to determine and modernize the cell state of the network given the new data. Then the final step is to obtain the filter vector by passing the earliest hidden state and current input data with the help of a sigmoid activated neural network to obtain the filter vector. This filter vector is applied to the cell state by point wise multiplication. The new hidden state gives the output. These details are shown in Fig. 1. While creating a LSTM model, the number of LSTM cells to be included throughout the layer is considered. This number depends on which application will be used by the LSTM model. In the proposed system, four LSTM layers with 50 neurons each are considered. The single dense layer is also thought to be the dense NN layer, where the dense layer connects each cell to another in the next layer. The LSTM model contains four layers, three hidden LSTM layers with 50 neurons and one dense layer. The output of the first hidden layer is connected to another hidden layer then that layer is connected to the third layer and lastly connected to a dense layer. 25 epochs are taken into consideration during experimentation. Batch size is the number in which the entire data set is divided. There are 32 batches in the proposed system. RMSE is used for time series forecasting as the model’s loss function. Adam is used as an optimizer due to its high performance and convergence. For measuring the accuracy of the LSTM model are able to make a prediction based totally on the past information by making a prediction from the history statistics [8]. This may be done by feeding the LSTM version the last prediction that turned into fed to the LSTM model, on the way to get the remaining access information of the training set, it will then make a prediction which will represent the next adjusted closing price. The time step is set to 502, which means the last 503 days closing value will be considered to calculate the 504th day closing value. The prediction is done for the next seven days. The results are discussed in Sect. 4.

3.4 Linear Regression In arithmetic and calculation, linear regression is the relationship between a scale response and one or more descriptive variables (independent and independent variables). When there is one descriptive variable it is called a simple line reverse; and if there is more than one, the process is called multi-line retraction. In linear regression, relationships are modeled using linear predictive functions that are estimated to model parameters in the data. Such models are called linear models.

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As with all types of descent analysis, the point of regression is located in the conditional distribution of the given number of predictions, rather than in the spread of combined opportunities for all of these variables, which is the area of variance. Line reversal has many practical uses. Most applications fall into one of the following two categories: If the objective is to predict, or reduce errors, linear regression can be used to match the predictive model in the hired data set of response values and descriptive variables. If the goal is to define variability in response variability then linear regression analysis can be used to measure the strength of the relationship between response and descriptive variables [9]. The equation for linear regression is as follows: Y = θ1 X 1 + θ2 X 2 + .... + θn X n

(1)

Here, x 1 , x 2 ….x n represent the independent variables while the coefficients θ 1 , θ 2 …θ n represents the weight.

3.5 ARIMA The ARIMA model is commonly used in mathematical problems and in the time series analysis. These models are used to better comprehend time series data and forecast future points in the series (prediction). When the data reveal indications of statistical instability but not variance, and the first step of the variance can be employed to eliminate the intermediate function instability, ARIMA models are applied. Since, the ARIMA model, according to Wold’s declining theory, is theoretically sufficient to define a general term (known as purely non-deterministic) with a broader sense, it committed to making a fixed time series, using variance before using the ARIMA model. In the ARIMA framework, there are certain non-specific yet intermediate components. Profit volatility is reversed by remaining values, as seen by the AR component of ARIMA. The retroactive mistake, according to the MA section, is a linear combination of words with errors that happened at different periods in the past. The difference between the data values and the prior values has flipped the data values (and this division process may have been performed more than once). Each of these aspects has the goal of modeling the data as precisely as feasible. Here, p is the number of autoregressive terms, ‘d’ is the degree of variance (the number of times the data is extracted from past values) in the autoregressive model, and q is the model order in the moving average model. ARIMA seasonal models are commonly referred to as (P, D, Q) m where m refers to the number of time steps in each season, and capital letters P, D, Q refer to autoregressive, differencing, and intermediate terms [10].

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3.6 Sentiment Analysis Twitter data is used to analyze the sentiments about trading a stock. What people are talking about a stock is really important as they get to know about their views. Famous entrepreneur like Elon Musk’s tweets have lots of effects on the market. In 2021 he famously tweeted that Tesla’s price was “too expensive,” sending shares down more than 10% almost immediately, although they quickly recovered. In the experimentation, a CSV file of Yahoo Ticker Symbols is used. Twitter API gives real-time tweets of that symbol and name. Then Sentiment Analysis is performed to check the polarity of the tweets and a pie chart is given which informs whether the tweets are positive/negative/neutral [11].

3.7 Root Mean Square Value For checking the error in the predicted stock, Root Mean Square Error (RMSE) value is used. RMSE is the standard deviation of the residuals. Residuals are the distance between the regression line and data points. It gives information about how closer the data is around the line. The errors and RMSE values of various cases are analyzed in Sect. 4 [12].

4 Results Section 4.1 describes the Stock Prediction results obtained by different machine learning techniques. Section 4.2 explains output of Sentiment Analysis.

4.1 Stock Prediction Using Machine Learning The proposed models are trained and tested using the Yahoo dataset finance. The dataset is converted into two sets training and testing. The predictions made by the all models are given as follows: In Fig. 2 the stock prices for Apple company are given, which is used as a training model. The performance of LSTM is obtained when the time step is set to seven days. This means that the stock market values of the past two years are used to predict the value of the next seven days. In figures, the proximity of the two lines shows the efficiency of the model. When a significant amount of time has passed, the prediction approximates real patterns. Larger datasets provide more accurate results. Moreover, here in the above Figs. 3, 4, 5 the red line shows the actual price where the blue line shows the predicted

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Fig. 2 Training dataset

future prices. To observe the accuracy of each given model it can be seen the difference between the prices of actual measured price lines and the predicted lines. The accuracy of ARIMA is greater than the other two models, as the lines are getting overlapped.

Fig. 3 Prediction by LSTM model

Fig. 4 Prediction by LR model

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Fig. 5 Prediction by ARIMA model

4.2 Sentiment Analysis Twitter API is used to categorize tweets. It takes 300 tweets and judges them with the help of Text Blob. It is able to analyze sentiment of each tweet into positive, negative, or neutral. The result is representing a percentage of positive, negative, and neutral sentiment tags. Depending on the result and percentage the overall sentiment is predicted.

5 Comparative Analysis The models are tested on a few real-time scenarios and conditions. The LSTM model, Linear Regression, and ARIMA are trained for the stock companies like Apple. ARIMA model shows results with good accuracy. LSTM and ARIMA have improved the accuracy of the predictions, resulting in positive outcomes, with the ARIMA model proving to be particularly effective. The results are promising, leading to the conclusion that utilizing machine learning techniques, it is possible to predict stock markets more accurately and efficiently. The RMSE value which is calculated and used to check the error in the predicted stock; these RMSE values are in Table 2. From the respective analysis, it is clear that the ARIMA model has performed way better than the LSTM and Linear Regression model. Also it should be noted Table 2 RMSE values for different techniques

Company

LSTM

ARIMA

Yesbank

1.7

0.49

Apple

5.42

2.33

Adani power

9.24

3.8

ONGC

5.55

2.63

LR 2.22 9.14 13.9 6.67

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that the ARIMA model performs better when the market has less volatility. ARIMA results are less deviated and the plot for ARIMA is also close to constant. Also, there are situations where the predictions from the ARIMA model aren’t accurate. Using an ARIMA model will not help us to minimize the error during volatile periods of the index because the volatility happens when, e.g., during a financial crisis or if some negative news concerning the index comes out to the public. Situations like the COVID-19, where the market has a global impact, creates large uncertainty in stock prices [13].

6 Conclusion The proposed system is an attempt to generate prediction models utilizing machine learning algorithms to predict the correct prices of stocks using LSTM, Linear Regression, ARIMA algorithms, and Sentiment Analysis. The main contribution of the proposed system is the use of the ARIMA model which shows results with good accuracy. All of them, LSTM, Linear Regression, and ARIMA have shown improvements in the accuracy of the predictions, thus producing positive results with the ARIMA model proving to be very effective. The results lead to the conclusion that it is possible to predict stock markets more accurately and effectively using machine learning strategies. In this digital age Sentiment Analysis gives us an idea of what investors think of a particular stock.

7 Future Scope In the future, the accuracy of the stock market forecast model can be further enhanced using a larger database and taking into account other factors affecting the market. In addition, some emerging machine learning models can also be considered to improve the accuracy further. In-depth learning-based models like CNN can also be used for speculative purposes. One of the major technical analyses is support and resistance level indicators which are taken here to forecast the future trend of the stock prices. The investors may analyze the uptrend and downtrend to determine the buying or selling of shares and to identify these levels using the Fibonacci retracement. It is considered as an efficient tool which supports the analysis of traders. This major task can be added in future work. Also it can’t totally rely on only mathematical models and calculations. In the future, it can take more features like NIFTY, sensex, etc.

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References 1. Lawal ZK, Yassin H, Zakari RY (2020) Stock market prediction using supervised machine learning techniques: an overview. In: IEEE Asia-Pacific conference on computer science and data engineering (CSDE) 2. Iyer M, Mehra R (2018) A survey on stock market prediction. In: Fifth International conference on parallel, distributed and grid computing (PDGC) 3. Vats P, Samdani K (2019) Study on machine learning techniques in financial markets. In: IEEE international conference on system, computation, automation and networking (ICSCAN), India 4. Jia H (2016) Investigation into the effectiveness of long short term memory networks for stock price prediction. arXiv [cs.NE] 5. Lu W, Li J, Li Y, Sun A, Wang J (2020) A CNN-LSTM-based model to forecast stock prices. Complexity (1) 6. Mandloi L, Patel R (2020) Twitter sentiments analysis using machine learning methods. In: International conference for emerging technology (INCET) 7. Istiake Sunny MA, Maswood MMS, Alharbi AAG (2020) Deep learning-based stock price prediction using LSTM and Bi—Directional LSTM model. In: 2nd novel intelligent and leading emerging sciences conference (NILES) 8. Wang Y, Liu Y, Wang M, Liu R (2018) LSTM model optimization on stock price forecasting. In: 17th international symposium on distributed computing and applications for business engineering and science (DCABES) 9. Cakra YE, Distiawan Trisedya B (2015) Stock price prediction using linear regression based on sentiment analysis. In: 6th international conference on advanced computer science and information systems (ICACSIS) 10. Shrivastav LK, Kumar R (2019) An empirical analysis of stock market price prediction using ARIMA and SVM. In: 6th international conference on computing for sustainable global development (INDIACom) 11. Yadav N, Kudale O, Gupta S, Rao A, Shitole A (2020) Twitter Sentiment analysis using machine learning for product evaluation. In: International conference inventive computation technologies (ICICT) 12. Vazirani S, Sharma A, Sharma P (2020) Analysis of various machine learning algorithm and hybrid model for stock market prediction using python. In: International conference on smart technologies in computing, electrical and electronics (ICSTCEE) 13. Qi Y, Yu W, Deng Y (2021) Stock prediction under COVID-19 based on LSTM. In: IEEE Asia-Pacific conference on image processing, electronics and computers (IPEC)

Parkinson Disease Screening Using UNET Neural Network and BWO Based on Hand Drawn Pattern Pooja Gautam Waware and P. S. Game

1 Introduction Parkinson’s disease is a nervous system ailment that affects the human body’s motor activities. It is a progressive chronic illness, meaning it gets worse over time. With each passing day, the symptoms get more severe. Neurons, which are important nerve cells in the brain, malfunction and, as a result, cease to operate indefinitely. This has an impact on the substantia nigra, which houses the majority of these neurons in the brain. A substance called dopamine is created as a result of the malfunctioning of these neurons. Dopamine is crucial for the body’s mobility and coordination. The effective amount of dopamine in the body declines as Parkinson’s disease progresses, resulting in a loss of control over body movements. Although the actual number of Parkinson’s sufferers is unknown, the disease is affecting an increasing number of people. Parkinson’s disease can strike anyone at any age, while it is extremely rare for those under the age of 30 to develop symptoms. In the majority of cases, the age is greater than 60. Parkinson’s disease grows more common as you become older. The degree of the disease diagnosed in the patient determines the amount of Parkinson’s symptoms. Because of most precise analysis to be shown as the outcome of the Parkinson’s disease detection. For the categorization, the Decision Tree implements the IF–then rules. This results in a successful and practical implementation of the approach, which employs machine learning techniques to deliver very accurate Parkinson’s disease identification. Section 2 of this paper is used for reviewing the past work on Parkinson’s disease detection models. In Sect. 3 proposed the disease’s mildness, early indications and symptoms may go unreported. Symptoms usually begin on one side of the patient’s body and progressively move to the opposite side. P. G. Waware · P. S. Game (B) Computer Engineering, Pune Institute of Computer Technology, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_43

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The following are the primary signs and symptoms of Parkinson’s disease, Tremors are the trembling or tremors of the limbs (both hands and feet) even in mild Parkinson’s disease, are a regular occurrence. Slowed movement (bradykinesia)—Reduced capacity to move quickly. Even simple chores take longer when the muscles are shaking. Rigid muscles—Muscle stiffness causes pain and limits movement. Stooped posture and poor balance are symptoms of poor posture and balance. Loss of automatic movements—All of us have unconscious behaviors that we don’t even realize we’re doing such as winking, Blinking, and other human behaviors, which are reduced and also, Smiling is becomes less and less common. Slurring, stammering, or speaking too quickly are all examples of speech alterations that are noticed in the patients. Parkinson’s disease has no established causation. There is no solution in order to get rid of it despite the fact that the symptoms can be managed if caught early enough, by treating it through medication or surgery. However, there are no conventional Parkinson’s disease detection procedures such as MRI and PET scans represent examples of traditional tests on brain disease patients. CT scans, for example, are ineffective in detecting Parkinson’s disease. This research proposes a Parkinson’s disease model to address this issue where machine learning techniques are used to create a detection mechanism. Therefore, there is a need for an effective technique for the purpose of enabling an accurate and timely detection of the Parkinson’s disease. The implementation of the machine learning techniques can be of immense assistance in this regard and allow the accurate determination of the Parkinson’s disease detection in a timely manner. The methodology proposed in this paper implements Black Widow Optimization and Decision Tree to effectively classify the Parkinson’s disease. The input is the patient’s handmade spirals, which are preprocessed and optimized using the Black Widow Optimization method. The Black Widow algorithm was created from the Black Widow spider’s mating ritual. This is one of the most effective and efficient strategies for improving our training results. This is ideal for implementation because it considerably reduces the strain on the system. This technique is combined with the use of the Decision Tree approach, which provides successful categorization of the output obtained from the spiral image processing. The output is separated by the Decision Tree, which allows just the methodologies are explained in detail. The obtained results are evaluated using the Sect. 4 and finally Sect. 5 concludes this paper.

2 Literature Survey In [1] explains that Parkinson disease is one of the most problematic occurrences that are highly difficult to detect and effectively diagnose for a patient. There are various techniques that are used which require extensive amounts of time and resources for the purpose of diagnosis and detection. Therefore, to provide a solution to this problem the authors propose the use of freezing of gait for the purpose of Parkinson

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disease detection through the utilization of Support Vector machines and wavelet transformation. The presented technique approaches effective and promising results. In [2] discusses that there has been an increase in the number of diagnosis of Parkinson disease patients across the world. This increase in the number of patients has been attributed to the improved accuracy of the detections that are being performed. Therefore, to further improve the paradigm and implement an effective and useful diagnosis of Parkinson’s disease authors have proposed the utilization of dysphonia speech and its feature analysis for monitoring the growth of the Parkinson disease. Experimental results have been performed on the down sampled signals which has achieved an effective utilization for telemedicine in the future. In [3] elaborate on the various impacts that the Parkinson’s disease can have on individual and the countries and economy as the whole. Large number of older individuals formed the larger section of society that is suffering from this debilitating disease. Most of the Pieces of the Parkinson’s disease are elderly individuals that have been diagnosed in the later years of their life. Increase in the number of patients and the high cost of detection and diagnosis of this disease has led to a large impact on the various resources especially the developing countries. The photo authors of proposed an effective text mining technique for identification of the relationship between Parkinson’s disease-based researches and the gait analysis of the patient. In [4] expresses that there has been lot of innovation that has been targeted toward the diagnosis and detection of Parkinson disease. This disease is highly debilitating and can lead to a lot of psychological as well as physical problems for the patient. Most of the patients are old age individuals that are suffering to this disease. To improve the detection capabilities of the diagnostic process the authors propose the utilization of a force sensitive platform to perform gait analysis which has been evaluated through extensive experimentation. In [5] discuss that the development of Parkinson disease takes a very long and inordinate amount of time. During this time, the disease is often undetected and can be very hard to diagnose for the individual. The problematic occurrence for this Parkinson’s analysis is the Time taken for performing the diagnosis which can be the factor and the survival of the patient. Therefore, there is a need for an accurate and timely implementation of the diagnosis of the patient. Therefore, the authors of proposed utilization of image processing approaches to be utilized on the brain CT scan images for automatic and highly accurate detection of Parkinson’s disease. In [6] narrates that it is highly complex and difficult for the categorization and classification of Parkinson disease effectively. This is highly difficult for the purpose of providing timely treatment to the patient as it can take a very long time to detect the disease and provide effective treatment. Therefore, the traditional techniques need to be improved and effectively implemented in a fast manner for the treatment to be formed as soon as possible. For this purpose, the authors have proposed synthetic minority quota sampling technique along with random forest classification for the purpose of classifying speech signal to detect the presence of Parkinson’s disease are suffering to this disease. To improve the detection capabilities of the diagnostic process the authors propose the utilization of a force sensitive platform to perform gait analysis which has been evaluated through extensive experimentation.

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In [7] narrates that there are multiple different types of nervous disorders that affect a lot of individuals all across the world. One of the most problematic out of these occurrences is the Parkinson’s disease which effectively reduces the quality of life for that individual significantly. Parkinson disease is highly difficult to detect which makes it really late when it is diagnosed. The authors comment that there has to be an effective technique that can provide results in a fast and effective manner. For this purpose, the authors have provided an effective classification system for the purpose of classifying the presence and prediction of Parkinson disease in an individual. In [8] explains that Parkinson disease is a highly degenerative nervous disease which is second to only Alzheimer’s in terms of severity. Parkinson disease usually affects elderly individuals which is diagnosed pretty late leading to a decreased quality of life for these patients. To significantly improve the process of identification and detection the authors have proposed and effective mechanism for Parkinson disease detection through the implementation of image processing. The authors have utilized the database which contains the progression markers for Parkinson’s disease using magnetic resonance imaging. The authors have implemented segmentation of the texture based on intensity. In [9] elaborate on the neurological defect or a degenerative disease known as Parkinson’s disease. This disease is highly problematic and usually affects the elderly or old population significantly. The disease is highly painful and the effective diagnosis of this disease is very time-consuming and often results in inaccurate diagnosis. Therefore, to improve the diagnostic abilities and provide an effective and useful parameter for the detection of Parkinson’s disease the authors have utilized voice-based features. The technique has been highly effective in the detection and diagnosis of the Parkinson’s disease through the use of the voice of the patients with very high accuracy. In [10] discuss the prevalence of Parkinson’s disease which is a neurological disability that affects the central nervous system and results in the loss of motor reflexes for an individual. Which disease is mostly seemed to occur in individuals that have aged significantly and results in impaired balance and tremors. The process of diagnosis of Parkinson disease is not as effective and is a times consuming procedure. Therefore, the authors propose an intelligent computing approach for the purpose of identification of Parkinson’s disease in an individual. The authors have utilized ANN, KNN, and SVM and compared the results for these approaches in machine learning applications. In [11] explains that there had been largescale advances in the paradigm of processing the information that is conveyed nonverbally. One of the most effective and predominant approaches for non-verbal communication has been the speech signals for the humans. The cognitive responses can also be effectively identified and correlated using the speech signals from the particular subject. The authors have explained that the speech analysis can be vital to achieve an effective diagnosis for the purpose of classification of the Parkinson’s disease effectively. The authors have proposed the use of ratio of transient parts for the purpose of Parkinson disease classification.

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In [12] elaborates on the topic of Parkinson’s disease which is a highly problematic and can lead to further complications if not detected and treated on time. The Parkinson’s disorder is usually noticed within increased frequency in individuals with advanced age. The age factor severely limits the mobility and can lead to increased tremors and shaky hands. The problems can be difficult to gauge and identify for the purpose of diagnosis. To provide a solution to this problem, the authors have proposed the use of gait analysis for performing the diagnosis of the ailment. The researchers have utilized the length of the strides and the steps for their classification and achieved effective accuracy. The main drawback of this approach is the lack of a prediction base approach for the detection purposes. In [13] states that the process of detection of a serious neurological ailment such as Parkinson’s is a convoluted and a complex procedure. The diagnosis takes a lot of time to process and results in a diagnosis with a low accuracy rate. This is an undesirable circumstance as the timely and prompt treatment of Parkinson’s is necessary to ensure the survival and pain free life for the patient. Therefore, there is a need for an effective technique for the identification and classification of the Parkinson’s disease. The main drawback noticed in this approach is the lack of a technique to reduce the class imbalance effectively. In [14] introduces the concept of Parkinson’s disease which is a neurological disorder that can negatively affect the motor cortex of the individual which can lead to impaired voice and movement, which is due to a compromised central nervous system. The detection of Parkinson’s disease on time can be helpful in reducing the impact of the disease on the motor functions of the individuals. Therefore, the researchers in this methodology propose an effective technique for the detection of Parkinson’s disease using a text mining approach that extracts the relationship between gaits Parkinson’s disease from Parkinson’s disease researches. In [15] discusses Parkinson’s disease that has been increasingly being diagnosed for more and more individuals all over the world. Parkinson’s disease has also been the cause of a large number of fatalities which are increasing every year. The Parkinson disease does not have a cure or a remedy, but if the disease is identified on time, it can be highly beneficial for the patient as there are medications that can significantly reduce the effects and provide a healthy life to the patient. Thus, the researchers in this study define a novel concept for the detection of Parkinson’s disease through the feature analysis of dysphonia speech of the Parkinson patient monitoring. In [16] narrates that Parkinson’s disease is one of the problematic diseases that leads to the slow degradation of the neurological system of an individual. This leads to a reduction in motor acuity and fine motor skills. Parkinson’s disease can also lead to the inclusion of various tremors and inability to coordinate the muscles for performing day to day tasks. This leads to degradation in the quality of life for the patients. Therefore, the authors in this publication have outlined an effective technique for the implementation of image processing in the detection of arytenoid cartilage feature points using laryngeal 3D CT images. In [17] explains that the process of progression of Parkinson’s disease as it causes the degradation of the nerve cells in the striatum region of the brain. This structure is one of the largest structures on the basal ganglia of the brain. There are various

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techniques to study the reduction of dopamine in the brain to identify the onset of Parkinson’s disease. The authors have utilized F-FDOPA for the purpose of realization of the lowering dopamine levels in the brain. The authors utilize the images to effectively identify and achieve the accurate measurement of Parkinson’s disease progression. In [18] states that the detection of Parkinson’s disease is a highly complicated as well as a complex maneuver. This is highly difficult to achieve the diagnosis and the longer it takes to diagnose, the more badly the condition of the patient. This is due to the fact the condition of the patient deteriorates considerably with time while suffering from Parkinson’s disease. Thus, detection and diagnosis of Parkinson’s disease in a timely manner is extremely necessary. For this purpose, the authors have presented an innovative approach that utilizes the speech and the ratio of transient parts of speech for the detection of Parkinson’s disease effectively. In [19] introduces the concept of Parkinson’s disease detection using voice data of the patients. Parkinson’s disease is one of the most depreciating diseases that gets increasingly worse as it progresses. The Parkinson disease does not have a cure, but the symptoms can be managed effectively if is detected and treated early. Therefore, the researchers in this publication have provided an innovative scheme for the identification and detection of Parkinson’s disease onset in an individual through the use of voice data feature sets using nonlinear Decision Tree classification to achieve their goals. The proposed methodology has been experimented with to extract its performance, which is within the expectations.

3 Proposed Methodology ALGORITHM 1: Region of Interest image formation. In [20] narrates that the process of identification and detection of Parkinson’s disease is a very critical concept that is utilized for the purpose of enabling effective diagnosis. The process of diagnosis of Parkinson’s disease is one of the most complex as well as complicated maneuvers. This type of neurological disorder impacts the central nervous system and the symptoms are an indication of the degenerative disease. The authors in this paper try to identify the presence of Parkinson’s disease using the freezing of gait symptoms. This is achieved by the detection of a failure to gain initiation using Support Vector. The thorough evaluation of the related works has been crucial in the development of the Parkinson’s disease detection approach. The literature survey has provided a valuable insight into the process that has helped develop the proposed approach in this research article. There have been a number of drawbacks and other limitations noticed in the studied papers, such as low accuracy, which have been successfully overcome in the presented approach. The presented technique for the Parkinson’s disease detection is displayed in Fig. 1. The sequential steps that are carried out in this process are expressed in the section given.

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Fig. 1 System overview diagram

Step 1: Dataset Collection—The proposed detection model for Parkinson’s disease diagnosis employed a Parkinson’s disease dataset which is downloaded from the URL: https://www.kaggle.com/kmader/parkinsons-drawings?. This dataset is divided into two different categories, testing and training images, which are acquired from the Parkinson’s patients and healthy individuals. The dataset comprises of wave and spiral structures sketched by both types of the individuals, healthy and diseased. Step 2: Preprocessing—As the dataset is provided as an input to the system it is effectively preprocessed. The path of the folder is provided to the system that extracts the filenames and they are stored in a list. These images are then extracted and preprocessed through rescaling the images to a particular height as 200 and width as 200. After the images have been rescaled, these images are then utilized for region of interest evaluation in the next step of the procedure. Step 3: Region of interest—Every image from the input dataset is examined to retrieve the RGB components in this step of the region of interest evaluation. The RGB components of the dataset images are verified for a value above 200. If any pixel’s RGB value is less than 200, the pixel’s RGB components are set to (255,255,255), which represents white color. This white color pixel represents the spiral or wave pattern line pixels. If the pixel readings are higher than 200, these pixels are assigned a value of (0, 0, 0), indicating a black pixel. The white pixel levels indicate the likelihood of a pattern region, which is noted and forwarded to the next phase of

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the UNET deep learning model. The following algorithm 1 depicts the process of estimating the region of interest. Step 4: UNET model—This is the strongest deep learning model, and it is commonly used in image categorization for biometric images. This UNET architecture receives input pictures, which are inspected employing a convolution layer comprised of a 3 × 3 kernel. The region of interest has produced a binarized image, which is an image consisting of only black and white pixels. The hand drawn patterns are converted into a white color and the rest of the image is in a black color, which highlights the pattern effectively. But it is noticed that there is a lot of noise in the form of white pixels in the image. This needs to be eliminated in the convolution layer to reduce the errors in the detection in the later stage of the U-Net. The convolution procedure is the initial stage of the U-Net architecture where each of the pixels in a binary image is traversed and the white pixels are identified. Once a white pixel is detected, the convolution procedure initiates and a white pixel is searched in the vicinity of the white pixel in 8 directions. The kernel of 3 × 3 is used to scan the surroundings and understand if the pixel is part of the pattern or it is just a noise pixel. If it not a pixel from the pattern but just a stray noise white pixel, it is effectively converted to black, which cleans the image and gets rid of the noise. This resultant image is stored in the form of a blob object in the database as the training data. This procedure is done for spiral and wave images of the healthy and Parkinson’s patients drawing. Step 5: U-Net Network Layer and Black Widow Optimization—In this step of the procedure, the user of the system can effectively utilize the interactive user interface and provide the system with the testing images. The testing image is then provided as input, consisting of either the spiral or the wave images. This image taken as an input is subjected to the entire process of preprocessing through resizing, along with the region of interest evaluation and the convolutional layer the U-Net. The resultant image is achieved and is considered as the initial population image. // Input: Preprocessed Image PIMG //Output: ROIIMG // function: roiFormation (PIMG) 1: 1: Start 2: ROIIMG = ∅ 3: for i = 0 to size of Width of PIMG 4: 4: for j = 0 to size of Height of PIMG 5: PSIGN = PIMG [i,j] RGB 6: R = (PSIGN > > 16 & HD) 7: G = (PSIGN > > 8 & HD) 8: B = (PSIGN > > 0 & HD) 9: if ( R < = 200 AND G < = 200 AND B < = 200) 10: ROIIMG[i,j] = setRGB(255,255,255) 11: else 12: ROIIMG[i,j] = setRGB(0,0,0) 13: end if

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14: end for 15: end for 16: return ROIIMG 17: stop // Output: Probability Score PS Function: probabilityScoreEvaluation (TIMG, DIMG) 0: 0: start 1: PS = 0, W = 8, H = 8 [ Width and Height Generations] 2: for x = 0 to Size of TDIMG 3: K=0 4: BLx = TIMG / W 5: BLy = TIMG / H 6: for i = 0 to W. 7: for j = 0 to H 8: SUBTIMG = TIMG [BLxi,BLyj] 9: SUBDIMG = DIMG [BLxi,BLyj] 10: BR1 = avgBrigtnessOf(SUBTIMG) 11: BR2 = avgBrigtnessOf (SUBDIMG) 12: if ( | BR1 – BR2|< = 25),then 13: K++ 14: end if 15: end for 16: end for 17: if (K > PS),then 18: PS = K 19: end if 20: end for 21: return PS 22: Stop The images of the selected pattern which are stored in the database are selected and subjected to Black Widow Optimization. The initial population image and the trained image are first divided into 64 generations. These generations are blocks of these two images that are then compared for their average brightness as given in the algorithm 2 below. If the comparison factor for the average brightness of the generations between these images is less than or equal to 25, a count is iterated. This process is repeated for all the healthy images in the database and the Parkinson’s images too. A max count for both the sets of trained image comparisons in the form of a score is maintained and provided to the next step of the procedure for classification. The mathematical depiction of the procedure is provided in the Eq. 1 given below. The process of the probability score evaluation is depicted in the algorithm 2. ( p) =



i = i = N 0|IMIMG(F) − DBIMG(F)| ≤ 25

(1)

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where, N IMIMG (F) (F) f(p)

Number of generations. Initial Mutation Image Factor DBIMG Database Image Factor Probability Score Function.

Algorithm 2: Probability ScoreEvaluation. // Input: Test image TIMG, Train Database image TDIMG. The performance metric of precision and recall is successfully used to identify the effectiveness of the Parkinson’s disease detection technique. The empirical examination is covered in the section that follows. Performance Evaluation Through Precision and Recall: Precision, recall, and accuracy are measured to achieve an accurate assessment of the performance of the presented technique. Here in this experimentation, precision, recall, and accuracy are used to measure the performance attained for the Parkinson’s disease categorization. Among the most fundamental evaluation parameters which extracts the system’s effectiveness is precision and recall evaluations. The precision is governed by the model’s relative accuracy. Recall, on the other hand, may be conceived of as being the model’s exact efficiency. Equations 2, 3, and 4 are shown below for a more thorough description of precision and recall performance metrics. Step 6: Decision Tree—The obtained healthy and Parkinson’s scores from the previous steps are utilized as an input for the purpose of classification. The scores are compared with one another to determine if the test image consists of a Parkinson’s diagnosis or not. The scores are utilized by the Decision Tree model to determine the higher score between the two scores obtained. This model effectively applies efficient IF–then rules to assess the existence of Parkinson’s disease for the supplied sample sketch. The collected findings are presented to the user in the form of a graphical user interface.

4 Result and Discussions The presented methodology for Parkinson’s disease identification is developed in Java and runs on a computer based on the Windows operating system. The NetBeans IDE is used to write code for this technique. The implementation device is provided with an Intel Core i5 CPU, 6 GB of RAM, and a 500 GB hard drive. The MySQL database server facilitates the storage responsibilities. The efficacy of detection of the presence of Parkinson’s in a patient must be evaluated in order to achieve a successful application of Parkinson’s disease identification. This method uses a pattern of spirals and sine waves sketched by the patients as input.

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Before using the method for detection, these pictures must be trained to an image binarization algorithm. Precision = A/(A + B)

(2)

Recall = D/(A + B)

(3)

Accuracy = (A + D)/(A + B + C + D)

(4)

where, A The number of accurate Parkinson’s disease detections (True Positive). B The number of inaccurate Parkinson’s disease detections (False Positive). C The number of accurate Parkinson’s disease detections s not detected (False Negative). D The number of inaccurate Parkinson’s disease detections s not detected (True Negative). The precision and recall metrics were used to evaluate the Parkinson’s detection competence. The results of comprehensive investigation on the system for a number of attempts with different types of samples being delivered with each repetition which are summarized in Table 1. Figure 2 illustrates the outcomes of the Parkinson’s disease detection in the format of a chart. The precision, recall, and accuracy scores evaluated for Parkinson’s disease detection have demonstrated effectiveness of the proposed system in utmost detail. The prescribed approach has been effectively compared with the methodology mentioned in [12]. Our methodology achieves 94.37% precision, 94.37% recall, and 94.37% of accuracy. The comparison of the Polar Expression Feature technique with the presented technique has been illustrated in [12] has been depicted in a tabular format in Table 2 given. As it can be seen in Fig. 3, the deep learning technique stipulated in this research article reliably outclasses the Polar Expression Feature approach given in [12]. This is due to the U-Net architecture and the Black Widow Optimization that have been deployed to boost the accuracy of Parkinson’s disease detection substantially. These results are highly acceptable as the presented system and achieve the accuracy established through the performance scores.

5 Conclusion and Future Scope This paper deals with the early prediction of the Parkinson’s disease detection based on the hand written drawing sample of the patients. Basically, these drawing of the patients generally deliver an idea behind applying artificial intelligence based on the

No. of accurate predictions for Parkinson’s or healthy (A)

9

9

7

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Input type

Spiral healthy

Spiral Parkinson’s

Wave healthy

Wave Parkinson’s

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1

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No. inaccurate predictions for Parkinson’s or healthy (B)

Table 1 Precision and recall performance

0

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Relevant predictions Irrelevant predictions not detected for not detected (D) Parkinson’s or healthy

100

87.5

90

100

Precision

100

87.5

90

100

Recall

100

87.5

90

100

F Measure

100

87.5

90

100

Accuracy

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Fig. 2 Graphical representation of the precision and recall values Table 2 Precision, recall, and accuracy comparison Performance metric

U-Net and Blackwidow optimization

Polar expression feature [12]

Precision

94.37

93.72

Recall

94.37

86.79

Accuracy

94.37

90.84

Fig. 3 Comparison with polar expression feature depicted in [12]

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smoothness of the drawing. Hence, this paper collects the sample drawing of the patients and then they are rescaled to a specific length and width. These rescaled images are subjected to the binarization process to convert the image into black and white, this process, in turn, produces an image into a black and white model, where black pixels represents the background and the white one indicates the drawn lines by the patient. This binary image ultimately feed to the UNET deep learning model to estimate the prediction score of the Parkinson’s disease. The obtained scores are used by the Black Widow Optimization model to estimate the pattern list based on the prediction score. These patterns list is finally used by the Decision Tree model to predict the presence of the Parkinson’s disease for the fed dataset images. Indepth evaluation of the system using precision, recall, and accuracy score indicates the betterment of the system toward the identification of the Parkinson’s disease in comparison with the conventional approaches. For the future enhancement of this model an interactive API can be developed to use the model by the other developers and researchers. And also huge dataset can be used to train the model so that it will be used in other applications like mobile and web.

References 1. Raza M, Awais M, Singh R, Imran M, Hussain S (2021) Intelligent IOT framework for indoor healthcare monitoring of Parkinson’s disease patient. IEEE J Sel Areas Commun 39(2):593–602 2. Cai J, Liu A, Mi T, Garg S, McKeown MJ, Jane Wang Z, Trappe W (2019) Dynamic graph theoretical analysis of functional connectivity in parkinson disease: the importance of fiedler value. IEEE J Biomed Health Inf 23(4):168–194 3. Demrozi F, Bacchin R, Tamburin S, Cristani M, Pravadelli G (2019) Towards a wearable system for predicting freezing of gait in people affected by Parkinson’s Disease. IEEE J Biomed Health Inform 24(9):168–194 4. Er O, Cetin O, Serdar Bascil M, Temurtas F (2016) A comparative study on Parkinson’s disease diagnosis using neural networks and artificial immune system. IEEE J Med Imaging Health Inf 6(1):264–268 5. Pérez-Ibarra JC, Siqueira AG, Krebs I (2020) Identification of gait events in healthy and Parkinson’s disease subjects using inertial sensors: a supervised learning approach. IEEE Sens J 20(24):558–748 6. Sakar BE, Isenkul M, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 17(4):168–194 7. Lei H, Huang Z, Zhou F, Elazab A, Tan E, Li H, Qin J, Leif Zhou B (2018) Parkinson disease diagnosis via joint learning from multiple modalities and relations. IEEE J Biomed Health Inf 23(4):168–194 8. Ghoraani B, Hssayeni MD, Bruack MM, JimenezShahed J (2019) Multilevel features for sensor-based assessment of motor fluctuation in Parkinson’s disease subjects. IEEE J Biomed Health Inform 24(5):168–194 9. Monajemi S, Eftaxias K, Sanei S, Ong S (2016) An informed multitask diffusion adaptation approach to study tremor in Parkinson disease. IEEE J Sel Top Sign Process 24(7):168–194 10. Pasluosta CF, Gassner H, Winkler J, Klucken J, Eskofier M (2019) An emerging era in the management of Parkinson’s disease: wearable technologies and the internet of things. IEEE J Biomed Health Inf 19(6):168–194

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11. Shahbakhi M, Far DT, Tahami E (2014) Speech analysis for diagnosis of Parkinson’s disease using genetic algorithm as support vector machine. IEEE J Biomed Sci Eng 7(4):147–156 12. Shi Y, Yang W, Thung KH, Wang H, Gao Y, Pan Y, Zhang L, Shen D (2020) Learning-based computer-aided prescription model for Parkinson disease: a data-driven perspective. J Biomed Health Inf 6(5):168–194 13. Chen X, Wang ZJ, Member S, McKeown MJ (2014) A ThreeStep multimodal analysis framework for modeling muscular activity with application to Parkinson disease. IEEE J Biomed Health Inf 18(4):168–194 14. Yang TL, Kan PJ, Lin CH, Lin HY, Chen WL (2019) Using polar expression features and nonlinear machine learning classifier for automated Parkinson disease screening. IEEE Sens J 1(1):558–748 15. Zham P, Arjunam SP, Raghav S, Kumar DK (2017) Efficacy of guided spiral drawing in the classification of Parkinson’s disease. IEEE J Biomed Health Inform 22(5):168–194 16. Sanguansuttigul P, Saleewong T, Khamwam K, Bongsebandhu S (2020) Modelling the concentration chan of 18FFDOPA using compartmental model in Parkinson patients. In: IEEE Journal of electrical engineering/electronics, computer, telecommunications and information technology, pp 281–486 17. Kiss G, Bendegúz A, Sztahó D, Vicsi K (2018) Detection possibilities of depression and Parkinson’s disease based on the ratio of transient parts of the speech. IEEE J Cognitive Info Commun Bp Univ 5(2):559–700 18. Aich S, Younga K, Lee Hu K, Abdul Hakim A, Absi A, Sain L (2018) A nonlinear decision tree-based classification approach to predict the Parkinson’s disease using different feature sets of voice data. IEEE J Biomed Health Inf 4(1):105–120 19. Tran Lya Q, Ardi AM, Gilatb M, Chaia R, Ehgoetz Martensb A, Georgiades M (2017) Detection of gait initiation failure in Parkinson’s disease based on wavelet transform and support vector machine. IEEE J Eng Med Biol Soc 7(3):345–456 20. Yang T, Hung Lin C, Ling Chen W, Yu Lin H, Liang C (2020) Hash transformation and machine learning-based decision making classifier improved the accuracy rate of automated Parkinson’s disease screening. IEEE J Neural Syst Rehabil Eng 28(1):320–534

A Comprehensive Review for Optical Character Recognition of Handwritten Devanagari Script Pragati Hirugade, Rutwija Phadke, Radhika Bhagwat, Smita Rajput, and Nidhi Suryavanshi

1 Introduction Optical Character Recognition (OCR) is a method for digitizing handwritten or machine-printed text from scanned images. It has applications in areas such as post-office automation, government and private office automation, searching and extracting data from papers and books, and bank check processing [1]. OCR is divided into two categories—Printed Character Recognition (PCR) and Handwritten Character Recognition (HCR). Because different people have varied writing styles, HCR is more complicated than PCR [2]. In Devanagari Character Recognition systems, image acquisition, pre-processing, segmentation, feature extraction, and classification are all critical tasks. The pre-processing techniques are used to improve the images to make further steps easy. Grayscale images in the dataset are binarized using various segmentation methods [3]. Further steps involve the removal of shirorekha. The following process includes the extraction of relevant features [4] and the classification of characters. There may be requirements for additional steps like inversion, skeletonization, and normalization. The use of additional steps depends on the dataset and the model deployed. P. Hirugade (B) · R. Phadke · R. Bhagwat · S. Rajput · N. Suryavanshi Cummins College of Engineering for Women, Pune 411052, India e-mail: [email protected] R. Phadke e-mail: [email protected] R. Bhagwat e-mail: [email protected] S. Rajput e-mail: [email protected] N. Suryavanshi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_44

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This paper aims to review the various research work done in the field of Devanagari HCR and can be a useful resource for researchers working in this domain. It entails studying, analyzing, and justifying the optimal technique suitable for Devanagari HCR. The paper is organized as follows: Sect. 2 presents-related work in the field of Devanagari HCR. Section 3 focuses over the benefits and drawbacks of each method, as well as the obstacles that must be overcome in order to have an efficient and accurate Devanagari HCR system. The conclusion is presented in Sect. 4.

2 Literature Survey This section examines the most recent research describing the work done in the field of Devanagari Handwritten Character Recognition. The first part of this section presents the notable work done using the traditional machine learning methods while the second part focuses on the work done using recent deep learning techniques.

2.1 Traditional Methods All the earlier work done in Devanagari HCR was done using traditional machine learning techniques. It was necessary to train the system with images of each character and to work on one font at a time [4]. Gaur et al. [5] presented a handwritten Hindi characters recognition system based on K-means clustering and Support Vector Machine (SVM). K-means clustering reduces the feature vector, making computation easier. The findings were derived using two classification algorithms, one using Euclidean distance and the other with SVM. The authors reported that the SVM results outperformed the Euclidean distance results. The accuracy of 81.7% was attained with Euclidean distance. However, the dataset used in the work is too small and a larger dataset must be used for better results. Puri et al. [6] proposed a Confounder Correcting SVM (CC-SVM) model for shirorekha-less character recognition and classification. Average accuracies of 98.35% and 99.54% were obtained for handwritten and printed images, respectively. This study shows the need for having a large dataset with a variety of characters including compound characters. Pande et al. [7] proposed the Character Recognition System’s flowchart is shown in Fig. 1. To recognize the characters, a few well-known classifiers such as Decision Tree, Nearest Centroid classifier, K-neighbors, Extra Trees, and Random Forest classifier were used. The authors reported that the Random Forest classifiers and Extra Trees classifiers gave the highest accuracy of 76.82% and 78.19%, respectively. The authors further reported that a hybrid approach can be implemented to achieve better accuracy. Histogram of Oriented Gradient (HOG) was used as a feature extraction method by Narang et al. [8]. The work used three classification techniques for recognition:

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Fig. 1 Flow chart of character recognition system [7]

SVM, Decision Tree, and Naïve Bayes. The accuracy was reported to be 90.70% when using an SVM classifier with an RBF kernel. However, the authors stated that as the number of classes increases, recognition accuracy may suffer to some extent. Kale et al. [9] used the Zernike moment technique to present a recognition system. SVM and the k-NN neural network approach were used to recognize handwritten Marathi Devanagari. The authors reported that the system can only handle 45 compound characters and that, due to a lack of a large data set, the system had a 0.37% recognition rate. Garg et al. [10] attempted an offline HCR system for the Hindi language, using a segmentation-based approach and SVM. The authors achieved an accuracy of 89.6%. The authors stated that the system can be improved by using other classifiers. Based on the foregoing, traditional machine learning algorithms can be used for small datasets. Selection of the various steps like pre-processing, feature extraction, and classifier used affects the performance of the model. The work done in Devanagari Character Recognition systems using traditional machine learning algorithms is summarized in Table 1.

2.2 Deep Learning Methods In recent times, deep learning techniques have developed rapidly. All the current work in HCR focuses on deep learning techniques due to their advantages over traditional machine learning algorithms. Dessai et al. [11] utilized LeNet-1, a CNN architecture. Data samples from 15 different characters were collected. The accuracy obtained was 89.34%. For the recognition of handwritten Hindi characters, Chaudhary et al. [12]

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Table 1 Comparative study of machine learning algorithms for Devanagari Character Recognition Sr. No

References

1

Language

Dataset used

Technique used Accuracy (%)

Gaur et al. [5] Hindi

Hindi dataset consisting of 430 images

Euclidean distance and SVM

95.86

2

Puri et al. [6]

Hindi, Sanskrit, Marathi

Dataset of all 3 languages written by 2 authors and 60 printed documents

SVM

99.54

3

Pande et al. [7]

Not mentioned

43,000 images (32 × 32 pixels) of 43 characters

Extra trees classifiers

78

4.

Narang et al. [8]

Not mentioned

5484 characters segmented from ancient Devanagari manuscripts

Naïve Bayes, SVM and Decision Tree

90.70

5.

Kale [9]

Marathi

Dataset created using 250 volunteers of different age groups

Zernike moment technique

98.37

6.

Garg et al. [10]

Hindi

Database created using 15 different writers

Segmentation approach with SVM

89.6

used a LeNet-5 architecture. The authors trained models using 96,000 character sets which resulted in a validation set accuracy of 95.72% using Adam optimizer and 93.68% using RMSprop optimizer. The authors reported that as LeNet-5 has 5 layers with learnable parameters; it resulted in an increase in accuracy than LeNet-1. Singh [13] proposed an ANN approach in which 70% of the dataset was used for training and 15% of the dataset was used for validation and testing. The number of hidden layers was initially set to 5 and then increased to 20 layers. For 15 hidden layers, the accuracy using Artificial Neural Network (ANN) and Histogram of Oriented Gradient (HOG) was 97.06%. However, the model was able to recognize only vowels and the base form of consonant characters. Pandey et al. [14] proposed using Convolutional Neural Networks to recognize Devanagari Handwritten Characters. The work utilized an online-based model with six layers, including two convolutional layers, two max-pooling layers, and two fully connected layers, yielding a 95.6%. Another author Narang et al. [15] used 3 convolutional layers and obtained an accuracy of 93.73%. They used a dataset consisting of 33 classes with a total of 5484 characters. It was observed that with an addition in convolutional layers and datasets; there is an increase in the accuracy rate. Dokare et al. [16] compared 4 different architectures of CNN by changing positions of different layers and bagged an accuracy of 97.56%. They introduced the use of a dropout layer which gave good results. Bisht et al. [17] suggested the use of double CNN architecture for better results with compounded words and the accuracy achieved was 95.97%. They used an 8620 characters dataset with 70% for training. They also compared SVM + HOG and CNN with single and double architecture.

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Fig. 2 Six-layer architecture of CNN [19]

Mathew [18] proposed Multilingual OCR for Indic Scripts. Their recognition system had two Recurrent Neural Networks. These two were used for script identification and for recognition, respectively. The authors developed an OCR system for 12 Indian languages along with English. The accuracy achieved was more than 95% for all the languages. However, the authors stated that the words having matras have larger error rates than words that do not contain matras. Bhagat et al. [19] presented a recognition system of Devanagari Handwritten Characters using Deep Convolutional Neural Networks. An offline-based model was used. The work used four convolutional layers, two max-pooling layers. The accuracy obtained by the model was 99.65%. A dropout layer was used to prevent overfitting. However, due to the close resemblance between a few characters, the authors reported that there are chances of wrong predictions. Figure 2 shows the architectural model with different layers. Aneja [20] recognized Devanagari Handwritten Characters using transfer learning for Deep Convolutional Neural Network. The work used a pre-built model for a task as the basis of another task. The challenge of overfitting due to the unavailability of large datasets was solved using transfer learning. The work reported that AlexNet gives good accuracy and is relatively faster. The Inception model achieved an accuracy of 99%; however, the model provided low accuracy for highly similar characters. Using the CNN, Mohite et al. [21] presented a Devanagari Handwritten Character Recognition System. For their application, the authors used transfer learning of pre-trained architecture such as AlexNet, GoogLeNet, and ResNet. For Devanagari characters, the identification accuracy was 91.23%, while for Devanagari numerals, it was 100%. However, the accuracy could have been improved by using large and different datasets. Avadesh et al. [22] presented a Sanskrit OCR. They presented a novel technique for using convnets as classifiers for Indic OCRs, demonstrating that convnets outperform SVMs and ANNs for multi-class image classification problems. Another approach was proposed by Dineshkumar [23] using ANN. After the training process, the corresponding characters were sent as input to the neural network with divergent sets of neurons in hidden layers to calculate the recognition accuracy rate for different Sanskrit characters. The accuracy level obtained was 98%. Table 2 shows the summary of the various work done in Devanagari Character Recognition using deep learning. It gives details of the language and the dataset used, the deep learning architecture utilized, and the accuracy achieved.

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Table 2 Comparative study of deep learning algorithm for Devanagari character recognition Sr. No

References

Language

Dataset used

Architecture

Accuracy (%)

1

Dessai et al. [11]

Devanagari (Hindi, Sanskrit, Marathi, Nepali, Konkani, etc.)

1500 samples per character for training and 250 samples per character for testing

LeNet-1

89.34

2

Chaudhary et al. [12]

Hindi

96,000 samples

LeNet-5

95.72

3

Singh [13]

Hindi

70% for training and 15% for testing

Minimum 5 layers up to 15 layers of ANN and HOG

97.06

4

Pandey et al. [14]

Hindi, Marathi etc

3,78,951 images

6 layers of CNN 95.6

5

Narang et al. [15]

Not mentioned

33 classes with a total 3 layers of CNN 93.73 of 5484 characters

6

Dokare et al. [16]

Not mentioned

48 classes, DHCD—92,000 images

CNN with a dropout layer

97.56

7

Bisht et al. [17]

Not mentioned

8620 characters with 70% for training

Double CNN

95.97

8

Mathew [18]

12 Indian languages and English

Indian script consisting of 0.8 M words

Two RNN layers

> 95

9

Bhagat et al. [19]

Not mentioned

300 images per iteration and 15 such iterations

6 layers of CNN 99.65

10

Aneja and Aneja [20]

Not mentioned

92,000 images

AlexNet

98

11

Mohite et al. [21]

Not mentioned

The database developed by ISI Kolkata

CNN

91.23

12

Avadesh et al. [22]

Sanskrit

10,106 images of size 32 × 32 × 3 (RGB),having 602 classes

8 layers of CNN 93.32

13

Dineshkumar [23]

Sanskrit

20 × 30 pixel character

ANN with 1 hidden layer

98

3 Discussion Various deep learning and traditional machine learning models are presented in the literature for Optical Character Recognition. All the earlier work is based upon traditional machine learning algorithms. These models do not require very huge datasets for training while deep learning models

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may not give good accuracy if the dataset is scarce. With small data sizes, traditional machine learning algorithms are more suitable, but these models give less accuracy for highly similar characters compared to deep learning models. In Devanagari, some characters differ from each other just by a small circle or curve or single line. To overcome all these limitations, the deep learning approach is used by many researchers, and the accuracy given by these models is comparatively very high. It has been found that for pattern recognition CNN gives good results. Many researchers have used CNN for OCR. CNN performs comparatively better in the case of highly similar characters. However, to train the CNN model a huge dataset is necessary. Availability of publicly open large datasets with high variations in Devanagari Handwritten Character Recognition is limited. Techniques like data augmentation [12] and transfer learning can be used to overcome the problem of small datasets and to achieve high accuracy. Even with all the work done in Devanagari Handwritten Character Recognition, some challenges still need to be addressed. Deep learning requires a huge dataset for training; however, publicly available large datasets for Devanagari Handwritten Character Recognition are sparse. The system is complicated by the presence of shirorekha. The system’s intricacy is exacerbated by the wide range of writing styles. This necessitates the use of a large dataset to train the model. Approaches for increasing the number of training images have been shown to improve model performance [24]. An android-based application for OCR would be beneficial for people working in government offices to digitally store their data written in Devanagari script which will help in the automation of such systems.

4 Conclusion Optical Character Recognition has emerged over the last few decades. Machine learning techniques have proved helpful in giving good accuracy results for OCR models. With changing times, deep learning has received an upper hand over machine learning in tasks like object detection and image recognition. This review presents a comparative study between traditional and deep learning models used for Devanagari Handwritten Character Recognition. The study reports that most of the researchers suggest the use of CNN as it specifically outperforms among all models. Even with all positive points, CNN lags in terms of data. To fit a model with good accuracy, CNN requires a good number of training data. Also, training a CNN model with scanty data without overfitting is difficult to achieve. In India, there is an urge for an automated Devanagari OCR system to digitize forms, documents, and postal addresses. If an accurate OCR system is built, it will not only benefit the common people but also the banking and government sectors of India. Future steps would involve the creation of an accurate and automated OCR system. Such a system would only exist when there is a humongous and openly available dataset. This will encourage many researchers to test and develop automated OCR systems.

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References 1. Hamdan YB (2021) Construction of statistical SVM based recognition model for handwritten character recognition. J Inf Technol 3(02):92–107 2. Bhagyasree PV, James A, Saravanan C (2019) A proposed framework for recognition of handwritten cursive english characters using DAG-CNN. In: 2019 1st international conference on innovations in information and communication technology (ICIICT). IEEE 3. Sharma R, Mudgal T (2019) Primitive feature-based optical character recognition of the Devanagari script. In: Progress in advanced computing and intelligent engineering. Springer, Singapore, pp 249–259 4. Chaudhuri A et al. (2017) Optical character recognition systems. In: Optical character recognition systems for different languages with soft computing. Springer, Cham, pp 9–41 5. Gaur A, Yadav S (2015) Handwritten Hindi character recognition using k-means clustering and SVM. In: 2015 4th International symposium on emerging trends and technologies in libraries and information services, pp 65–70, https://doi.org/10.1109/ETTLIS.2015.7048173 6. Puri S, Singh SP (2019) An efficient Devanagari character classification in printed and handwritten documents using SVM. Procedia Comput Sci 152:111–121 7. Pande SM, Jha BK (2021) Character recognition system for Devanagari script using machine learning approach. In: 2021 5th international conference on computing methodologies and communication (ICCMC). IEEE 8. Narang SR, Jindal MK, Sharma P (2018) Devanagari ancient character recognition using HOG and DCT features. In: 2018 fifth international conference on parallel, distributed and grid computing (PDGC), pp 215–220. https://doi.org/10.1109/PDGC.2018.8745903 9. Kale KV (2014) Zernike moment feature extraction for handwritten Devanagari (Marathi) compound character recognition 10. Garg NK, Kaur L, Jindal M (2013) Recognition of offline handwritten Hindi text using SVM. Int J Image Process (IJIP) 7(4):395–401 11. Dessai B, Patil A (2019) A deep learning approach for optical character recognition of handwritten Devanagari script. In: 2019 2nd international conference on intelligent computing, instrumentation and control technologies (ICICICT), pp 1160–1165. https://doi.org/10.1109/ ICICICT46008.2019.8993342 12. Chaudhary D, Sharma K (2019) Hindi handwritten character recognition using deep convolutional neural network. In: 2019 6th international conference on computing for sustainable global development (INDIACom), pp 961–965 13. Singh N (2018) An efficient approach for handwritten Devanagari character recognition based on artificial neural network. In: 2018 5th international conference on signal processing and integrated networks (SPIN), pp 894–897. https://doi.org/10.1109/SPIN.2018.8474282 14. Gupta P, Deshmukh S, Pandey S, Tonge K, Urkunde V, Kide S (2020) Convolutional neural network based handwritten Devanagari character recognition. In: 2020 International conference on smart technologies in computing, electrical and electronics (ICSTCEE), pp 322–326. https:// doi.org/10.1109/ICSTCEE49637.2020.9277222 15. Narang SR, Kumar M, Jindal MK (2021) DeepNetDevanagari: a deep learning model for Devanagari ancient character recognition. Multimedia Tools Appl. 80(13):20671–20686 16. Dokare I et al. (2021) Recognition of handwritten Devanagari character using convolutional neural network. In: 2021 3rd international conference on signal processing and communication (ICPSC). IEEE 17. Bisht M, Gupta R (2021) Offline handwritten Devanagari modified character recognition using convolutional neural network. S¯adhan¯a 46(1):1–4 18. Mathew M, Singh AK, Jawahar CV (2016) Multilingual OCR for indic scripts. In: 2016 12th IAPR workshop on document analysis systems (DAS), pp 186–191. https://doi.org/10.1109/ DAS.2016.68 19. Bhagat et al. (2020) Devanagari handwritten character recognition using convolutional neural networks. In: 2020 International conference on electrical, communication, and computer engineering (ICECCE). IEEE

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20. Aneja N, Aneja S (2019) Transfer learning using CNN for handwritten Devanagari character recognition. In: 2019 1st international conference on advances in information technology (ICAIT) 21. Mohite A, Shelke S (2018) Handwritten Devanagari character recognition using convolutional neural network. In: 2018 4th international conference for convergence in technology (I2CT). IEEE 22. Avadesh M, Goyal N (2018) Optical character recognition for Sanskrit using convolution neural networks. In: 2018 13th IAPR international workshop on document analysis systems (DAS), pp 447–452. https://doi.org/10.1109/DAS.2018.50 23. Dineshkumar R, Suganthi J (2015) Sanskrit character recognition system using neural network. Indian J. Sci. Technol. 24. Hirugade P, Suryavanshi N, Bhagwat R, Rajput S, Phadke R (2022) A survey on optical character recognition for handwritten Devanagari script using deep learning (10 Feb 2022)

Emotion Detection Using Machine Learning Algorithms: A Multiclass Sentiment Analysis Approach Sumit Shinde and Archana Ghotkar

1 Introduction Emotions, especially negative emotions and stress are related to each other, understanding this relation is important to help individuals who are in stress, to deal with their negative emotions. Being under psychological pressure is stressful. For the events happening in day-to-day life, the human body reacts and responds mentally, chemically, and physically. Various stressful events each individual may face in his day-to-day life. If these events and associated feelings are not settled appropriately, then that person has to suffer from negative emotional consequences with high probability. There are few well-known negative emotions such as fear, anxiety, anger, shame, and danger. Usually, certain negative emotions arise from stressful events when self finds such events uncontrollable and leads to feeling a high level of stress [1]. Sentiment analysis [2, 3] is the technique which helps to analyze the text and identify the polarity of the text to categorize them as positive, negative, or neutral. Sentiment analysis helps to analyze the emotions behind the mode of communications such as text, audio, and video. Emotion detection and sentiment analysis, these are the two terms being used interchangeably in natural language processing. Sentiment analysis helps to identify the meaning of sentence as positive, negative, or neutral, likewise emotion detection helps to identify the emotion of human. Nowadays almost all are using social media platforms to express their emotions and feelings arising from daily life events, these expressed views can be in the form of text, images, or videos. If this data is analyzed properly for emotion detection and sentiment analysis technique, emotions of the concerned can be identified with ease. As negative S. Shinde (B) · A. Ghotkar SCTR’s Pune Institute of Computer Technology, Pune, India e-mail: [email protected] A. Ghotkar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_45

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emotions and stress are associated, stress level of a concerned person can be identified at primary stage of stressful events [4, 5]. Machine learning technique such as classification plays very important role here to classify the text into different classes dependent on our problem statement. Classification can be binary or multiclass classification. Multiclass classification gives us opportunity and superiority to classify our text in more related class. Individual face many life challenges in day-to-day life, some of them may lead to negative consequences if these challenges are not settled properly. Psychological research shows that stress response given by an individual often shows the negative emotions associated with it [6]. These negative emotions in turn affects the health of a sufferer. It is important to detect emotions and see its polarity of being classified into positive or negative emotions group. This classification will help to assess the individual for early detection of stress level with Artificial Intelligence system, and hence, preventive measures can be taken prior its severeness. Following sections of this paper discuss more about the detection of emotions from the text.

2 Literature Survey Imran et al. [7] used deep learning technique to identify the emotions of the people of different culture and geographical areas coming out of the decisions taken by their respective governments for the COVID-19 pandemic. They used Deep Long Short-Term Memory (LSTM) model for the analysis of emotions and sentiment polarity from the collected twits and performed validation on the sentiment 140 dataset. Dependent on sentiment polarity they have classified the tweets as positive or negative and then emotion recognition is being performed for the classification of twit text into suitable emotion class. Nandwani and Verma [8] carried out extensive survey in the domain and highlighted the important facts about sentiment analysis and need of emotion detection in association with it. Considering text as an important media to express the viewpoints about the life events, sentiment analysis and understanding emotion status of a person from the given text is a challenging task. They have highlighted the various challenges faced for sentiment analysis and emotion detection along with different levels of sentiment analysis and models of emotion detection from text. Huddar et al. [9, 10] presented a new approach called attention-based multimodal contextual fusion strategy, for automatic sentiment analysis and emotion detection. With attention model they have used bidirectional LSTM (Large Short-Term Memory) for the extraction of contextual information from the utterances. They have validated their model on IEMOCAP and CMU-MOSI datasets for emotion detection and sentiment analysis, respectively. They have also presented a study for novel approach for the extraction of the context at multiple level and inter-modal utterance understanding.

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Rothe et al. [11] conducted an online survey of child and adults with and without mental health conditions to assess their emotions and stress perceived during COVID19 pandemic. For this study they have considered 284 children and adolescents and 456 adults. What social restrictions and potential health risks affects emotions and perceived stress before pandemic and during pandemic is well explained in this study. Zhang et al. [12] proposed a graph-based emotion detection model, which incorporating the social correlation, emotion labels correlation, and temporal correlations. Multiple emotions detection from the online social networks is mainly addressed in this study.

3 Binary Versus Multiclass Sentiment Analysis Sentiment analysis which is also termed as opinion mining techniques helps to identify the emotion behind the text, video, or image. Generally binary classification of sentiments is a widely used technique and it just gives classification of a statement as positive or negative. On the other side, we have a multiclass or fine-grained sentiment analysis technique, which helps to infer more precise information of a given sentence with their classification and expressed emotions. This multiclass technique is useful for dualpolarity sentences such as “The company is good… but people working there are not happy”, where binary classification may fail to predict the correct result.

4 Experimental Analysis Classification of text data in five different classes to know the emotions attached with it is being carried out in this work. For experimental analysis, we first carried out the task of dataset preparation using nltk and regular expressions and vectorizing the words using TF-IDF (Term Frequency-Inverse Document Frequency) metric. Classifiers provided by scikit-learn were used to perform fine-grained classification to classify the given sentence into five different classes which express the emotion categories such as joy, sadness, anger, fear, and neutral.

4.1 Importing Dataset In natural language processing, detecting emotions from the text is one of the challenging tasks as available data is unstructured and unlabeled which gives multiclass representation of data. We also encounter class misbalancing in collected data

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Fig. 1 System architecture

because for each emotion getting enough sample records is a problem. For this experimentation, we have used a labeled dataset for the task of emotion detection using scikit-learn classifiers. Dataset was prepared from the two different datasets: ISEAR [13], dailydialog [14], emotion-stimulus [14] to create a balanced dataset with 5 labels: joy, sad, anger, fear, and neutral. The texts mainly consist of short messages and dialog utterances, with emotion attached with it (Fig. 1).

4.2 Text Pre-processing Text processing was carried out using nltk and regular expression. As text collected from the user’s tweets, feedbacks, and suggestions are not structured and ready enough to pass directly to classifiers, it is to be pre-processed and cleaned to obtain much important information for our classification task. Here are some pre-processing steps to consider: • Removing Noise and Punctuations: Social media data is unstructured and consists noise elements in the text such as html markups, URLs, non-ascii symbols, and trailing whitespaces. With the help of regular expressions or python packages such htmlparser, text can be denoised efficiently. Punctuation in the text needs to be handled with its priority, important punctuation to be retained and others to be discarded.

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• Normalizing Emotions: Normalizing the text samples for each emotion classification is important hence to maintain balance in the training dataset. This technique will allow the model to be trained without bias. • Negation Handling: Negation available with the word may change the polarity of the sentence. Hence, determining the scope of negation and handling it not to affect the polarity of sentence is important. • Tokenization: In tokenization, longer strings are converted into smaller pieces called word tokens. This process is also called as lexical analysis. Selection of appropriate segmentation strategy to identify the segmentation boundary is important in this process of text processing. • Stopword Removal: Commonly used words such as a, an, and the won’t add meaning to the sentence are called stopwords and can be ignored. • Stemming and Lemmatization: Bringing all words from its grammar tense to its original lemma using lemmatization and to its linguistic root form using stemming is necessary to maintain the uniformity in the sentiment analysis process.

4.3 Text Representation Processed text is vectorized using Term Frequency technique (Term Frequency (TF)—Inverse Dense Frequency (IDF)), TF-IDF measures the importance of a word in a given corpus. Importance of a word ‘w’ is proportional to the frequency of word ‘w’ appearance in a document and inversely proportional to the frequency of word ‘w’ appearance in the given corpus [8]. TF - IDF = TF ∗ IDF.

(1)

Here, • TF = (‘w’ word occurrences in a document)/(Total word counts of a document) • IDF = log (Total documents in a given corpus/Total documents containing the word ‘w’)

4.4 Classification Scikit-learn, a python library provides useful algorithms for the purpose of classification in machine learning tasks and is easy to implement. The following are some classifiers we used for the emotion detection task. Naïve Bayes: It is a simple and widely used classification algorithm for large data. This algorithm is based on the Bayes probability theorem to predict membership probability of each given class based on the likelihood of data points belonging to the corresponding classes.

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E1 P E2

 =

P(E 1 ).P



P(E 2 )

E2 E1

 .

(2)

Here, P(E 1 ) = The probability of occurrence of event E 1. P(E 2 ) = The probability of occurrence of event E 2. P(E 1 |E 2 ) = The probability of occurrence of event E 1 given event E 2. P(E 2 /E 1 ) = The probability of occurrence of event E 2 given event E 1. Logistic Regression: It is designed for binary classification, but can be used for multiclass classification with one-to-all approach. Multinomial logistic regressiona logistic regression model, learn and predict multinomial probability distribution is used for multiclass classification. Configuration of logistic regression can be done by setting “multi-class” argument to “multinomial” and “solver” argument to “lbfgs”. Model will be fitted using cross-entropy loss to predict integer value for each integer encoded class label. XGBoost: It is a trending algorithm using the concept of gradient boosting. This machine learning technique is used for the ensemble learning; hence, it works well in multiclass text classification. Support Vector Machine: It is a supervised machine learning (SVM) technique used for classification as well as for regression tasks. Best decision boundary between the data points those who belong to a group and not belong to a group is determined using a Support Vector Machine. Binary classification tasks are natively carried out by SVM, but multiclass classification problems can be solved by breaking the problem into multiple problems of binary classification. One approach is one-toone, and it transforms data into high dimensional space to achieve linear separation between each pair of classes. Second approach one-to-rest splits multi-classification problems into binary classification problems, over each binary classification binary classifier is trained to predict the most confident model [15]. According to two approaches discussed above, if we want to classify the data points into ‘c’ classes. • In the first one-to-rest approach, ‘c’ SVMs will be used by the classifiers where each SVM would predict the membership in one of the ‘c’ classes. • In the second one-to-rest approach, c(c−1) SVMs will be used by the classifier. 2

4.5 Model Evaluation Model performance evaluation is the integrated part of the model building process. Classification model evaluation focuses on the number of data points or tuples we classified correctly as well as those who are classified incorrectly. Confusion matrix gives us the number of correct and incorrect classification data points or tuples as compared to actual target values in the dataset. It is an N*N matrix, where N is

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the number of target classes. To evaluate the performance of a classification model choosing the right evaluation metric is important. There are some well-known metric measures available such as accuracy, recall, precision, F1 score, log loss, and AUC/ ROC curve. Here are two evaluation metrics we used for the classification model. Accuracy: Accuracy measure depicts the performance of a classification model by evaluating the number of data points predicted correctly divided by total number of predictions. Accuracy =

Number of Correct Predictions by Model Total Number of Predictions by Model

(3)

If we are working on an imbalanced dataset then accuracy will not be a standalone measure to judge the model performance. F1 score will be a better choice in this condition. F1 Score: It gives harmonic mean between precision and recall. If we want to maintain the balance between precision and recall of a model then F1 score metric is the best choice. This measure is more suitable when we observe imbalance in class distribution and to avoid Type-I and Type-II errors. F1 Score for each individual class is calculated and averaging is done to receive averaged F1 Score by examining different averaging methods such as macro, micro, and weighted (Fig. 2; Table 1). Calculation of F1 Score for class = Joy: F1 Scoreclass= Joy

  2 × Precisionclass=Joy × Recallclass=Joy  . =  Precisionclass=Joy + Recallclass=Joy

Here,

Fig. 2 a Confusion matrix of XGBOOST b Confusion matrix of Linear SVC

(4)

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Table 1 Performance measures of classifiers

Classifier

Accuracy (%)

F1 score (Micro)

Naïve Bayes

67.08

67.08

Logistic regression

69.21

69.21

XGBOOST

71.03

71.03

Support vector machine

72.80

72.80

Precisionclass=Joy =  Recallclass=Joy

  TPclass=Joy

. TPclass=Joy + FPclass=Joy   TPclass=Joy . = TPclass=Joy + FNclass=Joy

(5)

(6)

Here, Linear SVC classifier performed well with an accuracy of 72.80%. We have tested our model for Marathi speaking individuals by translating the Marathi sentence spoken by the user into an English sentence and model performed well to identify the emotion class of a user. Current state-of-the art methods working on some specific datasets of English texts, here we are taking audio input in Marathi language, audio-to-text procedure is carried out to get the text in Marathi. Marathi sentence is translated to English sentence and used as an input to the model for emotion detection. Using the concept of deep learning LSTM and BERT accuracy can be improved further.

5 Conclusion As stress and negative emotions are interrelated, to reduce the emotional symptoms associated with stress sources in human life precautionary measures are to be taken. Artificial Intelligence systems to detect the stress level and associated emotions will play a great role to recognize it early and help to prevent it with suitable precautionary measures. This work performed well with Linear SVC classifier with an accuracy of 72.80% to identify the emotion of Marathi and English-speaking individuals. Future scope for this work is to identify the mental stress condition of the user and display the mental stress level.

References 1. Plaza-Del-Arco FM, Molina-González MD, Ureña-López LA, Martín-Valdivia MT (2021) A multi-task learning approach to hate speech detection leveraging sentiment analysis. IEEE Access 9:112478–112489 2. Veltmeijer EA, Gerritsen C, Hindriks K (2021) Automatic emotion recognition for groups: a review. IEEE Trans Affective Computing

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3. Zhang D, Lin H, Zheng P, Yang L, Zhang S (2018) The identification of the emotionality of metaphorical expressions based on a manually annotated chinese corpus. IEEE Access 6:71241–71248 4. Luo J, Bouazizi M, Ohtsuki T (2021) Data augmentation for sentiment analysis using sentence compression-based SeqGAN with data screening. IEEE Access 9:99922–99931 5. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113 6. Yuan JH et al (2020) Recent advances in deep learning-based sentiment analysis. Sci Chin Technol Sci 63:1947–1970 7. Imran AS, Daudpota SM, Kastrati Z, Batra R (2020) Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on covid-19 related tweets. IEEE Access 8:181074–181090 8. Nandwani P, Verma R (2021) A review on sentiment analysis and emotion detection from text. Soc Netw Anal Min 11 9. Huddar MG, Sannakki SS, Rajpurohit VS (2021) Attention-based multimodal contextual fusion for sentiment and emotion classification using bidirectional LSTM. Multimedia Tools Appl 80:13059–13076 10. Huddar MG, Sannakki SS, Rajpurohit VS (2020) Multi-level context extraction and attentionbased contextual inter-modal fusion for multimodal sentiment analysis and emotion classification. Int Multimedia Inf Retrieval 9:103–112 11. Rothe J, Buse J, Uhlmann A, Bluschke A, Roessner V (2021) Changes in emotions and worries during the Covid-19 pandemic: an online-survey with children and adults with and without mental health conditions. Child Adolesc Psychiatry Mental Health 15 12. Zhang X, Li W, Ying H, Li F, Tang S, Lu S (2020) Emotion detection in online social networks: a multilabel learning approach. IEEE Internet Things J 7(9):8133–8143 13. Scherer KR, Wallbott HG (1994) Evidence for universality and cultural variation of differential emotion response patterning. J Pers Soc Psychol 66(2):310 14. Li Y, Su H, Shen X, Li W, Cao Z, Niu S (2017) DailyDialog: a manually labelled multi-turn dialogue dataset. IJCNLP 15. Kanakaraddi SG, Chikaraddi AK, Gull KC, Hiremath PS (2020) Comparison study of sentiment analysis of tweets using various machine learning algorithms. In: 2020 International conference on inventive computation technologies (ICICT), pp 287–292

Deep Learning-Based Methods for Automatic License Plate Recognition: A Survey Ishwari Kulkarni, Dipmala Salunke, Rutuja Chintalwar, Neha Awhad, and Abhishekh Patil

1 Introduction In the current trends, automatic vehicle plate recognition (AVPR) has contributed a crucial half within the growth of better towns as a management system for automobile trailing, traffic regulation, and imposing strict traffic rules and policies [1]. In this paper, we will study how to categorize a number plate using various methods, models, algorithms, and features and then apply this knowledge for making a real-time system. Deep learning models are used to localize the vehicle license plates. Importantly, we use a 4-layer convolution neural network (CNN) model to observe the text regions present in the image given as an input and then this model helps us to differentiate the vehicle license plates from typical text characters [2]. There are a variety of models that can be used, including: 1. CNN: It is a type of model that permits operating with the photographs and videos; CNN takes the image’s raw component information, trains the model, and then extracts the options mechanically for higher classification. 2. R-CNN: R-CNN means regions with CNN. This method is an update to CNN that adds a phase of classifying images into around 2000 regions as a pre-processing step before identifying the image using a bounding box. The rest of the processes are the same as CNN’s. 3. Fast-R-CNN: In this method instead of creating regions directly, the image is converted into a convolutional map. Then the extraction of features is done. 4. Faster-R-CNN: Instead of selective search, this object detection algorithm learns from the network region proposals. It makes use of feature extraction and can be described as a CNN that has been pre-trained. 5. YOLO: YOLO means that you only look once. It works like this: an image is divided into grids, and each grid is bent to accept m bounding boxes at intervals. I. Kulkarni · D. Salunke · R. Chintalwar · N. Awhad (B) · A. Patil Department of IT, JSPM’s Rajarshi Shahu College of Engineering, Tathawade, Pune, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_46

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The network produces output for each of the bounding boxes based on the class and value of the bounding box. The box that fits on top of a threshold value is picked and used to locate the item at intervals across the image. Most of the previous real-time systems have in some way restricted their operating conditions, like limiting them to indoor scenes, stationary backgrounds, fastened illumination, prescribed driveways, restricted vehicle speeds, or selected ranges of the gap between camera and vehicle [3]. Some key advantages using above techniques rather than arbitrary machine learning method is higher productivity as it helps in quick data retrieval, increase storage space and data security. In this paper, we study various models, extraction features, and various types of pre-processing and different methodologies. The main goal of this paper is to look at several models and comprehend their views. There is no suggested legislation in India for uniform car plates, which means that they have varied stickers, are only three-numbered, and have a variable typeface. We want to come up with an effective real-time system that can provide precise accuracy, as well as analyze and learn from multiple perspectives. Specifically dominant functions such as traffic management, increased safety, and police activity, as well as management, are considered to be of primary importance in the smart town management applications proposed by this system [4] (Fig. 1).

Fig. 1 Flow diagram of methods discussed in paper

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2 Localization Techniques for AVPR AVPR comprises of three stages: localization, segmentation, and character recognition. In this section, different localization ways are compared. To locate the license plates, a variety of features are used, including shape, color, exposure, and frequency. Colors and shapes of the objects are among the most popular features that are employed. There are various algorithms used for number plate localization like, localization using signature analysis, localization using characteristics of alphanumeric characters, and localization using a novel approach.

2.1 Localization Using Signature Analysis A license plate includes characters which can be linked via means of their exclusive intensities from the background. A perpendicular stroke is described because of the hand of a license plate. Because the plate is positioned within the drop a portion of the vehicle, this evaluation will look for a signature of a plate within the five components [5]. Each row might be divided into three rows. The program calls for as a minimal two rows to have a legit signature before the specific phase certified for the posterior process. Signature is taken into consideration as reiteration of perpendicular edges and holes among peaks. A threshold technique is used to validate a hand in a plate (2). Adaptive thresholding is used to increase effectiveness. This is how signature analysis works.

2.2 Localization Using Characteristics of Alphanumeric Characters Alphanumeric characters have particular parcels in double images along with length, white pixel viscosity, atomicity, etc. This way of proposed method for localization of license plates from a picture is defined below. (a) Reversed “L” Masking’s having length same to maximum character length is used, having a form of reversed “L”. Input snap of the volume plate is preprocessed for the elimination of literal history noise. (b) The pre-processed snap is binarized to make it applicable for localization in this method. Otsu’s algorithm is used for instant binarization. (c) In the end, Otsu’s algorithm is used for instant binarization.

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2.3 Localization Using Novel Approach The AVPR system is mainly split into 3 major regions, along with the license plate localization, character segmentation, and character recognition. The localized plate is the first element. This consists of: a. Noise-intensity range is change to identify and rectify noise. b. Changing color area—The RGB area in image is changed into gray space for precision. c. Intensity revision—Histogram equalization method is used to add intensity. d. Edge discovery—Edge detection is done using various operators. e. Separating objects from picture—This is used for discovering context. f. Identifying connected component—Connected objects are identified by 8 and 4-ary connectivity. Basically, we understand from this that, localization is a process of identifying license plates from the image captured. Objective of this being recognizing the exact region of the number plate placed on the vehicle.

3 Pre-processing Methods in AVPR Binarization and gray-scaling are two sub-processes used in pre-processing of number plates [6]. Color-based approaches are inferior than gray-based methods in terms of performance [7]. Reducing number of colors is the fundamental goal of using color conversions (Fig. 2). The Binarization technique splits the RGB components of a 24-bit color image and generates an eight-bit grey value, transforming the grayscale image to black and white

Fig. 2 Pre-processing techniques

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Fig. 3 a Input image, b HSV cropped and converted, c HSV channels [10]

or 0/1 [8]. According to some studies, image augmentation refers to image manipulations such as contrast enhancement, translation, sharpening, rotating, mirroring, and Gaussian noise that are applied to images to create distinct versions of comparable content. After plate localization, the main issue is image deskewing, which is used to determine the angle at which the number plate was rotated in the original image. The Hough transform is employed to accomplish this [9]. Morphological processing, also known as dilatation, erosion, and closing, is a useful technique for catching up on illumination variations. As the brighter areas of the image expand, the dilation procedure fills in the image’s darker areas, causing the darker region to vanish and the stroke width of the character to rise [10]. RGB to HSV conversion is another method of color space change (shown in Fig. 3). HSV channels have the benefit of separating color description from brightness, allowing the approach to work in a variety of lighting circumstances [11]. The grey levels of any images are in inhomogeneous distribution. The histogram equalization in image enhances contrast gradients by dispersing various grey levels. After normalizing the original image, the compressed original image’s density latitude now ranges from (0 to 255) to (0, 1). As a result, image binarization and noise removal are done [12]. Noise removal is also a crucial stage in image processing [13]. We utilize a bilateral non-linear filter to preserve edges, smooth out images, and reduce noise. To reduce the impact of noise on the derivation of Sobel operations, the photo is sometimes filtered with a median filter. In another study, the input image is transformed to grayscale, then converted to binary image using Otsu’s approach, white pixel density of each likely character is computed, and all other background noise is removed [14]. After plate localization and before character segmentation some steps (pre-processing) are often executed gray shade transformation, gaussian blur, thresholding by Otsu’s method, OpenCV contouring, etc.

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4 Segmentation Process There are different methods of segmentation histogram projection-based segmentation, region-based segmentation, thresholding-based segmentation, edge-based segmentation, and clustering-based segmentation. The segmentation is done using a histogram projection approach [15]. The vertical projection profile for each column is calculated by adding all of the pixel values from all of the rows. This approach separates the characters based on the pixels intensity histograms for each column. This method has a precision of roughly 94.04%. In a different way, number and text segmentation is accomplished by generating vertical and horizontal histograms and then separating each text [16]. In region-based segmentation the principal goal is to concentrate at the region of interest and simplify. The further process [3, 17] makes use of smearing algorithms, median filters, and morphological algorithms. The image is filtered first in which all noises are removed. To separate the characters in the image, an elongation operation is performed. Then vertical and horizontal smearing are implemented for locating the characters. Segmentation accuracy is about 97%. The thresholding-based segmentation method changes the pixels which makes it easier to analyze the image. The threshold is modified so that the image can be segmented in all lighting conditions which gives optimal segmentation results [18]. As a consequence, the SVM can recognize 97.1% of the segmented characters. In another research, threshold filter is implemented using Otsu’s binarization [19]. Otsu’s method gives optimal threshold value. The edge-based segmentation method identifies the edges and boundaries present inside the image [20]. Edge detection is done using canny edge detector then contours are drawn on the pre-processed image. Once these contours are arranged in proper order the edges are counted and further process is continued. The clustering-based segmentation divides the image into various clusters. Here, comparison is done among the different groups (Fig. 4).

Fig. 4 Segmentation methods

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5 Feature Extraction in AVPR Feature extraction is one of many object detection techniques, and it is the process of creating features to use in vehicle identification. It is divided into 3 steps: number plate detection, character segmentation, and character recognition. Two methods of feature extraction are discussed in this section like color features and deep learning features.

5.1 Color Feature In color feature, we use clustering, in which constant quantity of clusters and variable quantity of clusters are formed. The proposed technique [21] preserves the color distribution in the photograph and decreases the deformation that occurred at some point of the feature extraction technique by the usage of binary quaternion second preserving (BQMP). In the new proposed color characteristic extraction approach [21], the constant quantity of clusters and variable quantity of clusters holds the color distribution.

5.2 Deep Features Numerous deep learning models are now trained over a big quantity of statistics and is extensively utilized in AVPR. In paper [13], CNN methods are used. Hence, datasets which include vehicle images and number plate snap shots are required. Recent system makes use of the YOLO algorithm in which CCTV photos are transformed into frames. The frames are exceeded through YOLO set of rules to stumble on the vehicles in it. The detected vehicles are saved in separate images in a folder. These images are checked for number plates. The detected number plate might be cropped and saved in some other folder. The characters in those number plates are diagnosed with the use of Optical Character Recognition (OCR). The extracted textual content is then copied to an excel sheet with the time, date, and car number.

6 Classification and Recognition In AVPR system we predict the classes of different alphabets and numbers. Out of many classification methods, SVM has proved to be of better performance. In handwritten numeral recognition it has been successfully used [22]. In it, two sets of SVMs are designed. One set is planned for distinguishing characters and another for characters which are representing different states [23].

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We then compared, K-nearest neighbor algorithm, which is used to classify the characters within the localized plate [6]. It assigns a classification to 36 alphanumeric characters (10 numeric digits + 26 letters). Fully connected layers of neural networks are widely utilized to accomplish classification tasks. A CNN model was proposed in another study to achieve categorization of two classes [24]. The model was trained on single images of characters and numbers rather than a dataset of number plates. Each tree in the decision tree is based on a randomly selected subset of the training data in the random forest classification algorithm. This is multiclass problem which is useful in our subject case. It has found that this algorithm gives higher accuracy of 90.9% among K-NN, CNN, and SVM. After some pre-processing, character segmentation is done using column sum vector charts [12]. Finally, the characters are recognized using a probabilistic neural network (PNN). In this segment of binary images are labeled according to their color to enable classification. PNN is sort of feed forward neural network which is alternative to classic back propagation neural network (Fig. 5). Next type is the YOLO model which can be used for both number plate detection and recognition. In this work to recognize all character’s A to Z (except O) and 0–9 they have trained 37-class CNN. Neglected O from training as O and 0 are found to be same [25]. Finally, the result is sorted from left to right to provide the correct number plate order. To filter out the remaining non-plate candidates, a trained CNN-based classifier is used. For classification and recognition feed forward back propagation neural network is utilized. Now total 36 neurons are containing in an output layer [9]. Then there is template matching, which is an OCR technique that compares a cropped image to an existing template database [26]. Without any indirect input, it can recognize the characters automatically. OCR converts handwritten or printed Fig. 5 Classification models

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text’s images into a machine text [13]. This system uses tesseract engine one of several OCR engines.

7 Dataset There are open data sets available online that have been used in certain cases, and primary data sets have been used in others. Car datasets that are openly available are (Fig. 6; Table 1). Aside from this, open data sets may be found on numerous websites such as scale.com and openintro.com. Some papers have built their own secondary data sets

Fig. 6 Visual representation of the data sets

Table 1 Datasets 1. Reference No.

2. Dataset

3.

4.

6. [27, 28]

7. Caltech

8. 126

9. 37

10. 896 × 592

1. [5]

2. Stanford

3.

4. 196

5. 600 × 400

12. 681 13. 757 14. 611

15. –

16. 320 × 240

20. 183

21. –

22. 640 × 480

Images

5. Resolution Classes

16,185 6. [29, 30]

7. 8. 9. 11.

AOLP Access control traffic law Enforcement Road Patrol

17. [27]

18. Microsoft Research Cambridge Object Recognition 19. Image database

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Table 2 Performance measures Performance measures

Evaluation

Accuracy (A)

A=

Tp+Tn Tp+Fp+Tn+Fn

Precision (P)

p=

Tp Tp+Fp

F-score

F-score = 2 ×

Prediction

1 1+e(−(Y 0+Y 1∗x1+Y 2∗x2))

Average precision (AP)

AP = =

Horizontal projection Discrete Fourier transform (DFT) Intersection over union (IoU)

P(c) =

 r

DFT(u) = IoU =

Precision×Recall Precision+Recall

1 Prediction total images(N )

N 

Pi

i=1

f (r, c) 1 N

N−1  x=0

−i2π ux VP(x)eN

area of overlap area of union

by taking photographs of cars in their environs with a camera or mobile device [25, 31].

8 Performance Measures The information is analyzed and evaluated using performance measures. Table 2 lists some general performance indicators.

9 Summary Overall, we understand that relying just on machine learning algorithms will not provide us with the desired accuracy, as each action is dependent on the previous one. To improve accuracy, we need to combine it with deep learning algorithms. Table 3 provides a quick comparison of the articles studied and the accuracy levels observed.

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Table 3 Summary S. No. Paper title

Year

Accuracy (%)

Algorithms

1

Automatic number plate recognition using deep learning

2021

93

CNN

2

Detecting vehicles number plate in video using deep learning

2021

92.89

NPD and CNN

3

Detecting the vehicle’s number plate in the video using deep learning performance

2021

96.91

Connected component analysis (CCA) CNN

4

Automatic number plate detection in vehicles using faster R-CNN

2020

99.1

Faster R-CNN OCR

5

Comparative analysis of deep learning approach for automatic number plate recognition

2020

90

CNN YOLO v3

6

Automatic license plate recognition for Indian 2019 roads using faster-RCNN

88.5

CNN OCR Faster RCNN

7

Real time Indian license plate detection using deep neural networks and optical character recognition using LSTM Tesseract

2019

95

YOLO CNN OCR Faster RCNN

8

Indian car number plate recognition using deep learning

2019

91

YOLO

9

Anonymous vehicle detection for secure campuses

2019

Not mentioned

Faster R-CNN Tesseract OCR

10

A new approach for vehicle number plate detection

2018

97.1

CAA SVC Threshold modification

11

Detection and recognition of multiple license plate from still images

2018

Not mentioned

SVM Artificial neural network (ANN)

12

An embedded automatic license plate recognition system using deep learning

2018

97

YOLO v3 CNN

13

Automatic number plate recognition

2018

82.6

OCR

14

A machine learning algorithm for automatic number plate recognition

2017

99

HOG SVM

15

Automatic number plate recognition using CNN based self synthesized feature learning

2017

90

CNN

16

Deep learning system for automatic license plate detection and recognition

2017

93

CNN SVM

17

Car plate recognition based on CNN using embedded system with GPU

2017

95

CNN (continued)

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Table 3 (continued) S. No. Paper title

Year

Accuracy (%)

Algorithms

18

Entry and exit monitoring using licence plate recognition

2017

98.5

CNN

19

Extraction of number plate images based on image category classification using deep learning

2017

20

Automatic number plate recognition (ANPR) system for Indian conditions

2014

CNN SVM ALEX-NET 82

Feature-based localization feature extraction

10 Conclusion This research explores a variety of methods for predicting, identifying, and classifying car number plates, including OCR, CNN, R-CNN, YOLO, and others. These models have recently improved: YOLO, SSD, and RETINANET are realtime detectors and single shot detectors, both of which are referred to as one-time approaches. Although numerous models exist, the core structure stays the same, namely, employing CNN or OCR models in conjunction with an updated model to get a result. Future directions in these algorithms still have important problems, such as the lack of a uniform composition for number plates, regardless of which country we consider; and the need for high-resolution cameras to capture proper images/videos. Models have changed, and we believe that an ensembled approach combined with appropriate tools will prove to be very efficient and have higher potential. The accuracies of the models we looked at varied depending on the intricacy of the learning and the amount of the data set, with results ranging from early 80% to 97%. We also conclude that with deep learning, enormous data sets can be processed quickly, with high accuracy outcomes, and for a wide range of applications.

References 1. Singh J, Bhushan B (2019) Real Time Indian license plate detection using deep neural networks and optical character recognition using LSTM tesseract. In: International conference on computing, communication, and intelligent systems (ICCCIS). IEEE, pp 347–352 2. Gupta S, Singh RS, Mandoria HL (2020) A review paper on automatic number plate recognition system 3. Kumari R, Sharma SP (2017) A machine learning algorithm for automatic number plate recognition. Int J Comput Appl 174(1) 4. Th V, Lydia L, Mohanty S, Alabdulkreem E, Al-Otaibi S, Al-Rasheed A, Mansour R (2021) Deep learning based license plate number recognition for smart cities. Comput Mater Continua 5. Gnanaprakash V, Kanthimathi N, Saranya N (2021) Automatic number plate recognition using deep learning. IOP Conf Ser Mater Sci Eng 1084(1):012027

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Modern Predictive Modelling of Energy Consumption and Nitrogen Content in Wastewater Management Makarand Upkare, Jeni Mathew, Aneesh Panse, Archis Mahore, and Vedanti Gohokar

1 Introduction Wastewater is a polluted form of freshwater. It is also known as sewage. It is generated from a variety of applications like cleaning, washing, flushing, industrial treatment, etc. Wastewater consists of various pollutants like organic matter, suspended solids, plant nutrients, and microbes. The more the organic material present in the wastewater, the higher the Biological Oxygen Demand (BOD). BOD is the amount of oxygen required by the organisms to decompose the organic matter in sewage. As industrial sewage contains more organic matter, hence BOD for it is more than that of domestic sewage. Along with BOD, Chemical Oxygen Demand (COD) which is the amount of oxygen consumed by the contaminants present in wastewater can be used for wastewater analysis. Nitrogen is also one of the contaminants present in the wastewater, which has huge environmental problems associated with it, and hence, its quantity in wastewater needs to be determined at both input as well as output levels of Waste Water Treatment Plant (WWTP). Analysing wastewater properties is critical because high levels of these properties in wastewater make disposal unsafe and improper wastewater disposal in the environment can lead to serious environment as well as public health issues. Hence, appropriate wastewater treatment becomes important. There are various WWTP empowered with the work of wastewater treatment. Bar screening, primary clarifier, aeration, secondary clarifier, chlorination, water analysis, testing, and finally effluent disposal are the steps involved in wastewater treatment. Advanced machine learning (ML) techniques in data science have equipped us with the tools to predict the output of various processes in many domains, one such domain is WWTP. It requires a huge amount of energy for its operation; energy is M. Upkare · J. Mathew (B) · A. Panse · A. Mahore · V. Gohokar Department of Chemical Engg, Vishwakarma Institute of Technology, Pune, Maharashtra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_47

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not only required for processing the wastewater but also in its transportation and discharge. Given the fact that it is an energy-intensive procedure, still it is extremely essential for sustainable development. In addition, with the help of data science techniques, it has become possible to predict the energy consumption of such plants. This prediction of energy consumption will not only help in analysing the energy consumption distribution at various stages but also help make the process more sustainable by shifting to renewable sources of energy where possible. With the help of ML algorithm, it has also become possible to predict total nitrogen in the wastewater by knowing the values of BOD, COD, ammonia present, etc. It not only saves the time for its estimation but also the resources required [1]. In this study, an innovative approach to implementing ML algorithms in deriving quantitative results in the field of wastewater management was proposed. This work aimed to establish a relationship between wastewater parameters with energy consumption and total nitrogen. Multiple predictive ML algorithms such as regression, support vector regression (SVR) and ensemble Model were implemented. Various data pre-processing steps like data cleaning, feature selection, and normalization were also included in the overall project execution. The predictive models were evaluated based on Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). Lastly, the number of inputs for these models was optimized to find out what all parameters and what number of inputs can give the lowest MSE value. The work provided a practical perspective as well as a set of parameters and models for wastewater management.

2 Literature Review In study [1], authors aimed to evaluate the effect of seven different feature selection methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen in the WWTP influent flow. Four scenarios based on feature selection suggestions were defined and compared by supervised machine learning algorithms. Decision tree algorithms [Random Forest (RF) and Gradient Boosting Machine (GBM)] revealed better performance. GBM gave the highest accuracy. In work of [2], authors reported the feature selection technique using Principle Component Analysis (PCA) to enhance the accuracy of the model. PCA was used for dimensionality reduction that is to select the most prominent genes to increase the accuracy of the model. As explored in [3], the polynomial regression model can be used when the relationship between two variables is curvilinear. Parameters used for modelling were estimated using the least square method. The paper discussed the importance of polynomial regression ineffectively fitting the data. Authors of [4] gave an overview of parameters that influenced energy consumption patterns and introduced them as energy Key Performance Indicators (KPI’s). It also suggested analysing energy consumption in WWTPs per unit of the pollutant

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removed. Pollutants are usually considered according to the literature that includes BOD, COD, or Nitrogen removed. In [5], the goal of the study was to predict the daily energy consumption of the Melbourne East WWTP by using various ML algorithms out of which the GBM algorithm had proved to be the best in predicting nonlinear irregular patterns with the lowest RMSE value. It also discusses the dependency of energy consumption in Melbourne WWTP on parameters like atmospheric pressure, wind speed, BOD, ammonia, temperature, humidity, and influent flow. In the following paper [6], which focuses on various forms of energy used in WWTP, include electrical, manual, chemical, fuel energy, etc. The methodology of this paper focuses on small-scale WWTP in the institutional area. The results of this paper state that electrical energy is only half of the total energy consumption. It also states that manual energy also has a significant share in total energy consumption. Jae et al. [7] devised a support vector machine (SVM) algorithm for the bankruptcy prediction problem. The grid search technique employed five-fold cross-validation to discover the optimal parameter value of the kernel function of SVM. This model was utilized in this study to reduce complex problems into simpler problems that could be fitted by linear models. The Radial Basis Function (RBF) kernel was employed as the fundamental kernel, and because kernels can be problem specific, choosing the right kernel is critical to the SVM model’s effectiveness. Summarizing the works by the authors it can be inferred that various improvization can be done. Various assumptions for performing regression models were considered in a few studies. Thus, to check whether the assumptions are true or not advanced statistical tests can be employed. Although the algorithms worked well, the confidence interval in a few papers revealed a specific error, indicating that further optimization could be used to reduce error.

3 Methodology 3.1 Case Study on Melbourne Melbourne city, capital of the state of Victoria, Australia is located at the head of Port Phillip Bay, on the south-eastern coast. It comprises mainly two wastewater treatment plants (WWTPs): Eastern Treatment Plant (ETP) and Western Treatment Plant (WTP). ETP is located in the south-eastern suburbs which is using innovative techniques to turn sewage water into the highest quality of recycled water (Class A). Due to increasing pressure and load on WTP Melbourne came up with ETP to nullify it. The plant consists of primary, secondary, and tertiary treatment tanks, sludge collectors, sludge thickeners, and effluent basins as depicted in Fig. 1. In this case study, various compositions of data were generated at different period and the report was made.

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Fig. 1 Melbourne eastern treatment plant

3.2 Data Collection The dataset which was considered was taken from “Mendeley Data” [8]. The data is of full-scale WWTP which describes the energy usage/consumption, weather and also emphasizes on wastewater characteristics of Melbourne ETP for a period of 6 years that is from 2014 to 2019. The dataset is made with the inner joining of open access data which is produced by Melbourne airport weather and Melbourne water stations. The dataset consists of parameters such as daily total grid power consumption (MWh), average outflow (m3 /s), average inflow (m3 /s), ammonia NH4 H (mg/L), BOD (mg/L), COD (mg/L), total nitrogen (mg/L), average temperature (T avg ) (°C), and average relative humidity (H) (%). In all, there are 1382 data point which were considered. The dataset was divided into a training set and a testing set. Training set comprises 85% and testing set comprises 15% of the data. For programming and for implementing necessary plots, Python 3.8 via Google Colab Notebook was used.

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3.3 Data Pre-processing and Visualization Data pre-processing and visualization are critical steps that must be completed before passing the data to the model for training. Boxplot technique was utilized to identify outliers in the data, and noisy data was removed by replacing the corrupted data points with the median of the data points present in that column. Data was normalized and rescaled from its original range to a new range that is between 0 and 1 as a part of data pre-processing. It is a very crucial step, especially when using the algorithms which use the weights of the input parameters like the ANN and the regression models. In this work, the min-max scalar function was used to scale the input parameters from 0 to 1 range. This process of data normalization helped in increasing the performance and accuracy of the model. Feature Selection Feature selection is a technique used for dimensionality reduction. It helps in optimizing predictive model as well as in saving computational cost. In order to select input variables for this work, Correlation Matrix was plotted to determine the correlation between the attributes of the dataset. As seen in Fig. 2 the parameters with weak correlations were not considered. Next step would be to create a new data frame that has all the independent attributes. In order to increase the accuracy of the model and to avoid the problem of overfitting, multicollinearity was checked [10]. To check for multicollinearity PCA was used. From the Fig. 3 it is clear that six attributes were enough for explaining more than 90% of variance. Hence, the attribute which contributed very less to the prediction

Fig. 2 Plot of correlation matrix

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Fig. 3 Principal component analysis for energy consumption

of dependent variable, i.e., humidity was removed from the dataset. In this manner six-input parameters were selected for energy prediction model. One more model was built, with the three inputs indicated. The same procedure was used to select input parameters for total nitrogen prediction, where PCA was used to select fourinput parameters: COD, BOD, ammonia, and average temperature, and two-input parameter model with COD and BOD as inputs.

3.4 Modelling Approach Four various ML techniques were studied and used to make predictions in this study. Regression, SVR, and ensemble models were employed, and all of the models were optimized to produce the best results. The objective was to compare the outcomes and find the optimum algorithm for predicting total nitrogen content and energy consumption. The ML algorithms used to generate the models are discussed below along with the model description, and the model development process flow is depicted in Fig. 4. Regression Model Multiple Linear Regression Multiple Linear Regression is a statistical method in which multiple independent variables are used in predicting their relationships with a single dependent variable. The relationships are linearly modelled giving a straight line as the regression curve. Equation (1) shows the mathematical representation of this method. yi = b0 + b1 xi1 + b2 xi2 + · · · + b p xi p + Ri for i = 1, 2, . . . n.

(1)

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Fig. 4 Process flow construction

In order to build the model in Python, linear regression functions from the sklearn packages were used. Polynomial Regression Polynomial regression as opposed to linear regression analyses the relationship between dependent variable and independent variables in an “nth” degree polynomial (2). y = b0 + b1 x1 + bx 2 + bx 3 + · · · + bx n

(2)

To acquire the best fit model for any dataset, degree of order needs to be chosen prudently, which can behave as a hyper parameter. In this work, the model with two-input parameters that predicted total nitrogen gave the best results with a degree of order of four, whereas the rest of the models performed best with a degree of order of two.

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Support Vector Regression SVM follows supervised learning methodology which can be used in classification as well as regression. SVR, a more advanced SVM algorithm, can be used to deal with regression problems. In SVM, a hyperplane is used to draw a line between two data classes; however, this line is used in SVR to find the continuous output. In order to optimize this model, three hyper parameters of SVM were chosen: soft margin constant “C”, margin of tolerance “epsilon”, and “kernel”. SVM kernels can be linear or nonlinear, which includes RBF, hyperbolic tangent and polynomial. Performance of linear, polynomial, and RBF kernels with SVR were assessed, followed by optimization of the two hyper parameters, C and epsilon. Ensemble Model Ensemble Models are data science techniques that use previously developed models and amalgamate them to predict the relationship between the dependent and independent variables. In this work, stacking regressor was utilized which is based on the concept of meta-regressor. Here, base models or first-level regressors are created and the predictions of these base models are fed into the meta-model or meta-regressor as a training feature. Multiple linear regression and SVR with linear kernel were employed as base models. SVR with polynomial kernel served as the meta-regressor.

3.5 Workflow of the Predictive Model As depicted in Fig. 4, the study began with data collection, after which the raw data was analysed and pre-processed to reduce the complexity of the model training and to manage any noisy or missing data. Feature selection and normalization was also performed as a part of data pre-processing in order to get a better prediction model. Following data splitting, the model was built and trained. Finally, validation metrics were used to evaluate each model, and if in case strong performance was not achieved, hyper parameter optimization was required to improve the performance.

4 Results and Discussions The experimental results are discussed in this section, which are based on model evaluation metrics and plots. The performance of regression, SVR, and ensemble models were compared and the prediction results of these models were analysed. The dependability and suitability of using the selected input characteristics as a forecasting medium were investigated holistically in order to establish their suitability for predicting the output parameters, which are energy consumption and total nitrogen.

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Table 1 Energy consumption prediction using three- and six-input parameters Energy consumption prediction Three-input parameter model

Six-input parameter model

MSE

RMSE

MAE

MSE

RMSE

MAE

Multiple regression

0.01900

0.13785

0.10931

0.01882

0.13720

0.10795

Polynomial regression

0.01850

0.13600

0.10813

0.01838

0.13559

0.10740

SVR linear

0.017153

0.130097

0.10333

0.01711

0.13079

0.10316

SVR polynomial

0.018569

0.13627

0.10695

0.01726

0.13139

0.10364

SVR rbf

0.017206

0.13117

0.10274

0.01693

0.13013

0.10217

Ensemble model

0.019937

0.14120

0.11220

0.01952

0.13971

0.11033

4.1 Insights from Energy Consumption Prediction When different ML algorithms used for energy consumption prediction with threeinput parameters were compared, it was discovered that the SVR model with linear kernel, SVR model with RBF kernel, and polynomial regression model had the lowest Mean Square Error (MSE) values, as shown in Table 1, thus proving to be appropriate for prediction. Multiple regression, polynomial regression, and ensemble models were found to give the closest predicted value to the actual value based on the prediction plots. Therefore polynomial regression and multiple regression were proven to be the most effective models in terms of both MSE and suitable prediction graphs. Similarly for the six-input parameter model, the SVR models were found to be giving low MSE values and from the prediction graphs, polynomial regression, and ensemble model performed well. The model with six-input parameters using SVR model with an RBF kernel had the lowest MSE value of all the energy consumption prediction models, which was 0.01693. By visualizing the proximity of predicted values to real values and analysing MSE values, it is reasonable to infer that the polynomial regression model can better predict rapidly changing data. With models used in study [5], best RMSE score possible was 33.9 where as in case of the study presented in this paper the best RMSE score for energy consumption prediction is found to be 13.6. Hence clearly indicating the significance of this work in decreasing RMSE by approximately 40%.

4.2 Insights from Total Nitrogen Prediction The same ML algorithms were used to estimate total nitrogen. A two-input parameter model and a four-input parameter model were employed in this study. The SVR model

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with RBF kernel produced the best results for both types of input parameter model sets. Except for the SVR model with linear kernel, it was clear from the actual and predicted data plots that most of the models performed well. The lowest MSE value for total nitrogen prediction was reported to be 0.00099 as shown in Table 2 for an SVR model with RBF kernel and four-input parameters. Taking all of these factors into account, it is possible to conclude that the SVR model with RBF kernel is the best for total nitrogen prediction. With the models chosen, it was clear from the plots shown in Figs. 5 and 6 that total nitrogen prediction was more efficient and produced better results than energy consumption prediction, owing to significant correlations between total nitrogen and the input parameters used to predict it. Table 2 Total nitrogen prediction using two- and four-input parameters Total nitrogen prediction Two-input parameter model

Four-input parameter model

MSE

RMSE

MAE

MSE

RMSE

MAE

Multiple regression

0.00141

0.03749

0.02612

0.00112

0.03345

0.02405

Polynomial regression

0.00138

0.03709

0.02569

0.00111

0.03325

0.02384

SVR linear

0.00135

0.03673

0.02419

0.00180

0.04242

0.02708

SVR polynomial

0.00155

0.03939

0.02501

0.00127

0.03568

0.02241

SVR rbf

0.00119

0.03450

0.02239

0.00099

0.03142

0.02006

Ensemble model

0.00140

0.03743

0.02504

0.00110

0.03312

0.02146

Fig. 5 Energy consumption prediction using six parameters polynomial regression model

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Fig. 6 Total nitrogen prediction using four parameter SVR-RBF model

4.3 Impact of Input Parameters on Model Performance Initially, input variables were directly selected by looking at the correlation of the input with the output, considering the goal of selecting input variables that would affect output more than the other variables. This method yielded a three-input parameter model for predicting energy consumption and a two-input parameter model for predicting total nitrogen as discussed in the previous sections. However, in order to carry out the selection of input variables in a systematic manner, PCA was used, yielding a six-input parameter model for energy consumption and a four-input parameter model for total nitrogen prediction. When the performance of these two types of models were compared, the models with input parameters directly chosen from correlations, i.e., the two- and threeinput parameter models, did not perform as well as the models with input parameters chosen using PCA, i.e., the four- and six-input parameter models, respectively, in predicting total nitrogen content and energy consumption. This is also evident from the results obtained in the preceding two sections, where the lowest MSE value for energy consumption prediction was found with a six-input parameter model and the lowest MSE value for total nitrogen prediction was found with a four-input parameter model. Alternatively, it can be inferred that when the model is given a greater number of input variables that explicitly affects the output variable, the modelling approach performs better. The best-formed model for energy consumption that is polynomial regression with six inputs can be used to predict and accordingly minimize the energy consumption

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in a WWTP. Considering the wastewater characteristics and the prediction model for total nitrogen, SVR with RBF kernel using four inputs was found out to be best using this model. Thus, total nitrogen content of wastewater by measuring COD, BOD, ammonia, and average temperature could be predicted which will save resources and time.

5 Conclusion The importance of energy and water resources is paramount for the existence of the human race. This work aims to provide analytical solutions to the problems faced in energy consumption in managing wastewater, thus having great potential. In this study, two types of models were built for energy consumption and total nitrogen content prediction, one with inputs chosen using PCA and the other with inputs chosen directly by looking at correlations. Six and three-input parameters were chosen for energy prediction, while four and two-input models were created for total nitrogen content prediction. The prediction was made using ML algorithms such as multiple linear and polynomial regression, SVM, and ensemble models. By evaluating all of the models, it was discovered that the SVR model with RBF kernel provided significant performance for both energy consumption prediction and total nitrogen prediction. Along with SVR model with the RBF kernel, polynomial regression models had encouraging results. PCA-based models performed well, implying that it is a viable method for determining input parameters. Due to a higher correlation between the inputs and outputs in the total nitrogen content prediction model than in the energy consumption model, better results were obtained. It can be inferred from the evaluation of the model metrics that the models are ambitious enough to be applied in the real-life.

References 1. Bagherzadeh F, Mehrani M-J, Basirifard M, Roostaei J (2021) Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance. J Water Process Eng 41:102033. https://doi.org/ 10.1016/j.jwpe.2021.102033 2. Kavitha KR, Ram AV, Anandu S, Karthik S, Kailas S, Arjun NM (2018) IEEE 2018 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, India (2018.12.13–2018.12.15). In: 2018 IEEE international conference on computational intelligence and computing research (ICCIC)-PCA-based gene selection for cancer classification, 1–4. https://doi.org/10.1109/iccic.2018.8782337 3. Ostertagová E (2012) Modelling using polynomial regression. Procedia Eng 48:500–506. https://doi.org/10.1016/j.proeng.2012.09.545 4. Longo S, d’Antoni BM, Bongards M, Chaparro A, Cronrath A, Fatone F, Lema JM, MauricioIglesias M, Soares A, Hospido A (2016) Monitoring and diagnosis of energy consumption in

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wastewater treatment plants. A state of the art and proposals for improvement. Appl Energy 179:1251–1268. https://doi.org/10.1016/j.apenergy.2016.07.043 Bagherzadeh F, Nouri AS, Mehrani M-J, Thennadil S (2021) Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach. Process Saf Environ Prot 154:458–466. https://doi.org/10.1016/j.psep.2021.08.040 Singh P, Carliell-Marquet C, Kansal A (2012) Energy pattern analysis of a wastewater treatment plant. Appl Water Sci 2(3):221–226. https://doi.org/10.1007/s13201-012-0040-7 Min JH, Lee Y-C (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. 28(4):603–614. https://doi.org/10.1016/j.eswa.2004. 12.008 Bagehrzadeh F (2021) Full scale wastewater treatment plant data. Mendeley Data V1. https:// doi.org/10.17632/pprkvz3vbd.1 Ridzuan F, Wan Zainon WMN (2019) A review on data cleansing methods for big data. Procedia Comput Sci 161:731–738. https://doi.org/10.1016/j.procs.2019.11.177 Alin A (2010) Multicollinearity 2(3):370–374. https://doi.org/10.1002/wics.84

Automatic Text Document Classification by Using Semantic Analysis and Lion Optimization Algorithm Nihar M. Ranjan, Rajesh S. Prasad, and Deepak T. Mane

1 Background Text classification is the process of assigning one or more than one predefined category for the text document based on its content analysis. In general, the text classification process involves two prevalent issues [1–3]. The first one is the extraction of features (keywords) during the training phase of the classifier and the second one is the use of extracted features during the testing phase [4]. Text classification is basically carried in two ways one manual and other automatic. In the manual type of classification, the documents are classified by extracting the knowledge and the interest of the users who predicts the end results [5]. Automatic text classification (using algorithms) methods are capable of classify the document with importance considering the parameters of time and accuracy [6]. Prime motivation of our work is to address the problem of exponential unstructured data generation. These data have huge hidden potential which can be revealed through the analysis. Text classification is one of the methods to analyse these unstructured data. Prime objectives of our proposed methods is to perform the semantic analysis of the available text, extract the features and design, and develop a Neural Network classifier with an optimization algorithm. Feature selection is one of the most critical task during the text classification process. Selection of good-quality features is one of the major challenges. Many work has been carried out to reduce the input features, correlation-based subset selection N. M. Ranjan (B) Information Technology, JSPM RSCOE, Pune, India e-mail: [email protected] R. S. Prasad Computer Engineering, MIT Art, Design and Technology University, Pune, India D. T. Mane Computer Engineering, Vishwakarma Institute of Technology, Pune, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_48

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is a new dimension reduction technique of input features and classifies those input on the basis of four different types of emotions (positive, negative, depressed, and harmony) [7]. Graph-based representation of the dataset can be preferred over the traditional representations. However graph-based method requires additional modelling because as the dataset require to be represented as a graph structure. This representation visualizes the relationship among the features-based graph-theory principles which helps to identify good-quality feature subsets [8].

2 Major Challenges In a micro-blog analysis for text categorization, the knowledge extraction requires the tedious task of collecting the behaviour of the user to undergo extraction using chat communication [9]. Dimensionality for text classification issues remains high, in spite of removing the stop words and stemming, because of the high dimensions of the feature space the classification consumes more time, mainly for real-time systems. Sparsity is another issue with the text categorization [10]. Other existing methods of automatic text classification suffer from the demerits of the unstructured text, dealing with a huge number of attributes, and finding effective pre-processing techniques. Moreover, handling the missing metadata and selecting the effective text classifier is another challenge [11]. Capturing high-level semantic is one of the major challenge in text classification. It is difficult to capture the high-level semantics from the text and concepts of natural languages just by an observance of the text. Words have semantic uncertainties, like hyponymy and synonymy [12]. Accuracy of the classifier [13] of different Machine Learning approach is another issue need to be considered.

3 Gap Analysis After evaluation of the state-of-the-art technologies and classification methods, following gap has been identified. Though the Poisson Naive Bayes text classification method is simple, efficient, and guaranteed incremental learning, its performance is poor compared to the traditional SVM because of the limitation in training documents [14]. The performance of DAN is worse on a sentence containing linguistic phenomenon like double negation [15]. The automated text classification method doesn’t include the practical algorithms and compressible schemes for the discovery of knowledge [16].

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The two major drawback of the combinational scheme are, the boundary detection in text segment is tougher and human effort required for creating and testing templates for new sites and products are high [17]. The main drawback of WordNet concept is that a specific word has different synonyms; it is not so simple to automatically identify the correct synonym in use [18]. The limitation of the HDP method is that with the exponential increase in the total number of topics, and this scheme becomes unrealistic [19]. The ALTO method limits users to view 20 K documents only at an instance and allows assignment of only one label per document. The obtained topics are also static and do not support users in browsing documents and assignment of multiple labels [20].

4 Methodology The very first step in text classification is pre-processing which have two common steps stop word removal and stemming. After pre-processing, the extracted keywords are applied to the semantic word processing. Semantic processing provides the mechanism to predict the significant words from the text document. The philosophy behind the text feature classification is that the multi-words or the important words or the periodically occurring phrases are grouped together as a similar text category. The two major issues during semantic word processing are ‘synonymy’ and ‘polysemy’. In order to solve these issues, semantic word processing using the bag of word and WorldNet ontology method for the feature selection is used. Lion algorithm is a recent optimization algorithm inspired by the social behaviour of the lion. Usually training of the Neural Network has the problem of over fitting, convergence, and local maximum problem. These problems can be effectively solved by using Lion Optimization Algorithm, other algorithm which can be used are genetic and particle swarm optimization. The extracted synonyms are expressed as U = { U1 , U2 , . . . , U j , . . . , Uc }, where U j ; 1 ≤ j ≤ c is the indication of the extracted synonym of a keyword of the document dz and its limit is between 1 and c. The extracted hyponym is expressed as shown in equation. V =

{

} V1 , V2 , . . . V j , . . . , Vc ,

where V j ; 1 ≤ j ≤ c is the indication of the extracted hyponym of a keyword of document dz and its range varies from 1 to c. In order to extract the hyponym and synonym, the processed keyword wl is fed as an input to the word net ontology. Synonym and hyponym are expressed as follows:

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Fig. 1 Semantic word processing

Sli ; 1 ≤ l ≤ n; here Sli indicates the lth keyword’s synonym in ith document, which has the hyponym, represented as, Hli ; 1 ≤ l ≤ n, where Hli indicates the lth keyword’s hyponym in ith document (Fig. 1). By the integration of the keywords, hyponym, and synonyms from the document, a word dictionary is devised. The newly created word dictionary is expressed as } { DT = wli ||Sli ||Hli . The feature dictionary comprising of the important keywords is constructed on the basis of semantic keywords, FT =

{

} Tf ; 1 ≤ f ≤ Q .

The feature matrix is calculated from the conferred topic terms using the equation below, ( F=

) Ai f ; 1 ≤ i ≤ N . 1≤ f ≤Q

High dimensionality of the feature space needs to be reduced by calculation the information gain. The entropy of the feature vector is calculated. [

]

Entropy F f = −

uf ∑

Pi log Pi .

i=1

The selected features are expressed as shown in equation, Fs = [N × M]; M < Q. Neural Network with backpropagation is considered for the experimentation. The Lion algorithm is an optimization algorithm which updates the weights of the network to get the maximum accuracy and minimum errors.

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The Neural Network’s input is the feature words extracted from the input database, and it is represented as Input = {A1 , A2 , ........ A M }. The target output consists of the label which is represented as Target Output = {B1 , B2 , . . . , B N S }. { The Neural }Network’s actual response is given by Actual response = C 1 , C 2 , . . . C Ns . Input of our proposed methodology is the extracted features based on the semantic analysis, and these features are processed by our Neural Network classifier. The weights of the neurons are updated continuously to achieve better classification accuracy by using Lion Optimization Algorithm. Output of our algorithms is the classified documents belongs to different categories.

5 Algorithm 1

Input: features

2

Output: classified text documents

3

Begin

4

Read the input and the target output

7

{ } Compute the actual output response by Equation Actual response = C1 , C2 , . . . C Ns ] ∑ h [ C j ∗ Wit Compute the output neuron by Equation o j = Nj=1 √ ∑N p Calculate the error by Equation, E tot = N1p s=1 (B Su − C Su )2

8

+ Update weight by Back Propagation algorithm (WSUV(BP) )

9

+ Update weight by Lion Optimization algorithm (WSUV(LION) )

10

+ + Find error of E error (Wsuv(BP) ) and E error (Wsuv(LION) )

5 6

11

If

12

+ + E error (Wsuv(BP) ) > E error (Wsuv(LION) )

13

NEW = W + Wsuv suv(LION)

14

else

15

NEW = W + Wsuv suv(BP)

16

Update bias

17

Perform iterations on the newly updated weights

18

End

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N. M. Ranjan et al.

6 Results and Discussions Two standard available datasets are used for the experimentation are (i) 20 Newsgroups and (ii) Reuters-21578. Three commonly used evaluation parameters accuracy, sensitivity, and specificity are used to evaluate the performance of our proposed methodology. The percentage of correctly classified test tuples are called as the accuracy of a Neural Network classifier; and it is expressed by using equation Accuracy =

T P + TN . TP + FP + FN + TN

The proportion of positive tuples, which are correctly predicted, is termed as sensitivity, which is also known as true positive rate and it is expressed as Sensitivity =

TP . TP + FN

The proportion of negative tuples, which are correctly predicted, is termed as specificity, which is also known as true negative rate, and it is expressed as Specificity =

TN . TN + F P

During experimentation percentage of training data and the number of hidden layers are kept varying from 50 to 70% and 1 to 4, respectively. The comparative analysis of our proposed classifier with other existing classifiers like Naive Bayes, K-NN, and Neural Network with Back propagation is discussed in details. Experimental result analysis has been discussed in Table 1 with 20 Newsgroup dataset, four numbers of hidden layers is used with the three parameters of accuracy, sensitivity, and specificity. Maximum value of 68.37, 73.62 and 64.16 is observed for accuracy, sensitivity, and specificity, respectively, with a single hidden layer. If number of hidden layers is increased to two then the observed values are 68.42, 74.13 and 65.06, respectively. Maximum value of 67.01, 75.36, and 66.57 is observed respectively with four numbers of hidden layers. Experimental result analysis has been discussed in Table 2 with Reuters dataset, and four numbers of hidden layers are used with the three parameters of accuracy, Table 1 Result analysis with 20 newsgroup dataset

No. of hidden layers

Accuracy

Sensitivity

Specificity

1

68.37

73.62

64.16

2

68.42

74.13

65.06

3

69.34

74.81

65.88

4

67.01

75.36

66.57

Automatic Text Document Classification by Using Semantic Analysis … Table 2 Result analysis with Reuters-21578 dataset

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No. of hidden layers

Accuracy

Sensitivity

Specificity

1

87.31

91.89

80.89

2

87.38

92.01

81.02

3

91.10

95.06

84.48

4

91.86

95.54

84.96

sensitivity, and specificity. Maximum value of 87.31, 91.89 and 80.89 is observed for accuracy, sensitivity, and specificity, respectively, with a single hidden layer. If number of hidden layers is increased to two then the observed values are 87.38, 92.01, and 81.02, respectively. Maximum value of 91.86, 95.54, and 84.96 is observed, respectively, with four numbers of hidden layers.

7 Comparative Analysis The performance parameters of our proposed algorithm are compared with three existing methods K-NN, Naïve Bayes, and Neural Network. On all three performance parameters accuracy, sensitivity, and specificity our proposed algorithm outperformed the existing methods. Table 3 and Fig. 2 show the comparative analysis of our proposed algorithm with three existing popular classifiers, and maximum values of 67.01, 75. 36, and 66.57 are observed for the three evaluation parameters accuracy, sensitivity, and specificity, respectively. The dataset used is 20 Newsgroup. In case of Reuters dataset, maximum values of 91.86, 95.54, and 84.96 are observed for the three evaluation parameters accuracy, sensitivity, and specificity, respectively. Our proposed algorithm outperformed the existing popular classifiers. This analysis is shown in Table 4 and Fig. 3. Table 3 Comparative analysis with 20 newsgroup dataset

Classifiers

Accuracy

Sensitivity

Specificity

K-NN

61.39

66.12

55.88

Naïve Bayes

61.46

65.39

56.01

NN

66.32

74.26

65.29

LNN

67.01

75.36

66.57

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Fig. 2 Comparative analysis with 20 newsgroup dataset

Table 4 Comparative analysis with Reuters dataset

Classifiers

Accuracy

Sensitivity

Specificity

K-NN

73.76

76.82

69.06

Naïve Bayes

75.53

77.02

69.12

NN

86.54

85.88

75.16

LNN

91.86

95.54

84.96

Fig. 3 Comparative analysis with Reuters dataset

8 Conclusions Objectives of our research work are to design and develop an efficient text classification system for the unstructured databases. The Lion Neural Network is used for the automatic text classification documents in the database based on their labels. The semantic processing has been incorporated for feature extraction during the classification process in order to tackle the dimensionality issue by the elimination of recurring words and words with similar meaning. A novel training algorithm entitled as Lion algorithm was proposed by the consolidation of the Neural Network and Lion Optimization Algorithm for optimal weight update and error reduction in the NN.

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The f experimentation is carried out with the two publically available datasets: 20 Newsgroups and Reuters-21578 by employing the Lion Neural Network (LNN) classifier. The performance analysis and comparative analysis were analysed based on the accuracy, sensitivity, and specificity. The maximum performance is attained by the proposed Lion Neural Network at high training data percentage with maximum number of hidden layers. The maximum accuracy measure for 20 Newsgroups dataset is 67.01 and for the Reuters-21578 dataset is 91.86%. The obtained maximum sensitivity measure for 20 Newsgroups dataset is 75.36% and for the Reuters-21578 dataset is 95.54%. The maximum specificity measure for 20 Newsgroups dataset is 66.57% and for our proposed model is dominant with maximum accuracy, sensitivity, and specificity compared to the other existing methodologies.

References 1. Khan A, Baharudin B, Lee LH, Khan K (2010) A review of machine learning algorithms for text- documents classification. J Adv Inf Technol 1(1):4–20 2. Ikonomakis M, Kotsiants S, Tampakas V (2005) Text classification using machine learning techniques. WSEAS Trans Comput 4(8):966–974 3. Harish BS, Guru DS, Manjunath S (2010) Representation and classification of text documents: a brief review. In: IJCA issue on recent trend in image processing and pattern recognition 4. Ranjan N, Chakkaravarthy M (2020) A brief survey of machine learning algorithms for text documents classification on incremental database. Test Eng Manag 83:25246–25251. ISSN 0193-4120 5. Raghvan P, Amer-Yahia S, Gravano L (eds) Structure in text: extraction and exploitation, vol 67. In: 7th international workshop on the web and databases, ACM SIGMOD. ACM Press 6. Li CH, Park SC (2009) An efficient document classification model using an improved back propagation neural network and singular value decomposition. Expert Syst Appl: 3208–3215 7. Chakraborthy S, Chakladar DD (2018) EEG based emotion classification using “correlation based subset selection. In: Article in biologically inspired cognitive architectures, May 2018 8. Goswami S, Das AK, Guha P et al (2019) An approach of feature selection using graphtheoretic heuristic and hill climbing. Pattern Anal Appl 22:615–631. https://doi.org/10.1007/ s10044-017-0668-x 9. Shang W, Huang H, Zhu H, Lin Y, Qu Y, Wang Z (2006) A novel feature selection algorithm for text categorization. Expert Syst Appl: 1–5 10. Ranjan N, Chakkaravarthy M. Evolutionary and incremental text document classifier using deep learning. Int J Grid Distrib Comput 14(1):587–595. ISSN 2005-4262 11. Wang Y, Wang XJ (2005) A new approach to feature selection in text classification. In: 4th international conference on machine learning and cybernetics, vol 6. IEEE, pp 3814–3819 12. Liu H, Motoda H. Feature extraction construction and selection: a data mining perspective. Kluwer Academy Publishers, Boston, Massachusetts 13. Ranjan NM, Prasad RS (2017) Automatic text classification using BPLion-neural network and semantic word processing. Imaging Sci J: 1–15. ISSN 1368-2199 14. Chen J, Huang H, Tian S (2009) Feature selection for text classification with Naïve Bayes. Expert Syst Appl 36:5432–5435 15. Ranjan N, Prasad R (2013) Author identification in text mining for used in forensics. Int J Res Advent Technol 1(5):568–571. ISSN 2321-9637 16. Lee H-M, Chen C-M, Hwang C-W. A neural network document classifier with linguistic feature selection. Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

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17. Aggarwal CC, Zhai CX. A survey of text classification algorithms. IBM T. J. Watson Research Center Yorktown Heights, NY 18. Wang Z-Q, Sun X, Zhang D-X, Li X (2006) An optimal SVM-based text classification algorithm. Fifth international conference on machine learning and cybernetics, Dalian, pp 13–16 19. Wang S, Mathew A, Chen Y, Xi L, Ma L, Lee J (2009) Empirical analysis of support vector machine ensemble classifiers. Expert Syst Appl: 6466–6476 20. Pilászy I. Text categorization and support vector machines. Department of Measurement and Information Systems, Budapest University of Technology and Economics

Text-Based Emotion Recognition: A Review Heer Shah , Heli Shah , and Madhuri Chopade

1 Introduction In today’s technological world, people from various backgrounds share information about current events and project their perspectives on them. Text-based input has become a popular channel for humans to share their opinions and emotions about a product or service via online social media, shopping platforms, and so on. Every day, vast amounts of textual data are collected on the internet through blogs, social media, and other means. There is a need to analyze people’s emotions to understand and recognize the behavior of such large amounts of textual information. Emotion recognition in text has grown in popularity in recent years due to its wide range of potential applications in marketing, political science, psychology, human–computer interaction, artificial intelligence, and other fields. Humans are prone to making mistakes when interpreting emotions, particularly those derived from text. Hence, emotion recognition models are widely used by intelligent systems to improve their interaction with humans. This is significant because systems can adapt their responses and behavior tendencies in response to human emotions, attempting to make interaction more natural. In this paper, we have reviewed various papers that used different methodologies for detecting and recognizing emotions from texts. All the algorithms are analyzed and compared, and the best method, giving the maximum accuracy, has been highlighted. The content of the paper has been divided into different sections. Section 2 defines the machine learning types. Section 3 is the literature review, i.e., the basic evaluation of the papers that have been reviewed, wherein all the methods used in these papers have been discussed. In Sect. 4, a comparative study of the methods that gave the best results in the respective papers has been given. Among H. Shah · H. Shah · M. Chopade (B) Gandhinagar Institute of Technology, Gandhinagar, India e-mail: [email protected] H. Shah e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_49

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all the approaches, the discussion of those giving the highest accuracy is explained in Sect. 5. Section 6 explains the limitations and future work of surveyed papers. Followed by, conclusion, conflict of interest, and acknowledgements.

2 Machine Learning Types The below-given diagram depicts the different types of machine learning approaches, proposed for the process of emotion detection, in the reviewed papers (Fig. 1).

2.1 Supervised In this type of machine learning (supervised), firstly, by using some input data that is previously tagged with the output (also called labeled training data), the machines are trained and educated. Secondly, on the basis of such data, the output is predicted with ease. It was indicated in [3] that the multinomial Naive Bayes classifier is suited for categorization with discontinuous features, for multinomial models, and in [7, 23], its benefits and utility were demonstrated through tests. In [7, 15], it was observed that SVM gives better accuracy than unsupervised ML algorithms. In [11], this algorithm

Machine Learning

Supervised

Naïve Baye’s

Semi-Supervised

Unsupervi sed

LSTM

K-means

SVM CNN Random Forests Fig. 1 Different types of ML approaches

Reinforce ment Hybrid Method

CEAC Key-word based

Learning based

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553

to create models for large-scale data was used. In [6], researchers built a CNN architecture for the required CNN model and discovered that this algorithm outperformed classical ML algorithms. In [16], various algorithms were experimented out of which random forest gave the best results for sentiment and emotion analysis based on a ten-fold cross-validation. In [18], authors performed techniques like KNN, NN, and SVM and concluded that the proposed work with NN is best suitable for classifying the emotions. It was concluded in [20] that generalized linear model performs best out of all other algorithms.

2.2 Semi-supervised Basically, this type is like a junction where supervised and unsupervised machine learning meet. In this case, the model is trained on a dataset that includes both labeled and unlabeled data. Most probably, unlabeled. LSTMs are a complicated branch of deep learning. This technique has been used by the authors of [2, 6, 8, 11] in their respective papers. They practiced and experimented with the LSTM approach and derived that it gave far better results than the previous classical ML methods. In [2], this deep learning approach that combines the sentimental and semantic features from user utterances and evaluates the real-world textual conversations was proposed. In [11], the writers used the nested LSTM approach to implement a temporary hierarchy. It is a simple extension of LSTM that creates another LSTM via nesting instead of creating a stacked LSTM.

2.3 Unsupervised This type is the complete opposite of supervised learning as it does not use labeled data for training. Here, the models are specifically trained with unlabeled data and are then allowed to predict output without supervision. K-means and CEAC are the algorithms that are unsupervised. In [7], the authors have explained that it works differently as it needs a whole dataset to work on, and its result is in the form of Kclusters where these K-clusters, based on the frequency distribution of emotion, are allotted a label. The introduction of a new technique in natural language processing (NLP) was done in [9]. CEAC, i.e., Cause-Emotion-Action Corpus was presented that manually annotates the cause events and action events along with the emotions. In [21], BERT gave best results.

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2.4 Hybrid/Reinforcement This type of learning is about taking appropriate actions depending on the occurring situations to maximize the profit/reward. It usually chooses the optimal path or solution for the problem. However, it is bound to act according to its previous experiences. In [1, 4, 22], authors have proposed this methodology that consists of two approaches for detecting emotions from text: the keyword-based method and the learning-based method. In [13], the authors proposed a Novel emotion detection-based reinforcement learning framework (EDRLF) wherein multi-modality performed better than uni-modality. In [17], based on comparative reports, it is clearly stated that the hybrid model gave more accurate and efficient results when compared to LSTM and Naïve Bayes. Others are bidirectional gated recurrent unit (GRU), label semantics, and proposed unsupervised machine learning algorithm are some algorithms which are stated in [5, 10], and [12], respectively. In [5], it was shown that by using BiGRU, the performance of the models can be remarkably improved by capturing more meaningful and important features from the text. In [10], the label classes are modeled via label embeddings that capture the surface semantics of the respective label names, and the mechanisms that track the correlation between the labels are added to improve the prediction. In [12], researchers have proposed a different method using an unsupervised machine learning algorithm. Its approach consists of two phases: firstly, a data corpus is built to train the system using YouTube comments. Secondly, unsupervised machine learning is run to categorize new text entries. In [14], an integrated combination model was proposed using a Twitter dataset (self-labeled) that outperforms state-of-the-art models.

3 Literature Review This section provides an overview of the methods, algorithms, and techniques used, and the results obtained, in the reviewed papers. In Ramalingam et al. [1], used a hybrid method that gives higher accuracy than that of the keyword-based and learning-based methods given individually. The approach takes the word, along with the surrounding words, to depict the result. However, it is difficult to find the most effective combination. In Gupta et al. [2], proposed the SS-LSTM algorithm to evaluate real-world problems, which outperforms CNN and LSTM along with other machine learning algorithms. Here, SSLSTM was found to be the best among all, with an average accuracy of 71.34%. In Ab Nasir et al. [3], experimented with four different methodologies: Naive Bayes, support vector machine, decision trees, and K-nearest neighbors, and discovered that the multinomial Naive Bayes algorithm gave the finest results, with an accuracy of 64.08%. The disadvantage is that mixed emotions may appear in complex sentences.

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In Arya and Jain [4], proposed a model that is partially keyword-based and partially learning-based for text-based emotion detection that gave 81.89% accuracy. In Seyeditabari et al. [5], compared their model with Wang et al.’s model using bidirectional GRU, which resulted in a difference of 26.8% accuracy. BiGRU gave 80.4% accuracy. In Pamnani et al. [6], experimented with multiple machine learning models and observed that LSTM and CNN outperformed the baseline score by 14% and gave an accuracy of around 60–65%. In Salam et al. [7], used supervised ML (Naive Bayes and support vector machine) and unsupervised ML (K-means) where supervised ML showed an accuracy of 23.45% and an accuracy of 39.6% was observed in unsupervised ML. But the system works on a syntactic level only. In Al-Omari et al. [8], presented the EmoDet system using deep learning architectures whose F1-score surpasses the performance of the baseline model. The system uses the LSTM algorithm, which gives an F1-score of 0.67. In Liu et al. [9], introduced a new technique called Cause-Emotion-Action Corpus (CEAC) using natural language processing that includes adding the corresponding cause and actions of the emotions to improve the result of emotion detection. In Gaonkar et al. [10], compared the performance of various baseline models with their own models that used label semantics. Out of all the baseline models, BERT proved to be the best with 60.96% accuracy, and the label embeddings were added to improve the efficiency of the model where the semi-supervision was concluded to be the finest with 65.88% accuracy. It showed significant improvement in overall baselines. In Haryadi et al. [11], evaluated that for multi-class emotion detection, SVM, LSTM, and nested LSTM can be used. Although the best overall accuracy was achieved by nested LSTM at 99.167%, as compared to the LSTM model that has an accuracy of 99.154%, LSTM has a better average performance in terms of precision (99.22%), recall (98.86%), and F1-score (99.04%). In Yasmina et al. [12], presented an unsupervised machine learning algorithm that uses YouTube comments as a data corpus as it does not need labeling and is flexible and easy to update. This system results in a precision of 92.75% and an accuracy of 68.82%, which is quite similar to the SVM algorithm’s results. In Huang et al. [13], derived a Novel emotion detection-based reinforcement learning framework (EDRLF) that gave an average accuracy of 59.7%. The unimodality EDRLF was performed with an accuracy of 58.7%, whereas the multimodality framework achieved an accuracy of 60.2%. In Vijayvergia et al. [14], proposed an integrated approach considering self-labeled Twitter datasets. The proposed model, giving an accuracy of 86.16%, was evaluated by the combination of two basic models as feature extractors. In Lee et al. [15], experimented with various methods like multinomial Naïve Bayes, SVM, and Bi-LSTM by proposing their features. They concluded that for binary classification, their features achieved the best accuracy of 70.13% using SVM classifier, and in the case of multi-sensation classification, the effectiveness of the proposed features achieved an outstanding result of 72.02%.

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In Balakrishnan et al. [16], investigated the sentiment and emotions of digital payment application consumers using both supervised and unsupervised ML techniques and concluded that random forest produced the best results based on tenfold cross-validation. The F1-score for emotion analyses was found to be 73.8%, and that for sentiment analyses was analyzed as 58.8%. In Madhuri et al. [17], detected emotions using hybrid and NLP pre-trained models. They concluded that the hybrid model gave the best results with 76% accuracy, whereas the Naïve Bayes approach gave an accuracy of 64% and that of Bi-LSTM was 61%. In Srinivas et al. [18], proposed a neural network-based emotion recognition model in Twitter text data using methods like KNN, SVM, and NN and concluded that the overall best accuracy achieved was 89.5% by the NN technique. In Seal et al. [19], described a process of emotion detection through natural language processing techniques that detect emotions by searching for keywords from an emotion database. The proposed system improved performance over existing systems, trying to overcome several limitations. It gave an accuracy of 65% and the F1-score was found to be 66.18%. In Chowanda et al. [20], used various supervised ML algorithms and found generalized linear model to give best results with 90% accuracy and F-score. In Chiorrini et al. [21], used bidirectional encoder representations from transformers (BERT) models for both sentiment analysis (92%) and emotion recognition (90%) of Twitter data and proposed an architecture on its basis. In Deshpande et al. [22], proposed a novel hybrid method for emotion detection using machine learning and NLP semantic analysis focusing on twelve emotions and got accuracy of 88.39%. In Suhasini et al. [23], used Naive Bayes (NB) and K-nearest neighbor algorithm (KNN) to detect the emotion of messages on Twitter where NB outperformed KNN with 72.60% accuracy.

4 Comparative Study Among the various categories of machine learning algorithms, supervised and unsupervised are recurrently chosen in various papers. Supervised learning algorithms require direct human intervention for labeling the data and are more accurate. Unsupervised learning algorithms, on the other hand, yield inconsistent performance but are maybe a good option for the emotion detection task. Hybrid algorithms, being a combination of two or more algorithms, may generate good results but generalized linear model performed way better than others in paper [20]. Below-given approaches are stated from the paper [20] which are applied on the same dataset called “AffectiveTweets” and GLM resulted well as given (Fig. 2).

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Fig. 2 Approaches [20]

Emotion Detection from Text Naive Bayes GLM FLM ANN Decision Tree Random Forest SVM

5 Discussion In [20], it has many algorithms performed on a single dataset which is thereby explained below through tables and diagram. The link of the dataset used is as follows: (from [20]). https://github.com/felipe bravom/AffectiveTweets/tree/master/benchmark/dataset (Table 1). After implementing all these algorithms on the previous given dataset (link), satisfying results were obtained and they are stated below in the form of table (Table 2). Table 3 comprises of all the research papers taken into consideration along with their proposed/preferred methodologies and their accuracies/F1-score. Best results giving methods/techniques are mentioned here according to the respective authors. Moreover, by analyzing, methods such as generalized linear model, neural networks, hybrid approach, nested LSTM, and so on performed outstandingly. Table 1 The dataset profile (AffectiveTweets) [20] Class

Actual

% Each class (%)

Sampling

% Changes (%)

Train

Test

Anger

1701

24

1701

0

1446

255 255

Sadness

1533

22

1700

11

1445

Fear

2252

32

1700

− 25

1445

255

Joy

1616

23

1700

5

1445

255

Total

7102

100

6801

−4

5781

1020

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H. Shah et al.

Table 2 Results [20] Algorithm

Avg. recall

Avg. precision

Avg. F1-score

Accuracy

Naïve Bayes

0.809

0.806

0.806

0.806

GLM

0.902

0.902

0.901

0.902

FLM

0.872

0.874

0.871

0.872

ANN

0.885

0.886

0.884

0.885

Decision tree

0.607

0.772

0.587

0.608

Random forest

0.62

0.783

0.601

0.62

SVM

0.848

0.85

0.849

0.85

The bolded row shows GLM outperforms other algorithms in accuracy, recall, precision, and F1score

5.1 Generalized Linear Model It was proved in [20] that GLM approach gave much better results than other algorithms (Fig. 3).

6 Limitations and Future Works In hybrid systems, it is difficult to find the most effective combination. A few limitations were found in [4], which indicated an inability to recognize genuine emotions in the absence of an emotion keyword, and it was difficult to classify the word into the appropriate emotion class due to word ambiguity. In [17], because of incorrect association between emotions, the initial approaches for emotion detection had numerous failures. The comparative study showed that hybrid models were more accurate and efficient than other models. However, it is expected that in the future, more models will be added to increase the overall performance. In [13], a new method, EDRLF, was introduced, but the results were unsatisfactory. Therefore, further studies will be carried out to model conversations with arbitrary turns and speakers for more accurate emotion detection in the future. In [15], it was proved that SVM gave comparatively better results, but there were a few limitations for which future enhancements like designing more elaborate features for boosting the performance of sensation classification, constructing a sensation knowledge database, and opening the database to the public for supporting further sensation-related studies are mentioned. In [16], the study’s scope was confined to English evaluations only, so it does not represent any other language. Future research builds on the study by refining the present tools for dealing with the local language or by utilizing manual human annotation, but this method can be costly. In [20], emotions like fear and sadness were difficult to recognize in some models. As future research, deep learning algorithms can be explored. In [21], there was overfitting and they would study the influence of

Text-Based Emotion Recognition: A Review

559

Table 3 List of methodologies and accuracies/F1-scores of reviewed papers Research paper

Methodology

Accuracy/F1-score

Ramalingam et al. [1]

Hybrid

High than learning- and keyword-based

Gupta et al. [24]

SS-LSTM

71.34%

Ab Nasir et al. [3]

Multinomial Naïve Bayes

64.08%

Arya et al. [4]

Proposed

81.89%

Seyeditabari et al. [5]

Proposed model

BiGRU: 80.4% Pre-trained: 63.2%

Pamnani et al. [6]

CNN and LSTM

Approx. 65%

Salam et al. [7]

Unsupervised ML (K-means)

39.6%

Al-Omari et al. [8]

LSTM

67% (F1-score)

Liu et al. [9]

CEAC

Improved the result

Gaonkar et al. [10]

Semi-supervised label semantics model

65.88% (F1-score)

Haryadi et al. [11]

Nested LSTM

99.167%

Yasmina et al. [12]

Unsupervised ML using YouTube comments

68.82%

Huang et al. [13]

Multi-modality EDRLF

60.2%

Vijayvergia et al. [14]

Integrated approach

86.16%

Lee et al. [15]

SVM

Binary: 70.13% Multi: 72.02%

Balakrishnan et al. [16]

Random forest

Sentiment: 73.8% (F1-score) Emotion: 58.8% (F1-score)

Madhuri et al. [17]

Hybrid

76%

Srinivas et al. [18]

Neural network (NN)

89.5%

Seal et al. [19]

Proposed emotion detection algorithm

Accuracy: 65% F1-score: 66.18%

Chowanda et al. [20]

Generalized linear model

Accuracy: 0.92 F1-score: 0.901

Chiorrini et al. [21]

BERT

Accuracy: 0.90

Deshpande et al. [22]

Hybrid

Accuracy: 88.39% F-score: 81.31%

Suhasini et al. [23]

Naïve Bayes

Accuracy: 72.60%

BERT-Base by substituting it with other BERT distributions or regular word embeddings. In [22], the model could progress toward the inclusion of images, videos, social media data, and other languages for emotion recognition analysis.

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Training Model

Training Data Feature Extraction

GLM Classifier

Testing Data

Training Process

Emotion Detection

Test Process

Fig. 3 GLM flow diagram [20]

7 Conclusion This paper encompasses the review of various machine learning algorithms experimented with and evaluated in the referenced research papers for detecting and recognizing emotions from text. It discusses several text-based emotion detection methods along with their shortcomings. The discussed emotion recognition systems encounter certain challenges while interpreting raw text, which includes plain text, comments, tweets, and short messages. This survey analyzes various ways of resolving raw textual data processing issues that are currently available. Different approaches give different accuracies that too on different dataset. So, we have discussed experimentation of paper [20] where various approaches were implemented on single data set and high accuracy was found.

References 1. Ramalingam VV, Pandian A, Jaiswal A, Bhatia N (2018) Emotion detection from text. J Phys Conf Ser 1000 2. Gupta U, Chatterjee A, Srikanth R, Agrawal P (2017) A sentiment-and-semantics-based approach for emotion detection in textual conversations. In: Proceedings of Neu-IR 2017 SIGIR workshop neural information retrieval, Shinjuku, Tokyo, Japan, 11 Aug 2017 (Neu-IR ’17), 6 p 3. Ab Nasir AF et al (2020) Text-based emotion prediction system using machine learning approach. IOP Conf Ser Mater Sci Eng 769 4. Arya P, Jain S (2018) Text based emotion detection. Int J Comput Eng Technol 9:95–104 5. Seyeditabari A, Tabari N, Gholizadeh S, Zadrozny W (2019) Emotion detection in text: focusing on latent representation. https://arxiv.org/abs/1907.09369 6. Pamnani A, Goel R, Choudhari J, Singh M (2019) IIT Gandhinagar at SemEval-2019 task 3: contextual emotion detection using deep learning. In: Proceedings of 13th international workshop semantics evaluation (SemEval-2019), Minneapolis, Minnesota, USA, June 6–7, 236–240. https://doi.org/10.18653/v1/s19-2039

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7. Salam SA, Gupta R (2018) Emotion detection and recognition from text using machine learning. Int J Comput Sci Eng 6:341–345 8. Al-Omari H, Abdullah M, Bassam N (2019) EmoDet at SemEval-2019 task 3: emotion detection in text using deep learning. In: Proceedings of 13th international workshop semantics evaluation (SemEval-2019), Minneapolis, Minnesota, USA, June 6–7, pp 200–204. https://doi. org/10.18653/v1/s19-2032 9. Liu P, Du C, Zhao S, Zhu C (2019) Emotion action detection and emotion inference: the task and dataset. https://arxiv.org/abs/1903.06901 10. Gaonkar R, Kwon H, Bastan M, Balasubramanian N, Chambers N (2020) Modeling label semantics for predicting emotional reactions. In: Proceedings of 58th annual meeting association of computing linguistics, 4687–4692. https://doi.org/10.18653/v1/2020.acl-mai n.426 11. Haryadi D, Kusuma GP (2019) Emotion detection in text using nested long short-term memory. Int J Adv Comput Sci Appl 10:351–357 12. Yasmina D, Hajar M, Hassan AM (2016) Using YouTube comments for text-based emotion recognition. Procedia Comput Sci 83:292–299 13. Huang X et al (2021) Emotion detection for conversations based on reinforcement learning framework. IEEE Multimed 28:76–85 14. Vijayvergia A, Kumar K (2021) Selective shallow models strength integration for emotion detection using GloVe and LSTM. Multimed Tools Appl 80:28349–28363 15. Lee J, Jatowt A, Kim KS (2021) Discovering underlying sensations of human emotions based on social media. J Assoc Inf Sci Technol 72:417–432 16. Balakrishnan V, Lok PY, Abdul Rahim H (2021) A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews. J Supercomput 77:3795–3810 17. Madhuri S, Lakshmi SV (2021) Detecting emotion from natural language text using hybrid and NLP pre-trained models. Turkish J Comput Math Educ 12:4095–4103 18. Kumar B, Suresh Kumar S, Janaki V (2021) Neural network based emotion recognition model in Twitter text data. SSRN Electron J. https://doi.org/10.2139/ssrn.3884051 19. Seal D, Roy UK, Basak R (2020) Sentence-level emotion detection from text based on semantic rules. In: Advances in intelligent systems and computing, vol 933. Springer, Singapore 20. Chowanda A, Sutoyo R, Tanachutiwat S (2021) Exploring text-based emotions recognition machine learning techniques on social media conversation. Procedia Comput Sci 179:821–828 21. Chiorrini A, Diamantini C, Mircoli A, Potena D (2021) Emotion and sentiment analysis of tweets using BERT. In: CEUR workshop proceedings, vol 2841 22. Deshpande A, Paswan R (2020) Real-time emotion recognition of twitter posts using a hybrid approach. ICTACT J Soft Comput 6956:2125–2133 23. Suhasini M, Srinivasu B (2020) Emotion detection framework for twitter data using supervised classifiers. Springer, Singapore, 565–576. https://doi.org/10.1007/978-981-15-1097-7_47 24. Gupta U, Chatterjee A, Srikanth R, Agrawal P (2017) A sentiment-and-semantics-based approach for emotion detection in textual conversations. arXiv preprint arXiv:1707.06996.

AURA—Your Virtual Assistant, at Your Service Janhvi Pawar, Disha Shetty, Aparna Ajith, and Rohini Patil

1 Introduction Virtual assistant is an application program that understands the human language and fulfill their demands helping them to save their time. It is the program that takes the input of the humans in the form of voice or text and outputs it in the similar manner. It has played and is still playing a most important part in the race of advancement of technology in the field of artificial intelligence because though being a machine it makes people feel like there are interacting to a normal human being. In this running world, people want something that is fast and not time-consuming. Hence, the virtual assistants were created. They are effortless to use. Nowadays, the voice searches are much more influenced over text search. Web searches conducted via mobile devices have only just overhaul to those carried out using a computer. The virtual assistants have been gaining the popularity since the 1990s due to the evolution of the first virtual assistant Simon of IBM. It was a digital voice recognition technology that became a feature of the personal computer. And by the yearly advancement and improvements, the most known google assistant, Cortana and Siri, were created. One of the reasons of popularity being they could be even accessed using the mobile phones. Making it more stress reliving for the users. The main purpose of virtual assistant is to answer the questions that the user may ask it. The most important part being the use is not just limited for one field but also in business setting, such as on a company website with a conversational interface. The virtual assistant offers a call-button driven service on the mobile platform that asks the user “How may I help you?” and then replies to vocal input. The virtual assistant’s motto is to assist the user with daily activities such as common J. Pawar (B) · D. Shetty · A. Ajith · R. Patil Terna Engineering College, Nerul, Navi Mumbai, India e-mail: [email protected] R. Patil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_50

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human conversation, searching queries in Google, taking screenshots, taking images, telling jokes, surfing for videos, providing daily weather conditions, fetching images, playing songs, information related to Covid-19 counts, and informing the user about the latest news.

2 Research Work The author provides an overview of the advanced IoT technology that is being utilized to help visually impaired persons with impairments. Tesseract is the Optical Character Recognition engine utilized here, which supports Unicode and has the ability to recognize over 100 languages [1]. The author describes the voice assistant online software, which allows users to have a hands-free experience while answering all of their questions and obtaining information about college and associated skeptic doubts and inquiries on this page [2]. The author has created a virtual assistant specifically for blind people. The proposed system reacts promptly to user voice requests. The virtual assistant offers different mobile device functions such as network connection and application management using just voice commands [3]. The author outlines how voice-activated services will become a part of millions of people’s daily lives. In order to build the virtual assistant device, they made certain adjustments to existing models such as the ASR model, gesture model, graph model, and interaction model in this proposal [4]. The author explains how to make a gadget that wakes up instantaneously when called to lessen users’ labor and provide them with a more convenient lifestyle. This method also takes advantage of the Internet of Things (IoT), which allows additional devices, such as smartphones, to be linked together. The user only has to supply input via speech, and the gadget will handle the rest [5]. The working ideas of voice assistants are discussed, as well as their significant shortcomings and limitations, by the author. It is described how to set up a local voice assistant without using Google or Amazon cloud services. The major focus is on developing a voice assistant using Python as a programming language [6]. The current study explored how the perceived acceptability of utilizing the voiceactivated personal assistant in smartphones effects its reported use, according to the author of this work. Participants preferred to use the VAPA in a private setting, such as their home, but were still hesitant to use it to input private or individually identifiable information when compared to more generic, non-private data [7]. Devices have a track of getting illegal access and inflicting harm to their owners. This document attempts to solve the issue of security by classifying commands as permission-based or permission-less. To preserve the owner’s confidentiality and privacy, authorization instructions must go through an authentication process [8]. This paper suggests creating a voice assistant that provides statistical data on Covid-19, such as their country, city, and location. The voice assistant speaks to

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us and offers us information regarding Covid-19’s most recent statistics. Because it operates with a voice, it is beneficial to everyone, but especially to handicapped persons [9]. The author explains how their voice assistant gadget uses text-to-speech conversion to help travelers get to their desired location. It gives directions based on GPS and translates them to spoken form [10]. The author describes their “ERAA” virtual personal assistant device, which is a smart phone application that uses Google Dialogflow and machine learning to help users with things like writing emails, accessing Google, and so on. Because the application has both picture and capture capabilities, users may utilize the latter to look up object descriptions. Because it has a built-in text-to-text interaction function, it can even make small conversation with users [11]. According to the author, their virtual assistant program uses face recognition to provide security to users, which is a feature that other virtual assistants lack. Before getting access to the device, unauthorized individuals must first seek the user’s authorization. Because it is entirely safe, this tool may be used by numerous family members [12]. The virtual assistant was created using the Linux operating system by the author. This digital assistant not only saves time, but also makes the user’s job easier. It makes our lives less stressful since we do not have to plan our excursions without checking the weather forecast, or we do not have to consider the music listing, or we can look for nearby restaurants or YouTube searches; the entire thing might appear to be really calming [13]. This Cortana Intelligent Personal Digital Assistant was created by the author. Only one person can train the Cortana at a time. Only when Cortana unlocks the screen will it do a restricted set of tasks, not all of them, in order to protect your privacy [14]. Speech recognition and language processing are among the outstanding capabilities of this system. Users may take use of a variety of services on this platform, which will allow them to modify and respond to user demands. The main goal of this project is to provide required services via voice and to allow a larger audience to be pleased by this program [15].

3 Proposed Methodology We have made our virtual assistant AURA using Python IDLE. Python is not the only language you can use to create a virtual assistant, and it is certainly not one of the most popular. Despite this, it is one of the finest languages for this purpose due to its vast library ecosystem and intrinsic characteristics. Overall flowchart is shown in (Fig. 1). • Step 1: Start • Step 2: Import modules and libraries mentioned in (Table 1).

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Fig. 1 Flowchart of the proposed virtual assistant

Table 1 Modules and libraries used Packages

Description

Speech Recognition

This software understands human voice and then turns it to text

Pyttsx3

This is a software converts text to speech

Wikipedia

The Wikipedia package extracts the information that the user has requested

ecapture

It takes the pictures that the user desires

Datetime

This package displays the current date and time on the device

Web browser

The web browser package extracts data from the web

Request

Any sort of HTTP request may be sent using the Request library

Pywhatkit

It plays tunes, YouTube videos, performs Google searches, and obtains information on a certain topic

• Step 3: The speech engine setup is done using the pyttsx3 which is used to convert the text in to speech. In this, we want our virtual assistance voice to be male or female. We alter 0 and 1 in the voices[].id. where 0 being male and 1 being female. The runAndWait function handles the queue and broadcasts the speech to the rest of the system. • Step 4: This step is about taking the audio input from the user. We set the input language to “en” that is English. • Step 5: All instructions and trigger phrases are condensed with their execution times in the if, elseif, and else loop.

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Fig. 2 Wish me function and what can you do command

• Step 6: In this step if the user has mentioned a trigger word in the command. then the desired output will be provided. If not, then AURA will reply “I did not understand the command” and then the execution will end. • Step 7: End.

4 Result and Discussion This project primarily focuses on the user’s voice requests and how our virtual assistant AURA executes them by offering hands-free service to the user. The commands that our virtual assistant executes are listed below.

4.1 Commands Executed Wish Me and What Can You Do Command. The wish me function makes our AI wish or greet us according to the time on the device. The what can you do function tells us the activities or tasks performed by our virtual assistant AURA shown in (Fig. 2). Weather Command, Time Command, and Covid Command. The Weather will tell us the weather of a particular city mentioned by the user. The Time command tell us the current time on the device. The Covid command will give us the live updated counts The Covid command provides the live updated counts of the total number of active cases, deaths, confirmed cases, and the total number of recovered cases shown in (Fig. 3). Screenshot and Take a Picture Command. When we ask AURA to take a screenshot, it will take the screenshot of the page of which we want the screenshot of. This command take the photo on behalf of the user. To execute this command, we just have to ask AURA to take a photo. Screenshot command captures the image of the screen using imagegrab imported from pyscreenshot module, whereas picture commands clicks the photo of anything that is in front of the camera using ecapture shown in (Figs. 4 and 5). News Command. This command tells us the updated news provided by the sources of The Times Of India shown in (Fig. 6).

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Fig. 3 Weather command, Time command, and Covid command

Fig. 4 Screenshot taken by AURA

Search command. The search command will search any information, images or anything you wish to search shown in (Fig. 7). Open Google, Gmail, and YouTube Command. This command will open the Google tab. If we ask our virtual assistant AURA to Open Google mail, then it will open the Google mail in the new tab. The open YouTube command shown in (Fig. 8) will open the YouTube in new tab and you can watch any videos of your choice.

AURA—Your Virtual Assistant, at Your Service

Fig. 5 Photo taken by AURA

Fig. 6 News command executed by AURA [16]

Fig. 7 Butterfly images searched by AURA [17]

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Fig. 8 Google, YouTube, and Gmail command [18]

Play Songs Command. The play songs command shown in (Fig. 9) will play any song of your choice. While giving the command to AURA, you will just have use the keyword play and the name of the song you wish to hear. Joke Command, Wikipedia, and Goodbye Command. The joke command is added to our program to lighten up our moods. Whenever you feel low, AURA will crack some jokes to boost your mood up. The Wikipedia command will give us the information about anything we want to know about. In the goodbye command when we

Fig. 9 Play song command execution [19]

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Fig. 10 Joke, Wikipedia, and goodbye command

want to stop the execution, then we just have to say the keyword bye, goodbye or stop. Using this keyword, AURA will understand that she has to stop the execution shown in (Fig. 10).

5 Conclusion AI virtual assistants are rapidly emerging nowadays. Voice-based personal assistants are a huge benefit to today’s consumers, especially in the age of smart homes and smart gadgets. We have automated several services and simplified most of the user’s tasks using our virtual assistant AURA, such as serving Covid-19 information, searching the web for information, getting weather forecasts, clicking pictures, taking screenshots, and even entertaining us by cracking jokes, playing videos on YouTube, and medical-related queries. We are working on improving their skills, such as speech recognition and language processing. Users may take use of a variety of services on this platform, which will allow them to modify and respond to user demands. We intend to make our project a success by assisting physically challenged and visually impaired individuals, since our project’s main and fundamental goal is to provide vital services using speech and to allow a larger number of people to be amused by this program. Our exhibited project will be extremely beneficial and have a wide range of possible applications and values in industries all over the world. Online and automated learning are two further applications in the subject that may be combined with the present system. Speech recognition is required for a few applications; therefore, robotic process automation can be used. For functions that do not require an online connection, the assistant will become offline.

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References 1. Dubey I, Verma JS, Mehendale A (2019) An assistive system for visually impaired using Raspberry Pi. Int J Eng Res Technol (IJERT) 8(5):608–613 2. Deny N, Praveen S, Sai A, Ganga M, Abisree RS (2019) Voice assistant application for a college website. Int J Recent Technol Eng (IJRTE) 7(6S5):2065–2070 3. Kulhalli KV, Sirbi K, Abhijit JP (2017) Personal assistant with voice recognition intelligence. Int J Eng Res Technol 10(1):416–419 4. Këpuska V, Bohouta G (2018) Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). In: 2018 IEEE 8th annual computing and communication workshop and conference (CCWC), 7(9), pp 99–103 5. Dekate A, Kulkarni C, Killedar R (2016) Study of voice controlled personal assistant device. Int J Comput Trends Technol (IJCTT) 42(1):42–46 6. Shende D, Umahiya R, Raghorte M, Bhisikar A, Bhange A (2019) AI based voice assistant using python. J Emerg Technol Innov Res (JETIR) 6(2):506–509 7. Moorthy EA, Vu KPL (2014) Voice activated personal assistant: acceptability of use in the public space. In: Human interface and the management of information. Information and knowledge in applications and services, 8522(3), 1–112 8. Bhonsle VS, Thota S, Thota S (2022) AKIRA—a voice based virtual assistant with authentication. Commun Control Robot Syst Smart Innov Syst Technol 229(8):908–917 9. Primkulov S, Urolov J, Singh M (2021) Voice assistant for covid-19. Intell Human Comput Interact IHCI 12615(7):776–789 10. Keerthana R, Kumar AT, Manjubala P, Pavithra M (2020) An interactive voice assistant system for guiding the tourists in historical places. In: International conference on system, computation, automation and networking (ICSCAN). IEEE, Pondicherry, India, pp 1–5 11. Patil J, Shewale A, Bhushan E, Fernandes A, Khartadkar R (2021) A voice based assistant using Google Dialogflow and machine learning. Int J Sci Res Sci Technol (IJSRST) 8(3):06–17 12. Praddeep P, Balaji P, Bhanumathi S (2019) Artificial intelligence based person identification virtual assistant. Int J Recent Technol Eng (IJRTE) 8(2S11):2315–2319 13. Sharma RR (2020) Linux based virtual assistant in C. Int J Adv Res Idea Innov Technol 6(1):25–27 14. Bhat RH, Lone AT, Paul MZ (2017) Cortana—intelligent personal digital assistant. Int J Adv Res Comput Sci 8(7):55–57 15. Suvarna D, Srinivas J, Ramaiah VC (2021) Virtual assistant using python. Int J Sci Eng Res 8:1–5 16. TOI homepage. https://timesofindia.indiatimes.com/home/headlines. Last accessed on 16 Jan 2022 17. Google Search, 2022. https://www.google.com/search?q=%20butterfly%20images. Last accessed on 16 Jan 2022 18. Google Homepage, Google Account Homepage, YouTube Homepage. https://www.google. com/, https://myaccount.google.com/?utm_source=account-marketing-page&utm_medium= go-to-account-button, https://www.youtube.com/. Last accessed on 16 Jan 2022 19. YouTube.https://www.youtube.com/watch?v=WMweEpGlu_U. Last accessed on 16 Jan 2022

Design and Develop Sign Language to English Script Convertor Using AI and Machine Learning Bhausaheb Khamat, Mrunal Bewoor, and Sheetal Patil

1 Introduction Indian communication via gestures is a widely used methodology for gesture-based communication from the time when the main handicap/impacted people who cannot speak and individuals have been correspondence related, and they cannot involve communicated, thus the main way to communicate the talks via the signed and gestured-based communication. Delivering messages between the peoples of impacted and non-impacted (who cannot speak or hear and who can speak or hear) in different way such as signs, gestures, hand signals, etc. The ways of communicating of the thoughts in every place is differ or it will also differ in the native language wise. Signals and gestures are the nonverbal ways of communication. This nonverbal and gesture communication are very hard to communicate and called as gesture-based communication. Communicating the thoughts via signs and gesture in this language are classified into the three major parts.

B. Khamat Computer Engineering Department, Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune, Pune, India e-mail: [email protected] M. Bewoor (B) · S. Patil Computer Engineering Department, Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune, Pune, India e-mail: [email protected] S. Patil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_51

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Finger spelling

Example

Non-manual features

Used to communicate the words letter by letter

Used for the communication of deaf and non-speaking peoples. Will use the gesture- and sign-based language

Expression of the face, fingers, signs gestures, position of the hand, fingers, and other physical or some signs

In our research, we basically keeping our focus on the creating a model which can take input as a gesture and finger/signals to frame and gather all the inputs, then it will be processed in our model. Our model will be focused on the input, we will train the input given and will be provide the output. This gesture-based input will always processed by the CNN and gesture extraction model and will be saved in AI/ML training set for indexed and used next time.

2 Literature Survey Many explorations have been made on communications acknowledgment via gestures. We have self-analyzed different accessible exploration papers and concocted a point of interaction that converts communication through signing to message, gives the element of adding a word and ideas in view of the word being interpreted. A framework has been proposed which perceives active hand signals for English numeric (0–9) continuously utilizing Hidden Markov Model. Gee is profoundly subject to the likelihood of the secret states, thus there are a greater number of boundaries to realize which is tedious [10, 11, 13, 18, 21]. The proposed framework contains two phases such as pre-processing for gesture based and classification for hand following and to perceive motions respectively. Secret Markov Model is used for the dynamic signal acknowledgment whose normal acknowledgment rates are 99.167% and 93.84% separately. HMM also known as Hidden Markov Model is used for the motions for order and the arrangements of model with active and dynamic parts of motions [20, 22]. Signals are extracted from the video or the pictures arrangement through following skin-shading masses comparing with the hand to body–face space fixated on the substance of client. The objective is perceiving two types of signals: deictic and representative. The picture has been sifted from utilizing a quick look to an ordering table. Post-separating, skin-shading pixels have been accumulated to masses. [1] A calculation has been proposed by Pradumn Kumar and Upasana Dugal which was utilizing TensorFlow considering advanced convocational neural networks to recognize proof of plants. This examination paper inspired us for involving convolutional neural networks for Indian Sign Language images ID. Uncommonly utilizing CNN is considered to be highly moving method for deep learning w.r.t PC perspective. ImageNet has been created numerous assumptions through resulting invigorating end results. CNN here [6, 7, 9, 12] holds the most difficult errand of recognizable evidence of plants by making use of their total picture or any of their pieces. At the same time,

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others handle process like right off the bat individually. They hold a specific living being (like blossoms, leaves and bark and so forth) then, at the same point, complete picture of creatures [5, 23]. CNN has some restrictions such as it is not that much good with exceptionally enormous arrangements of images or non-appearance of informative power. Hence, advanced CNN will replace CNN because advanced CNN is tiny in size as compared to CNN in perceiving pictures. Enormous models can effectively be increased here, and these models are little to the point of preparing fasting, through such advanced technology, we can get rid of novel thoughts and will be having a decent opportunity for probing different strategies too. The advanced CNN [1516, 17] design is multi-facet consisting of substitute utilization of convolution layers and nonlinearities. This large number of layers are dragged through entirely associated layers moving into a SoftMax classifier. Such model provides an adequate precision result within couple of times when we run on a GPU. It represents a large-scale survey of profound knowledge gaining and fosters a new arrangement idea for breaking down the current way of writing process in profound learning [19]. It partitions profound learning calculations to four divisions according to the essential model they have received from: convolutional neural networks, restricted Boltzmann, sparse coding, machines, and autoencoder. The cutting-edge approaches of four divisions have been talked about and dissected exhaustively. Regarding applications in PC vision space, this paper predominantly states the headways of CNN-based plans, since it is the most widely used as well as generally reasonable in terms of pictures. Most strikingly, a few under-way articles [3, 4] have announced exciting advances showing that some CNN-based calculations have effectively over-shadowed the exactness of human raters. Despite favorable outcomes announced up until this point, there can be a large space for additional advances. For instance, the fundamental hypothetical foundation does not clarify yet that under which circumstances they will perform satisfactorily or outflank different methodologies, and how to decide the absolute design for a particular assignment. This paper depicts such complications and sums up recent fads in planning and developing profound brain organizations, along with a few bearings that can additionally investigated from here on out. Convolution brain network [14] has for quite some time been utilized in the field of computerized picture handling and discourse acknowledgment and has made extraordinary progress. Before the proposal of convolutional brain network, both picture handling and discourse acknowledgment were finished by conventional AI calculations. Albeit incredible outcomes were accomplished, it was hard to make further forward leaps, so CNN appeared. As of now, CNN for picture handling [2] and discourse acknowledgment are moderately developed. The hypothetical exploration as well as the modern application were extremely effective, which has been advanced the jump of CNN for improvement ahead. The achievement of CNN in picture handling as well as discourse acknowledgment has invigorated its exploration free for all in regular language handling. The current CNN

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to deal with regular language has been generally utilized, albeit a few accomplishments have been made, the current impact is not excellent. The reason for is to give a clearer clarification of the design of CNN. Simultaneously, give a concise outline and prospect of flow CNN research in picture handling, discourse acknowledgment, and regular language handling. Results saw in the similar review with other customary strategies propose that CNN provides more satisfactory precision and more effectively lifts framework presentation because of exceptional elements like shared loads and neighborhood network. CNN is superior to other profound learning techniques [24, 25] w.r.t applications related with PC vision as well as normal language handling since it diminished many customary issues. For decreasing the issue of overfitting, dropout technique has been included. Dropout is a strategy for working on brain networks through diminishing overfitting. Standard backpropagation knowledge develops fragile cotransformations that will do the job of preparing information however do not add unobtrusive content. Dropout which are not regular distributes these variations when there is availability of a specific secret untrustworthy. This innovative way was established to work on the exhibition of brain neural network in a large assortment of use areas combinedly discourse acknowledgment, record arrangement, examination of computational science information, object grouping, and digit acknowledgment. This analysis shows that method of dropout is an overall procedure and not clear any area. The dropout significantly worked on exhibition of standard brain nets onto different informational collections too. Restricted Boltzmann machines and other graphical models can hear this thoughts as well. Taking a huge model that overfits effectively and more than once test and train more modest sub-models from it is to take the focal thought of dropout. Many researchers combinedly find out that fostered a finger gesture communication through signing interpreter is gotten which has an exactness of 95%. They made a work area application that utilizes a PC’s webcam to catch an individual marking signals for ASL and make an interpretation of it into relating text and discourse progressively. The deciphered communication through signing motion will be procured in text which is further changed over into sound. As such they are carrying out a finger spelling communication via gestures interpreter. To empower the recognition of motions, they utilized CNN. This research paper demonstrates and gave the knowledge regarding the referring model. In the other hand for referring paper, SVM was utilized as AI technique. They demonstrated and introduced an acknowledgment technique for fingerspelling’s in Japanese gesture-based communication, which utilizes arrangement tree in view of example acknowledgment and AI. Fingerspelling’s of Japanese [8] communication through signing depend on Indian letters in order, most of it are contributed by Japanese symbols, signals, and implications [14, 15]. They have developed an order so that they can handily perceived fingerspelling’s and furthermore utilized AI for troublesomely perceived ones. Utilizing this model they are accomplish a precision of 85%. Handling a high volume as well as high request information is a fundamental issue, particularly in AI. The research

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paper fostered model utilizing LDA. The LDA, in any case, requests that input information ought to be. Such an imperative is a critical disadvantage to communicate complicated information.

3 Research Methodology For converting sign language to English uses methodology which is having two layers of calculation to foresee the last image of the user. There are plenty of application which demonstrates the idea of converting such signs to particular language, the model used in existing system is static model. The system that we are developing will be adaptive model, which accepts multiple data sets as input. This input or data set is taken not only from administrators but also mainly the live users’ data. Let say particular user A interacts with the system and giving input set for one word. The system will take that data set and will pass it to our Algorithmic Layer and then to CNN model after passing the input from multiple CNN model layer the input will then processed through the dropout layer and optimizer. Now the system has the input in processed format, this input will then passed to ML trained data set and then will get the required output. While passing this output back to application in text format, the system will simultaneously proceed for this input ML seeding. Means the input is now the training data set for the system and will add to data set. So next time when similar optimized data set passed as input then without any extra validation, the system will give the output. Below is the figure to illustrating high-level understanding of system (Fig. 1):

4 Observations and Applications • Low discovery exactness of customary AI and profound learning order calculations. • Miniature article location issue because of usage of homogeneous element extraction and choice strategies. • Overfitting issue because of repetitive element determination during the discovery and grouping. • Information spillage issues when frameworks used disseminated information transmission draws near. • About 18 million individuals in India have been assessed for facing difficulty in hearing as indicated by the National Deaf Association (NAD). Subsequently an undertaking with the thought that this could take care of an enormous level of this kind of weakened local area through furnishing some assistance to speak with the remainder of the world utilizing communication through signing.

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Fig. 1 Illustration of high-level design

• This prompts the end of the center individual who for the most part goes about as a mechanism of interpretation. Because of the straightforwardness of the model, it can likewise be carried out in versatile application and is viewed as our tentative arrangement to do as such. • Individuals who are deaf do not possess such numerous choices to speak to any consultation individual, also, every one of the choices in all actuality do have significant blemishes. Translators are not normally accessible, and furthermore could be costly. Our task as referenced before is very affordable and requires insignificant measure of the board. Subsequently it is very worthwhile as far as cost decrease. • The approach called pen and paper approach is just like a poorly conceived notion: it’s entirely awkward, muddled, tedious for both hard of hearing and hearing individual. Our interpreter "ANUVADAK" in this way tackles the issue by eliminating the need of any composed methodology for correspondence.

5 Conclusion With the relentless forward jump of mind network in man-made thinking, PC point of view and other similar environments, network of brain or cerebrum has put forth advanced steps in corresponding examination through marking affirmation considering vision. In this system, we proposed an acknowledgment technique for hands sign in Indian Sign Language, which utilizes PC vision in light of example acknowledgment and convolutional neural network (a deep Learning calculation). Prepared

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our model for an aggregate many images (English letter sets and a “clear” image for dividing in the middle of the sentences). Our CNN network classifier’s which we had the option to accomplish nearly exact 96%. To test our classifier in this system, a key graphical user interface application has been made. These users of application allow clients to shape characters, words, and sentences as per their requirements, also, through giving various plans to the relating word being outlined further helpers in correspondence. The standard goal has been achieved, or if nothing else, the necessity for a middle person has been removed.

References 1. Kumar P, Dugal U (2020) Tensorflow based image classification using advanced convolutional neural network. Int J Recent Technol Eng (IJRTE) 8(6), ISSN: 2277-3878, March (2020) 2. Shaina AR, Singh N (2021) Indian and Indian sign language translation using convolutional neural networks 3. Chophuk P, Chamnongthai K (2021) Backhand-view-based continuous-signed-letter recognition using a rewound video sequence and the previous signed-letter information 4. Taylor MM (2017) Interpretation skills: English to Indian sign language. Interpreting Consolidated, Lexington, KY, USA, Tech. Rep. 324490974487 5. Jia X (2017) Deep learning on image recognition method based. CCDC, 2017. 978-1-50904657-717 6. Yang J, Li J (2017) Deep convolution neural network- applications. IEEE 978 1 5386 1010 7/ 17 7. Ojha A, Pandey A, Maurya S, Thakur A, Dayananda P (2020) Real time using CNN sign language to text & speech translation. Int J Eng Res Technol (IJERT), ISSN: 2278-0181, NCAIT—2020 Conference Proceedings 8. Mukai N, Harada N, Chang Y (2017) Japanese fingerspelling recognition based on classification tree and machine learning. NICOGRAPH International 9. Aarthi M, Vijayalakshmi P (2016) Sign language to speech conversion. In: 5th international conference-recent trends in information technology 10. Hema B, Anjum S, Hani U, Vanaja P, Akshatha M (2019) Survey on gesture language & gesture recognition system. IRJET V6-I3-2019 11. Ahmed S, Islam M, Hassan J, Ahmed MU, Ferdosi BJ, Saha S, Shopon M et al. (2019) Hand sign to Bangla speech-A deep learning in vision based system for recognizing hand sign digits & generating Bangla speech 12. Bheda V, Radpour D (2017) Indian sign language—Using deep convolutional networks for gesture recognition 13. Srinivas B, Sasibhushana Rao G (2019) IJRTE. 8(2), July 2019. ISSN: 2277-3878 14. Jeong J (2019) The most intuitive and easiest guide for convolution neural network. Towards Data Sci 15. Rao GA, Syamala K, Kishore P, Sastry A (2018) Deep CNN for sign language recognition. In: 2018 Conference 16. Patil, S.S., Patil, S.H., Azfar, F.N., ...Kumar, S., Patel,Medicinal plant identification using convolutional neural network, I. AIP Conference Proceedings, 2023, 2890(1), 020023 17. Bewoor, M., Patil, S., Kushwaha, S., ...Trivedi, S., Pawar,Face recognition using open CV and VGG 16 transfer learning , A. AIP Conference Proceedings, 2023, 2890(1), 020019 18. Patil, S.S., Patil, S.H., Pawar, A.M., ...Sharma, S., Bewoor, M.S "Employee Churn walkthrough using KNN ." 2022 2nd Asian Conference on Innovation in Technology, ASIANCON 2022, 2022

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19. Zunair H, Mohammed N, Momen S (2018) Unconventional wisdom: new transfer learning way applied to Bengali numeral classification. In: 2018 international conference on bangla speech and language processing (ICBSLP) 20. Raikwal JS, Saxena K (2012) Performance evaluation of SVM and K-Nearest Neighbours Algorithm over medical data set. Int J Comput Appl 50(14), 0975–8887 July 2012 21. Oyedotun OK, Khashman A (2017) Vision-based static hand gesture recognition—Deep learning. Neural Comput Appl 28(12) (2017) 22. Vidhya A (2017) Understanding support vector machine from examples. Sunil Ray 23. Ji Y, Kim S, Lee KB (2017) Sign language learning system with image sampling and convolutional neural network. Rob Comput (IRC), IEEE 24. Garcia B, Viesca SA (2016) Convolutional neural networks-Real-time Indian sign language recognition. CNN for Visual Recogn 25. Kuznetsova A, Leal-Taixe L, Rosenhahn B (2013) Consumer depth camera—Real-time sign language recognition. In: Proceedings IEEE international conference on computer

Akademy: A Unique Online Examination Software Using ML Hartik Suhagiya, Hardik Pithadiya, Hrithik Mistry, Harshal Jain, and Kiran Bhowmick

1 Introduction The entire education system was most affected due to the pandemic. Educational institutions had to adapt online teaching and assessment methods. This drastically changed the evaluation method. Due to this, the students had to face many conflicts regarding the functionalities present in the application or lack of proper resources such as Internet connectivity, poor access to technology, aggressive online proctoring; and also, from the faculty’s perspective, there may be discomfort during question paper generation and difficulty in evaluation of a candidate’s potential. Many higher institutions have started using their customized e-examination forms to test knowledge, such as the University College of London, which uses the course examination system model and uses online quizzes and examinations [1]. The University of Nottingham also developed Rogo e-examination, an open-source assessment management system [2]. Online examination has several benefits for the student and faculty, like saving travel time, comfort zone, etc. But at the same time, there are many obstacles too as discussed above. Therefore, a solution is proposed that would try to overcome all the gaps in malpractice prevention, which would make a huge change in fair evaluation by utilizing various ML models.

H. Suhagiya · H. Pithadiya · H. Mistry · H. Jain (B) · K. Bhowmick Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Bhaktivedanta Swami Rd, Mumbai, Maharashtra 400056, India e-mail: [email protected] K. Bhowmick e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_52

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2 Literature Review A comprehensive literature review of existing research on online education and examination is done to identify gaps and need to make a newer system. The authors of the work [3] provided a way to improve the resilience for posture and illumination fluctuations by performing an incremental training procedure with it. The work done in the paper [4] compares various models such as FaceNet and YOLO-face face detector, and the result was that MTCNN performs better. The paper [5] discusses the approaches and features that should be there in an ideal online examination system. A cutting-edge computer vision-based video content analysis system was supplied by the authors of the article [6] for the automatic creation of video summaries during online assessments. Facial detection and recognition, tab locking mechanisms used in conjunction with a secure browser can help lessen malpractice in online examinations, according to the author of the study [7]. In the paper [8], a method is proposed that classifies the person’s improper behaviour by training model. The literature discussed above describes various approaches to the prevention of malpractices, such as using models on videos, models on images, microphone analysis, and tracking user activity. However, none of these discussed about the additional layer of authentication, which is user verification and continuous user detection. This paper provides an efficient examination-taking solution with simpler UI having advanced features.

3 Proposed Methodology This paper’s main focus is on following supreme features.

3.1 Tab Switch Detection In online examinations, students can commit malpractice by switching the browser tabs and do illegal practices. To stop this, the tool can detect the number of times the student switches the tabs or even lock the current tab such that switching between them is not allowed.

3.2 Random Question Generation The faculty must schedule the MCQ tests, which must include all fields such as test description, data and time of the scheduled test, level of difficulty (easy, medium,

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Fig. 1 Possible scenarios of face detection

hard), and a field to upload an Excel file containing MCQ questions. Randomizing the MCQ is the efficient way to reduce malpractice as it presents different questions for different students but with the same difficulty for all the students as set by the faculty.

3.3 Examination The two important subsections based on ML in the developed application are as follows:

3.3.1

Student Face Detection

The face detection model detects number of people in an image captured by the system. There could be multiple faces in an image (people may assist the examination taker). To detect faces, an image is provided as input to MTCNN (face detection algorithm), and the output result will be all the faces identified/detected from the image. The possible scenarios for the output are shown in Fig. 1.

3.3.2

Student Face Verification

The student’s captured image and the profile image are passed through the MTCNN algorithm, which generates the bounding boxes for the faces found in the image. The faces in the bounding boxes are compared by the dissimilarity measure using the cosine similarity. As a result, if the student’s profile image matches the captured image, then the user is allowed to give the test, else the image verification process will repeat again. This verification is done using mathematical measures as discussed in Sect. 4.

3.4 Analysis Different analyses of student’s performance are conducted based on the examination results. These are graphical and includes:

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1. Mark and rank analysis: In this analysis, the student’s marks are displayed with the percentage. 2. Top 10 rank analysis: This provides the list of top 10 students from all the students who gave the test. 3. Difficulty analysis: In case of mix questions, this analysis gives the total number of question attempted out of all questions for each difficulty level. 4. Stand across groups and participation analysis: Student score compared with topper, average, and minimum scorer is also calculated.

4 Evaluation Methods For face verification, a dissimilarity measure between captured and profile picture is evaluated using cosine similarity. The value of this measure lies between 0 and 1.If the student’s newly captured image has a dissimilarity measure of less than 0.5, then the student will be provided access to the resources/tests, else the model will run again after giving an appropriate alert to student. X.Y . ||X || ∗ ||Y || Dis(X, Y ) = 1 − Cos(X, Y ).

Cos(X, Y ) =

(1)

Here, X. Y Dot product of two vectors ‘X’ and ‘Y ’. || X || and || Y || Magnitude of two vectors ‘X’ and ‘Y ’. || X || * || Y || Cross product of two vectors ‘X’ and ‘Y ’.

5 Results Various outputs shown below depict the result of the proposed solution.

5.1 Face Detection and Verification A web application with useful features and constraints was developed by integrating the MTCNN model. The test environment having student verification and detection takes the input image through the camera and gives the appropriate output. The results provided by the model are displayed below in Figs. 2 and 3. Figure 2 displays the evaluation of student detection whilst giving exam, and Fig. 3 shows verification of the student’s recently captured image with the profile image on the application.

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Fig. 2 Face detection

Detected 1 face

Detected 2 face

Fig. 3 Face verification

Captured Image

Profile Image

Fig. 4 Tab switch detection

5.2 Tab Switch Whenever the student tries to switch to another tab in the browser, it is detected and the appropriate action is taken against it. Figure 4 shows that tab switch is detected.

5.3 Test Analysis Figure 5 shows the four analyses: mark analysis, top 10 analysis, difficulty analysis, and overall test analysis.

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Mark analysis

Top 10 Rank analysis

Difficulty analysis

Overall Test Analysis

Fig. 5 Test analysis

6 Conclusion and Future Scope The solution includes numerous features that have been created, such as remote proctoring for observing the candidate and detection of switching between browser tabs or desktop tabs. The suggested approach makes use of face verification during online examinations. In the future, proctoring could be enhanced using mobile applications by gaining access to the rear camera as well, which will provide a wider view of the environment in which the student is giving the examination and also unique features. Other machine learning models can also be involved in the development of good examination-taking software.

References 1. Designing effective online assessment, https://www.ucl.ac.uk/teaching-learning/assessment-res ources/designing-effective-online-assessment 2. Asep HS, Bandung Y (2019) A design of continuous user verification for online exam proctoring on m-learning. In: 2019 international conference on electrical engineering and informatics (ICEEI). IEEE, pp 284–289 3. Ganidisastra AHS, Bandung Y (2021) An incremental training on deep learning face recognition for m-learning online exam proctoring. In: 2021 IEEE Asia pacific conference on wireless and mobile (APWiMob). IEEE, pp 213–219 4. Muzaffar AW, Tahir M, Anwar MW, Chaudry Q, Mir SR, Rasheed Y (2021) A systematic review of online exams solutions in e-learning: techniques, tools, and global adoption. IEEE Access 9:32689–32712 5. Cote M, Jean F, Albu AB, Capson D (2016) Video summarization for remote invigilation of online exams. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–9 6. Sasikala N, Sundaram BM, Kumar VN, Sumanth J, Hrithik S (2022) Face recognition based automated remote proctoring platform. In: 2022 second international conference on artificial intelligence and smart energy (ICAIS). IEEE, pp 1753–1760 7. Susithra V, Reshma A, Gope B, Sankar S (2021) Detection of anomalous behaviour in online exam towards automated proctoring. In: 2021 international conference on system, computation, automation and networking (ICSCAN). IEEE, pp 1–5 8. Kashid MM, Karande KJ, Mulani AO (2022) IoT-based environmental parameter monitoring using machine learning approach. In: Kumar A, Ghinea G, Merugu S, Hashimoto T (eds) Proceedings of the international conference on cognitive and intelligent computing. Cognitive Science and Technology. Springer, Singapore

Selection of Reactive Load to Correct Power Factor, Cost-Effectiveness Somnath Lambe and Kailash Karande

1 Introduction It is very important to use energy efficiently in recent times. The loss of energy while transforming energy should be minimal [1]. At present, the industrial load is of inductive type like motors, the average power factor of motors lies between 0.7 and 0.8, lagging, and it changes as per the torque. Now, consider a transformer of 100 KVA and its power transferring efficiency is 95%, then it delivers an average power of 70 KW as an active power, and it produces tentative reactive power of 20 KVAR [2]. This happens due to a lagging power factor. Then, to reduce power loss, it is needed to compensate by using reactive load and then power loss can be minimized. To reduce this loss, it is needed to add reactive load. The power factor should be close to 0.95, so a capacitor bank always be used. However, to check the feasibility of all these things, let us find how much energy is lost annually without adding a capacitor bank. Then, compare it with expenses needed after adding the reactive load from which it can be concluded which one is economical. For experimentation, it is proposed to use 1 KVA, 3φ input–3φ output, isolation transformer, 1 HP 3φ motor load, 0.5 KVAR calculated reactive load and its overall comparison. Everywhere it is found that today, there is a load sharing? The central/state government today is giving a lot of impetus to solar energy so that maximum energy can be generated from renewable sources. In order to reduce pollution, it is needed to save energy going to waste [3]. Due to the gap between demands and supplies, farmers are provided with electricity at night, facing endless difficulties. Unfortunately, farmers have to irrigate at night. The big cities are growing S. Lambe (B) SKNSCOE, Punyashlok Ahilyadevi Holkar Solapur University, Pandharpur, M.S., India e-mail: [email protected] K. Karande SKNSCOE, Punyashlok Ahilyadevi Holkar Solapur University, Pandharpur, India KIT, Shelve, M.S., India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_53

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fast, and their electricity needs are also increasing. So, considering all of the above factors, saving energy is the only good way to stay balanced and reduce pollution [4].

2 Related Work Karimeh et al. [5] proposed a modified system of adaptive power factor correction. They proposed the best algorithm to choose an appropriate value of reactive load to be selected. There were choices to choose from. Among these, a best value was chosen. Selection of reactive load time was low so that the lifetime of switching relay and capacitors was increased. A fixed algorithm was developed by considering different phase voltages, different currents, constant 50 Hz frequency and required capacitor value according to these coefficients were developed [6]. Zhang et al. [7] proposed a self-protected single-stage LLC resonant rectifier unit to protect circuits from overvoltage generated. The diode is used to avoid overvoltage. Resonant inductor and capacitors are used to pass only fundamental component, and other components were blocked. This has added to power factor correction also [8]. Qi et al. [9] developed a new system of power factor correction that is a singlephase three-level flying capacitor PFC rectifier without electrolytic capacitors. Traditionally, in AC–DC converters, electrolyte capacitors are used for filtration purposes. Here, they used a pulsating power buffering (PPB) function within each switching period. In this way, power factors were corrected. Konchaki et al. [10] designed a passive LCL filter to filter out third harmonics generated from non-linear loads. Harmonics were filtered, and stable reliable units were developed. Kabir et al. [11] proposed an automated power factor correction and energy monitoring system. Active power was measured, and compensating for reactive load to be added is calculated and correspondingly added to improve the power factor. The power factor was maintained at about 0.95. Arduino microcontrollers were used in this method. Mahlatsi et al. [12] proposed an innovative method for power factor correction using a solar plant as a source of reactive load. Solar plants at night are at an idle stage. So, in the night time, solar plants are used as a source of reactive load. In this way, they worked on solar energy generation at daytime and as a reactive load at nighttime. This method was really innovative. Christopher et al. [13] did a comparative study on power factor correction—a fresh look at today’s electrical system and gave an explanation of the need for power factor correction and advantage. Victor et al. [14] proposed a power quality enhancement in residential smart grids through power factor correction stages. A smart grid was proposed where THD in current and voltage is measured, and compensating methods were proposed [15].

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3 System Architecture 3.1 Block Diagram Figure 1 shows the block diagram of the power factor improvement system. For testing purposes, a 3φ isolation transformer with a 1:1 ratio is used. At different stages, MCBs are used to break circuits from the mains supply. 1 HP three-phase motor is used as a load. Fuses are used for mains circuit breaking purposes. Two different stage power factors are observed with VFD and direct 3φ to motor. In order to improve PF from 0.9 to 0.97, a reactive load must be connected. The value of the reactive load to be connected is calculated as below: Let us assume, Initial power factor (PF1) = 0.9 Required power factor (PF2) = 0.97 Load connected (P) = 1HP = 0.746 KW Now calculate phase angle. Say initial phase angle is φ 1 , and the required phase angle is φ 2 , ∴ COS(φ1 ) = 0.9 ∴ φ1 = COS−1 (0.9) = 26◦ Similarly, ∴ φ2 = COS−1 (0.97) = 14◦ Now, calculate reactive load to be connected to achieve 0.97 power factor, KVAR(to be added) = P(TAN(φ1 )−TAN(φ2 ))

Fig. 1 Block diagram

(1)

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Fig. 2 Actual connection diagram

∴ KVAR = 0.746 (TAN(26◦ )−TAN(14◦ ) ∴ KVAR = 0.746 (0.48−0.24) ∴ KVAR = 0.746 × 0.24 ∴ KVAR = 0.300 (3φ reactive load to be used) Figure 1 shows the block diagram of the proposed system. It shows an isolation transformer output connected to the motor through a set of fuses, PF meter and MCB. There is a provision to connect reactive load at the o/p of the transformer. There is also provision to connect 3φ to the motor through VFD. A changeover is used to select one of the inputs of the motor. MCBs are used in each line for extra protection from overloading. The photograph of the actual system used for testing is shown in Figure 2

3.2 System Flowchart Figure 3 shows the flowchart of designed a system. In this system, 3φ supply to the motor is provided in two ways. Initially, 3φ supply is given to a fuse. The reactive load has been added through MCB. Then, the power factor can be measured on the power factor meter. After that, the power supply to the motor goes in two ways, one through direct MCB and another through VFD. In both cases, the reactive load is added to correct the power factor. Figure 4 shows the different powers per phase, i.e., active power, reactive power and apparent power. It also shows phase voltage, line-to-line current and power factor

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Fig. 3 Flowchart of system

of these lines. Figure 5 shows line-to-line voltage, average line current and frequency of line voltage. It also shows the rpm of the motor. Figure 6 shows line-to-line voltages and phase voltages of RYB phases. Figure 7 shows the line voltages when the VFD is bypassed. Figure 8 shows the line voltages when a motor is supplied via VFD. It is seen that line voltages are increased by 2 V due to a non-linear drive VFD. It generates harmonics of the fifth-, seventh-order majority. Figure 9 shows the phase voltage, average line current and average power factor. Figure 10 shows the phase voltage, average line current and average power factor after adding reactive load. So, here it is seen that the power factor is improved.

3.3 Results See Figs. 4, 5, 6, 7, 8, 9 and 10.

3.4 Cost-Effectiveness Case 1: Consider 3φ, 1-KVA transformer supplying power to a 1 HP, 3φ motor with 0.9 PF. Here, 0.15 KWH is a power loss per hour. If a motor runs 8 h a day, the total loss is 1.2 KWH per day and that leads to 36 units loss per month and 400 units loss per year. If the electricity charge is Rs. 10 per unit, then it gives a loss of Rs. 4000/- per year.

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Fig. 4 Different powers, phase voltage, current and PF

Case 2: As per the calculations presented in III.I above, the reactive load of 0.5 KVAR is required to improve PF, which costs only Rs. 200/- to compensate for the loss of Rs. 4000/- per year. It shows the proposed system is economical. Here is another example. A 204-KVA transformer has been installed at Sinhagad Campus, and they have installed a 100-KVAR reactive load to improve the power factor. Of these, 20 KVAR is permanently connected, and 80 KVAR is automatic switching. With this arrangement, they save about Rs. 25,000 per month. They have incurred a cost of Rs. 150,000 in total. However, by considering all other unseen factors, the payback period for reactive power is a maximum of one year. Considering

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Fig. 5 Line voltage, frequency and RPM of motor used

these two examples, the power distribution company needs to adopt a strict policy to address the power factor issues and improve the PF of each type of transformer. So, power factor correction is very beneficial. First of all, the power requirement is reduced, and, as detailed below, (1) Power demand reduction: Due to the reduction of current flowing in the wire (reduction in power demand) is achieved, the wire does not get heated, and hence, its life is extended. (2) Improvement in the voltage: Due to low power factor, more current flows through the wire, so by increasing the voltage drop in the wire, the load gets less voltage. Conversely, if the power factor increases, the current flowing in the wire decreases, so there is a minimum voltage drop in the wire, and the load meets the good voltage. (3) Increase in the current carrying capacity of load: Increasing the power factor increases the current carrying capacity of the load, so more work is done and more benefits are obtained.

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Fig. 6 Line-to-line and phase voltages

Fig. 7 Line voltages, when VFD is bypassed

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Fig. 8 Line voltages, when the motor is supplied via VFD

Fig. 9 Average 3φ voltage, current and power factor

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Fig. 10 Average 3φ voltage, current and power factor after adding reactive load

(4) Lower side billing charges slot: If the demand decreases, then the monthly bill will be reduced, and the bill will not go to the upper slot, so fewer charges will be applied. (5) Low power factor penalty: The electricity distribution company will not charge any charges for low power factor. If there is a correction in the power factor, such charges will not be levied.

4 Conclusion It has been shown that power factor correction is essential if power generation is to be put to good use. This paper sheds light on how much the added reactive load costs, and its return period to improve the power factor. If reactive load is not added, how much energy is lost annually, then when to get back the one time investment made for the system installed to prevent power factor, all these things are studied in-depth through this paper. Power factors for all types of transformers like agricultural, industrial should be inspected, and instructions should be given by the electricity distribution company to add reactive load for power factor correction. The responsibility for its maintenance and service should be given to the respective stakeholders. Doing so will increase

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the efficiency of electricity distribution, and all these will result in the progress of the country. Results show that the method is understood for the system of a 1-KVA transformer, 1 HP/3φ load for it, then the calculation of how many KVAR reactive load should be added to make power factor 0.97 if the initial power factor was 0.90. It is studied for experimentation purposes. This methodology can be applied to different transformers like 75-KVA, 100-KVA, 500-KVA transformers, and it can be calculated how much reactive load to add by considering the actual active load. Comparative observations have shown that investing costs for power factor correction have better advances than traditional ways. In fact, if power factor improvement has so many benefits, then why should not the Electricity Distribution Board compulsory the customers? If customers are benefiting so much, then it is necessary to take care of the transformers by uniting the customers. It is also important to consider the reduction in unit usage and bill. If electricity generation is costly due to such problems, then the Electricity Distribution Board should make it compulsory to improve the power factor. Maybe by going to the higher slot on the customer’s electricity bill, they can charge the customer more, so there is also a conflict of interest about the electricity distribution board. He really is. For this, consumers need to look after their own interests. Acknowledgements The authors are grateful to the Management of SKNCOE, Pandharpur, for providing a Basic Electrical Laboratory to install an experimental setup, 3φ electrical connection and other required facilities for the experimentation. We are also very grateful to Deshpande Sir who is head of Power House of Sinhagad College of Engineering, Pandharpur, for engineering every minor factor related to power factor at every distribution stage very well. They gave us detailed guidance on how many KVA transformers are there in Sinhagad, how many KVAR reactive loads they have, how much they benefit each month. We are also thankful to the commercial provider of a customized 1-KVA isolation transformer, VFD drive and 1 HP, 3φ motor from Pune.

References 1. Wu H, Wong S-C, Tse CK (2018) Single-phase LED drivers with minimal power processing, constant output current, input power factor correction, and without electrolytic capacitor. IEEE Trans Power Electron 33(7):6159–6170, July 2018 2. Liu X, Liu W, He M, Wang W (2021) Boost-type single-stage step-down resonant power factor correction converter. IEEE Trans Ind Electron 68(9):8081–8092, Sept 2021 3. Reiman AP, Somani A, Alam MJE (2019) Power factor correction in feeders with distributed photovoltaics using residential appliances as virtual batteries. IEEE Access 7:99115–99122, July 2019 4. Lopez-Martin VM, Azcondo FJ, Pigazo A (2018) Power quality enhancement in residential smart grids through power factor correction stages. IEEE Trans Ind Electron 65(11), 8553– 8564, Nov 2018 5. Heger CA, Sen PK, Morroni A (2012) Power factor correction—A fresh look into today’s electrical systems. In: IEEE, San Antonio, TX, USA, 14–17 May 2012 6. Liu J, Xu W, Chan KW (2020) A three-phase single-stage AC–DC wireless-power-transfer converter with power factor correction and bus voltage control. IEEE J Emerg Sel Top Power Electron 8(2):1782–1800, June 2020

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7. Karimeh A, Nabil K, El-Sheikh H (2016) A modified adaptive power factor correction technique. In: IEEE, Beirut, Lebanon, 2–4 Nov 2016 8. Gu L, Liang W, Praglin M (2018) A wide-input-range high-efficiency step-down power factor correction converter using a variable frequency multiplier technique. IEEE Trans Power Electron 33(11):9399–9411, Nov 2018 9. Kabir Y, Mohsin YM, Monir M (2017) Automated power factor correction and energy monitoring system. In: IEEE, Coimbatore, India, 22–24 Feb 2017 10. López-Martín VM, Azcondo FJ, Pigazo A (2018) Power quality enhancement in residential smart grids through power factor correction stages. IEEE Trans Ind Electron 65(11):8553–8564, 09 March 2018 11. Kouchaki A, Nymand M (2017) Analytical design of passive LCL filter for three-phase twolevel power factor correction rectifiers. IEEE Trans Power Electron 33(4):3012–3022, 17 May 2017 12. Qi W, Sinan L, Siew CT, Hui SY (2018) A single-phase three-level flying-capacitor PFC rectifier without electrolytic capacitors. IEEE Trans Power Electron 34(7):6411–6424, 20 Sept 2018 13. Zhang G, Junming Zeng Z, Wenxun X (2020) A self-protected single-stage LLC resonant rectifier. IEEE J Emerg Sel Top Power Electron 9(3):3361–3372, 21 Sept 2020 14. Malatji EM, Billy C (2018) Innovative method for power factor correction using a solar plant as a source of reactive power. In: IEEE, Mon Tresor, Mauritius, 6–7 Dec 2018 15. Qin S, Lei Y, Ye Z (2019) A high-power-density power factor correction front end based on seven-level flying capacitor multilevel converter. IEEE J Emerg Sel Top Power Electron 7(7):1883–1898, Sept 2019

Machine Learning Algorithm Recommendation System Haider Nakara, Prashant Mishra, and Hariram Chavan

1 Introduction In recent years, machine learning has made significant progress in many applications. There is an increasing need for machine learning systems that can be effectively used by newcomers to machine learning. As a result, more and more companies like DataRobot, Google Cloud AutoML, Dataiku, H2 O, JADBio AutoML, Enhencer, etc. are working to meet this demand. Our library’s primary job is to solve the major problem of determining which machine learning algorithm is utilizing for a given dataset, whether and how its characteristics should be preprocessed, which features to include, and how to fine-tune all its hyperparameters. This library aims to reduce repetitive tasks in the machine learning pipeline, and machine learning algorithm recommendation systems (Mlars) promise significant performance gains and high efficiency for data scientists, machine learning engineers, and machine learning researchers. It automates the current method of generating, testing, and deploying ML models. It also can help machine learning professionals by increasing efficiency by automating time-consuming tasks such as hyperparameter optimization (HPO).

H. Nakara (B) · H. Chavan Department of Information Technology, K. J. Somaiya Institute of Engineering and Information Technology, Mumbai, India e-mail: [email protected] P. Mishra · H. Chavan University of Mumbai, Mumbai, Maharashtra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_54

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1.1 Motivation Machine learning should evolve the business rather than be a burden on the company. ML practitioners in these companies should invest more of her time on business issues rather than wasting time on repetitive ML workflows and processes. We want to make machine learning more approachable for new businesses.

1.2 Problem Analysis Traditional machine learning processes are time-consuming, laborious, and iterative. Demand for data scientists is high, but this job is not very attractive. Data scientists and data analysts spend most of their time cleaning and preprocessing data for model generation. So, the library’s goal is to make machine learning tasks more non-repetitive and user-friendly, making them usable for new ML practitioners and non-technical people.

1.3 Objectives To make a developer tool or set of tools that can help data scientists in their workflow by recommending the best algorithm for a given problem and dataset along with hyperparameters which reduces errors in applying ML algorithms.

1.4 Scope To create a developer tool or set of tools to assist data scientists in their workflow by recommending the best algorithm for a given problem and dataset, as well as hyperparameters that reduce errors in applying ML algorithms.

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2 Literature Review 2.1 Related Work We have studied many literature surveys on ML algorithm recommendation systems and techniques used to analyze and explore the datasets. Machine learning (ML) algorithms have achieved outstanding results on a variety of problems, including language modeling, object detection, and image recognition. A high-quality ML system for a specific activity, on the other hand, is heavily dependent on human skill, limiting its widespread application. While this is happening, automated machine learning (AutoML) is a promising approach for developing a machine learning system with the least amount of time and effort. A thorough and up-to-date examination of the state of the art in AutoML is done by He et al. [1] in their opinion. The majority of the tools and frameworks used today cannot be regarded as user-friendly. To deploy and use them, sophisticated technical skills are still required. Its usability and widespread acceptance among non-technical users and domain specialists, who frequently have limited technical skills, are constrained by such a constraint. One strategy to address these issues is to give such a system an interactive and lightweight web interface. ¨ Zoller and Huber [2] present AutoML as a bi-level optimization problem, where one problem is nested inside of another to look for the best solution in the search space. Although in AutoML, correctness of each solution can be verified quickly, this study also demonstrates that it is a bi-level optimization problem and generalizes AutoML heuristic techniques from the iterative solver. One of the key elements in the effective adoption of AutoML techniques is efficiency. Some AutoML techniques yield solutions in a reasonable amount of time. (Finding capable pipes in a CPU takes 500 s.) Choosing a machine learning algorithm to apply to a given dataset, deciding whether to preprocess the dataset’s features, and determining how to configure all hyperparameters are the core issues that need to be resolved for any machine learning service to be effective. We address this issue in the work by Feurer et al. [3], who attempted to expand the AutoML approach in several ways that significantly improve its effectiveness and robustness, based on principles that apply to a wide range of machine learning frameworks. It proved that the meta-learning and ensemble enhancements for AutoML produce greater robustness and efficiency. To adopt a machine learning model to different tasks, its hyperparameters must be changed. The performance of a machine learning model is directly impacted by selecting the best hyperparameter configuration.

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It often requires a thorough understanding of machine learning techniques and suitability. Industrial users, data analysts, and researchers can more efficiently create machine learning models by selecting the right hyperparameter configurations thanks to a survey paper by Yang and Shami [4]. The most recent findings in the field of hyperparameter optimization and their theoretical and practical applications to various machine learning models have been thoroughly explored in this work. Different optimization techniques can be applied to each ML framework to maximize the hyperparameters of ML models. There is currently no one optimization strategy that outperforms all other strategies when processing multiple datasets with varying metrics and hyperparameter types. Instead, numerous optimization techniques each have their advantages and disadvantages in certain situations.

3 Proposed System We have developed a Python library to create an ML algorithm recommendation system to recommend the best algorithm for a particular dataset with the best hyperparameters. This library will be easy to use, reducing the work of model generation to a few lines of code.

3.1 Proposed Approach and Details We have developed a Python library to solve the above problem statement, and this library is in the Python language and uses the sklearn library as a dependency. Anyone with a working Python environment can install and use this library on any dataset of their choice. The two main components of the controller are the data preprocessor and model generation. Data preprocessor: Data cleansing is the correcting or removal of inaccurate, corrupted, redundant, or incomplete data from a dataset. Data duplication or labeling errors are more likely to occur when combining multiple data sources. If the data are not cleaned, the results and methods, even if they appear to be accurate, cannot be trusted. Model generation: Model generation is the process where the model is trained with a preprocessed dataset using different algorithms (random forest, AdaBoost, gradient boosting, extra trees, decision tree, GaussianNB, MultinomialNB, BernoulliNB, XGBoost, CatBoost, SVM) and then fine-tuning the hyperparameters to get the best results.

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Model selection: Calculating each model’s accuracy, precision, F1, and recall numbers lead to model selection. Accuracy is a crucial parameter to take into account, but it does not always provide a whole picture. For this reason, we select the model with the highest overall average score. To provide a well-balanced model does not require any adjustments or optimizations and can be utilized right away. Selecting the top three models is another possibility.

3.2 Innovation in Idea We have developed a free and open-source library in the Python language. It simplifies the whole complex process of ML model generation and unifies all of the components to create one simple workflow eliminating all of the repetitive processes of trying and testing the accuracy, recall, F1, precision, and finding a perfect balance between all. With the help of our library, anyone can make a production-level model with only a few lines of code.

4 Implementation Details and Results We have implemented the project using Python for the library and sklearn for the ML model.

4.1 Technology Stack Python: We chose Python as the primary language to write this library because Python is one of the most popular languages overall and one of the first choices of most people when it comes to machine learning or data analysis with a fairly easyto-use syntax, beginner-friendly, and massive community support. There are many machine learning libraries like TensorFlow, OpenCV, sklearn, and PyTorch. Sklearn: The most effective and reliable Python machine learning library is scikitlearn (sklearn). It provides a number of useful approaches for statistical modeling and machine learning, such as dimensionality reduction, clustering, and classification, via a consistent Python interface. This library, which is based on NumPy, SciPy, and Matplotlib, was mostly developed in Python.

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Pandas: pandas is a python library that focuses on data analysis and processing of data for machine learning. It is an easy-to-use library with excellent documentation. Specifically, it provides data structures and operations for modifying time series and numeric tables. NumPy: NumPy is a Python library that supports large multidimensional arrays and matrices, as well as a variety of advanced mathematical operations that can be performed on these arrays.

4.2 Implementation Parameters In this project, we have tested our library on the following dataset: Heart Failure Prediction Dataset: Cardiovascular diseases (CVDs) are the largest cause of mortality globally, causing an estimated 17.9 million fatalities annually, or 31% of all deaths worldwide. Four out of every five CVD fatalities result from heart attacks or strokes, with premature deaths making up one-third of these deaths in those under the age of 70. CVDs frequently result in heart failure, and this dataset contains 11 indications that might be used to predict the onset of heart disease. A machine learning model can be highly effective for the early diagnosis and management of persons with cardiovascular disease or at high cardiovascular risk (because of the existence of one or more risk factors such as hypertension, diabetes, hyperlipidemia, or preexisting illness). There are 12 characteristics and 918 occurrences in the collection. Car Evaluation Dataset: This database was built with a basic hierarchical decision model that was originally developed to illustrate DEX. For input characteristics, lowercase letters are displayed. The model incorporates three intermediary ideas in addition to the goal concept (CAR): price, technology, and comfort. In the original approach, each idea has a collection of instances that connect to its lower-level descendants. The car rating database provides an instance with the structural information removed to rapidly link a car with six input attributes: purchase, service, door, people, trunk, and safety. Because of the established underlying conceptual frameworks, this database can be particularly valuable for testing constructive induction methods and structure discovery. There are 1728 occurrences and 6 characteristics in the collection.

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Student Grade Prediction: This dataset looks at secondary school student academic achievement at two Portuguese schools. Data were gathered through the use of school reports and questionnaires, and it is comprised of student performance, demographic information, and socioeconomic and educational factors. For performance in two independent courses, mathematics and Portuguese, two datasets are offered. Both datasets were subjected to binary/five-step classification and regression tasks. The target property g3 suggests an essential association with the qualities g2 and g1. There are 649 occurrences and 33 characteristics in the collection. Seeds Dataset: Rosa, Kama, and Canada wheat types were chosen at random to serve as the study’s three research groups. These three wheat varieties each include 70 distinct components. The inner core structure was shown in excellent detail using the soft X-ray technique. Compared to other, more sophisticated imaging technologies like scanning microscopes and laser techniques, it is non-destructive and far less expensive. The Polish Academy of Sciences’ Agrophysics Institute in Lublin gathered and mixed wheat grains from a laboratory experiment for use in the study. 210 instances and seven characteristics are present in the dataset.

5 Results The result shows us that the library is giving models with a fairly balanced score of accuracy, precision, recall, and F1, which gives the user a ready-to-use model with few lines of code. The library is tested on the dataset as follows (Figs. 1, 2, 3 and 4; Table 1): Code for generating model using our library import pandas as pd from mlars import main df = pd.read_csv("data.csv") model = main(df, "HeatDisease")

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Fig. 1 Heart disease dataset: All published tests mention employing a subset of 14 of its 76 qualities, which includes the anticipated attribute. The “target” field indicates that the patient has a heart illness

Fig. 2 Cars dataset: Information on used vehicles can be found in this dataset. Many different uses of this data are possible

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Fig. 3 Student dataset: This dataset contains the grades that students have earned in a variety of subjects

Fig. 4 Seeds dataset: For the experiment, the dataset contains 70 elements from each of three different wheat varieties

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Table 1 Analysis of the best model provided by Mlars for the above datasets Dataset

Name

Accuracy

Precision

Recall

F1

Average

Hyperparameter

Heart disease

Gaussian Naive Bayes

88.58

88.28

88.13

88.2

88.30

Var_ smoothing (1e-09)

Cars

Gradient Boosting

99.71

98.86

99.67

99.25

99.37

Learning_rate (0.5)

Student

Gaussian Naive Bayes

43.03

42.03

40.53

39.1

41.20

Var_ smoothing (0.01)

Seeds

Gradient Boosting

97.5

97.91

97.61

97.69

97.68

Learning_rate (0.1)

6 Conclusions In this paper, we discussed the need for a machine learning algorithm recommendation system. We built a tool Mlars that recommends algorithms and provides the best model for a given dataset which helps machine learning engineers and data scientists by reducing repetitive tasks and letting them focus more on problems instead of models. This library is available on PyPi at https://pypi.org/project/mlars/, so anyone can install using pip (package installer for Python) and generate a machine learning model using a few lines of code. There are certain flaws in our system as well, which we want to fix in future. We have not yet addressed semi-supervised issues, for instance. scikit-learn mostly works very well with small- to medium-sized structured datasets, and extending our approaches with TensorFlow and PyTorch is a clear future goal, which produces a cutting-edge performance on huge datasets.

References 1. He, Zhao K, Chu X, AutoML: a survey of the state-of-the-art. Department of Computer Science, Hong Kong Baptist University. https://arxiv.org/pdf/1908.00709.pdf 2. Z¨oller M-A, Huber MF, Benchmark and survey of automated machine learning frameworks. [email protected] USU Software AG R¨uppurrer Str. 1, Karlsruhe, Germany. [email protected] Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Allmandring 25, Stuttgart, Germany & Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12, Stuttgart, Germany 3. Feurer M, Klein A, Eggensperger K, Tobias J, Efficient and robust automated machine learning. Springenberg Manuel Blum Frank Hutter Department of Computer Science University of Freiburg, Germany{feurerm, kleinaa, eggenspk, springj, mblum,fh}@cs.uni-freiburg.de 4. Yang L et al, On hyperparameter optimization of machine learning algorithms: theory and practice, Li Yang, et al. and| Neurocomputing Department of Electrical and Computer Engineering, University of Western Ontario, 1151 Richmond St, London, Ontario, Canada N6A 3K7

Crop Recommender System Shivanoori Sai Samhith, T. V. Rajinikanth, Burma Kavya, and Alley Yashwanth Sai Krishna

1 Introduction In today’s world of increasing population, agriculture plays a vital role. For the cultivation of crops, farmers need to think a lot, i.e., which crop would give a better yield, how the weather would be and lot more. Predicting which crop would give better yield involves different factors like soil pH value, rainfall, area, temperature and many more. This helps the farmer to get a good crop with good revenue. By this, farmers will also be able to lead a better life. Knowing which crop to grow is a very important aspect before we cultivate any crop. Here, in this paper, we would be using few factors and predicting which crop to grow so that it gives a better yield. The factors which we are going to use in this are from a dataset which has N, P, K values, temperature in that area, humidity in that area, pH values and rainfall. Here, N, P, K values involve the values of nitrogen, phosphorus and potassium levels in the soil. These values play a vital role in knowing how the soil is and will help in knowing which crop would grow better that soil. Rainfall is also an important aspect to know the weather conditions and how many centimeters rainfall might occur. We would be using those factors and predicting which crop to grow like rice, paddy, coffee, maize, etc. We are going to split the data and then apply few machine learning-based techniques to predict which crop to grow and then predicting the accuracy percentage to know which algorithm is going to work better to predict the crop. The machine learning algorithms involves decision tree classifier algorithm, random forest algorithm and k-nearest neighbor algorithm. After predicting which crop to grow, we would calculate some accuracy score so that we would be able to know which algorithm is giving best prediction among them. By getting the accuracy score, we can

S. S. Samhith (B) · T. V. Rajinikanth · B. Kavya · A. Y. S. Krishna Sreenidhi Institute of Science and Technology, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_55

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help farmer by predicting which crop to grow in their farm by using the best algorithm among them which we applied. We also applied few dimensionality reduction techniques like PCA and LDA to get better accuracy and also cross-validated the accuracy predicted. By predicting the accuracy percentage to know which algorithm works better, we also represented that graphically to get it easily understandable.

2 Literature Review Suganya et al. [1] paper is about predicting the yield of the crop using different algorithms in machine learning techniques like k-nearest neighbors, support vector machine, logistic regression, random forest and decision tree. By applying all those algorithms, they concluded that logistic regression works better among all the algorithms and support vector machine gives worst performance among all of them. Venugopal et al. [2] paper is about predicting which crop to be grown and which crop gives the maximum yield for the farmer. The factors on which the crop and yield are predicted are temperature, rainfall, area, etc. In this, different machine learning algorithms like naïve Bayes, logistic regression and random forest are applied on the dataset having the abovementioned factors for predicting the crop and also for getting the accuracy percentage. Among them, random forest gives the best accuracy. Anbananthen et al. [3] paper is about helping the farmer to know which crop to grow so that it gives good yield. Here, different hybrid machine learning techniques are applied like random forest regressor, gradient-boosted tree regression and stacked generalization so that we can analyze which algorithm gives better accuracy for recommending the crop to the farmer. From the above three hybrid machine learning algorithms, stacked generalization gives the best accuracy of yield and this algorithm can be used to know which crop can be grown. Suresh et al. [4] paper is about predicting the crop and also yield of the crop based on the factors of location such as state name, district name and also based on the season. Here, for predicting the crop, they used the machine learning algorithm named decision tree, and for predicting the yield, they used machine learning algorithm named linear regression, and the model gives a good accuracy percentage when predicted. Kakaraparthi et al. [5] paper is about predicting the price of the crop based on the factors rainfall and wholesale price index. Here, they used machine learning technique named decision tree for estimating the price of the crop for next twelve months. This prediction of price would help the farmer in getting a good revenue. Nagini et al. [6] paper is about predicting the yield of a particular crop in a particular region based on the factors like soil characteristics, soil moisture, surface temperature, nitrogen, water, rain water. Here, they used various regression-based machine learning techniques like linear regression, multiple linear regression, non-linear models. The accuracy percentage of predicted yield among different algorithms is compared to know which algorithm works better. Jadhav et al. [7] had stated about working with some advanced farming technologies like GPS technology app that captures the user details and locations

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are given to it and it will provide yield prediction and crop through various machine learning algorithms applied on the data. Suruliandi et al. [8] have conveyed that for healthy production, we need to prefer latest procedures like comparing and selecting the feature selection methods with various classification or machine learning models based on the accuracy to get the optimal result. Choudhary et al. [9] have expressed about providing the importance of latest farming techniques and machine learning algorithms to farmers that prevent imprecision farming and also identify crop as well as classify plant disease through a GUI app. Kadam et al. [10] have portrayed the content which explains about consistency in yield will be regularized by a framework called crop recommendation model that includes deep learning algorithm and predefined data are linked with AgroSYS neural network architecture to give valid accuracy. Anguraj et al. [11] have mentioned that the procedure for a crop to be grown is of capturing soil parameters with help of IOT sensor device that collects the data and that is given to the GUI which performs operation and displays the required crop based on given attributes. Vaishnavi et al. [12] have explained about planting crops with help of its content related to productivity and season which are given to machine learning models that allow to choose the right crop for cultivation that reduces the climatic risk factors and maintains healthy agricultural environment. Gupta et al. [13] have stated about an android application that operates on machine learning platform which depicts the crop yield and type as soil and weather parameters are concatenated as well as given to train the models and will provide suitable crop according to given attributes gained from soil testing laboratory and agriculture experts. Palanivel et al. [14] stated that how various machine algorithms are useful in the prediction of yield because of number of factors like water shortage, soil fertility affect the crop yield and accuracy. They achieved the prediction of crop yield using algorithms in big data computing paradigm. Romero et al.’s [15] main objective is that to predict the durum wheat yield through the machine algorithms and compare them to detect which algorithm gives the best result. Ying-Xue et al.’s [16] project is about to generate rice development stage and yield prediction model and then it is used to integrate into SBOCM system. SBOCM system is used for perennial simulation and one-year rice predictions within certain scale ranges. Shekoofa et al. [17] analyzed a large number of physiological and agronomic traits by screening, clustering and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree is the most used tool. The results showed that the model techniques are useful tools for crop physiologists. Everingham et al. [18] stated that a data mining method like random forest is used to generate a prediction model for sugarcane. They have significantly saw the changes in the yield of the sugarcane, and the algorithm is working perfectly. Better crop predictions allow farmers to improve their nitrogen management to meet the demands of the new crops. Champaneri et al. [19] stated that prediction of the crop yield in advance of its harvest would help the farmers and policy makers for making appropriate measures for marketing and storage. Random forest is the most powerful algorithm capable of performing both classification and regression tasks. It predicts the yield of the crops accurately. Mufamadi et al. [20] had viewed the concept related to choosing appropriate crop on the basis of applying and performing machine learning algorithms and

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its related operations such as support vector machine and random forest on the soil features, namely nitrogen, potassium and phosphorus as well as pH value which in turn gives the right crop.

3 Motivation Agriculture, as the world’s largest industry, is finding it increasingly difficult to forecast profits as the world’s population grows exponentially. Our country, India, has over 70 percent of its population dependent on agriculture. Recently, most of the farmers are leaving farming and doing other daily wages work for their livelihood because of the losses they are making in the agriculture they cannot live with loses. If they know the exact yield, they can produce before the harvesting, then they can escape from their losses, to do that there are different approaches in the technologies to predict the yield before the harvesting. With the perfect decision-making algorithm, we can predict the accuracy of each crop and make them cultivate so that they can get profits. With the exact values of the soil content and the factors affecting the agriculture, we predict which crop should be grown.

4 Methodology In this methodology, we would be discussing about the different aspects of the project such as the dataset attributes, the algorithms applied and the steps involved in getting the crop recommendation.

4.1 Dataset Retrieval In agriculture crop prediction, we consider various factors for prediction and each has a unique importance for their consideration. The dataset is collected and should be preprocessed before any machine learning techniques are applied on the dataset. In this project, we considered few factors and based on those factors we would be predicting the crop. The attributes of dataset involve temperature, pH value, humidity, rainfall and nutrients like nitrogen (N), potassium (K), phosphorous (P). Using these, we would be predicting the crop name. The independent attributes of the dataset involve temperature, pH value, humidity, rainfall and nutrients. The dependent variable will be the label which is the name of the crop, and it is predicted using all the independent variables. We would be applying different algorithms on this dataset like decision tree, random forest and KNN. We would also be applying the dimensionality reduction techniques like PCA and LDA before applying any algorithm to improve the accuracy percentage of crop prediction.

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4.2 Algorithm Building Three machine learning algorithms make up the model in this study. The algorithms make predictions depending on the correctness of each individual’s work. In some cases, the accuracy supplied by the algorithms may be identical, but we have incorporated extra features to forecast the best method in addition to the accuracy provided by the algorithms. K-nearest neighbor, decision tree, random forest are the algorithms employed. To acquire the projected crop accuracy, we apply several algorithms to the dataset during algorithm development. We compare the predicted crop accuracy with the different algorithms with cross-validation and without cross-validation (Fig. 1). We have applied dimensionality reduction methods like principal component analysis (PCA) and linear discriminant analysis (LDA) to the algorithms, and we have compared the predicted crop accuracy with cross-validation and without crossvalidation. In PCA dimensionality reduction, it reduces the number of input variables for dataset and increases the accuracy of the crop prediction. In LDA, it is used to project the features in higher dimension space to the lower dimension space and increase the accuracy, and it is compared with cross-validation and without cross-validation. The key benefit of applying dimensionality reduction is to improve the accuracy of the model and the model to run as soon as possible with least amount of time complexity while still providing the best output with the best-predicted values.

4.3 Accuracy Prediction and Comparison 4.3.1

Accuracy Prediction Without Any Dimensionality Reduction Technique

This graph shows the accuracy of different algorithms with cross-validation and without cross-validation. The x-axis represents the algorithm names, and y-axis represents the predicted accuracy on the scale of 20 in the range of 0–120 (Fig. 2).

4.3.2

Accuracy Prediction Using PCA Dimensionality Reduction Technique

This graph shows the accuracy of the different algorithms when PCA dimensionality reduction method is applied with cross-validation and without cross-validation. The x-axis represents the names of the algorithm, and y-axis represents the predicted accuracy on the scale of 20 in the range of 0–100 (Fig. 3).

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Splitting of data into train and test data

Without Dimensionality Reduction Technique

With Dimensionality Reduction technique

Algorithms applied Decision Tree, Random Forest, KNN

Predict Accuracy

Apply cross validation and predict accuracy

Compare accuracy with and without cross validation

Calculate Time complexity

Fig. 1 Architecture of crop recommendation system

4.3.3

Accuracy Prediction Using LDA Dimensionality Reduction Technique

This graph shows the accuracy of the different algorithms when LDA dimensionality reduction method is applied with cross-validation and without cross-validation. The x-axis represents the names of the algorithm, and y-axis represents the predicted accuracy on the scale of 20 in the range of 0–100 (Fig. 4).

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Fig. 2 Accuracy using different algorithms

Fig. 3 Accuracy using different algorithms with PCA dimensionality reduction technique

Fig. 4 Accuracy using different algorithms with LDA dimensionality reduction technique

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Table 1 Accuracy percentages without any dimensionality reduction technique Algorithms

Without cross-validation

With cross-validation

Decision tree

90.0

92.18

KNN

97.2

97.6

Random forest

98.8

99.18

Table 2 Accuracy percentages with PCA dimensionality reduction technique Algorithms

Without cross-validation

With cross-validation

Decision tree

71.3

75

KNN

95.6

96.7

Random forest

96.1

96.4

5 Result 5.1 Accuracy Prediction Table 5.1.1

Without Any Dimensionality Reduction Technique

These are the accuracy values of different machine learning algorithms obtained before and after applying cross-validation on the dataset; the above table shows that random forest has given the highest percentage and responded positively for both with and without cross-validation compared to other algorithms (Table 1).

5.1.2

With PCA Dimensionality Reduction Technique

The table represents the accuracy results of three algorithms gained after applying dimensionality reduction technique called principal component analysis to get much better output; here, a difference to be noticed is that by applying cross-validation on KNN, we got highest value of 96.7 than other techniques (Table 2).

5.1.3

Accuracy Prediction Table with LDA Dimensionality Reduction Technique

The second dimensionality reduction technique that is used to improve the working of algorithms is linear discriminant analysis applied on the dataset which leads to the compression of dataset gives more efficient and the highest results that random forest has got up to 99.18% with cross-validation (Table 3).

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Table 3 Accuracy percentages with LDA dimensionality reduction technique Algorithms

Without cross-validation

With cross-validation

Decision tree

90.0

92.18

KNN

97.2

97.6

Random forest

98.8

99.18

Table 4 Time complexity without any dimensionality reduction technique Algorithms

Without cross-validation (s)

With cross-validation (s)

Decision tree

0.05

0.06

KNN

0.06

0.19

Random forest

0.3

0.91

Table 5 Time complexity with PCA dimensionality reduction technique Algorithms

Without cross-validation (s)

With cross-validation (s)

Decision tree

0.19

0.64

KNN

0.07

0.23

Random forest

0.2

1.14

5.2 Time Complexity for Accuracy Predicted 5.2.1

Without Any Dimensionality Reduction Technique

The time complexity table shows and states that in how much duration that each algorithm has taken to provide the result and accuracy. From above dataset, we can say that the random forest has taken the least and minimum time of 0.3 s to provide the result (Table 4).

5.2.2

With PCA Dimensionality Reduction Technique

The above data in table show the time complexity for principle component analysis which has provided the least time that random forest has taken without cross-validation (Table 5).

5.2.3

With LDA Dimensionality Reduction Technique

The above table shows about time taken by each algorithm after applying linear discriminant analysis, we can see that without applying cross-validation on KNN

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Table 6 Time complexity with LDA dimensionality reduction technique Algorithms

Without cross-validation (s)

With cross-validation (s)

Decision tree

0.10

0.58

KNN

0.08

0.32

Random forest

0.24

1.43

has taken 0.08 milliseconds of time to complete the task which is very less compared to other algorithms (Table 6).

6 Conclusion This project ensures the reduction of farmer’s suicide rate by improving the production per yield, due to which there will be a healthy society. By using machine learning algorithms, we are able to predict which type of crop to be grown based on given conditions, as it performs some operations like classification and regression and applies algorithms such as decision tree, random forest and KNN on the given dataset which contains values related to weather and soil. Among all the classifiers, the random forest has given the best accuracy of 98.8%.On comparing dataset accuracy percentages of different machine learning algorithms along cross-validation, we can state that without and with applying dimensionality reduction technique such as principle component analysis, both got exact potentiality values and there percentages got increased. We can conclude from above research that the cross-validation has increased the accuracy percentage of crop prediction in each and every case whether we applied dimensionality reduction technique or not. But we can also say from above results that the accuracy prediction when LDA dimensionality reduction technique is applied gives very similar accuracy to that when no dimensionality reduction technique is applied. Among all the algorithms applied, i.e., decision tree, random forest and KNN, the random forest gives the best accuracy with or without any dimensionality reduction technique and also random forest works the best among three algorithms even when cross-validation is applied.

References 1. Suganya M, Dayana R, Revathi R (2020) Crop yield prediction using supervised learning techniques. Int J Comput Eng Technol 11(2):9–20 2. Venugopal A, Aparna S, Mani J, Mathew R, Williams V (2021) Crop yield prediction using machine learning algorithms. IJERT, NCREIS 09(13), 2021 3. Anbananthen KSM, Subbiah S, Chelliah D, Sivakumar P, Somasundaram V, Velshankar KH, Ahamed Khan MKA (2021) An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms. 11 Nov 2021. https://doi.org/10.12688/f1000rese arch.73009.1

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4. Suresh A, Monisha K, Pavithra R, Marish Hariswamy B (2020) Crop selection and it’s yield prediction. Int J Recent Technol Eng (IJRTE) 8(6), Mar 2020 5. Kakaraparthi GS, Prabhakar Rao BVANSS (2021) Crop price prediction using machine learning. Int Res J Modernization Eng Technol Sci 03(06) June-2021 Impact Factor- 5.354. e-ISSN: 2582-5208 6. Nagini S, Kanth TVR, Kiranmayee BV (2016) Agriculture yield prediction using predictive analytic techniques. In: 2016 2nd international conference on contemporary computing and informatics (IC3I), pp 783–788. https://doi.org/10.1109/IC3I.2016.7918789 7. Jadhav R, Bhaladhare P (2022) A machine learning based crop recommendation system: a survey. J Algebraic Stat 13(1):426–430. https://publishoa.com. ISSN:1309-3452 8. Suruliandi A, Mariammal G, Raja SP (2021) Crop prediction based on soil and environmental characteristics using feature selection techniques. Math Comput Modell Dyn Syst 27(1):117– 140 9. Choudhary M, Sartandel R, Arun A, Ladge L (2022) Crop recommendation system and plant disease classification using machine learning for precision agriculture, 2022. In: Hiranwal S, Mathur G (eds) Artificial intelligence and communication technologies. Computing & Intelligent Systems, SCRS, India, pp 39–49 10. Kadam PD, Chavan RS, Kulkarni AM, Janrao SR (2021) AgroSys—A crop recommendation system. Int Res J Eng Technol (IRJET) 08(05), May 2021, e-ISSN: 2395-0056 11. Anguraj K, Thiyaneswaran B, Megashree G, Preetha Shri JG, Navya S, Jayanthi JF (2021) Crop recommendation on analyzing soil using machine learning. Turk J Comput Math Educ 12(6):1784–1791 12. Vaishnavi S, Shobana M, Sabitha R, Karthik S (2021) Agricultural crop recommendations based on productivity and season. In: 2021 7th international conference on advanced computing and communication systems (ICACCS). https://doi.org/10.1109/icaccs51430.2021.9441736 13. Gupta J, Chauhan A, Agarwal A, Ranghuvanshi AS, Saxena R (2018) Machine learning approach for crop yield prediction and crop variety recommendation in android application. MIT Int J Comput Sci Inf Technol 7(1), January 2018. ISSN 2230-7621 14. Palanivel K, Surianarayanan C (2019) An approach for prediction of crop yield using machine learning and big data techniques. Int J Comput Eng Technol 10(3):110–118 15. Romero JR, Roncallo PF, Akkiraju PC, Ponzoni I, Echenique VC, Carballido JA (2013) Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires. Comput Electron Agric 96, Aug 2013 16. Ying-Xue S, Huan X, Li-Jiao Y (2017) Support vector machine-based open crop model (SBOCM): case of rice production in China. Saudi J Biol Sci 24(3), Mar 2017 17. Shekoofa A, Emam Y, Shekoufa N, Ebrahimi M, Ebrahimie E (2014) Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture, Published: 15 May 2014 18. Everingham Y, Sexton J, Skocaj D, Inman-Bamber G (2016) Accurate prediction of sugarcane yield using a random forest algorithm, Published: 19 Apr 2016 19. Champaneri M, Chachpara D, Chandvidkar C, Rathod M (2020) Crop yield prediction using machine learning. Apr 2020 Int J Sci Res (IJSR) 9(4 April 2020):2 20. Mufamadi TO, Ajoodha R, Crop recommendation using machine learning algorithms and soil attributes data. In: 3 IEEE global humanitarian technology conference: South Asia satellite (GHTC-SAS)

256-Bit AES Encryption Using SubBytes Blocks Optimisation R. Kishor Kumar, M. H. Yogesh, K. Raghavendra Prasad, Sharankumar, and S. Sabareesh

1 Introduction The advanced encryption standard (AES) was released by (NIST) National Institute of Standards and Technology, a symmetric key block, in December 2001. A fixed data block of 128 bits is encrypted and decrypted by this non-Feistel block cypher. Three alternative key lengths are available. The process of transforming regular actual text/message into unintelligible information and vice versa is known as cryptography. Hash functions, public (asymmetric) key cryptography, and private (symmetric) key cryptography are the three different types of cryptographic techniques. Only one key is used for encryption as well as decryption also. Advanced encryption standard (AES) and data encryption standard (DES) are symmetric key algorithms. It is a lot quicker, simpler to use, and uses less computing power. The AES is an iterative technique that employs the transformations SubBytes, ShiftRows, MixColumns, and KeyAdditions in various rounds. S-box is used for SubBytes transformation. The S-box plays a key role in determining the AES architecture’s performance and throughput. Due to the long ROM access time, the ROMbased technique takes a lot of storage and also results in low latency. Therefore, the implementation of S-box (substitution) is better suited to composite field arithmetic.

R. Kishor Kumar (B) · M. H. Yogesh · K. Raghavendra Prasad · Sharankumar · S. Sabareesh Don Bosco Institute of Technology, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_56

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2 Related Work NIST released a draught FIPS-197 including the advanced encryption standard (AES) technique in 2001. It has received various hardware implementation suggestions, most of which have focused on the AES with a 128-bit key size. For the majority of commercial applications, when using larger key sizes is viewed as a waste of resources, this key size is thought to be adequate. To provide the highest level of security, key sizes of 192 bits and 256 bits are frequently utilised in top-secret military applications [1]. “FPGA Implementation of Combined S box and Inv S box of AES”, 2017. For ByteSubstitution & InvByteSubstitution transformations, a combinational memoryfree substitution-box and Inv-substitution-box (together) implementing on the same hardware. LUTs were originally used, which used a large amount of RAM memory and storage. The proposed structure in this work is realised by composite field arithmetic (CFA) in Galois field GF (28 ), which offers benefits over the LUT method based on hardware complexity; there is a significant reduction in gate count and area. Additionally, power consumption is decreased by S-Box and Inv S-Box sharing resources for the multiplicative inverse module [2]. “High Speed VLSI Designs for the AES Algorithm” was published in 2004. This paper shows new high-speed methodology for the advanced encryption standard (AES) method hardware implementation. Combinational logic is used to implement SubBytes/InvSubBytes in order to prevent the irreducible delays of LUTs in the prior design and to further explore the advantages of subpipelining. Utilising composite field arithmetic further reduces the complexity of the hardware, and many approaches to performing inverted in sub-field are assessed [3]. “Pipelined implementation of AES encryption based on FPGA”, 2010. The AES128 encryption processor’s outer-round only pipelined design is presented in this work for an FPGA implementation. The proposed solution makes use of two different types of block RAM and uses it to store the S-box data. We can shorten the crucial delay by merging the procedures into a single cycle, in line with the increase in transmission speed to Gbps. By combining all the operations in single round, critical delay is decreased.

3 AES Algorithm The AES algorithm is a private key block cypher. Data with a block size of 128 bits are encrypted. It employs three key sizes in three different versions: 128, 192, and 256 bits. Three different round procedures are used by AES. Three different AES versions’ round counts are displayed in Table 1. However, the end round key in each version is 128 bits.

256-Bit AES Encryption Using SubBytes Blocks Optimisation Table 1 Number of rounds and round key size in three versions of AES [4]

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No. of rounds

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128-bit

10

128-bit

192-bit

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128-bit

256-bit

14

128-bit

3.1 Architecture of AES Algorithm The four operations substitution bytes, shifting rows, mixing column, and adding round key are used to build the AES algorithm. The 256-bit AES algorithm’s design is depicted in Fig. 1. There are a total of 14 operation rounds—14 for encryption and for decryption as well. After encryption, ciphertext will be sent across the channels. The message will be decrypted at the recipient end using the same encryption key. The key size in 256-bit AES technique is 256 bits; however, all the size of data is just 128 bits. Data consist of the encrypted message, the ciphertext, and the unencrypted message. The internal architecture of 128-bit message is shown in Fig. 2. The 128-bit data set is employed as matrix of 4 × 4, with each of matrix’s elements being 8 bits. We transform the 128-bit message into matrix of 4 × 4 with 8 bits for each member since all four operations are carried out on a column basis.

Fig. 1 Architecture of 256-bit AES algorithm [5]

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Fig. 2 128-bit message data structure

3.2 Existing Methodology In 14 cycles, a 256-bit AES encryption/decryption block is implemented. Adding round key, substitution bytes, shifting rows, and mixing column make up each round. As showed in Fig. 3, round 0 only comprises of AddRoundKey operations. SubBytes, ShiftRows, and AddRoundKey actions make up round 14. Rounds 1 through 13 include each of the four operations depicted in Fig. 3. We perform a different operations per clock cycle. Therefore, a same hardware can be used for all four rounds after it has been implemented for adding round key, substitution bytes, shifting rows, and mixing column [6, 7]. Four stages of each round are as follows: Substitute Bytes: At the encryption site, SubBytes is employed as the initial transformation. It uses a substitution table to do a non-linear substitution of bytes independently on each byte in the state matrix (S-Box). The relevant values obtained from the lookup table are substituted for each of the 16 bytes of state. InvSubBytes is utilised during decryption. The InvSubBytes table is used to replace the state’s bytes.

Fig. 3 Conventional implementation structure

256-Bit AES Encryption Using SubBytes Blocks Optimisation Fig. 4 Word key generation

Fig. 5 g function

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Fig. 6 S-box architecture in composite field

Shift Rows: Each row’s state bytes are moved left during encryption. ShiftRows operation is what it is known as. The state matrix’s row number (0, 1, 2, or 3) determines how many shifts there are. Row 0 bytes is left as it is, and rows one, two, and three are pushed to the left by one, two, and three bytes, respectively. Mix Columns: At the column level, the MixColumns transformation operates. Each column in the state is changed into a new column. A state column is really multiplied by a fixed square matrix to produce the transformation. The Galois field serves as the workspace for all mathematical operations (finite field). The bytes are not handled as numbers but as polynomials. Add Round Key: AddRoundKey moves forward one column at a time. In this way, it is comparable to MixColumns. Each column in a matrix is given a round keyword by AddRoundKey. The AddRoundKey stage is when the matrix addition operation is carried out.

3.3 Key Expansion The process of creating all round keys from initial input key is referred to as key expansion. When encrypting data, the first round key serves as the original key, and when decrypting it, the final group of keys produced by key expansion serves as the original keys. Each round key in AES 256 is created as a 256-bit array in the manner described below. The input key is 256 bits long and is divided into 8 columns that each contain 32 bits. The final column is selected and used as input for the S-box. Shift rows action is applied to the S-box’s output.

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4 Proposed Methodology 4.1 Architecture for S-Box During in the non-linear AES SubBytes transformation, state’s each byte is converted to a separate byte. The SubBytes transformation is performed out via S-box. Two methods exist for performing substitutions: using a ROM table and composite field arithmetic. When implemented using a ROM, the SubBytes transformation, which is accomplished using S-box mapping, is computationally inefficient. However, it is ineffective for applications that need a high throughput because ROM reading requires one full clock cycle to map one 8-bit state element, which results in the 128 bits of data being changed using 16 clock cycles (16 bytes) [8, 9]. Use composite field arithmetic, which depends mainly on logic components, to create an S-box. The most difficult phase in terms of price and execution is substitution. Its hardware optimisation for VLSI implementation is crucial to minimising the AES architecture’s size and power consumption. Due to the long ROM access time, the ROM-based technique takes a lot of memory and also results in low latency. Therefore, the implementation of S-box (substitution) is better suited to composite field arithmetic. The two main transformations are for S-box. The affine transformation is one, and multiplicative inversion is another. SubBytes and InvSubBytes are displayed in figure. The function is multiplicative inversion to affine transformation in SubBytes. The operation is Inv affine translation to multiplicative inversion in InvSubBytes. Transformation of Affines (AT): Affine transformation involves multiplying a matrix and then adding a vector. The total of a byte’s several rotations is a vector. Here, the XOR operator serves as the addition operation. Inverse Affine Transformation (ATI): Inverse affine transformation is the opposite procedure. The multiplicative inversion prevents the composite field of GF(28 ) from being applied directly. The complex form of GF(28 ) is broken down into its lower-order forms, GF(22 ), GF(21 ), and GF((22 )2 ), to perform the computation. The irreducible polynomial has historically supported addition, multiplication, inversion, and square root operations. The most expensive discipline is multiplicative inversion. These are made simpler by the straightforward XOR–AND gates.

5 Results See Fig. 7.

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Fig. 7 Simulation waveform

6 Conclusion We used 256-bit key-based AES for 128-bit data in this project. We employed the composite field S-box method to replace the ROM-based substitution transformation, and we optimised the system by reusing the S-box block. The suggested approach is universal and may be used to keys with sizes of 128, 196, and 256 bits. Multilevel confusion matrix was included to strengthen the key in this straightforward architecture, which is employed for key selection.

References 1. Sharma RK, Rajeswara Rao M (2017) FPGA implementation of combined S-box and InvS-box of AES. In: 2017 4th international conference on signal processing and integrated networks (SPIN) 2. Iyer NC, Anandmohan PV, Poornaiah DV (2010) Mix/InvMixColumn decomposition and resource sharing in AES 3. Parhi KK, Zhang X (2004) High speed VLSI designs for the AES algorithm. IEEE 12(9) 4. Kumar A, Shaik F, Rahim BA, Kumar DS (2016) Signal and image processing in medical applications. Springer, Heidelberg. https://doi.org/10.1007/978-981-10-0690-6 5. Stallings W (2007) Data and computer communications. Pearson Education India 6. Majumdar A, Bhargav S, Chen L, Ramudit S (2008) 128 bit AES decryption. CSEE 4840embedded system design, Columbia University 7. Vyawahare MV, Kshirsagar RV, Borkar AM (2011) FPGA implementation of AES algorithm 8. Sai Srinivas NS, Akramuddin M (2016) FPGA based hardware implementation of AES Rijndael algorithm for Encryption and Decryption. In: 2016 international conference on electrical, electronics, and optimization methods (ICEEOT) 9. Zhang Y, Wang X (2010) Pipelined implementation of AES encryption based on FPGA. In: International information theory and information security conference 2010. IEEE

Detection of Food Freshness H. R. Poorvitha and R. Ruthu

1 Introduction Our daily lives depend greatly on the food we eat. Globalization is causing a daily decline in food quality. Various food processing techniques are used to keep the food fresh [1, 2]. Food is given a variety of substances or preservatives to make it appear appealing or fresh [3, 4]. Nowadays, the bulk of food is chemically preserved, which contaminates the eatables. The customer wants wholesome food as a result of the ailments caused by this pollution. People desire organic food in order to lead healthy lifestyles [5, 6]. So, a technology that aids in judging food quality is required if we are to prevent the problems associated to food without human interpretation [7]. It is necessary to have a tool that instructs us on how to eat hygienically. So, to be able to satisfy this client desire, we created a gadget that assesses the quality of the meal [8, 9]. Many sensors used in the food industry are illustrated in this study. The status of food may be determined with the use of pH sensors, gas sensors, and temperature sensors are examples of sensors. To interact with customers, this technique is useful at restaurants, homes, small businesses, and retail establishments.

H. R. Poorvitha (B) · R. Ruthu Department of ECE, Don Bosco Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_57

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2 Literature Survey Paper 1 Fresh—A Food Freshness Detection Device 2018 September In this study, electrical and biosensors were used by authors Naveed Shahzad and Usam Khalid to assess the freshness of food. A sophisticated technique, such as with agricultural goods, meat, and fruits, can determine the food’s freshness. To make intelligent food, three sensors—a moisture sensor, a gas sensor, and a hydrogen ion concentration device—are chosen and identified whether or not it should be consumed or thrown away [10]. The type of food to be examined may be chosen using an Android application. The mechanism guarantees the food’s quality, whether it is fit for consumption or not. It does not offer the ability to file a complaint if the device does not offer the ability to file a complaint if the device [11]. Using an electronic nose and a support vector machine, Paper 2 from 2017 identifies and categorizes germs in regular street meals Focusing on the categorization of bacteria in street food, authors Julius T. Sese, Crissa Vin R. Babaan, and Jessie R. Balbin wrote the article. Street food has a significant cultural influence, but because there is a lack of knowledge on how to properly prepare food, the hygiene and quality of street food are overlooked. By employing using image processing and an electronic nose, a harmful bacteria may be identified. In order to classify whether the three primary Enterococcus faecalis, Escherichia coli, and Staphylococcus aureus are germs that may be detected on street food and are present both before and after cooking. This article will design an electronic nose with gas sensors. The electronic nose system detects bacteria in the sample street food during the pre-cooking step, and the support vector machine detects the same bacteria during the post-cooking stage. Other characteristics like moisture content and food gas levels cannot be detected by this method [12]. Real-Time Milk Monitoring System, Paper 3, 2018 The condition of smart city services, which are offered to manage the city’s assets by fusing information and communication technology (ICT) and the Internet of things, is described by authors Professor Kadam P. R. and Miss. Shinde K. P. (IoT). To handle various sensors, terminals, applications, and topologies, as well as security is required. Food safety concerns are brought on by the deteriorating food quality that is being produced on a daily basis to make money. The study’s model indicates that degradation may be detected in raw milk. Over the past 10 years, scientists have created reliable techniques for spotting milk degradation. Since this research mentions A real-time method to check the quality of milk served to the public or used to make dairy products must be developed in light of several studies that demonstrate raw milk includes bacteria that are three dangerous to humans. The suggested solution makes use of various sensors connected to an Arduino board in order to function. The information is then delivered to an Android app, where the system determines the milk’s quality based on its value and makes it easy for the user to identify the

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three types of milk they are getting. For a system to be effective, it must also consider other factors in addition to milk [8]. The Vegetable Freshness Monitoring System Using RFID with Oxygen and Carbon Dioxide Sensor, 2012, Paper 4 An oxygen and carbonic acid gas concentration observation according to authors Ki Hwan Eom and Min Chul Kim, a method for freshness management that supports radio frequency identification has been presented in this study (RFID). Wetness, temperature, oxygen, and carbonic acid gas are just a few of the variables that may be used to determine freshness. This essay focuses on carbon dioxide and oxygen. These two gases’ concentrations have an impact on food and are connected to food freshness. This system utilizes a gas observation device and links it to an associated RFID tag. The management of the RFID system is not too difficult. The freshness of vegetables was calculated using this combination technique [7]. A wireless electronic nose for the detection and categorization of fruits is the topic of Paper 5. 2016 The purpose of the study described in this paper by authors. The wireless electronic nose developed by Deshmukh L.P., Kasbe, and Mujawar T. H will be used to determine the stages of fruit ripeness. This is exclusively applicable to fruits [6]. Paper 6, Smart packaging: Sensors for food quality and safety monitoring, 2011 For online quality control and safety in terms of customers, authorities, and food producers, technology is required, according to the authors Ahmad M., Kuswandi B., Wicaksono Y., and Heng L. It also offers enormous promise for creating fresh sensing systems that are incorporated into food packaging and go beyond the limitations of currently used traditional technologies, such as weight, volume, colour, and aesthetic control restricted to fruits, vegetables in packaging [5]. Paper 7, An IoT-based system for measuring fruit quality, 2016 The authors Ray P, Pradhan S, and Sharma R K compare the pre-calibrated indexing table to the scattered ripening index. This is used for knowledge processing in real time, and storage information is transferred through an IoT-based cloud platform. This system’s drawback is that it can only count the quantity of spoiled fruits and recognize ripe fruits [4]. A deep learning-based automatic nutrition monitoring system in the IoT is the subject of Paper 8, 2018 The deep learning-based vision system was put on a citrus processing line, and the authors Kesavan K, Sundaravadivel P, and Mohanty S P Kougianos provided the approach for quick online sorting. This system’s drawback is that it does not account for the length of food spoilage [3].

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Paper 9, An approach to IoT combining food distribution and health care IoT, 2016 The wireless sensor network would be made up of physical items that were implanted with software, sensors, and electronics, according to the technique suggested by authors S. Bhushan, B. Bohara, P. Kumar, and V. Sharma. Public health, energy efficiency, and sustainable development are interconnected factors that may change an environment or a system for the betterment of people and the earth. Sensor and smart device integration should increase energy efficiency and guarantee that sustainable targets are met [2]. Paper 10, Smart system for monitoring food quality using chemical and biological sensors, 2018 The current state of by Fatima Mustafa and Silvane Andreescu, chemical and biological sensors for food monitoring and intelligent packaging are described. Chemical sensors’ disadvantages include temperature sensitivity, whereas biological sensors take a lot of time and have poor stability. Consequently, the food detection’s accuracy is flawed [1]. Paper 11, Freezing: An underused tool for food safety, 2004 According to author Archer DL, freezing can preserve certain microorganisms for a long time by stopping the activity of rotting microbes in and on foods. This system’s drawback is that it can only temporarily extend food’s shelf life and is unable to identify food that has gone bad [4].

2.1 Aim of the Work The primary goal of this work is to determine if food products for consumption and storage are fresh or not.

2.2 Objectives • To serve and preserve food in an insufficient manner. • To lessen sickness brought on by tainted food. • The research’s primary goal is to determine whether or not people who consume food, fruit, and dairy products are healthy.

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2.3 Scope The report’s focus is on sensors that are effective in finding volatile organic molecules. To show the spoiled food.

2.4 Future Enhancements The project can only determine whether food is fresh, but in the future, we can build a system that can also determine how long it will take for the expert to ruin it.

3 Proposed Methodology This technique aims to create an electronic gadget that can identify food deterioration. Therefore, in the suggested system, we use temperature, gas, and pH sensors that operate based on the transducer type to count (calculate) values for the food item’s temperature, gas content, and pH. Electrochemical, optical, mass, and calorimetric sensors are all possible. An ESP32 microcontroller serves as the brain of this system, including the LCD, temperature sensor, gas sensors, pH sensor, and so on. Through the interpretation of readings obtained from food, this sensor determines the amount a measure of the food’s quality and freshness. We can readily determine if the output is good or awful because it is displayed on an LCD. To check for contamination, a pH sensor with a working pH of 4 is dipped into the meal. Since the temperature, pH, and gas sensors. The ESP32 microcontroller interfaces with sensors, and the microcontroller outputs values to the LCD. By doing this, it will be easy to tell if the food is tainted or not. Whether or not the food is spoilt will be determined by the sensor’s overall value. Each sensor will have predetermined threshold values that, when crossed, indicate that the food is tainted. PH sensor: Using the PH scale, alkalinity or alkalinity of a solution is determined, which stands for potential of hydrogen. Because it detects the difference in electrical potential between a pH electrode and a reference electrode, the pH metre is usually referred to as a potentiometric as part of its operation. The pH or acidity the change in electrical potential indicates the effectiveness of the remedy. A pH sensor, which has a range of 0–14, aids in determining the water’s acidity or alkalinity. Water begins to get as the pH value drops below 7, it becomes more acidic. Any number greater than seven denotes a higher alkaline level. Different pH sensors operate in different ways to gauge the water’s purity (Fig. 1). MQ4 sensor: The MQ4 sensor is used to detect the presence of alcohol gas in food using a particular alcohol gas sensor. Gas sensor model MQ-135. Lead oxide is used to make MQ-135 (SnO2 ). One of this sensor’s finest qualities is its high sensitivity to dangerous gases found in tainted food, including ammonia, sulphur dioxide, smoke,

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Fig. 1 PH sensor

and ammonia. Therefore, the MQ135sensor may be used to determine whether food has begun to rot. The MQ4 methane gas sensor measures the amount of methane gas in the environment and creates an analogue voltage as a consequence. Leak detection is intended to detect concentrations between 300 ppm and 10,000 ppm. The detector, for instance, could pick up if a gas burner was left on but unlit (Fig. 2). Temperature sensor: A thermostat is a device that operates at full capacity until a certain temperature is achieved, at which point it goes out. Since food products must be served at a specific temperature, a thermostat is utilized affects how fresh the food is. The commonly used DHT11 temperature and humidity sensor contains a dedicated NTC for temperature measurement and an 8-bit microcontroller for serial data output of the temperature and humidity readings (Figs. 3 and 4). The project’s goal is to identify whether or not the food is spoiled, and the block diagram above illustrates the architecture of the proposed system.

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Fig. 2 MQ4 sensor

Fig. 3 DHT 11 sensor

4 Methodology The project’s implementation is broken down into the following five steps: 1. Infected or uncontaminated produce, dairy products, or food in general 2. Establishing the ambient temperature, humidity, pH level, and gaseous content 3. Using the Arduino IDE to communicate its threshold values to the hardware components 4. Determine if the food is spoiled or not 5. This is shown on the LCD display, and a mobile application will notify you of it.

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Fig. 4 Block diagram

5 Results When food spoils, the output is a loud buzzer that turns on and a display that says food is spoiled. When healthy food is provided as the input, the output is a loud buzzer that turns off and a display that the food has not spoiled. When the model is utilized and goes through the phases listed below, the predicted results are as follows (Figs. 5, 6, 7, 8, 9 and 10).

Detection of Food Freshness Fig. 5 Serial monitor output

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Fig. 7 LCD humidity display

Fig. 8 LCD gas detection display

Fig. 9 LCD temperature display

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Fig. 10 LCD pH value display

6 Conclusion We have come to the conclusion that a straightforward sensor combination can transform the food business after doing extensive study. This plan will encourage rivalry among food producers to increase marketing after integration nutritious foods and raise customer knowledge of the benefits of doing so. So that people may enjoy the fresh food.

References 1. Mustafa F, Andreescu S (2018) Chemical and biological sensors for food-quality monitoring and smart. Philos Trans Roy Soc London A247:529–551 2. Neethirajan S, Jayas DS (2011) Nanotechnology for the food and bioprocessing sectors. Food Bioprocess Technol 4:39–47. Jacobs IS, Bean CP (1963) Fine particles, thin films, and exchange anisotropy are discussed. In: Rado GT, Suhl H (eds) Magnetism, vol 3. Academic, New York, pp 271–350 3. Eissa S, Zourab M (2017) In vitro selection of DNA aptamers targeting beta-lactoglobulin and their integration in graphene-based biosensor for the detection of milk allergy. Biosens Bioelecton 91:169–174 4. Kumar A, Shaik F, Rahim BA, Kumar DS (2016) Signal and image processing in medical applications. Springer, Heidelberg. https://doi.org/10.1007/978-981-10-0690-6 5. Tentzeris MM, DeJean GR, Thai TT, Yang L (2011) Remote gas sensing is made possible by nanotechnology. IEEE Microwave Mag 12:84–95 6. Deshmukh LP, Kasbe MS, Mujawar TH, Mule SS, Shaligram AD (2016) A case study of a wireless electronic nose (WEN) for fruit detection and categorization. In: The 2016 international symposium on electronics and smart devices (ISESD), Bandung, Indonesia, 29–30 Nov 2016 7. Shaik F, Sharma AK, Ahmed SM (2016) Detection and analysis of diabetic myonecrosis using an improved hybrid image processing model. 2016 2nd International Conference

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on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, India, pp 314–317. doi: https://doi.org/10.1109/AEEICB.2016.7538298 Kougianos E, Mohanty SP, Sundaravadivel P, Kesavan K, Kesavan L (2018) Smart-log is an Internet of Things (IoT) automatic nutrition monitoring device. IEEE Trans Consum Electron 64:390–398 Kaur S, Puri D (2017) Active and intelligent packaging: a boon for food packaging. Int J Food Sci Nutr 2:15–18 Roya AQ, Elham M (2016) Intelligent food packaging: concepts and developments. Int J ChemTech Res 9:669–676 Sivayamini L, Venkatesh C, Fahimuddin S, Thanusha N, Shaheer S, Sree PS (2017) A novel optimization for detection of foot ulcers on infrared images. 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), Warangal, India, pp 41–43. doi: https://doi.org/10.1109/ICRTEECT.2017.36 Gunders D (2012) Wasted: how America is losing 40% of its food from farm to fork to landfill. Natural Resources Defense Council

COVID-19 Data Analysis and Forecasting for India Using Machine Learning K. T. Rashmi, S. Hamsa, M. G. Thejuganesh, and S. Yashaswini

1 Introduction Coronavirus affects human beings and even animals too. The word ‘corona’ obtained from the Latin word ‘corona’ that denotes ‘coronet’ and implies to the virus’s external prongs, which are cylindrical and enlarging gradually towards the end and resembling a crown. Where coronavirus consist of positive RNA having huge and reside strand. Coronavirus arises from the corona virinae genus family. Alpha, beta, gamma and delta are the four procreations of the virus corona. Alpha and beta viruses which affect the humans and animals and gamma and delta viruses which affects the birds, respectively. The coronavirus came into existence in the late years like 1940s after the virus detected in birds like chickens had respiratory diseases. After 1970s virus which affects the humans was found, and it named as coronavirus. This virus can be turned to even deadly virus where it will spread drastically through air which interprets the hardly few cases having symptoms like common cold and the virus can also be named as SARS having an abbreviation of severe acute respiratory syndrome and in the another form named as MERS having an abbreviation of middle east respiratory syndrome. Where the dealing with death rates and respiratory problems have been arose due to the both virus form named as severe acute respiratory syndrome and middle-east respiratory syndrome. Where the new coronavirus came into existence in the month of December 2019 in Wuhan, China which effects the human in a deadly manner. Wuhan city was acted as a main transportation place which spreads the virus rapidly having 12 million plus people had been affected. The major markets situated in Wuhan were mainly responsible for the spread of this coronavirus which well known for the selling of snakes, bats, marmots, chickens and other live animals. The maximum cases were found in the areas which present around the market regions. From December to January where the forty-four cases of respiratory disease problems K. T. Rashmi (B) · S. Hamsa · M. G. Thejuganesh · S. Yashaswini Department of ECE, Don Bosco Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_58

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are came into frame. So the virus was unidentified till the mentioned era. By the end of the January month of 2020, the Government of China and WHO discovered the new virus named as coronavirus which is the subpart virus of the severe acute respiratory syndrome virus family. China’s government had taken many steps to avoid this virus spread. At the time of January 2020, the virus spread add taken major turn, which had reached more than million cases all over the world [12]. And the virus is acknowledged by the WHO, and it had reached 20 countries through person to person. The virus had reached 32,118,316 humans all over the world at the time of September 2020, and it was named as pervasive, the data obtained from the www.worldometer.com. The United States had the most confirmed cases. The sudden increase in active cases and mortality cases in the United States and India astounded the entire world. Furthermore many other nations, such as India, Russia, Peru, Africa, Brazil, Spain, Colombia, Mexico and Africa, were amongst the top ten nations that were badly afflicted by coronavirus, and their death and positive rates are both high level. A very drastic change in the medical experiments could result in better overcome from this spreading of coronavirus. Due to this change in medical improvements towards health, which could even deals with coronavirus role in the emergency condition. Curing the corona-affected patients was complicated one. Machine learning techniques have two perspectives like forecasting and analysing these have been used to get the accurate output which can give precise predictions through self-learning machine learning. Though numerous diseases like Ebola virus and others had been effectively predicted through machine learning techniques. In order to incorporate the data obtained from corona patient’s cases, where machine learning techniques have been used to get the predicted output. Even many machine learning algorithms have been used in order to get the featured output. Data visualization accomplishes the machine learning. The importance of advanced structure such as machine learning and other techniques in controlling coronavirus applicable epidemic struggling has been examined in this work. The novel general health disaster menaces the overall country. The importance of advanced structure such as machine learning and other techniques in controlling coronavirus applicable epidemic struggling has been examined in this work. The novel general health disaster menaces the overall country [2, 5, 7, 10, 13, 17, 19, 21]. The contagious virus severe acute respiratory syndrome coronavirus 2 was a new virus that had developed quickly across the globe. COVID-19 increased quickly than the early viruses such as severe acute respiratory syndrome coronavirus called as SARS and middle-east respiratory syndrome coronavirus called as MERS, the foremost vital beta-corona virus in the human respiratory system. There were roughly 9,022,600 reported cases globally. Computed tomography scan had been used to recognize active and non-active cases; in order to identify the death rates and active cases, more methodology has been used.

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2 Literature Survey Many papers have used machine learning techniques in order to get the accurate outcome. They have been used many algorithms such as support vector machine, decision tree algorithm, random forest algorithm, KNN algorithm, logistic algorithm and linear regression algorithm where they have also used supervised and unsupervised learning algorithms in order get the expected output and which also useful for the future study. They have collected data set from Kaggle set having different states, country and region as base information.

3 Methodology Where the dataset used for the prediction, considering the positive cases in the comparison. Dataset: On 31 December 2019 World Health Organization was warned to many cases of respiratory disease in Wuhan, China. This new virus had not match with any other known virus. The main concern was that we had no idea about how the virus would affect the world. A daily information about this virus spread had given some interesting facts to make usable to the data science region [3]. Johns Hopkins University has made an excellent dashboard used the affected cases. Where data is extracted from the Google sheets associated. Where data is in the form of csv means comma separated values. This new coronavirus 2019 was a virus recognized as the cause of an eruption of an respiratory illness identified in Wuhan, China. During early stages in Wuhan, China come out with a huge of patients had some connection to a large seafood and animal market, indicating animal to human spread. Where it was only reporting that virus was spreading between person and person where it was not affecting animals and had not spread from person to animals. This dataset contains the daily level information based on the number of positive, mortality and recovery cases from COVID-19. Where the data set is the time series data, and the number is a cumulative number given on any day [3, 8, 14, 16]. At this time, it is unclear how easily or sustainably this virus is spreading between people—CDC. The data is available from 22 January 2020.

3.1 Column Description Main file in this dataset is covid_19_data.csv, and the detailed descriptions are below. covid_19_data.csv. Serial number denoted by Sno.

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MM/DD/YYYY which represent the observation date. Observation form for the state or province is represented as Province/State even it can have empty space when the values are missing. Observation form for the country region is represented as country or region. Observation form for the UTC having time updated in the row form for the taken country and province. It does not have standardized value so we have to clean the values before considering it. Observation form for the number of confirmed cases which are in the cumulative form is represented as confirmed. Observation form for the number of death cases which are in the cumulative form is represented as deaths. Observation form for the number of recovered cases which are in the cumulative form is represented as recovered.

4 Block Diagram Figure 1 contains data, training set, test set, model development, model evaluation and performance. Data pre-processing where raw data is being processed and its made to acceptable for machine learning model. Generally real word data having some noises, values are missing, and also in an unusable format which cannot be used for machine learning techniques. Where the cleaning the available data and make it acceptable for machine learning model. So that accuracy and effectual will gradually increase. Data pre-processing includes following steps-gathering the dataset, libraries are imported, data set are imported, missing data are recognized, categorical data are encoded, data set are splitted as training set and test set, and feature scaling [16]. Training set: Training data set is a subpart of data set which we have taken, where it is used to train the machine learning techniques, which have used in this project where we have already know the output of this training set result.

Fig. 1 Block diagram

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Test set: Testing data set is also a subpart of data set which we have taken, where it is used test the machine learning model based on the training set and which predicts the output based on the machine learning model, and here, we do not know about the output means where it is unable to predict the output before testing. It is always better to perform high with machine learning techniques; meanwhile it performs good with the training and testing data set. Model development and model evaluation: Machine learning model has two types of learning like supervised learning and unsupervised learning, where supervised learning trains the machines using well-labelled training data and based on that data predicts the output. The labelled data means where we already know about the output and some input data is labelled to the available data which has a process of providing input data and output data to the machine learning techniques. Whereas unsupervised learning is a type of machine learning model, where the trained data uses unlabelled dataset and allowed to use data without any supervision [1, 8].

4.1 Import Libraries List of libraries we are used here are pandas, matplotlib, seaborn, numpy, datetime and sklearn. Where pandas is used for data analysis and associated manipulation of tabular data in dataframes. Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Seaborn is a library for making statistical graphics in Python. Numpy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Python datetime module supplies classes to work with date and time. Sklearn is an open-source machine learning library that supports supervised and unsupervised learning [9].

4.2 Read Data Where csv files contain the available raw data which can be read using pd.read_csv.

4.3 Checking for Missing Values Missing values are labelled as 0 and 1 based on the data set which we have taken.

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4.4 Checking for Categorical and Variable Data In the classification target variable is identified. Seaborn library is used for feature distribution techniques.

4.5 Standardize the Data For analysis, data set must contain the variables in a numerical input form.

4.6 Data Splitting In order to obtain the expected accuracy in the machine learning model, here we are using algorithms mentioned below.

4.7 Support Vector Machine Support vector machine is also another effective technique which detects the anomalies. A support vector machine is a supervised learning and unsupervised learning based on this extension or even a class for instances values, which can be used to identify the anomalies based on the above two different learnings. Where the classification techniques classify or detect the data set or patterns into two classes as fraud or legitimate. This technique is known as binary classification. Pattern recognition includes such as face recognition, idiomatic expression and text categorization. XG Boost is successfully implemented by using SVM as a popular algorithm. Support vector machine determines the one class support vector machine which includes kernel, degree, gamma, nu and maximum iteration to obtain accuracy, precision, f1 score, macro average and weighted average [4, 6, 11]. Support vector machines give accuracy about 97.14.

4.8 Logistic Regression Logistic regression is a basically a major classification algorithm, and it is almost indistinguishable to linear regression. Logistic regression is more progressive and innovational than linear regression, because linear regression is limited to data set that are distributed in the given space but not for widely distributed space for given data

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space. Logistic regression is nothing but classifying the data. Where linear regression is to predict or identify the data. We used logistic regression because this algorithm will describe the data and also determine the difference between dependent binary variable and independent binary variable that can be a nominal or ordinal interval. Advantages of using logistic regression algorithm to predict accuracy for credit card fraud detection are 1. Logistic regression is uncomplicated method to implement the data’s than other algorithm like linear regression. 2. It does not make any assumptions regarding the distribution of classes in upcoming space. 3. Multinominal logistic regression means which is used to predict the data like data classes as 1 and 0 like checking the probability based on multiple independent variables. 4. Unknown record is easily classified by using this algorithm. By observing linear regression equation, we can obtain logistic regression equation as follows. The mathematical equation for straight line can be written as: y = m 0 + (m 1 ∗ x1 ) + (a2 ∗ x2 ) + · · · + (m k ∗ xk ) where y lies between 0 and 1, let us divide the equation by (1 − y): y1 − y0 for y = 0 and ∞ for y = 1 log[y1 −y] = y = m 0 + (m 1 ∗ x1 ) + (a2 ∗ x2 ) + · · · + (m k ∗ xk )

(1)

Equation 1 represents the result for logistic regression algorithm [14, 15, 20].

4.9 Testing the Classifier Here, we used cross-validation technique in order to divide the database into different parts having different testing data set. Each classifier is tested by using testing data set which allow us to use the whole database for training the classifier and also for testing. Accuracy for logistic regression is obtained by calculating the accuracy of each classifier [1].

4.10 Evaluating the Classifier Confusion matrix is basically a (N × N) matrix, which is used to determine the performance of classification model. The confusion matrix is formed based on following terms: True positive: The positive classes (records) which we have predicted have positive records, which are corrected by the classifier.

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True negative: The positive classes (records) which we have predicted have negative records, which are corrected by the classifier. False positive: The negative classes (records) which we have predicted have positive records, which are corrected by the classifier. False negative: The negative classes (records) which we have predicted have negative records, which are corrected by the classifier. TP + FN = P FP + TN = N where TP: True positive, FN: False negative, FP: False positive, TN: True negative. Accuracy = (TP + TN)/number of all records in the testing set Sensitivity = TP/P Error rate = 1 − accuracy where accuracy means based on dependent binary variables or independent binary variables in a data set are measured, used to determine the best outcome. Sensitivity determines the true values of each available category. Error rate is used to determine the number of all false prediction divided by the total number of the data sets taken [1, 9, 11, 15]. Logistic regression gives accuracy as 96.41.

4.11 Random Forest Random forest is a supervised learning algorithm. Where classification even regression can be done using this random forest algorithm method. Random forest consists of trees means decision trees on data sets or the data samples we taken. Here, ensemble method is better than having single decision tree method. In this problem, dependent variable is categorical. Where the classes are classified as x train and y train having random classified score as x test and y test. We get the accuracy as 92.14 [1, 4, 8, 14, 16].

4.12 K-Nearest Neighbour K-nearest neighbour is a laziest algorithm because it reads the data when it performs the operation before that it stores the data which does not perform any function. Where it has supervised learning algorithm.

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K-nearest neighbour determines the classifier as x train y train as KNN-fit and x test is considered as a KNN predict by y pred. And it has confusion matrix of y test and y prediction in order to get accuracy score as 96.96 [1, 9, 11, 15, 16, 18].

4.13 Decision Tree Decision tree classifiers have a readable classification model which gives potentially accurate in numerous contrasting applications contexts, including applications which has energy-based applications. Classification model is build based on decision tree as created by decision tree classifier. An attribute’s test specifies each node in the tree, and it has a possible values correspond to the node which has descending branch. Each label is represented by each leaf which has instance values. Navigating the instance values in the training set which are classified from the root of the tree down to a leaf gives the outcome of the tests according to the path. Which starts from the root node of the tree, instance are splitted by the each node into two or more sub spaces based on the attribute test condition. From moving down the tree branch a new node is created which corresponds to the attributes value. All records in the training set are classified based on the process as mentioned above for the subtree rooted at the new node. Here, its works based on top down manner having attribute test condition at every step that makes the split records best. Splitting the records in a best way has many measures to determine. It has an accuracy of about 98.06 [1, 11].

4.14 Gradient Boosting Classifier Gradient boosting classifier, the model is used to solve the regression as well as classification problems having ensemble forward learning. It ignores the all weaker predictors and chooses the stronger one. Its uses structure score in order to satisfying the structure having better version than decision tree which gains calculation and increased refined approximations. An additional classifier invokes the prediction performance of gradient boosting classifier. Without having any disturbance in speed which gives optimized accuracy. For model tuning and selection having effortless environment provides an easily distributable and parallelizable feature. The optimal accuracy of the big data handled by the new version of gradient boosting classifier model is as shown in Fig. 2. Minimum temperature, maximum temperature, minimum humidity, and maximum humidity are the four atmospheric parameters used for analysis which is used for computational complexity reduction and for the gradient boosting classifier model where it uses average of the maximum and minimum temperature and average of maximum and minimum humidity are used as an input. Which predicts the number of positive and negative cases accuracy of all individual states of India. Number of trees, number of folds, distribution function, and learning rate tune the

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Fig. 2 Algorithm of GBM

gradient boosting classifier model. Mean square error, mean average error, root mean square error, coefficient of determination, and mean residual deviance evaluate the performance of gradient boosting classifier model. Combining all test data sets of all states predicts the active and recovered cases of corona virus 2019. It has accuracy of about 89.06. Naïve Bayes: Naïve Bayes performs an impressive role in prediction even though it is called as a simple classification algorithm which comes under probabilistic classifiers based on Bayer’s theorem and uses conditional probability. Where it classifies the instance of the taken value, and it is denoted by a vector Y = (y1 , …, yn ) where the features of independent variables represented by ‘n’. Data sets are programmed and are considered based on group of cases. Having all the conditions and all the classes are calculated based on the probability which categorize the patient based on class with probability, it has an accuracy about 77.36 [15].

COVID-19 Data Analysis and Forecasting for India Using Machine … Table 1 Result of algorithms

Table 2 Symptoms and drugs name

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Sl. No.

Algorithm

Accuracy

1

Decision tree

98.068077

2

Support vector machines

97.148114

3

KNN

96.964121

4

Logistic regression

96.412144

5

Random forest

92.140174

6

Gradient boosting classifier

89.061785

7

Naïve Bayes

77.368905

Symptoms

Drug name

Fever

Paracetamol (crocin/calpol/fevago)

Dry cough

Robitussin and Mucinex

Sore throat

Ibuprofen or acetaminophen

Running nose

Chlorpheniramine and diphenhydramine

Asthma

Tezspire (tezepelumab-ekko)

Chronic lung disease Phosphodiesterase-4 inhibitors Headache

Acetaminophen

Heart disease

Antiplatelets

Diabetes

Insulin

Hypertension

Hiazide diuretics

Fatigue

Duloxetine

5 Result The coronavirus threatens the world through this infectious disease. Implementing various machine learning techniques gives accuracy based on the performances. Compared to all the algorithms which have been used here, support vector machines and Naïve Bayes achieve most accuracy than the other algorithms. Accuracy for respective algorithms is as mentioned in Table 1. With respect to symptoms, drugs are given as mentioned in Table 2.

6 Conclusion and Future Scope Coronavirus had appeared as a very effective infected spread virus like other global spread diseases. During the pandemic, active cases and mortality ratio were increasing rapidly all over the world. Classification and predictions were mainly used; instead of this method, machine learning techniques are used to predict the outcome.

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In this paper, many supervised machine learning algorithms are used, namely SVM, KNN, Naïve Bayes, gradient boosting classifier, decision tree, random forest, and logistic regression. All the models are evaluated having training data set of 80% and testing data set of 20% from the given data set. Compared to all the algorithms which have been used here, support vector machines and Naïve Bayes achieve most accuracy than the other algorithms. Using numerous machine learning techniques even we can predict different cases having particular data set which gives us a correct accuracy which could enhance the output for future work. Even deep learning and artificial intelligence can also be used.

References 1. Rohini M, Naveena KR, Jothipriya G, Kameshwaran S, Jagadeeswari M (2021) A comparative approach to predict corona virus using machine learning. IEEE 2. Chamola V, Hassija V, Gupta V, Guizani M (2020) A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE 3. Kumari R, Kumar S, Poonia RC, Singh V, Raja L, Bhatnagar V, Agarwal P (2021) Analysis and predictions of spread, recovery, and death caused by COVID-19 in India. IEEE 4. Wolfe G, Elnashar A, Schreiber W, Alsmadi I (2020) COVID-19 candidate treatments, a data analytics approach. IEEE 5. Vakula Rani J, Jakka A (2020) Forecasting COVID-19 cases in India using machine learning models. IEEE 6. Istaiteh O, Owais T, Al-Madi N, Abu-Soud S (2020) Machine learning approaches for COVID19 forecasting. IEEE 7. Yang Z, Chen K (2021) Machine learning methods on COVID-19 situation prediction. IEEE 8. Hasan MK, Ahmed S, Abdullah ZE, Monirujjaman Khan M, Anand D, Singh A, AlZain M, Masud M (2021) Deep learning approaches for detecting pneumonia in COVID-19 patients by analyzing chest X-ray images 9. Turabieh H, Karaa WB (2021) Predicting the existence of COVID-19 using machine learning based on laboratory findings 10. Gambhir E, Jain R, Gupta A, Tomer U (2020) Regression analysis of COVID-19 using machine learning algorithms. IEEE 11. Li M, Ma X, Chen C, Yuan Y, Zhang S, Yan Z, Chen C, Chen F, Bai Y, Zhou P, Lv X (2021) Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images. IEEE 12. Siddhu AK, Kumar A, Kundu S (2020) Review paper for detection of COVID-19 from medical images and/or symptoms of patient using machine learning approaches. IEEE 13. Cotfas LA, Delcea C, Roxin I, Ioan˘as¸ C, Gherai DS, Tajariol F (2021) The longest month: analyzing COVID-19 vaccination opinions dynamics from tweets in the month following the first vaccine announcement. IEEE 14. Khanday AM, Rabani ST, Khan QR, Rouf N, Mohi Ud Din M (2020) Machine learning based approaches for detecting COVID-19 using clinical text data. IEEE 15. Aljameel SS, Khan IU, Aslam N, Aljabri M, Alsulmi ES (2021) Machine learning-based model to predict the disease severity and outcome in COVID-19 patients 16. Mary LW, Raj SA (2021) Machine learning algorithms for predicting SARS-CoV-2 (COVID19)—a comparative analysis. IEEE 17. Rahman MM, Paul KC, Hossain MA, Ali GM, Rahman MS, Thill JC (2021) Machine learning on the COVID-19 pandemic, human mobility and air quality: a review. IEEE 18. Tiwari S, Chanak P, Singh SK (2022) A review of the machine learning algorithms for covid-19 case analysis. IEEE

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19. Podder P, Mondal MR (2020) Machine learning to predict COVID-19 and ICU requirement 20. Nasser N, Emad-ul-Haq Q, Imran M, Ali A, Al-Helali A (2021) A deep learning-based system for detecting COVID-19 patients. IEEE 21. Kunjir A, Joshi D, Chadha R, Wadiwala T, Trikha V (2020) A comparative study of predictive machine learning algorithms for COVID-19 trends and analysis. IEEE

Design of a Smart Safety Design for Women Using IoT G. Shruthi, R. Chandana, and P. Gagana

1 Introduction In the current global situation, women face many challenges. We hear more about the harassment of women than about their achievements [1]. There are many existing apps and devices to keep women safe through smartphones [2]. Although smartphone technology has grown rapidly, it is not possible to have the phone in hand to call or click on it, so here we present a new technique through a smartwatch that has an alarm function, a shock generator, and a camera to record the attacker’s image, GPS/ GSM to find the victim, the nearby police station, and circle there so it will be useful for the police to reach the place soon by GPS tracking, and such a system will lead to a safer and better environment.

2 Existing System Sting Rings: The sting ring is intended for personal protection. Compared to other stun guns on the market, this innovative model stands apart [3]. You may conceal the unit’s base in the palm of your hand, leaving the sting ring’s top exposed. When you grip it firmly, the rounded base feels natural in your hands, and it is nearly impossible for an assailant to take it from you. Squeeze-N-Stun technology can help you protect yourself in a panic while also saving you valuable seconds [4]. Turning off the fuse and tightening the handle will activate the device without you having to search for the correct buttons to push [5]. The Pepper Spray: With the use of pepper spray, a threat can be momentarily stopped, allowing the user to flee from bodily damage at the hands of an assailant G. Shruthi (B) · R. Chandana · P. Gagana Department of ECE, Don Bosco Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_59

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[6]. Use of pepper spray for any other purpose is prohibited and may result in both criminal and civil fines for the user. It causes involuntary closing of the eyes, breathing difficulties, and burning of the face and skin. Only use pepper spray if you or another person is in immediate danger of being physically harmed by a person or animal. For best effect, spray the attacker in the face and eyes [7]. Pepper spray may not be as effective on intoxicated people or those with a high pain threshold. We cannot rely entirely on pepper spray to stop an attack. The Ultrasonic Dog Repellent: The ultrasonic dog-repellent with LED lights uses powerful ultrasonic sound to prevent dogs from barking incessantly and unnecessarily by giving them indications to keep clear. Train your dog not to bark regularly. By emitting ultrasonic sound waves, the dog repellent trains dogs to not bark constantly, and the strobing LED flashlights keep you safe from stray dogs very effectively [8]. Thus, using ultrasonic sound and flashing lights could be a practical and effective way to train dogs.

3 Proposed System The main objective of the self-defense watch For women safety is that it helps women to defend themselves in situations of danger. The watch helps the victim to contact their relatives, friends, and the nearest police station. Meanwhile, the watch can assist with GPS position tracking for the victim, and for self-defense, it can produce shock waves that can render the attacker immobile for a certain time duration, helping the victim to escape or hide from the attacker. In addition to this, it also can alert nearby people through an alarm. This makes self-defending easier than the existing system. Our proposed system also provides the facility of an inbuilt camera to capture the image of the attacker that can help in taking legal actions against the attacker. The system consists of sections that describe a quick response cost a system of safety for people, especially for women, that enables a lady in need to summon assistance by pressing a button on this cutting-edge device. The equivalent of a smartwatch for women is a self-defense system for women’s security. Women can benefit from technology that are incorporated into small, portable gadgets. When a woman wearing this watch or band senses that she is about to be harassed, she can press a switch on the watch or band to send an SMS alert with her position to several predefined emergency numbers, alerting them and soon help is on the way. The equipment also has a camera that records the image of the assailant and sends it to the chosen contacts. Embedded hardware and software created specifically for this application will make up the system. As soon as the trigger key on the watch is depressed, the mechanism enables one to know the precise location of a person.

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Fig. 1 Block diagram

4 System Design The proposed system architecture is shown in Fig. 1.

5 Module Description 5.1 OpenCV OpenCV, short for open-source computer version, is a library with functions that focus primarily on real-time computer vision. With OpenCV, face detection can be performed using a pre-trained deep learning face detection model that comes with the OpenCV library which is mostly written in C++ and has a C++ interface, but still the traditional C interface is still present and is less complicated but more comprehensive. Tools for 2D and 3D features, ego motion estimation, facial recognition, gesture recognition, human–computer interaction (HCI), mobile robotics, motion comprehension, object identification, segmentation, recognition, and stereo vision: Depth perception from two cameras, structure from motion (SFM), motion tracking, and augmented reality are some of the application areas for OpenCV.

5.2 ARM11 Raspberry Pi 3 Board The Pi is a credit card-sized computer that connects to a computer monitor or television and employs keyboard and mouse inputs. It can do a wide range of tasks,

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Fig. 2 Specification of a Raspberry Pi

including database building, high-definition video playback, live gaming, internet browsing, and military applications (Fig. 2). A Raspberry Pi 3B board is used to implement the device. The Raspberry Pi is a credit card-sized computer created and designed in the UK by the Raspberry Pi Foundation for teaching fundamental computer science to children in schools and anybody else interested in computer hardware, programming, and DIY projects.

5.3 Raspbian OS Although Windows is more similar to Mac than the Raspberry Pi operating system, the desktop is most similar to Windows. Though initially unfamiliar, using Raspbian is essentially identical to using Windows (except for Windows 8, of course). In addition to a file manager, a web browser, and a menu bar, there are desktop shortcuts for pre-installed programmes. In order to develop “hard float” code that is optimized for the Raspberry Pi, Raspbian is an unauthorized port of Debian Wheezy armhf. For programmes that frequently employ floating-point arithmetic, this results in noticeably faster performance. Utilizing the sophisticated instructions of the Raspberry Pi’s ARMv6 CPU will improve the performance of all other programmes as well. Utilizing the sophisticated instructions of the Raspberry Pi’s ARMv6 CPU will improve the performance of all other programmes as well. Although Mike Thompson (mpthompson) and Peter Green (Pugwash) are the main contributors to Raspbian, the

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community of Raspberry Pi users who are eager to maximize their device’s potential also contributes significantly to the project’s success.

6 Methodology An algorithm, workflow, or process are represented by a flowchart, a type of diagram. Another way to think of a flowchart is as a schematic illustration of an algorithm (step by step to solve a problem). When the victim senses danger and presses the emergency button, the alarm system and shock generator are activated. The built-in camera then switches on and starts taking pictures of the immediate area as well as the user’s or the victim’s location in relation to the closest police station and any emergency contacts that have been saved in the Twilio Messenger module. This also activates the shock generator, which can be used as a self-defense weapon (Figs. 3, 4 and 5).

7 Experiment and Results • Press the emergency button to turn on the device. • The police station and any preloaded contacts for family members can get a message about the threat. • The message is delivered to the police station, and GSM or Twilio Messenger both offer user location monitoring. • Even the system takes a photo of the person and sends an email to the nearest police station. • Using the tiny camera included in the gadget, the user can take a picture of the assailant and send it along with the message to the police station. The position of the device will be promptly transmitted to predefined contacts when the user activates it by pressing the emergency button (Figs. 6 and 7).

8 Applications • • • •

Provides safety for women. Makes victim tracking easy so that help can be provided faster. Can be used for children and senior citizens’ safety as well. Just by modifying it we can use it for employee safety in industries.

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Fig. 3 Flowchart

9 Advantages • The proposed system helps women defend themselves in situations of danger. • The system is provided with four modules in a single watch, i.e., alarm system, GPS location tracking, shock generator, and an inbuilt camera.

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Fig. 4 System interface Fig. 5 System implementation

• The technology can also be used to keep kids and the elderly safe. • The system helps reduce crime rates against women. • As legal proof of a crime with precise location data, the system’s image can be used in court to support a prosecution.

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Fig. 6 Message intimation

Fig. 7 Image sent through mail

• The system will also help in finding the attacker with the help of the image captured through the camera.

10 Conclusion The project grants designing of a safety device for women in times of danger and will assist to clarify them scientifically with compressed kit and concept making use of wrist band, the mechanism like shock generator, alarm, location intimation through message, and capturing the image of the attacker. The above-mentioned product can run over the suffering of every woman in the world about her assurance and security.

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11 Future Work As the main aim in the world is to ensure women’s security, so by this model, we can achieve our aim. This would also slowly reach the rural areas. The benefit of this product is its affordable price, and a woman using this can leave home anytime without having to worry about her safety. This system can be more advanced by adding a calling feature, accelerometers for fall detection, and also a video capturing system.

References 1. Chitkara D, Sachdeva N, Vashisht YD (2016) Design of a women’s safety device. In: Humanitarian technology conference (R10-HTC), 2016 IEEE region 10. IEEE, pp 1–3 2. Hussain SM, Nizamuddin SA, Asuncion R, Ramaiah C, Singh AV (2016) Prototype of an intelligent system based on RFID and GPS technologies for women’s safety. In: 2016 5th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO). IEEE, pp 387–390 3. Study: 94% women victims of sexual harassment in public transport Dhaka Tribune [Online]. Available at: https://www.dhakatribune.com/bangladesh/crime/2018/03/07/study-94-womenvictimsexualharassmentpublic-transport. Accessed 15 Aug 2018 4. Smith MJ, Clarke RV (2000) Crime and public transport. Crime Just 27:169–233 5. Heise L, Ellsberg M, Gottemoeller M (1999) Ending violence against women. Popul Rep 27(4):11 6. Shaik F, Sharma AK, Ahmed SM (2016) Detection and analysis of diabetic myonecrosis using an improved hybrid image processing model. 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, India, pp 314–317. doi: https://doi.org/10.1109/AEEICB.2016.7538298 7. Toney G, Jabeen F, Puneeth S (2015) Design and implementation of safety armband for women and children using ARM7. In: 2015 international conference on power and advanced control engineering (SPACE), pp 300–303 8. Sivayamini L, Venkatesh C, Fahimuddin S, Thanusha N, Shaheer S, Sree PS (2017) A novel optimization for detection of foot ulcers on infrared images. 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), Warangal, India, pp 41–43. doi: https://doi.org/10.1109/ICRTEECT.2017.36

Ultravoilet Sterilization Disinfectant Robot for Combating the COVID-19 Pandemic Padmashree V. Kulkarni, K. M. Akash, G. Deepika, Kamal Nayan Singh, and K. Madhura

1 Introduction Smart technologies are the one which are used in the medical field. The most important task in the hospitals is to maintain the hygiene and cleanliness which are these maintained by the process. The process is referred as sanitization and disinfection. The smart surveillance UV-robot can be used for the process of sanitization and disinfection. The UV-robot involves in the work of real-time monitoring and control system in environmental and industrial applications. UV-robot is the one which has the ability to replace the security system of housing apartments and the offices and the hospitals and the schools. Even after the cleaning the floor for several times, a small microorganism exists on the floor, and hence, the UV-sterilization technology will play a very important role in reduction of such microorganism. Since late 2019, the COVID-19 has become a worldwide pandemic. Coronavirus on surfaces found large variability and ranges from 2 h to 9 days. Any person who is within 2 m or 6 m or 6 ft. away from a person who is infected by COVID-19 for a prolonged time will be considered as close contact. The process of sanitization is quite difficult during this time. Using the manpower for the process of sanitization increases the risk of chances of being infection, and to overcome this UV-sterilization robot can be used. UV-sterilization robot can be operated in patient’s room for their well-being and to reduce the chances of infection. Coronavirus dies very quickly when they are exposed to UV-light, and the UV-sterilization robot consists of UV-light in its technology. UV-sterilization robot covers 360-degree angle direction, it is employed with embedded system, and it is based on Arduino ATmega328 which will detect the P. V. Kulkarni (B) · K. M. Akash · G. Deepika · K. Nayan Singh · K. Madhura Department of Electrical and Electronics Engineering, Don Bosco Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_60

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obstacles or help the robot to recognize the obstacle. As additional feature, we used the solar panels to collect the solar energy and charge the battery to provide a power supply to the UV-sterilization robot.

2 Literature Survey Authors of this thesis have discussed regarding the translesion of DNA polymerase replication restart and how the recombinational repair takes place in DNA. They have also discussed regarding the relationship of DNA repair to regulation of cell fate [1]. Out of 294 patients who were operated in clean and contaminated operation theatre, 10.9% were confirmed with bacterial nosocomial infections. The rate of infections among clean and contaminated operations was 3.3% and 12.8%, respectively, to minimize the infections proper surgical process as to be implemented [2]. The germicidal properties of UV technologies will help to purify the water. The desired effects of UV exposure on various types of bacteria and effect of different UV wavelength on organic molecules, which helps in reduction of organic contaminants [3]. Patients admitted to hospital acquire a multi-drug-resistant organisms-related diseases and clostridium difficile from inadequately disinfected environmental surfaces. By using UV for the standard cleaning technologies, the infections were significantly lower, and cases were reduced [4]. The object surface brightness depends on surface luminous density and its luminous density and its luminous emitted per unit surface area; as per astronomy it depends on particular filter band or photometric system [5].

3 Problem Statement Considering present pandemic situation, the probability of human getting infected is more. If the sanitization is done by humans, especially in public areas, patient’s room, office, laboratories, and educational centers where the frequent sanitization is mandatory to ensure the cleanliness, there are certain places around us where large amount of bacteria of bacteria multiplication takes place and spreads the diseases to the surrounding common people.

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4 Existing Versus Proposed System Disinfection process using manpower is a quite difficult task, and it also involves the risk, which increases chances of infection. This problem was tackled with a help of recently proposed UV-robot which requires the external power supply to perform the disinfection and sanitization task. But this is quite difficult task to depend on external power supply source. Hence, we have proposed a prototype of UV-robot which uses the solar energy to perform the task of sanitization and disinfection.

5 Methodology The UV-robot gets the power supply with the help of solar panels. The solar panels absorb the solar energy and the charge the battery with the help of inverter which in return provides power supply to UV-robot, and this is the one we have implemented in the prototype. We have used the MQ-sensors in the prototype, which acts as an air-quality sensor and monitors the air quality in the environment and also provides the information regarding the hydrogen level present in the atmosphere, with the help of display board present on the UV-robot. We have introduced the sprayer in UV-robot for the purpose of eradicating the bacteria, microbes, and virus present in the environment.

5.1 Hardware Components 1. DC 12 V motor: The 12 V DC motor is used in UV-sterilization robot for the purpose of converting the direct current (DC), electrical energy into mechanical energy, which has a speed of 12,000 RPM and which provides 16 watts of power. The DC motor will be as shown in Fig. 1. 2. H-bridge: We used this as an electronic circuit to supply the current in two directions, where we can control the current flow in two paths. Voltage can be Fig. 1 5 V DC motor

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Fig. 2 H-bridge—L-293D

applied in any direction. This allows the DC motor to run in forward or backward direction (Fig. 2). 3. Air quality sensor: The air quality sensor monitors the air quality and hydrogen level in the environment and also displays the information regarding the hydrogen level of the environment in the display board (Fig. 3). 4. UV-LED lamp of 5 V, 105 mW: UV-LED lamps with their narrow wavelength are used for the eradication of microorganisms. UV-LED emits the light which reduces the ability if DNA to multiply and cause diseases to spread (Fig. 4). 5. Solar panel of 6 V—3 mA: Solar panel is connected to battery with help of inverter to generate large amount of electricity by the process of photovoltaic effect which is used to provide power supply to the battery for the operation of UV-sterilization robot (Fig. 5).

Fig. 3 Air quality sensor and display board Fig. 4 UV LED − 5 V, 105 mW

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Fig. 5 Solar panel 6 V—3 mA

6. Battery: The battery has a deep has a deep cycle. The state of the battery condition will help to determine the charging time (Fig. 6). 7. UV HC-SR04 sensor of 5 V: The UV-sensor provides 2–4 cm of non-contact measurement function ability and also determines the distance of any obstacle (Fig. 7). 8. Inverter of 24 V: The solar panel is wired to the inverter, which helps to convert the DC power produced by the panels to AC power. In-return acts as a power supply to the battery in the prototype (Fig. 8). 9. AURDINO-UNO: The open-source platform-based easy-to-use software and hardware used in activating of motor of UV-robot and helps in turning on the LED lamp to eradicate the microorganisms, bacteria, and viruses present in the environment (Fig. 9). Fig. 6 Battery − 12 V, 1.3 Ah

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Fig. 8 Inverter − 24 V

Fig. 9 AURDINO-UNO

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Fig. 10 Circuit connection of UV-sterilization robot

5.2 Circuit Connections In the improved prototype, the solar panel is connected to the battery with the help of inverter to convert the solar energy to electrical energy and to provide the power supply to the battery. The Arduino UNO gets the power supply from the battery, and it is also connected to the relay, UV-LED lamp, and the display board. The relay connected to Arduino UNO acts as a switch. The display board connected to Arduino displays the hydrogen level in the environment. The sprayer is connected to Arduino UNO in order to perform the operation of disinfection. When the hydrogen level is more than 2000 ppm, the UV LED lamp is intimated to glow, and its starts to glow in order to eradicate the micro organisms or bacteria or virus. Ultrasonic sensor is connected to Arduino UNO and the battery where it determines the obstacles during the process of disinfection (Fig. 10).

6 Result The prototype is successfully implemented by using solar panels as source of energy to operate the UV-robot. This solar panels convert solar energy into DC electricity and provide power supply to the battery of the system. The information regarding the movement is provided by the processor. The H-bridge enables the given voltage to act in either direction, such that to provide a command to a DC motor. To make the robot to move in clockwise and anti-clockwise direction. By varying the pulse width modulation, the speed of the motor will be controlled. Ultrasonic sensor mounted on UV-robot at its front head is used to detect any obstacle which is located at its path. According to the processed signals given by the processor

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UV-sensor detects the obstacle. The MQ-sensor will monitor the air quality in the environment at the range of 200 ppm and displays on the display board present in UV-robot. The sprayer is used in the function of disinfection and sanitization.

7 Conclusion Considering the problems involved in the process of sanitization and Disinfection process and also risk involved in chances of being infected if in case the Man-power is used for the process of sanitization and disinfection task. This UV Robot will be the best one for the process of sanitization and disinfection task. The potential UV-robot helps to provide sterilization process for drug-resistant bacteria in order to provide the safe and hygienic environment. The UV-robot uses the wireless network control system via Web site connecting the system for the purpose of sanitization and disinfection process. The prototype uses the Web site communication system connected with Wi-Fi network and enables to sterilize the entire operating systems. This prototype can be expanded in many ways to serve the medical field. The smart surveillance UV-robot prototype will be the best one for real-time monitoring purpose and control system in environmental and industrial application. This prototype would be the best model for the utilization of natural resource to eradicate the microbes and bacteria in the environment.

References 1. Friedberg EC, Walker GC, Seide W, Wood RD, Scultz RA, Ellenberz T (2006) DNA repair mutagenesis. ASN Press, Washington 2. Mulu W, Kibru G, Beyene G, Damtie M (2012) Postoperative nosocomial infections and antimicrobial resistance pattern of bacteria isolates among patients admitted at Felege Hiwot Referral Hospital, Bahiradar, Ethiopia. J Health Sci 3. Kano I, Darbouret D, Mabic S (2012) UV technologies in water purification systems. The R&D Notebook 9. A publication of the Lab Water Division of EMD Millipore 4. Spagnolo M, Ottirio GT, Amicizia D, Perdelli F, Cristin ML (2013) Operating theatre quality and prevention of surgical site infections. J Prev Med Hyg 5. Andreson J, Chen LF, Weber DJ, Moehring RW, Lewis S, Triplett PF et al (2017) Enhanced terminal room disinfection and acquisition and infection caused by multidrug-resistant organisms and Clostridium difficile (the benefits of enhanced terminal room disinfection study): a clusterrandomized, multicenter-crossover study. J Prev Med Hyg 6. The “Brightness and surface brightness”, Astronomy Department University of Michigan by Cordella Lackey, Sky Tapestry in the year 2011

Advancement in Sericulture Using Image Processing Kishor Kumar, N. Pavan, R. Yashas, R. Rajesh, and B. G. Rakshith

1 Introduction Silk is produced by rearing of silkworm. Silk is the elegant textile in the world, and it is “queen of textiles” due to its lightweight, soft touch, high durability, and so on. Silk industries provide lots of livelihood opportunities and even high employment orientation. The most important fact of this industry in the rural areas such as onfarm and off-farm is providing enormous employment potential, and this industry is among one of the most appropriate avenues for socioeconomic development. Silk, as we all know it, has become a part of life and culture of Indians. India is rich in silk production; hence, it stands in the second position. India’s educational and established bound has aided the country to obtain a leading position. India is the only country that produces all the five popular silks to a degree mulberry, sweltering tasar, stage tasar, eri, and muga. As we disturb experience any things about silk, allow us immediately experience what is the means entailed the help of fabric. Sericulture, is the rearing of silkworms to produce silk. It is a method of making fabric, and it is a usual farming-located manufacturing that plays an important function in the frugality of evolving country. Within a very short pregnancy and accompanying less capital expenditure this manufacturing provides a big job opportunity from homestead of host plant mulberry to silkworm raising and post swaddle exercises that provides diversified habits of wage creation through various stage of ventures. Since it determines a lot of employment conveniences, this sericulture industries uprightly named as “The manufacturing of weak”. Sericulture manufacturing forms of both farming and manufacturing. It resides of (1) Cultivation of mulberry (2) Rearing of silkworms (3) Production of fabric. The first two that is to say help of mulberry and building of silkworms come under land subdivision and dizzy of cocoons is located K. Kumar · N. Pavan · R. Yashas (B) · R. Rajesh · B. G. Rakshith Department of Electronics and Communication Engineering, Don Bosco Institute of Technology, Mysore Road, Kumbalagodu, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_61

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under modern area. To recover quality of fabric option of non-broken swaddle plays a main part.

2 Related Work Intelligent Sericulture Control System M.A. Dixit, Amruta Kulkarni, Neha Raste, and Gargi Bhandari are the authors. Year-2015 IEEE Sericulture is essentially the activity of raising silkworms to produce silk. During the lifecycle of a silkworm, strict control of a number of environmental factors, including temperature, relative humidity, light, air flow, and air quality, ensures an increase in the quality and quantity of silk. It should be noted that each molt, or stage of growth, in a silkworm requires a certain range of environmental parameter values in order to produce the best results. Every molt has a different requirement for this. Early stages of silkworms, for instance, need a temperature that is substantially higher since they are very active and consume quickly. The field of science known as machine learning is what enables computers to learn without explicit programming. The absence of any yield optimum curve at this time justifies the requirement for machine learning in this control system. As a result, the system must create its own curve from the raw data. Computerized sericulture structure powered by Arduino, written by Poornima G. R., Farheen Taj, Gavinya T. M., Madhu. G., and Madhubala B. N. Year: 2018. The lifting of silkworms and the creation of fabric are handled by the science of sericulture. Sericulture is the main rural activity in India and is the foundation for all economic, social, political, and intellectual advancements. Because of its appeal, sparkling sheen, impressibility, taste, durability, and malleability, silk is known as the queen of materials. Bombyx mori is the most well-known variety of silkworms, despite there being other marketable varieties. One of the most common domesticated insects is the silkworm, which converts mulberry leaves into rich fabric thread known as cocoon while still having worm ends. But the fabric movement has to go through several different processes as it develops from a larva to silk. The peasant faces a great problem every time they watch the silkworms. As a result, in this work we proposed an Arduino-based computerization of the sericulture process. It regulates significant environments, such as the farm’s temperature and humidity. Before a decade, there were numerous issues with computer vision that were saturating its accuracy. However, the accuracy of these issues significantly increased with the development of deep learning algorithms. Image classification, which is defined as determining the class of the image, was one of the main issues. One such instance of how the photos of cats and dogs are categorized is the cat and dog image classification. In order to achieve high accuracy, this work incorporates cutting-edge object detection techniques. The task of picture classification has been put into a convolutional neural network. These methods were employed in this experiment to categorize the sick and healthy silkworms.

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3 Proposed Approach Identifying an object’s location inside an image and identifying each object of interest within a given image are both tasks involved in object detection. This method in computer vision is utilized in programs for driverless vehicles, security cameras, and photo retrieval. The YOLO family of deep convolutional neural networks (DNN) for object detection is one of the most well-known (You Only Look Once). In order to train a unique object detection model in JavaScript, deploy it in production, and make real-time inferences in the browser using TensorFlow.js, we will create an end-to-end solution using TensorFlow. From processing a raw dataset to installing an effective machine learning model, AutoML is often a platform or open-source library that makes each step in the machine learning process simpler. In conventional machine learning, each stage of the process must be managed independently, and models are created by hand. The study and techniques discussed in this article end up becoming the strategies used to create computer frameworks the most commonly. In this method, framework configuration is used to characterize and create frameworks that satisfy the needs that the client has indicated. To train an object identification model, you provide AutoML Vision Edge with a series of images along with object labels and object borders. AutoML Vision Edge uses this dataset to train a fresh model in the cloud that can be utilized for on-device item detection. The object detection technique, which is used in computer vision and image processing, allows us to locate and identify a variety of objects in an image or video. This technique makes bounding boxes around the object or objects after identifying them in the image or video. These bounding boxes are defined by a point, a width, and a height. The things are then classified using terms like “faulty” and “healthy cocoon.” Convolutional neural networks, which are the foundation of the bulk of neural techniques, are capable of doing end-to-end object detection without specifying attributes (CNN) (Fig. 1). Keep in mind that the VSCode editor will be used to complete the steps. The npm start command must be used to start our react application. This command is sent from the versus code terminal. The command will then launch our react application and open a new browser. By default, it will go to “localhost 3000” automatically. Our React application will begin here. Please make sure the “npm” command is already installed on your computer before executing the “npm start” command. The “npm install” command should add a “module-json” file and related files folder to your list of files in the React Computer Vision Template folder. In the beginning, the sericulture unit’s digital camera will be used to take pictures of the silkworms, which will then be placed in the database. These photos will undergo processing, creating a dataset. Both damaged and unimpaired cocoons should be present in the dataset of silkworm images. The model built using the YOLO technique is trained using the silkworm dataset. We can create bounding boxes to illustrate their locations and classify them after identifying the objects in the camera footage and identifying them. Utilizing the utility.js function will let you achieve this. By using

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Fig. 1 System block diagram

the coordinates’ values, the canvas html element is utilized to draw the bounding boxes around the silkworm cocoon.

4 Design and Implementation The process of defining a framework’s architecture, pieces, components, interfaces, and information to satisfy the required prerequisites is known as system configuration. Using the principle of frameworks to advance objects could be seen as configuration of frameworks. The research and methods described in this article become the approaches most frequently utilized for designing computer frameworks. In this way, characterizing and creating frameworks to satisfy the client’s specified demands is accomplished through framework configuration. Figure 2 displays an input and output diagram where the input images are provided by video and scanners, which are then processed by laptops and presented on computer monitors. The training model, or TensorFlow models and dict files, can be saved in Google Cloud or on other devices. The point of view on the application that was formed during the higher-level arrangement is segregated into modules and tasks during the organized stage. Each program’s justification design is developed, and a while later it is noted as a program

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Fig. 2 Input/output design

point of interest. There is a unit test plan created for each software. The fundamental system architecture uses a video camera to input images, which are then analyzed to determine the health or defect of the silkworm cocoon. The research and methods described in this article become the approaches most frequently utilized for designing computer frameworks. In this approach, characterizing and creating frameworks to satisfy the needs that the client has specified is accomplished through framework configuration. You give AutoML Vision Edge a set of photos with accompanying object labels and object borders in order to train an object detection model. You can use AutoML Vision Edge to train a new model in the cloud for on-device object identification using the dataset it utilizes. System Flow We can find and pinpoint the locations of several things in an image or video using the object detection technique, which is used in computer vision and image processing. After identifying an object in the picture or video, this technique creates bounding boxes around the object or objects. These bounding boxes have a point, width, and height that define them. The items are then given class labels, such as faulty and healthy cocoon. Without defining features specifically, neural approaches can recognize objects from beginning to end. These techniques are often based on convolutional neural networks (CNN). The object detection technique, which is used in computer vision and image processing, allows us to locate and identify a variety of objects in an image or video. This technique makes bounding boxes around the object or objects after identifying them in the image or video. These bounding boxes are defined by a point, a width, and a height. The things are then classified using terms like “faulty” and “healthy cocoon.” Neural techniques may recognize objects from start to finish without describing

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Fig. 3 Dataflow

features directly. These methods frequently utilize convolutional neural networks (CNN) (Fig. 3). Finally, after identifying the items in the webcam footage, we may create bounding boxes to indicate their locations and assign class labels. Utilizing the utility.js function allows you to achieve this. By using the coordinates’ values, the canvas html element is utilized to draw the bounding boxes around the silkworm cocoon. The cocoon is then divided into two categories, healthy and defective, according to its precise location and labeling. Different colors are used for the bounding boxes to distinguish between the healthy and faulty cocoons. Red bounding boxes are utilized for defective cocoons and green bounding boxes for healthy cocoons. Even we are able to distinguish between a healthy and defective cocoon at once.

5 Result and Discussion We evaluated the detector using photos from the test dataset, real-time webcam images, and live video input after training the model to assess performance. The figure demonstrates that, with an accuracy score of 97%, the model correctly identified the instance of the object in the image as either a healthy cocoon or a defective cocoon (Figs. 4 and 5). Test results on several photos revealed that this new categorization method performed more accurately than the competition. Some photographs are evaluated using images from the database and are provided in the displayed result. When the camera is turned on, the live captured image properties have been extracted and compared with the dataset using the trained model created using the YOLO algorithm. After the process, the result is displayed on the output screen. In results analysis, the images shown are real time detected of both defective and non-defective cocoons.

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Fig. 4 Healthy cocoon

Fig. 5 Defective cocoon

6 Conclusion and Future Work In accordance with tradition, sericulture cultivation is expanding throughout India and meeting all expectations. Within a short period of time, sericulture, a feasible activity, established roots throughout the entire nation (Fig. 6). As a rising nation with agriculture as its foundation, issues are highly likely to arise in India. Through our project, we have acquired a new idea that will enhance farmers’ current conditions in order to inspire them to improve their economic situation with all of their efforts. Labor costs can be avoided by using newer technology, which are being investigated in our research, which is advantageous for farmers. A key element in the decline of silk production is the economy. Sericulture, a capital-intensive business, is confronted with significant economic limits and problems. We can achieve a better yield compared to prior cultivation by taking into account these economic aspects like temperature, humidity, and other seasonal changes that have been affecting the growth of sericulture cultivation.

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Fig. 6 Output screen showing classification of cocoon

The system that we have developed detects and segregates defective and healthy cocoons using image processing and YOLO architecture which drastically increases the quality of silk production. All of these techniques help us produce silk that is of a high caliber. Therefore, by carrying out our full project, we can strengthen India’s position in the production of sericulture.

References 1. Ananda Kumar MD, Kumar V, Ananthanarayana SR. Constraints in adoption of novel rearing methods in Bangalore’s rural district: a study. Department of Sericulture, Jnanabharathi Campus, Bangalore, 9–10 Mar, National conference on new strategies in research and development of sericulture—Indian perspective, pp 126–127 2. Anatharaman M (1977) Research on small- and marginal-farmer training needs. Tamil Nadu Agricultural University, Coimbatore, p 185 3. Yadav AK (2008) Yield gaps and restrictions in cocoon production in Karnataka: an econometric analysis. M.Sc. thesis, University of Agricultural Sciences, Dharwad 4. Anitha Kumari P, Kalavathy S (2001) Knowledge and acceptance of suggested actions by coconut producers 5. Anjaneya Gowda DM (1993) A research on the characteristics of major, small, and marginal sericulturists in Kolar district and their adoption behavior. STS dissertation, Central Sericulture Research and Training Institute, Mysore, p 93 6. Vanker PM (2000) Farmers from the scheduled caste in the Khambhat Taluka of Gujarat’s Anand District have been impacted by canal irrigation. G. A. U., Anand, M. Sc. thesis, unpublished 7. Venkararamana P, Rao S, Reddy PS (2002) Impact of integrated sericulture technologies in Telangana region of Andhra Pradesh. Indian Silk 41(4):19–23

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8. Venkatesh Kumar R, Afshan I, Chandra U (1999) Adoption of enhanced sericultural methods among multivoltine seed cocoon producers in Magadi taluk of Bangalore rural (now Ramnagar) district. In: NSTS proceedings, pp 88–92 9. Vijaya Prakash NB, Dandin SB (2005) Yield gap and restrictions in bivoltine cocoon production in Mandya district of Karnataka—an Empirical analysis. Indian J Sericult 44(1):50–54 10. Dandin SB, Vijaya Prakash NB (2005) An economic analysis of the factors influencing the adoption of bivoltine sericulture practices in the Mandya District of Karnataka. Indian J Sericult 44(1):55–58 11. Thiagarajan V (2002) Evaluation of adoption of innovative technology in sericulture with special reference to drought prone areas. Ph.D. thesis, University of Mysore, Mysore, pp 1–189

Model Based on Credit Card Fraud Detection Using Machine Learning T. R. Lakshmi Devi, S. Keerthana, Akshata B. Menasagi, and V. Akshatha

1 Introduction A form of identity theft known as credit card fraud (CCF) occurs when someone other than the account holder uses a credit card or account information for an unauthorized transaction. We recognize different types of credit card fraud; some of them are • • • •

Application frauds Card not present (CNP) Lost and stolen card fraud Card ID theft.

A credit card that has been misplaced, stolen, or fraudulently created may be the cause of fraud. Card-not-present fraud, or the use of your credit card number in e-commerce transactions, has become more prevalent as a result of the rise in online shopping. The growth of e-banking and various online payment environments has led to an increase in fraud, such as CCF, causing billions of dollars in losses annually. CCF detection has emerged as one of the key objectives in this era of digital payments. A cashless society is the way of the future. As a result, conventional payment methods will not be used going forward. Customers will not always come into the store carrying cash. They now prioritize using debit and credit cards. The model should be able to distinguish between fraudulent and non-fraudulent transactions in order to determine whether an incoming transaction is fraudulent or not. Numerous fundamental problems, including the system’s quick reaction time, cost sensitivity, and feature preprocessing, are at play. In the field of machine learning

T. R. Lakshmi Devi · S. Keerthana (B) · A. B. Menasagi · V. Akshatha Department of ECE, Don Bosco Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_62

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Fig. 1 Credit card fraud detection process

(ML), computers make predictions based on patterns in previously collected data (Fig. 1). Machine learning, which focuses on building computer programmes that can access data and use it to learn for themselves, is the ability of a system to automatically learn from experience and improve without being explicitly programmed. Additionally, a classifier is an algorithm that performs classification, especially in concrete implementation. It can also refer to a mathematical operation that an algorithm performs in order to categorize input data. The training set consists of observations that have been correctly identified, making it an example of supervised learning. Predictive modeling is a mathematical technique used to forecast upcoming events or results by examining patterns in a specific set of input data. We must divide our dataset into two sets: training datasets and test datasets, so that we can test the predictive analysis model we built. These datasets should be drawn at random from the population as a whole. The training and test datasets should use similar data. The training dataset typically outweighs the test dataset by a wide margin. We can prevent mistakes by using the test dataset. Test data is run against the trained model to determine its performance. The training model is a dataset consists of sample output data. The stream processing is computing on data directly as it is produced or received. The deployed model integrates it into an existing production of 1 or 0 where it can take in an input and return an output. So prediction is where we get the output whether its fraudulent or non-fraudulent; zero (0) represents it means non-fraudulent and one (1) represents it means fraudulent.

1.1 Problem Statement For international financial institutions, credit card fraud continues to be a major issue with annual losses in the billions of dollars. Modeling of previous credit card transactions while knowing they were fraudulent is a problem in the detection of credit card fraud. Then, a new transaction is evaluated using this model to determine whether it is fraudulent or not. Our goal is to determine whether a new transaction

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is fraudulent or not, and to catch every fraudulent transaction while reducing the number of false positives for fraud.

2 Methodology There are numerous techniques for detecting credit card fraud; here we highlight some of the most effective ones. Credit card fraud detection methods • • • • • • •

Logistic regression Random forest Isolation forest Local outlier factor Support vector machine Multilayer perception K-nearest neighbor.

Building the Classifier 1. Logistic regression Logistic regression is a basically a major classification algorithm, and it is almost indistinguishable to linear regression. Logistic3 regression is more progressive and innovational than linear regression, because linear regression is limited to dataset that are distributed in the given space but not for widely distributed space for given data space. Logistic regression is nothing but classifying the data where linear regression is to predict or identify the data. Here where we can observe that y lies between 0 and 1, as we coded in Fig. 1 where we have taken data class = 0 for fraud detection and data class = 1 for nonfraud detection. In Fig. 2, graph has s-curve indicated in red color, having threshold value at 0.5 (y-axis). Above graph is plotted is based on the values we got in Fig. 2. This type of technique is very relevant and favorable regression analysis to perform when we have binary dependent (Fig. 3). Values like the other regression analysis. We used logistic regression because this algorithm will describe the data and also determine the difference between dependent binary variable and independent binary variable that can be a nominal or ordinal interval. Fig. 2 Code

In [9]: ## Get Fraud and the normal datasetFraud = data[data[‘Class’] ==1] Normal = data[data[‘Class’]==0] In [10]: print(fraud,shape,normal,shape) (492, 31) (284315, 31)

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Fig. 3 Fraud detection curve

The mathematical equation for straight line can be written as: y = m 0 + (m 1 ∗ x1 ) + (a2 ∗ x2 ) + · · · + (m k ∗ xk ) where y lies between 0 and 1 as shown in Fig. 1, let us divide the equation by (1 − y): y1 − y0 for y = 0 and ∞ for y = 1. log[y1 −y] = y = m 0 + (m 1 ∗ x1 ) + (a2 ∗ x2 ) + · · · + (m k ∗ xk )

(1)

Equation 1 represents the result for logistic regression algorithm. Testing the Classifier Here we used cross-validation technique in order to divide the database into different parts having different testing dataset. Each classifier is tested by using testing dataset which allow us to use the whole database for training the classifier and also for testing. Accuracy for logistic regression is obtained by calculating the accuracy of each classifier. Evaluating the Classifier Confusion matrix is basically a (N × N) matrix, which is used to determine the performance of 4 classification model. The confusion matrix is formed based on following terms: True positive: The positive classes (records) which we have predicted that have positive records, which are corrected by the classifier. True negative: The positive classes (records) which we have predicted that have negative records, which are corrected by the classifier. False positive: The negative classes (records) which we have predicted that have positive records, which are corrected by the classifier. False negative: The negative classes (records) which we have predicted that have negative records, which are corrected by the classifier.

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TP + FN = P FP + TN = N where TN: True negative, FN: False negative, FP: False positive, TN: True positive. Accuracy = (TP + TN)/number of all records in the testing set. Sensitivity = TP/P Error rate = 1 − accuracy where accuracy means based on dependent binary variables or independent binary variables in a dataset are measured, used to determine the best outcome. Sensitivity determines the true values of each available category. Error rate is used to determine the number of all false prediction divided by the total number of the datasets taken. 2. Random forest A supervised learning algorithm is random forest. Using this random forest algorithm technique, regression as well as classification can be carried out. Decision trees on datasets or the data samples we took make up the random forest, which consists of trees. In this case, an ensemble method is preferable to a single decision tree method (Fig. 4). Here, we used scikit learn as (s k learn) library to get various features like classification, regression. In this problem, dependent variable is categorical. Where the classes are classified as x train and y train having random classified score as x test and y test. We get random score 0.91878172. 3. Isolation forest By randomly choosing a split value between the maximum and minimum values of the designated features, the isolation forest observation “isolates” a feature (Fig. 5). Here, we used classifier library in order to get estimator, maximum samples, and contamination to obtain accuracy which randomly selects features and selecting sampled values that are split as maximum and minimum values of selected features.

In [52]: from sklearn.ensenble import RandomForestClassifier rand_clf = RandomForestClassifier(n_estimators=1000,random_state =35) rand_clf.fit(X_train, Y_train) ranf_score = rand_clf.score(X_test, Y_test) ranf_score Fig. 4 Code

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In [33]: ##Define the outlier detection methods Classifiers = { “Isolation Forest”:IsolationForest(n_estimators=100, max_samples= len(X),Contamination=outlier_fraction,verdose=0), Fig. 5 Code Fig. 6 Concept of outlier forest

Output which we get by using this algorithm determines the accuracy and even precision while testing the values. 4. Local outlier forest This algorithm for detecting outliers is unsupervised. The local out layer factor uses the anomalies code of each sample and quantifies the sample data’s local deviation from its immediate neighbors, with the k closest neighbors providing the most accurate locality from which the local data is estimated (Fig. 6). On the distance and score we can easily identify that D is outside the boundary and the score is larger. So it is the local outlier factor for this figure. Here, local outlier factor determines the neighbors, algorithm, leaf size, metric, metric parameters, contamination factor to obtain accuracy, precision, f 1-score, macroaverage, and weighted average. 5. Support Vector Machine “Support vector machine” is also another effective technique which detects the anomalies. A support vector machine is a supervised learning and unsupervised learning based on this extension or even a class for instances values, which can be used to identify the anomalies based on the above two different learnings. Where the classification techniques which classify or detect the dataset or patterns into two classes as fraud or legitimate. This technique is known as binary classification. Pattern recognition includes face recognition, idiomatic expression, and text categorization. XG Boost is successfully implemented by using SVM as a popular algorithm (Fig. 7). Support vector machine determines the one class support vector machine which includes kernel, degree, gamma, nu, maximum iteration to obtain accuracy, precision, f 1-score, macroaverage, and weighted average. 6. K-nearest neighbor

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“Support Vector Machine”:OneClassSVM(kerne1=’rbf’, degree=3,gama=0.1,nu=0.05,max_iter=-1,) Fig. 7 Code

Fig. 8 Heat map

K-NN is a simple, non-parametric technique for categorizing data based on equivalences in distance metrics like Euclidian, Manhattan, Minkowski, and Orhamming distance. One of the most fundamental supervised learning-based machine learning algorithms is K-nearest neighbor. The K-NN algorithm, assuming that the new case and the available cases are comparable, assigns the new case to the category that most closely resembles the availability categories (Fig. 8). As you can see in Fig. 9, it would be interesting to learn if there is any correlation between our predictor and the class variable in particular. Using a heat map is one of the most effective ways to do that visually. You can see that some of our predictors do appear to have a correlation with the class variable. K-nearest neighbor determines the classifier as x train y train as KNN-fit, and x test is considered as a KNN predict by y pred. And it has confusion matrix of y test and y prediction in order to get accuracy score. 9. Multilayer Perception The input layers, which represent the available data, in this case the multispectral image and values, are divided into three parts as shown in Fig. 10. The output layer will contain the bathymetric data, and finally the hidden layer shows how the network training process works, and I will provide a fictitious example with four input layers, five hidden layers, and one output layer.

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In [69]: knn=KNeighborsClassifier(n_neighbors=2) knn.fit(X_train,Y_train) y_pred=knn.predict(X_test) cm=confusion_matrix(Y_test,y_pred) acc=accuracy_score(Y_test,y_pred) score=knn.score(X_test,Y_test) print(“Basic KNN Accuracy: % {}”.format(acc)) print(:Score:”,score) print(“CM:”,cm) Basic KNN Accuracy: % 0.9994382219725431Score: 0.9994382219725431 CM: [[56863 1] [31 67]] Fig. 9 Code

Fig. 10 Hypothetical example of multiplayer perception

2.1 Data Preparation Our time and number features have not been scaled, despite the anonymized features appearing to be centered around zero. We must keep this in mind before continuing with our analysis. If they were not scaled as well, certain machine learning algorithms that depend on a distance metric or assign weights to features (like logistic regression) would perform much worse. To solve this issue, I standardized the time and money columns. Fortunately, there are no missing values, so there is no need to worry about missing value imputation. A training set for a highly imbalanced dataset is being designed. The difficult part that comes next is making a training dataset that will allow our algorithms to recognize the precise characteristics that make a transaction more or less likely to be fraudulent. Given that over 99% of our transactions are legitimate, using the original dataset would be inappropriate. This is the exact opposite of what we want, despite the fact that it would produce an accuracy rate greater than 99%. We want to not only achieve 99% accuracy by never classifying a transaction as fraudulent, but we

Model Based on Credit Card Fraud Detection Using Machine Learning Table 1 Comparison of accuracy of various algorithms

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Logistics regression

99.86

2

Random forest

91.87

3

Isolation forest

99.73

4

Local outlier factor

99.64

5

Support vector machine

70.14

6

Multilayer perception

73.46

7

K-nearest neighbor

99.7

also want to identify and label fraudulent transactions. We must focus on two key issues in order to resolve this. To achieve high performance, we first use random undersampling to produce a training dataset with a balanced class distribution that will compile the algorithms to identify fraudulent transactions as such. We will not rely on accuracy to deliver performance.

3 Result Output As a result, we determined the accuracy of each algorithm. By comparing these values, it is predicted that one can accurately identify fraud that occurred during a transaction by taking into account the true positive, false positive, true negative, and false negatives (Table 1). Based on the accuracy, we obtain by comparing the values based on normal fraud transactions and fraud transactions, and we have used seven algorithms in this case to detect fraud transactions. The accuracy of the k-nearest neighbor algorithm is 99.7%, while the accuracy of the isolation forest algorithm is 99.73%, the accuracy of the local outlier factor is 99.64%, the accuracy of the support vector machine is 70.14%, the accuracy of multilayer perception is 73.46%, and the accuracy of the random forest algorithm is 91.87%. The data is inaccurate and has missing values, noisy data, and the issues mentioned above. A logistics regression affects the system’s accuracy rate as a solution to these issues. A classifier based on logistic regression is suggested as a solution to these issues. Logistic regression is made up of well-known models.

4 Conclusion and Future Enhancement Where credit card fraud detection is basically normal issues in present digital world though precaution is taken 100% accuracy cannot achieved. However, above algorithms are determined using all dataset which we have taken. Even fraud transaction

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is detected by plotting graphs, pie charts, etc. In this paper, we have implemented many algorithms to predict, read, and write the values to get correct accuracy. 99.65% accuracy reached using above methodology, where 28% remains as precision when it considered the tenth of the dataset. Entire dataset is taken while determining the above all algorithm, so then the precision rises to 33%. We are expecting high accuracy even though we had large imbalance between fraud transaction and normal transaction. By adding some more algorithms, this project can be done better in future. Even having high knowledge, having great hold on Python language and knowing usage of library while coding these techniques can made code even better to get high and perfect accuracy to identify the fraud transaction with respect to time.

References 1. V. Deepa, R. Dhanapal at research and development center, which was published on July 2012 and they explained about SVM 2. Masoumeh Zaria Poor at Jamia University, which was published on 2015, they explained about k-nearest neighbour algorithm 3. Sonal Mehandiratta, Mtech Scholar at Gurunanak Institute of Technology Mullana, Ambala, which was published on 8th august 2019 and they explained KNN & Navies Bayes 4. Vaishnavi Nath Dornadula at Vellore Institute of Technology Chennai, which was published on 2019, they explained local outlier factor, isolation forest, logistic regression 5. Tam Jothi das at Amity University Kolkata, which was published on November 2019 and they explained Isolation forest algorithm, EdA, SVM 6. Filip K. Chaan at Florida Institute of Technology, which was published on 2019, they explained multilayer perception 7. Papers of Suman and GJUS&T, where they used the technique of supervised and unsupervised learning 8. Papers of Rohilla where they used the classical methods for data classification 9. https://www.kaggle.com/mlg-ulb/creditcardfraud 10. https://www.kaggle.com/mingyi1202/eda-over-under-sampling-and-modeling-practice 11. https://www.kaggle.com/basel99/best-k-nearest-neighbors-parameters 12. https://www.kaggle.com/braindeadcoder/achieving-100-accuracy-precision-and-recall 13. https://www.kaggle.com/justingodden/credit-card-fraud-detection 14. https://www.kaggle.com/braindeadcoder/achieving-100-accuracy-precision-and-recall 15. https://github.com/krishnaik06/CreditCardFraudlent/blob/master/Anamoly%20Detection. ipynb 16. https://www.kaggle.com/dataengel/credit-card-fraud-detection-with-ml-and-dp

Implementation of Smart Mobile Health Application for COVID-19 Pandemic M. J. Akshath, M. B. Sudha, Y. S. Sahana, S. Spurthi, and S. Sahana

1 Introduction The COVID-19 epidemic is currently one of the biggest worldwide problems facing health agencies. As of 2021, there have been a total of 24.6 million confirmed SARSCOV-2 cases in India and more than 1.35 million coronavirus fatalities, demonstrating that the number of COVID-19 infections is rising globally. IoT technology has emerged as a crucial invention with multiple uses. Remote patient condition monitoring is a critical issue that has to be addressed [1]. Systems that enable doctors or other medical professionals to consult, diagnose, and treat patients remotely are referred to as remote health management systems. The main goal of continuous health monitoring is to deliver prompt medical treatment using telecommunications technology. With the number of older residents expanding, remote healthcare has become a crucial service. Being able to seamlessly connect individuals, medical devices, and providers of social and medical services is necessary for health monitoring, rehabilitation, and supported living for the elderly and medically challenged. This highlights the need for wearable, low-cost, dependable technology that will enhance the lives of many elderly and physically challenged persons. To provide the aforementioned healthcare services, the Internet of things (IoT) platform offers a promising technology [2], and it may further enhance the systems for providing medical services. IoT platforms may be used to gather the necessary data about the user and its surrounding environment, transmit it wirelessly, and then analyse or store it so that the user’s history can be tracked. When connected to devices and services, it will be possible to take preventive action (such as when a heart attack is anticipated) or offer emergency treatment. The Internet of things has a significant impact on health care, which makes it easier for patients and physicians to interact. Homecare is offered in place of the pricy. This provides clinical care and prevention M. J. Akshath (B) · M. B. Sudha · Y. S. Sahana · S. Spurthi · S. Sahana Don Bosco Institute of Technology, Bengaluru, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_63

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a competent healthcare system [3]. This assistance is needed. It produces outcomes that are more favourable.

2 Proposed System A health monitoring system is made up of multiple sensors that are attached to a patient and transfer data through a controller unit. A Wi-Fi connection is required to run an IoT-based health monitoring system project. A NodeMCU ESP8266 is used to link the microcontroller or Arduino board to the Wi-Fi network. This project will not be possible without a functional Wi-Fi network. NodeMCU ESP8266 is used to establish a Wi-Fi zone, or mobile hotspot can be utilised to create a Wi-Fi zone. The sensors provide constant input to the Arduino UNO board and collects the information from the patient and transformed into signals. The IoT based monitoring of health index of people will monitor and facilitate to take care of their health. Therefore, medical facilities can be provided in proper time. Pressure sensor, heartbeat sensor, temperature sensor, blood oxygen sensor and gyro sensor are interfaced with controller in order to detect and analyse the body parameters. The temperature and blood pressure of the patient is sensed by temperature sensor and digital BP sensor. Gyro sensor is used to detect body movement of the patient. If the values surpass a predetermined normal level, a warning is given through buzzer. Blood oxygen sensor detects the amount of oxygen in the blood and oxygen saturation, respectively. When the sensor detects abnormal level of oxygen in blood [4], the alarm gives warning. The heartbeat sensor detects the heartbeat rate; when the heartbeat crosses the threshold level, the system gives alarm [5]. Cough monitoring is implemented with the use of sound sensor [6]. When the sensor detects continuous coughing sound from patient, alert tone is produced. Furthermore, the monitored parameters are updated to the IoT which uses LCD for displaying the parameters with the help of ATMEGA328P controller.

3 Implementation and Results The implementation for health monitoring system is shown in Fig. 1. Design of a patient health monitoring system (PHMS) consists of heartbeat detection system, a fall detection system, temperature detection system, SPO2 detection system [7] along with monitoring blood pressure and cough. A doctor or health specialist can utilise the device to remotely monitor the patient’s or person of interest’s vital health metrics. An attempt to create a remote healthcare system using locally available components.

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SERV ER

TEMPERATURE SENSOR

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DIGITAL BP SENSOR

LCD ATMEGA328P

GYRO SENSOR

MICROCONTROLLER

BUZZER

HEARTBEAT SENSOR COUGH MONITOR BLOOD OXYGEN SENSOR

Fig. 1 Block diagram

(i) An accelerometer, wireless transmitter, and microcontroller are the components required for monitoring fall detector, temperature, and SPO2 modules. A receiver module receives the collected data wirelessly. (ii) SPO2 is developed for detecting heart signals, a liquid crystal display (LCD), and a non-invasive infrared finger detector. The analogue signal’s output is transformed into digital form, and the outcome is displayed on the LCD. (iii) A cloud server is maintained, so that the crucial information can be accessed remotely whenever required [8]. The model of implementation of smart mobile health application for COVID-19 pandemic is shown in Fig. 2. All the sensor readings are displayed in LCD as well as in Blynk IoT application. Temperature is mentioned as T, and the temperature is measured in degree Celsius. Fall detection is mentioned by G: NP or G: FP. Blood pressure is measured and displayed as P: Values. Oxygen level measured by BO and heart rate as H. Cough is

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Fig. 2 Kit implementation for an IoT-based health monitoring

also monitored and displayed by C and updated in Blynk application, respectively (Figs. 3, 4, 5, 6 and 7).

Fig. 3 Measurement of blood pressure

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Fig. 5 Monitoring the cough

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4 Conclusion A smart mobile health system for health applications is proposed. It represents an example for smart hospital management approach to transfer medical data of the patient to the hospital based on priority of the patient health status and to monitor the patient health regularly. In this project, the patient health can be monitored continuously to avoid emergency situation. The system can also be designed to efficiently track the location of the patients and elderly to provide timely medical services in an emergency situation. The advancement of integrated sensor technology has allowed many physiological parameters to be monitored accurately and measured remotely which are clinically useful in tracking disease progression in viral diseases. The proposed system can be used to identify a person under home quarantine who needs a higher level of treatment or a community where an emerging outbreak could be imminent and requires early intervention.

References 1. AASM Task Force (1999) Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep 22(5):667–689 2. Young T et al (2002) Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 165(9):1217–1239 3. Bradley TD, Floras JS (2009) Obstructive sleep apnoea and its cardiovascular consequences. Lancet 373(9657):82–93 4. Berry RB et al (2012) Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. J Clin Sleep Med 8(5):597–619 5. Marin JM et al (2005) Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 365(9464):1046–1053 6. Iber C et al (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Am Acad Sleep Med 7. Peppard PE et al (2013) Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 177(9):1006–1014 8. Verbraecken J (2013) Applications of evolving technologies in sleep medicine. Breathe 9(6):442–455 9. Varon C et al (2015) A novel algorithm for the automatic detection of sleep apnea from singlelead ECG. IEEE Trans Biomed Eng 62(9):2269–2278 10. De Chazal P et al (2003) Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans Biomed Eng 50(6):686–696

IoT-Based Weather Monitoring System M. J. Akshath, L. Amrutha, and Praveen S. Yarali

1 Introduction The impact of climate on human existence is significant, especially in light of the enormous expansion of industry, agriculture, and vehicle traffic. The building industry has a significant opportunity for energy savings, but in order to improve the calibration of energy simulation systems, precise weather data in the precise site where the structure is being constructed is required. The errors in the location-specific weather forecasting system can be reduced by developing a regulating local weather reporting system using IoT. It is possible to describe precision agriculture and farming as the art and science of utilising technology to increase agricultural yield. Using various technologies, a weather station gathers information on the environmental changes and the weather. Climate has a huge effect on human existence, particularly in light of the massive growth of industry, agriculture, and automobile traffic. The local environment is not accurately reflected by the most recent weather information a satellite provides.

2 Literature Survey IoT-based weather monitoring and reporting system model is described. You can gather, process, analyse, and show your measured data on a Web server using this system model. Devices, routers, gateway nodes, and management monitoring centres make up the wireless sensor network management paradigm. End device is in charge of a weather station collects data on the environment and the weather using a variety of technologies. M. J. Akshath (B) · L. Amrutha · P. S. Yarali Don Bosco Institute of Technology, Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 V. K. Gunjan et al. (eds.), Computational Intelligence in Machine Learning, Lecture Notes in Electrical Engineering 1106, https://doi.org/10.1007/978-981-99-7954-7_64

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Many pollution monitoring methods used today are created using various environmental factors. An existing system model for IoT-based weather monitoring with a NodeMCU reporting system is described. With this system, you may gather data, process it for analysis, and show your measured data on a Web server. Devices, routers, gateway nodes, and management monitoring centres make up the wireless sensor network management paradigm. End device is in charge of a weather station collects data on the environment and the weather using a variety of technologies.

3 Working Create all systems according to the circuit diagram. Utilising the Arduino IDE, programme the NodeMCU. You will receive verification once on your screen a programmable controller called the NodeMCU includes a Wi-Fi module built in. Connected are three sensors, namely BMP180 to NodeMCU, followed by DHT11 and the rain sensor. By which we can gather the necessary weather data using these three sensors data used for monitoring. The streamed data from this pool is the Internet, which allows for remote viewing and reading. When hardware is correctly programmed, the NodeMCU receives one IP address. From any Web browser, we can access this IP address such as Chrome, Firefox, Internet Explorer, and others, so display the necessary real-time data that was collected by sensors in a stunning graphical user interface. According to the circuit diagram, create all systems. Programming the NodeMCU requires the Arduino IDE. You will get confirmation on your screen once a Wi-Fi module is integrated inside the NodeMCU, a programmable controller. Three sensors are linked like BMP180 to NodeMCU, then the rain sensor and DHT11. By these three sensors provide us with the monitoring data, we need to compile the relevant weather information. The Internet, which enables distant viewing and reading, is the source of the streamed material from this pool. The NodeMCU obtains a single IP address when the hardware is properly programmed. We may access this IP address from any Web browser, including Chrome, Firefox, Internet Explorer, and others, allowing us to present the essential real-time data that was gathered by sensors in a convenient manner.

4 Design Methodology Hardware • • • •

ESP8266 NODEMCU Humidity and temperature sensor (DHT11) Sensor for pressure (BMP 180) Sensor for rain (YL83).

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Fig. 1 Block diagram

Software Arduino IDE and Things can speak (Fig. 1). There are presently various high-end techniques available for continuous weather surveillance. However, these systems are being designed to track the global weather in real time for a specific location. The differences in the environment and climate, such as temperature, humidity, pressure, and rain level, this weather monitoring system uses three sensors. The Web site receives the values after they are delivered there and marks them in graphic form. Online access to uploaded data from the newly launched system is available to everyone in the world. By analysing the current weather and sensor value data, the system will be able to present the current weather conditions. An ESP8266 microcontroller will control all of the data, while NodeMCU will serve as the client to receive sensor data from the ESP8266. The ThingSpeak, which was made to make it easier for users to check online platform, will have this system. The data will be analysed and compared with Jabatan Meteorologi Malaysia to confirm its accuracy and the state of the weather at the time it is gathered. Without the need for human verification, the Internet of things (IoT) will wirelessly and instantly connect the system with the user. Various highend methods are currently available for ongoing weather surveillance. These systems, however, are intended to track the local weather globally in real time. Everyone in the world has access to the newly launched system’s uploaded data online. The system will be able to provide the current weather conditions by analysing the sensor value data and the current weather data. All data will be controlled by an ESP8266 microcontroller, and NodeMCU will act as the client to receive it.

5 Implementation and Result First, a control unit system circuit was created that allows the ESP8366 microcontroller to control every weather parameter sensor, including the rain sensor, and BMP280, DHT11 (temperature, humidity, and pressure) sensor. After that, it was powered by a USB cable and used to upload a coding sketch to the ESP8266microcontroller.

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The Arduino IDE software’s serial monitor can be used to view sensor data. In order to establish the Web server that will display all of the sensor data, the ESP8266 will connect to the Wi-Fi hotspot that has been added to the system. Data from the weather station will be displayed on OLED to show how the weather station and sensor stations may communicate utilising a Wi-Fi hotspot. The channels of communication are successfully opened. The ESP8366 microcontroller can now control any weather parameter sensor, including the rain sensor, BMP280, and DHT11 (temperature, humidity, and pressure) sensor, thanks to the creation of a control unit system circuit. It was then used to upload a programming sketch to the ESP8266 microcontroller while being powered by a USB connection. The serial monitor of the Arduino IDE software can be used to display sensor data. The ESP8266 will connect to the Wi-Fi hotspot that has been added to the system in order to set up the Web server that will display all of the sensor data. To demonstrate how the weather station and sensor stations may communicate using a Wi-Fi hotspot, weather station data will be shown on an OLED display. The communication lines have been successfully opened (Fig. 2).

Fig. 2 Results

IoT-Based Weather Monitoring System

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6 Conclusion and Future Scope In order to achieve this, sensor devices must be placed, and it will capture real-time data by placing sensor devices in the surrounding area. Through the network, it can function together with other items. The user will then have access to the gathered data and the analysis’ findings over Wi-Fi. This study presents various concepts for an efficient, low-cost entrenched system and a smart way to monitor the environment. A weather station is a type of technological application that uses science to monitor and forecast the weather at a specific location. Sensor devices must be installed in the environment for data gathering and processing in order to accomplish this. By deploying sensor devices in the neighbourhood, it will gather data in real time. It can interact with other objects via the network. The obtained data and the analysis’ results will subsequently be accessible to the user over Wi-Fi. This study offers some ideas for an economical entrenched system as well as a clever technique to keep track of the environment. An example of a technical application that makes use of science to track and forecast the weather is a weather station. To do this, sensor devices must be in the environment for data collection, analysing, and processing. It will gather data in real time by placing sensor devices throughout the neighbourhood. Through the network, it can communicate with other things. The user will then have Wi-Fi access to the collected data and the analysis’ findings. This paper provides some suggestions for a financially sound embedded system as well as an innovative way to monitor the environment.

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