Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020 (Algorithms for Intelligent Systems) 9813366907, 9789813366909

Table of contents :
Preface
Contents
About the Editors
IOT Smart Locker
1 Introduction
2 Literature Review
3 Workflow of the System
3.1 To Open This Smart Locker, the User Has to Go Under These Steps to Access the Locker
3.2 Tool Kit
3.3 Proposed Model
4 Result from Observation
5 Conclusion
References
Brief Analysis on Human Activity Recognition
1 Introduction
1.1 Human Activity Recognition (HAR)
2 Related Work
3 Deep Neural Network (DNN)
4 System Design
5 Result
6 Conclusion
References
Lora-Based Smart Garbbage Alert Monitoring System Using ATMEGA 328, 2560, 128
1 Introduction
2 Literature Review
3 Existing System
4 Proposed System
5 Working
6 Cloud Database
7 Simulated Output of the System on the Cloud
8 Components Required
9 IR Sensor
10 LoRaWAN
11 Principle of Operation
12 Conclusion
References
Pre-birth Prognostication of Education and Learning of a Fetus While in the Uterus of the Mother Using Machine Learning
1 Introduction
2 Why Subsidizing in Early Brain Development?
2.1 Literature Survey
2.2 The Three Key Aspects What Baby Learns
2.3 How Early Staged Career Guidance Can Shape Future
3 How the Future Learning Can Be Predicted
3.1 Applying Decision Tree to Predict Educational Stream
3.2 Merits and Demerits of This Predication
4 Conclusion
References
Performance Analysis of Single-Stage PV Connected Three-Phase Grid System Under Steady State and Dynamic Conditions
1 Introduction
2 Photovoltaic System
2.1 PV System
2.2 Power Electronic Converter
2.3 MPPT Method
3 Simulation Result Analysis
4 Conclusions
References
Delay Feedback H_infty Control for Neutral Stochastic Fuzzy Systems with Time Delays
1 Introduction
2 Problem Description
3 Main Results
4 Numerical Example
5 Conclusion
References
Modeling Crosstalk of Tau and ROS Implicated in Parkinson’s Disease Using Biochemical Systems Theory
1 Introduction
2 Methods
2.1 Generalized Mass Action Kinetics
2.2 Michaelis–Menten's Kinetics
2.3 Hill Equation
3 Results
3.1 Increased Concentration Levels of ROS, Alpha Synuclein Aggregation and Oxidized DJ-1 in Diseased Conditions
3.2 Mitochondrial Dysfunction Leads to NFT Production in Diseased Conditions
4 Discussion
5 Conclusion
References
IoT-Based Patient Vital Measuring System
1 Introduction
2 Literature Survey
3 Hardware and Software Platform
4 Result and Performance Analysis
5 Security Compliance
6 Conclusion and Future Work
References
IoT-Enabled Logistics for E-waste Management and Sustainability
1 Introduction
2 Literature Review
3 E-waste Management Network
4 Conclusion
References
Navigation Through Proxy Measurement of Location by Surface Detection
1 Introduction
2 Related Works
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Feature Extraction
3.4 Model Selection
3.5 Performance Evaluation
4 Conclusion and Future Enhancements
References
Unsupervised Learning Algorithms for Hydropower’s Sensor Data
1 Introduction
2 Literature Review
3 Methodology
3.1 One Class Support Vector Machine
3.2 Isolation Forest
4 Conclusion
References
Feature Construction Through Inductive Transfer Learning in Computer Vision
1 Introduction
2 Deep Learning
3 Transfer Learning
4 Target Dataset—Place Dataset
5 Overall Approach
5.1 Data Pre-processing
5.2 Model Transfer
5.3 Training the Datasets
5.4 Figures and Tables
6 Result
7 Conclusion and Future Work
References
Decoding Motor Behavior Biosignatures of Arm Movement Tasks Using Electroencephalography
1 Introduction
2 Methods
2.1 Subject Summary and Data Collection
2.2 Design of Experiment and Execution Steps
2.3 Design of Experiment and Execution Steps
3 Results
3.1 Increased Θ and γ Activity in Frontal and Temporal Regions in Motor Planning Phase of Cognitive Tasks
3.2 Behavioral Variations in Θ and γ Rhythms in Frontal, Temporal and Parietal Lobes in Motor Execution Phase of Cognitive Motor Performances
3.3 Activity Related Increase of Theta and Gamma Waves in Frontal and Temporal Regions in Linear and Complex Motor Actions
4 Discussion
5 Conclusion
References
Emergency Robot
1 Introduction
1.1 Literature Survey
2 Description of Vehicle Section
2.1 Gas detector
2.2 Metal Detector
2.3 Web Camera
2.4 Gun Control
3 System Implementation
3.1 Work Flow of the Model
3.2 Troubleshooting
4 Proposed System
4.1 Controlling Section
5 Hardware Implementation
5.1 Circuit Diagram
6 Result
7 Conclusion
8 Future Scope
References
Type Inference in Java: Characteristics and Limitations
1 Introduction
2 Type Inference in Java
2.1 Java 5
2.2 Java 7
2.3 Java 8
2.4 Java 10
3 Limitations
3.1 Wildcard as Return Type
3.2 Chained Method Call
3.3 Local Variable Inference
4 Discussion
5 Conclusion
References
Detection and Correction of Potholes Using Machine Learning
1 Introduction
2 Literature Survey
3 System Architecture
4 Support Vector Machine
4.1 Working of SVM
4.2 Working of Pothole Detection Robot
5 Results and Discussions
5.1 Normal Support Vector Classifier (SVC) Where no Features Are Used Gives 62.5% Accuracy.
5.2 SVC with Corner Detection Gives 62.5% Accuracy.
5.3 SVC with Canny Edge Detection is 75% Accuracy.
5.4 SVC with Canny Edge Detection and Corner Detection 68.75% Accuracy.
6 Future Scope
7 Conclusion
References
Detecting COVID-19 Using Convolution Neural Networks
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Data Preprocessing
3.2 Implementation
4 Results and Discussions
5 Conclusion
References
Electroencephalography Measurements and Analysis of Cortical Activations Among Musicians and Non-musicians for Happy and Sad Indian Classical Music
1 Introduction
2 Methods
2.1 Subject Selection and Screening
2.2 Auditory Stimuli
2.3 Experiment Design
2.4 EEG Data Acquisition and Computational Analysis
3 Results
3.1 Brain Rhythms Shifts from Gamma-Alpha in Happy Music Perception and Alpha–Beta-Gamma for Other Auditory Cues in Musicians and Non-musicians
3.2 Behavioural Pattern Variations of Brain Rhythms for Sad Music Stimuli and Other Auditory Cues in Musicians and Non-musicians
4 Discussion
5 Conclusion
References
Signal Processing in Yoga-Related Neural Circuits and Implications of Stretching and Sitting Asana on Brain Function
1 Introduction
2 Methods
2.1 Experimental Protocol and Characteristics
3 Results
3.1 Increased Changes Attributed to Memory and Attention Observed in Practitioners
3.2 Statistical Analysis on the Task Scores Shows Homogenous Performance Between the Genders
3.3 Statistical Analysis on the Task Scores Shows There is Significant Difference Between the Groups
4 Discussion
5 Conclusion
References
Automation of Answer Scripts Evaluation-A Review
1 Introduction
2 Related Work
3 Challenges
4 Conclusion
References
Diabetes Mellitus Detection and Diagnosis Using AI Classifier
1 Introduction
2 Proposed Methodology
3 Results and Discussion
4 Conclusion
References
Review on Unit Selection-Based Concatenation Approach in Text to Speech Synthesis System
1 Introduction
2 The Process of Speech Production
2.1 Basic TTS System
3 Different Synthesizer Technologies
3.1 Concatenative Synthesis
3.2 Unit Selection Synthesis
3.3 Unit Selection and Specification Process
4 Comparison of Different Synthesis Methods
5 Conclusion
References
Enhancing the Security of Confidential Data Using Video Steganography
1 Introduction
2 Steganography and Cryptography
2.1 Steganography
2.2 Cryptography
3 Related Work
4 Frame Selection Approach
5 Methodology
6 Parameter Metrices
7 Results and Discussion
8 Conclusion
References
Data Mining and Analysis of Reddit User Data
1 Introduction
2 Related Works
3 Research Methodology
4 Result and Discussion
5 Conclusion
6 Future Scope
References
Analysis of DNA Sequence Pattern Matching: A Brief Survey
1 Introduction
2 Literature Review
3 Conclusion
References
Sensor-Based Analysis of Gait and Assessment of Impacts of Backpack Load on Walking
1 Introduction
2 Methods
2.1 Effect of Backpack on Lower Body
3 Results
3.1 Measured Gait Variability Across Different Subject Groups Using Froude Analysis
3.2 Attribute Selection to Classify Gait
3.3 Gender-Based Classification with Respect to Fr Number.
3.4 Effect of Backpack Load on Pelvis during Walking to Understand the Gait Pathological Condition
4 Discussion
5 Conclusion
References
Wireless Battery Monitoring System for Electric Vehicle
1 Introduction
2 Literature Survey
3 Methodology
4 System Block Diagram
4.1 Sensors
4.2 Wi-Fi Module
5 System Flow Chart
5.1 Electric Vehicle Battery Specification
6 Result
7 Software Preparation
7.1 Arduino IDE
7.2 AVR Studio
8 Conclusion and Future Scope
References
Iris Recognition Using Selective Feature Set in Frequency Domain Using Deep Learning Perspective: FrDIrisNet
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Image Augmentation
3.2 Image Pre-Processing
3.3 Robust Region Determination
3.4 Transformation Layer
3.5 FrDIrisNet: Convolutional Neural Network Architecture
4 Experiments
4.1 Experimental Results
5 Conclusion
References
Enhancement of Mammogram Images Using CLAHE and Bilateral Filter Approaches
1 Introduction
2 Literature Review
3 Proposed Methodology
4 Result and Discussion
4.1 Enhancement Techniques for Spatial Domain
4.2 Enhancement Techniques for Frequency Domain
4.3 Combination of CLAHE and Bilateral Filter
References
Supervised Cross-Database Transfer Learning-Based Facial Expression Recognition
1 Introduction
2 Related Work
3 Proposed Work
4 Datasets
4.1 FER2013 Dataset
4.2 ImageNet and VGG Dataset
5 Results and Discussions
6 Conclusion
References
Innovative Approach for Prediction of Cancer Disease by Improving Conventional Machine Learning Classifier
1 Introduction
2 Machine Learning Techniques
2.1 Supervised Learning
2.2 Unsupervised Learning
3 Literature Review
4 Machine Learning Classifiers
5 Dataset Description
6 Proposed Work
7 Conclusion and Future Work
References
Influence of AI on Detection of COVID-19
1 Introduction
1.1 Model Used for Prediction
2 Conclusions
References
Study of Medicine Dispensing Machine and Health Monitoring Devices
1 Introduction
2 Literature Review
3 Methodology
4 Conclusion
References
Building Image Classification Using CNN
1 Introduction
2 Methodology
2.1 Definition and Working Principle
2.2 Definition and Working Principle
2.3 Sub-Sampling or Pooling Layer
2.4 Fully Connected Layer
2.5 Optimizer
3 Design Steps
4 Results
4.1 Classification
4.2 Accuracy Versus Training Step
4.3 Loss Versus Training Step
References
Analysis of COVID-19 Pandemic and Lockdown Effects on the National Stock Exchange NIFTY Indices
1 Introduction
2 Literature Survey
3 Data Analysis Methodology
3.1 Data
3.2 Analysis Methodology
4 Conclusion
References
COVID-19 Detection Using Computer Vision and Deep Convolution Neural Network
1 Introduction
2 Background
3 Methodology of COVID-19 Detection Using CNN
3.1 CNN Model and COVID-19 Detection
3.2 Feature Extraction
3.3 Model Training
3.4 Prediction and Performance Evaluation
4 Conclusion
References
Prediction of Stock Indices, Gold Index, and Real Estate Index Using Deep Neural Networks
1 Introduction
2 Related Work
3 Data Preprocessing and Feature Extraction
3.1 Numeric Historic Data
3.2 Textual News Data
3.3 Flow Diagrams for Data Preprocessing and Feature Extraction
4 Data Preprocessing and Feature Extraction
4.1 Support Vector Machine
4.2 Deep Neural Network
5 Results
5.1 Visualization of Prediction Results
6 Conclusion
References
Signal Strength-Based Routing Using Simple Ant Routing Algorithm
1 Introduction
2 Related Work
3 Proposed Solution
4 Result Analysis
5 Conclusion
References
Fake News Detection Using Convolutional Neural Networks and Random Forest—A Hybrid Approach
1 Introduction
2 Related Work
3 Dataset
4 Proposed Model
4.1 URL Classification
4.2 Data Pre-processing
4.3 Model Architecture
5 Result Analysis
5.1 Performance Parameters
5.2 Results and Analysis
6 Conclusion
References
An Enhanced Fuzzy TOPSIS in Soft Computing for the Best Selection of Health Insurance
1 Introduction
2 Relative Work
3 Methods
4 Results and Discussions
4.1 Illustration Work
5 Limitation of Hesitant TOPSIS
6 Conclusion
References
Non-intrusive Load Monitoring with ANN-Based Active Power Disaggregation of Electrical Appliances
1 Introduction
2 Background
3 ANN Based Active Power Disaggregation
3.1 Data Collection and Feature Extraction
3.2 Training and Validating
3.3 Estimation or Prediction
4 Conclusion
References
Prediction of Dimension of a Building from Its Visual Data Using Machine Learning: A Case Study
1 Introduction
2 Methodology
2.1 Definition and Working Principle
2.2 Data Acquisition
2.3 Photogrammetric Instruments
2.4 Decision Tree
3 Prediction Model
3.1 Input Data
3.2 Working of the Model
4 Results
4.1 Output
5 Conclusion
References
Deep Learning Algorithms for Human Activity Recognition: A Comparative Analysis
1 Introduction
1.1 Surveillance System
1.2 Healthcare
1.3 Human–Computer Interaction
2 Related Work
2.1 Based on Sensor
2.2 Based on Wearable Device
2.3 Based on Radio Frequency
2.4 Based on Vision
3 Deep Neural Network
3.1 Convolutional Neural Network (CNN)
3.2 Recurrent Neural Network (RNN)
4 System Design
4.1 Data Collection
4.2 Data Pre-processing
4.3 Training and Test Set
4.4 Reshaping Data and Prepare for the ML Model
4.5 Building and Training the Model
5 Results
6 Conclusion and Future Scope
References
Comparison of Parameters of Sentimental Analysis Using Different Classifiers
1 Introduction
2 Related Work
3 Methodology Used
3.1 Data Description
3.2 Performance Metrics
3.3 Data Preprocessing and Text Cleaning
3.4 Feature Extraction
3.5 Classification Models
4 Experiment and Result Evaluation
5 Conclusion and Future Scope
References
System Model to Effectively Understand Programming Error Messages Using Similarity Matching and Natural Language Processing
1 Introduction
2 Methodology and Preprocessing
2.1 Tokenization
2.2 Stop Words Removal
2.3 Count Vectorizer
2.4 Similarity Matching
3 Algorithm
4 System Architecture
5 Scalability of the System
6 Conclusion and Result
References
Enhanced Accuracy in Machine Learning Using Feature Set Bifurcation
1 Introduction
2 Machine Learning Techniques
2.1 Machine Learning Techniques
2.2 Machine Learning Classifiers
3 Literature Review
4 Feature Set Bifurcation
5 Proposed Work
6 Results Obtained
7 Conclusion and Future Work
References
Author Index

Citation preview

Algorithms for Intelligent Systems Series Editors: Jagdish Chand Bansal · Kusum Deep · Atulya K. Nagar

Vinit Kumar Gunjan P. N. Suganthan Jan Haase Amit Kumar   Editors

Cybernetics, Cognition and Machine Learning Applications Proceedings of ICCCMLA 2020

Algorithms for Intelligent Systems Series Editors Jagdish Chand Bansal, Department of Mathematics, South Asian University, New Delhi, Delhi, India Kusum Deep, Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Atulya K. Nagar, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

This book series publishes research on the analysis and development of algorithms for intelligent systems with their applications to various real world problems. It covers research related to autonomous agents, multi-agent systems, behavioral modeling, reinforcement learning, game theory, mechanism design, machine learning, meta-heuristic search, optimization, planning and scheduling, artificial neural networks, evolutionary computation, swarm intelligence and other algorithms for intelligent systems. The book series includes recent advancements, modification and applications of the artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, fuzzy system, autonomous and multi agent systems, machine learning and other intelligent systems related areas. The material will be beneficial for the graduate students, post-graduate students as well as the researchers who want a broader view of advances in algorithms for intelligent systems. The contents will also be useful to the researchers from other fields who have no knowledge of the power of intelligent systems, e.g. the researchers in the field of bioinformatics, biochemists, mechanical and chemical engineers, economists, musicians and medical practitioners. The series publishes monographs, edited volumes, advanced textbooks and selected proceedings.

More information about this series at http://www.springer.com/series/16171

Vinit Kumar Gunjan · P. N. Suganthan · Jan Haase · Amit Kumar Editors

Cybernetics, Cognition and Machine Learning Applications Proceedings of ICCCMLA 2020

Editors Vinit Kumar Gunjan Department of Computer Science and Engineering CMR Institute of Technology Hyderabad, India Jan Haase Department of Computer Science Nordakademie, Elmshorn, Germany

P. N. Suganthan School of Electrical and Electronics Singapore, Singapore Amit Kumar Bioaxis DNA Research Centre (P) Ltd. Hyderabad, India

ISSN 2524-7565 ISSN 2524-7573 (electronic) Algorithms for Intelligent Systems ISBN 978-981-33-6690-9 ISBN 978-981-33-6691-6 (eBook) https://doi.org/10.1007/978-981-33-6691-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

Preface

Cognitive science is the study of human mind and brain, focusing on how mind represents and manipulates knowledge and how mental representations and processes are realized in the brain. The field is highly transdisciplinary in nature, combining ideas, principles and methods of psychology, computer science, linguistics, philosophy, neuroscience, etc. Brain–machine interfaces were envisioned already in the 1940s by Norbert Wiener, the father of cybernetics. The opportunities for enhancing human capabilities and restoring functions are now quickly expanding with a combination of advances in machine learning, smart materials and robotics. Automation, artificial intelligence (AI) and machine learning (ML) are pushing boundaries in the software and hardware industry to what machines are capable of doing. From just being a figment of someone’s imagination in sci-fi movies and novels, they have come a long way to augmenting human potential (reducing risk of human errors) in doing tasks faster, more accurate and with greater precision each time—driven by technology, automation and innovation. This is indeed creating new business opportunities and is acting as a clear competitive differentiator that helps analyze hidden patterns of data to derive possible insights. AI and ML can certainly enrich our future, thereby making the need for intelligent and sophisticated systems more important than ever. This book is comprised of selected and presented papers at the International Conference on Cybernetics, Cognition and Machine Learning Applications 2020. It consists of selected manuscripts, arranged on the basis of their approaches and contributions to the scope of the conference. The chapters of this book present key algorithms and theories that form the core of the technologies and applications concerned, consisting mainly of artificial intelligence, machine learning, neural networks, face recognition, evolutionary algorithms such as genetic algorithms, automotive applications, automation devices with artificial neural networks, business management systems, cybernetics, IoT, cognition, data science and modern speech processing systems. This book also covers recent advances in medical diagnostic systems, smart agricultural applications, sensor networks and cognitive science domain. Discussion

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Preface

of learning and software modules in deep learning algorithms is added wherever suitable. Hyderabad, India Singapore Elmshorn, Germany Hyderabad, India

Vinit Kumar Gunjan P. N. Suganthan Jan Haase Amit Kumar

Contents

IOT Smart Locker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anurag Narkhede, Vinit Mapari, and Aarti Karande

1

Brief Analysis on Human Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . Kaif Jamil, Deependra Rastogi, Prashant Johri, and Munish Sabarwal

9

Lora-Based Smart Garbbage Alert Monitoring System Using ATMEGA 328, 2560, 128 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anzar Ahmad and Shashi Shekhar

21

Pre-birth Prognostication of Education and Learning of a Fetus While in the Uterus of the Mother Using Machine Learning . . . . . . . . . . . Harsh Nagesh Mehta and Jayshree Ghorpade Aher

31

Performance Analysis of Single-Stage PV Connected Three-Phase Grid System Under Steady State and Dynamic Conditions . . . . . . . . . . . . V. Narasimhulu and K. Jithendra Gowd

39

Delay Feedback H∞ Control for Neutral Stochastic Fuzzy Systems with Time Delays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Senthilkumar

47

Modeling Crosstalk of Tau and ROS Implicated in Parkinson’s Disease Using Biochemical Systems Theory . . . . . . . . . . . . . . . . . . . . . . . . . . Hemalatha Sasidharakurup, Parvathy Devi Babulekshmanan, Sreehari Sathianarayanan, and Shyam Diwakar

55

IoT-Based Patient Vital Measuring System . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashwini R. Hirekodi, Bhagyashri R. Pandurangi, Uttam U. Deshpande, and Ashok P. Magadum

63

IoT-Enabled Logistics for E-waste Management and Sustainability . . . . . P. S. Anusree and P. Balasubramanian

71

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Contents

Navigation Through Proxy Measurement of Location by Surface Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Savitha, Adarsh, Aditya Raj, Gaurav Gupta, and Ashik A. Jain Unsupervised Learning Algorithms for Hydropower’s Sensor Data . . . . . Ajeet Rai Feature Construction Through Inductive Transfer Learning in Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suman Roy and S. Saravana Kumar

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Decoding Motor Behavior Biosignatures of Arm Movement Tasks Using Electroencephalography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Rakhi Radhamani, Alna Anil, Gautham Manoj, Gouri Babu Ambily, Praveen Raveendran, Vishnu Hari, and Shyam Diwakar Emergency Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Shubham V. Ranbhare, Mayur M. Pawar, Shree G. Mane, and Nikhil B. Sardar Type Inference in Java: Characteristics and Limitations . . . . . . . . . . . . . . 131 Neha Kumari and Rajeev Kumar Detection and Correction of Potholes Using Machine Learning . . . . . . . . 139 Ashish Sahu, Aadityanand Singh, Sahil Pandita, Varun Walimbe, and Shubhangi Kharche Detecting COVID-19 Using Convolution Neural Networks . . . . . . . . . . . . . 153 Nihar Patel, Deep Patel, Dhruvil Shah, Foram Patel, and Vibha Patel Electroencephalography Measurements and Analysis of Cortical Activations Among Musicians and Non-musicians for Happy and Sad Indian Classical Music . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Nijin Nizar, Akhil Chittathuparambil Aravind, Rupanjana Biswas, Anjali Suresh Nair, Sukriti Nirayilatt Venu, and Shyam Diwakar Signal Processing in Yoga-Related Neural Circuits and Implications of Stretching and Sitting Asana on Brain Function . . . . . . . . . . . . . . . . . . . . 169 Dhanush Kumar, Akshara Chelora Puthanveedu, Krishna Mohan, Lekshmi Aji Priya, Anjali Rajeev, Athira Cheruvathery Harisudhan, Asha Vijayan, Sandeep Bodda, and Shyam Diwakar Automation of Answer Scripts Evaluation-A Review . . . . . . . . . . . . . . . . . . 177 M. Ravikumar, S. Sampath Kumar, and G. Shivakumar Diabetes Mellitus Detection and Diagnosis Using AI Classifier . . . . . . . . . 185 L. Priyadarshini and Lakshmi Shrinivasan Review on Unit Selection-Based Concatenation Approach in Text to Speech Synthesis System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Priyanka Gujarathi and Sandip Raosaheb Patil

Contents

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Enhancing the Security of Confidential Data Using Video Steganography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Praptiba Parmar and Disha Sanghani Data Mining and Analysis of Reddit User Data . . . . . . . . . . . . . . . . . . . . . . . 211 Archit Aggarwal, Bhavya Gola, and Tushar Sankla Analysis of DNA Sequence Pattern Matching: A Brief Survey . . . . . . . . . 221 M. Ravikumar and M. C. Prashanth Sensor-Based Analysis of Gait and Assessment of Impacts of Backpack Load on Walking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Chaitanya Nutakki, S. Varsha Nair, Nima A. Sujitha, Bhavita Kolagani, Indulekha P. Kailasan, Anil Gopika, and Shyam Diwakar Wireless Battery Monitoring System for Electric Vehicle . . . . . . . . . . . . . . 239 Renuka Modak, Vikramsinh Doke, Sayali Kawrkar, and Nikhil B. Sardar Iris Recognition Using Selective Feature Set in Frequency Domain Using Deep Learning Perspective: FrDIrisNet . . . . . . . . . . . . . . . . . . . . . . . . 249 Richa Gupta and Priti Sehgal Enhancement of Mammogram Images Using CLAHE and Bilateral Filter Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 M. Ravikumar, P. G. Rachana, B. J. Shivaprasad, and D. S. Guru Supervised Cross-Database Transfer Learning-Based Facial Expression Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Arpita Gupta and Ramadoss Balakrishnan Innovative Approach for Prediction of Cancer Disease by Improving Conventional Machine Learning Classifier . . . . . . . . . . . . . . 281 Hrithik Sanyal, Priyanka Saxena, and Rajneesh Agrawal Influence of AI on Detection of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Pallavi Malik and A. Mukherjee Study of Medicine Dispensing Machine and Health Monitoring Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Aditi Sanjay Bhosale, Swapnil Sanjay Jadhav, Hemangi Sunil Ahire, Avinash Yuvraj Jaybhay, and K. Rajeswari Building Image Classification Using CNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Prasenjit Saha, Utpal Kumar Nath, Jadumani Bhardawaj, Saurin Paul, and Gagarina Nath Analysis of COVID-19 Pandemic and Lockdown Effects on the National Stock Exchange NIFTY Indices . . . . . . . . . . . . . . . . . . . . . . 313 Ranjani Murali

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COVID-19 Detection Using Computer Vision and Deep Convolution Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 V. Gokul Pillai and Lekshmi R. Chandran Prediction of Stock Indices, Gold Index, and Real Estate Index Using Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Sahil Jain, Pratyush Mandal, Birendra Singh, Pradnya V. Kulkarni, and Mateen Sayed Signal Strength-Based Routing Using Simple Ant Routing Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Mani Bushan Dsouza and D. H. Manjaiah Fake News Detection Using Convolutional Neural Networks and Random Forest—A Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Hitesh Narayan Soneji and Sughosh Sudhanvan An Enhanced Fuzzy TOPSIS in Soft Computing for the Best Selection of Health Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 K. R. Sekar, M. Sarika, M. Mitchelle Flavia Jerome, V. Venkataraman, and C. Thaventhiran Non-intrusive Load Monitoring with ANN-Based Active Power Disaggregation of Electrical Appliances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 R. Chandran Lekshmi, K. Ilango, G. Nair Manjula, V. Ashish, John Aleena, G. Abhijith, H. Kumar Anagha, and Raghavendra Akhil Prediction of Dimension of a Building from Its Visual Data Using Machine Learning: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Prasenjit Saha, Utpal Kumar Nath, Jadumani Bhardawaj, and Saurin Paul Deep Learning Algorithms for Human Activity Recognition: A Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Aaditya Agrawal and Ravinder Ahuja Comparison of Parameters of Sentimental Analysis Using Different Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Akash Yadav and Ravinder Ahuja System Model to Effectively Understand Programming Error Messages Using Similarity Matching and Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Veena Desai, Pratijnya Ajawan, and Balaji Betadur Enhanced Accuracy in Machine Learning Using Feature Set Bifurcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Hrithik Sanyal, Priyanka Saxena, and Rajneesh Agrawal Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437

About the Editors

Vinit Kumar Gunjan is an Associate Professor in the Department of Computer Science and Engineering at CMR Institute of Technology Hyderabad (Affiliated to Jawaharlal Nehru Technological University, Hyderabad) and an active researcher; he published research papers in IEEE, Elsevier and Springer Conferences, authored several books and edited volumes of Springer series, most of which are indexed in SCOPUS database. He is awarded with the prestigious Early Career Research Award in the year 2016 by Science Engineering Research Board, Department of Science and Technology, Government of India. Senior member of IEEE and an active Volunteer of IEEE Hyderabad section—he is presently serving as secretary for IEEE CIS and volunteered in the capacity of Treasurer, Secretary and Chairman of IEEE Young Professionals Affinity Group and IEEE Computer Society. He was involved as organizer in many technical and non-technical workshops, seminars and conferences of IEEE and Springer. During the tenure he had an honour of working with top leaders of IEEE and was awarded with best IEEE Young Professional award in 2017 by IEEE Hyderabad Section. P. N. Suganthan is Professor at Nanyang Technological University, Singapore, and Fellow of IEEE. He is a founding Co-editor-in-Chief of Swarm and Evolutionary Computation (2010–), an SCI Indexed Elsevier Journal. His research interests include swarm and evolutionary algorithms, pattern recognition, forecasting, randomized neural networks, deep learning and applications of swarm, evolutionary and machine learning algorithms. His publications have been well cited (Google Scholar Citations: ~33k). His SCI indexed publications attracted over 1000 SCI citations in a calendar year since 2013. He was selected as one of the highly cited researchers by Thomson Reuters every year from 2015 to 2018 in computer science. He served as the General Chair of the IEEE SSCI 2013. He is an IEEE CIS distinguished lecturer (DLP) in 2018–2020. He has been a member of the IEEE (S’91, M’92, SM’00, Fellow’15) since 1991 and an elected AdCom member of the IEEE Computational Intelligence Society (CIS) in 2014–2016.

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Jan Haase (M’07, SM’09) received his Bachelor, Master, and Ph.D. degree in computer sciences at University of Frankfurt/Main, Germany. Then he was project leader of several research projects at University of Technology in Vienna, Austria, at the Institute of Computer Science and a lecturer at Helmut Schmidt University, Hamburg, where he received his habilitation grade. 2016–2020 he held a temporal professorship for Organic Computing at University of Luebeck, Germany and now is a full professor at Nordakademie near Hamburg, Germany. His main interests are Building Automation, System Specification and Modeling, Simulation, Low-Power Design Methodologies, Wireless Sensor Networks, Automatic Parallelization and modern Computer Architectures. As a member of several technical program committees of international conferences he is involved in the review process of many research publications and repeatedly acted as TPC chair, track chair, etc. at these conferences. He (co)-authored more than 100 peer reviewed journal and conference papers and several book chapters. As an IEEE volunteer, he currently is Germany Section’s Chair, has been Austria Section’s Chair and is active in IEEE R8, having been R8 Conference Coordinator, R8 Professional Activities Chair, and a member of several R8 committees. In IEEE Industrial Electronics Society, he is a voting AdCom member and Chair of the Technical Committee on Building Automation, Control, and Management for 2020 and 2021. Furthermore, he serves and served on several society committees like the constitution and bylaws committee, the planning and development committee, the technical activities committee, membership committee, etc. On IEEE HQ level he currently serves on the Conference Finance Committee and chaired the Adhoc on Cultural Differences in the IEEE Conferences Committee. He also continuously served as a mentor on the IEEE VoLT program since the very first season. Amit Kumar, Ph.D. is a passionate Forensic Scientist, Entrepreneur, Engineer, Bioinformatician and an IEEE Volunteer. In 2005, he founded the first Private DNA Testing Company BioAxis DNA Research Centre (P) Ltd in Hyderabad, India, with an US Collaborator. He has vast experience of training 1000+ crime investigation officers and helped 750+ criminal and non-criminal cases to reach justice by offering analytical services in his laboratory. Amit was member of IEEE Strategy Development and Environmental Assessment Committee (SDEA) of IEEE MGA. He is senior member of IEEE and has been a very active IEEE Volunteer at Section, Council, Region, Technical Societies of Computational Intelligence and Engineering in Medicine and Biology and at IEEE MGA levels in several capacities. He has driven number of IEEE Conferences, conference leadership programmes, entrepreneurship development workshops, innovation and internship related events. Currently, he is also a Visiting Professor at SJB Research Foundation and Vice Chairman of IEEE India Council and IEEE Hyderabad Section.

IOT Smart Locker Anurag Narkhede, Vinit Mapari, and Aarti Karande

1 Introduction In the present day, security is the key issue for many people especially in the urban and rural areas. By trying to cheat people, risk of the safety of the money is most important. To avoid risks in day-to-day life, everyone relies on the banks to keep important documents, jewelry, or cash. As the times are changing, banks are increasing their branches due to public interest and hence it has become more imperative to secure the bank as well. This paper proposes a system that contains more security to keep the cash/jewelry/document in the locker, safe with multistage security by using biometric, password verification, internal security using an ultrasonic sensor, and android app to set the timer. This system works on the Internet of things sensors. Ultrasonic sensors are used to measure the distance to a locker using ultrasonic sound waves. Ultrasonic pulses are sent and received using transducer by an ultrasonic sensor. These pulses relay back information about locker proximity. System includes biometric system. The biometric system will be enabled when the timer is on. If the biometric system is off, the user cannot open the locker and the timer can be set using the android app. the system allows only unauthorized authorized person to recover money from the bank, home and office locker with four-phase security.

A. Narkhede (B) · V. Mapari · A. Karande MCA Department, Sardar Patel Institute of Technology, Mumbai, India e-mail: [email protected] V. Mapari e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_1

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2 Literature Review The Internet of things (IOT) refers to the ever-growing network between physical objects and systems. It is a system of interrelated computing tinny devices, mechanical and digital machines, objects. These components are provided with unique identifiers (UIDs). They have the ability to transfer data over a network without requiring human-to-computer or human-to-human interaction. A locker is a small cupboard or compartment. They are mostly found in cabinets, very often in large numbers, in various public places such as offices, homes, and locker rooms that may be locked especially. Smart locker is a locker with open base, on authentication and automated base on instructions (Table 1).

3 Workflow of the System 3.1 To Open This Smart Locker, the User Has to Go Under These Steps to Access the Locker Case 1: In step 1, the individual who wants to access the locker has to ask the admin to grant the access that will enable the password and biometric system and the timer will start. Here, OTP will be generated. In step 2, the user has to enter the right password (OTP) and also has to get verified by the biometric system once both get verified the locker will open. Once the timer ends and still the locker is open, then it will alert the admin of theft. If the admin has not given the access to the locker and the person enables the password and biometric system manually the theft alert will be sent to the admin that password and biometric have been enabled manually. The workflow of this is explained in the below flowchart (Fig. 1). Case 2: When unauthenticated user gets all the access and shows that he is an authenticated user. At this time, internal security will come into picture and ultrasonic sensor will take care of internal security system that will measure and store a record of distance between the objects. When unauthenticated users clear all the paths, that time internal security will check that is it a holiday or closing time?’ If yes, then it further checks ‘is distance updated?’ If yes, then send an alert to all the higher authorities. This is the secret part of the security where malicious users as well as an authenticated user are also unaware of this secret part. The workflow of this is explained in the below flowchart (Fig. 2).

3.2 Tool Kit a. Ultrasonic sensor: As the name says, an ultrasonic sensor or level sensor is used to measure the distance to a locker using ultrasonic sound waves. Ultrasonic pulses

IOT Smart Locker

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Table 1 Comparison analysis of referred papers P. No.

Approach

1

Fingerprint, user To improve the security of A malicious user can get authentication with OTP home which verifies user access of locker multiple as well as device times

Result

Disadvantage

2

They have used motion detector and GSM messaging module

It sends an unauthorized image detection signal to the microcontroller as well as alert message will be generated

The system is not reliable because of microcontroller

3

They have used RFID system, user authentication, and also biometric system

They have implemented a security system using matrix keypad, RFID tag, and GSM technology

A malicious user can hack the system

4

PIR sensor and email alert used

Motion is detected by the PIR sensor; then that will send email to the admin which will give the warning of theft

Theft detection message is delivered only by email

5

Face recognition, GSM, and Zigbee used

We can access the system through the Web where we can monitor as well as control the equipment

Face detection takes a more complex algorithm

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They have used dual key It has a dual key for safety lockers as well as opening the locker. For biometrics this, they have used special characters and for the biometric system the person who is authenticated with the special ID that has been provided

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They have used email alerting and SMS control

If the theft is detected, The system does not then it sends the SMS to a provide that much security user remotely as needed, and it is not reliable

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Fingerprint scanner for biometric system

The fingerprint is used for detecting malicious user where the identity of the fingerprint is unique

Security provided through fingerprint is not that much secure as compared to top-level security

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They have used PIR sensor, fingerprint, vibration sensor, and GSM

PIR sensor and fingerprint are used for the security where the vibration sensor used to detect the pressure which gives alert through the alarm

Identification of user is needed for detection of the malicious user but it does not provide

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Using PIR sensor, IOT, and Web server

We can control the system through mobile where we can on and off the system and access it remotely

If hackers hacked the mobile system, then they are able to get access to the locker

A malicious user can easily get access and take full control of the system

(continued)

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Table 1 (continued) P. No.

Approach

Result

Disadvantage

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Used microcontroller

They have used microcontroller based on an automated system which detects physical interference to the system and sends the warning message immediately

If microcontroller crashed, then it is not able to detect

are sent and receive the wave reflected back from the target using a transducer. Here, the ultrasonic sensor helps to identify if there is a theft attempt to forcefully breaking locker to open it. b. Android smartphone app: The app can be accessed by the android smartphone; the feature of the app contains the accessibility of the locker through the app; the admin has to login to the app; after login, he can give them access to the locker, can set the timer of the locker and also can get the notification of the system on the app.

3.3 Proposed Model See Fig. 3.

4 Result from Observation This system will be more secure as it has to get access from the admin to open the locker. If access is granted, the timer will begin, the person has to complete the process of verifying the password and the biometric and use of the locker need to be completed before the timer ends. The locker has to be closed; otherwise, the theft alert will be sent to admin.

5 Conclusion During this system building, lots of challenges were faced while gathering feedback from the back employees regarding the security of the lockers. After gathering the feedback, building the security system was a challenge. This system has an established process which will help us to control for accessibility of user usage. It will store the information of the users with their location and usage levels among the lockers also. This system will assure the owner about the locker’s security by the

IOT Smart Locker

Fig. 1 Flow diagram of case 1

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Fig. 2 Flow diagram of case 2

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IOT Smart Locker

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Fig. 3 IOT-based smart locker system using Tinkercad simulation tool

robber. This system can be used in any places where security is paramount. This system can even be better in the process execution by using various techniques of authentication using biometric verification of the users. This paper proposes a system which can be used to check the authenticity logins and access to the system.

References 1. Run, C.J., Reza, M., Ning, Y.: Improving home automation security; integrating device fingerprinting into SmartHome. https://doi.org/10.1109/ACCESS.2016.2606478,IEEE 2. Neeraj, K., Amit, V.: Development of an intelligent system for bank security. In: 2014 5th International Conference-Confluence The Next Generation Information Technology Summit (Confluence) 3. Ashutosh, G., Medhi, P., Pandey, S., Kumar, P., Kumar, S., Singh, H.P.: An efficient multistage security system for user authentication, pp. 3194–3197 (2016). https://doi.org/10.1109/ICE EOT.2016.7755291 4. Tanwar, S., Patel, P., Patel, K., Tyagi, S., Kumar, N., Obaidat, M.S.: An advanced internet of thing based security alert system for smart home. In: Fellow of IEEE and Fellow of SCS 5. Mrutyunjaya, S., Chiranjiv, N., Abhijeet, K.S., Biswajeet, P.: Web-based online HEmbedded door access control and home security 6. Srivatsan, S.: Authenticated secure bio-metric based access to the bank safety lockers. In: ICICES2014. S.A. Engineering college, Chennai, Tamil Nadu, India. ISBN No. 978-1-47993834-6/14 7. Balasubramanian, K., Cellatoglu, A.: Analysis of remote control techniques employed in home automation and security system 8. Salil, P., Sharath, P., Anil, K.J.: Biometric recognition: security and privacy concerns 9. Tejesvi, S.V., Sravani, P., Mythili, M.L., Jayanthi, K., Nagesh Kumar, P., Balavani, K.: Intellectual bank locker security system. Int. J. Eng. Res. Appl. 6 (2(Part-2)), 31–34 (2016). ISSN: 2248-9622 10. Safa, H., Sakthi Priyanka, N., Vikkashini Gokul Priya, S., Vishnupriya, S., Boobalan, T.: IOT based Theft premption, and security system. https://doi.org/10.15680/IJIRSET.2016.0503229 11. Rahul, B., Kewal, T., Hiren, K., Sridhar, I.: 3 tier bank vault security. In: Computer Science 2018 International Conference on Smart City and Emerging Technology (ICSCET) (2018)

Brief Analysis on Human Activity Recognition Kaif Jamil, Deependra Rastogi, Prashant Johri, and Munish Sabarwal

1 Introduction Human activity recognition is emerging as a prominent concept to understand and develop human interaction with technology as well as computer vision in the broad sector of computer science research. The core of scientific endeavours like security insurance, health assistance and human interaction with technology is formed by it. But the new field is infatuated with problems such as sensor placement, sensor motion, installing video cameras in monitoring areas, scattered background and the heterogeneity in ways the actions/activities involving movements and unique gestures are conducted by us (citation). To tackle the aforementioned challenges, a more structured approach would be to collect the data and process it, and this data is collected from sensors worn by the person or object’s body or is built in smartphone to track gestures and movements of the person. A tri-axial accelerometer which is a kind of sensor built in smartphones to track the users movement is used for this reason.

K. Jamil (B) · P. Johri · M. Sabarwal Department of Computer Science and Engineering, Galgotias University, Greater Noida 226001, India e-mail: [email protected] P. Johri e-mail: [email protected] M. Sabarwal e-mail: [email protected] D. Rastogi School of Computing Science and Engineering, Galgotias University, Greater Noida, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_2

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1.1 Human Activity Recognition (HAR) HAR is being the process of ‘assorting sequences of accelerometer data registered by smartphones into known well-defined manoeuvres’. It can also be said that the process to understand gestures or motion carried by humans using sensors to understand and learn the human activities or actions taking place is known as HAR. Our daily activities can be rationalized and automated if they are identified by any HAR system taking an example of lights (smart) maybe that apprehend hand movements. The most important part is that these systems can be supervised as well as unsupervised. A supervised system cannot function without prior training with allocated data sets, while the unassisted HAR system can configure with a set of rules during development (citation). A confined explanation of the various uses of HAR in different surroundings is provided below. A. Security System The installation of HAR security system through surveillance first took place at airports and banks to prevent unlawful activities occurring at social public places. This pattern of human activity prediction was introduced by Ryoo. The HAR surveillance system is able to recognize multiple in progress human interactions at the former stage, the results confirmed. A better system was proposed by Lasecki et al. named Legion:AR that would provide one of the best deployable, vigorous, robust activity recognition along with pre-existing systems with realtime activity recognition by utilizing the data sets gathered from the public. B. Health Assistance and treatment In the field of medical care, HAR is used in residential areas, hospitals and rehabilitation centres for multiple reasons like keeping an eye on the activities of elderly people living being monitored and treated in rehabilitation centres, disease preclusion and management of severe diseases. In reference to rehabilitation centres, HAR system is particularly helpful to track the activities of elderly people and monitor physical activities of children with disabilities (mainly motors), fall detection and monitor patients with dysfunction, disturbing motion conditions like ASD, patients with slowing of psycho-motor and having abnormal conditions for patients with cardiac problems. This monitoring will definitely help in ensuring timely clinical intermediation. C. Human interaction with technology This field utilizes HAR in applying it generally in exergaming and gaming such as Nintendo Wii, Kinect, other games based on motions for people and people with neurological disabilities. This system describes the human body movements and basic of that data instructs the execution of required tasks. Anyone with neurological disorders or lesion can perform a basic movements to communicate physically (interactions) with these games comfortably. This gives more control to the surgeons to monitor and make it more easy and handy for the affected people to perform tasks that they were earlier incapable of.

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2 Related Work In the last decade, a significant progress has been witnessed in HAR. There are numerous researches and studies which focus on different approaches which identifies human activity and the remarkable effect of it in the real-world context. The different methods can be classified into the following four categories. A. Sensor-Based Chen et al. had given a brief survey of the sensor-based work in HAR, this arranges the pre-existing research data in mainly two classifications: (a) Datadriven-based versus knowledge-driven-based and (b) vision-based versus sensorbased. Data centric activity recognition techniques is the main focus of this survey. A survey by Wang et al. brought out how sensors can be used by different deep learning approach for HAR. This work categories the literature in HAR on the basis of application area, deep model and sensor modality. The main focus of this survey was to show how deep model can be used for data processing of information acquired from sensors. B. Wearable Device-Based Lara and Labarador sketched the work performed in HAR using wearable devices that have sensors. It presents a brief overview of a number of issues related to design in the system, such as selecting attributes and sensors, protocol and data collection, energy consumption, processing methodologies and recognition performance. Cornacchia et al. provided a brief survey and presented the research work (existing) that describe the activities of interaction which have limited movement and those whose movements are not restricted by the whole body is in motion. This paper also gives a classification based on the how the sensors are placed on the body of a human and the type of sensor used. C. Radio Frequency-Based A study analysis and presentation of the work that has been researched in the field where activity recognition is of device-free and radio-based (DFAR) was presented by Scholz et al. This survey divides the already present work in DFAR and device-free radio-based localization (DFL). Amendola et al. proposed a brief study (research) which summarizes the things that are important and must be used like RFID tech for medical related use in IoT. This also comprised of RFID tags like passive environmental sensors like temperature sensor, body centric, etc. The existing work is categorized by four major groups: (a) Wi-Fi-based, (b) RFID-based, (c) ZigBee radio-based, (d) radio-based (e.g. microwave). Metrics like coverage, activity types, accuracy and distribution cost are used by the authors to compare all these techniques.

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D. Vision-Based A survey was presented by Vrigkas et al. about the research work that has used this approach, and based on this, he had done the classification of the literature into two categories: multi-modal and unimodal methodologies adopted. Another detailed overview of the research done in the action recognition field was done by Herath et al. This then is categorized into two major categories: representation-based solutions and DNN-based solutions.

3 Deep Neural Network (DNN) Deep learning or DNN is the sub-category of machine learning. A DNN constitutes various levels defined in a nonlinear working functionalities having many hidden layers known as neural nets. The main goal is to learn feature hierarchies, where lower level features help in establishing features that are at higher level of hierarchy. A deep learning model will be created by employing such network, two of them are mentioned below: A. Convolutional Neural Network (CNN) CNN, a feed forward type of artificial neural network, is largely used in recognition and processing of images and driving important info and analysis of the same. It performs a set of tasks mainly generative and descriptive using deep learning, and it often uses computer vision that involves video and image recognition. Convolution and pooling are two major operations performed by CNN. These are applied along the dimension of time of sensor signals. We will be using a 1D CNN since in it the kernel slides unidirectionally and since time series data is classified by HAR. B. Recurrent Neural Network (RNN) Unlike other types of established neural network where all the acquired data are independent of result, in RNN, the results from the preceding steps are given as the data for further next step analysis and results. In this paper, we will be using LSTM network. The LSTM design was put into use because of the error flow in the existing model of RNN, and the analysis was that the long duration lags were not accessible. The LSTM consists of layers that were having a recurring connected blocks, and these were known as memory blocks.

4 System Design The four phases of human activity recognition are shown in Fig. 1. These are: (a) Sensor selection and Deployment, (b) Collecting data from Sensor, (c) Data pre-processing and Feature selection and (d) Developing a Classifier to recognize activities.

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Fig. 1 Processes in human activity recognition

A. Data Collection If data needs to collect, smartphones have a tri-axial accelerometer that is used to collect data. The test users carried smartphones with them in their pockets thus performing activities that correspond to a rate of sampling of 20 Hz, the frequency of which was 20. These data were mapped in the tri-axial axes (X, Y, Z axes) by an accelerometer, which shows the movement of the test user in different directions like horizontal, upwards, downwards, etc. There two graphs are shown below (Figs. 2 and 3): B. Data Pre-processing It is the process which works to transform captured data in a format so that machine accepts it and further cater to the algorithm. The data then is processed as per the norms, it reads the data, and then, each component of accelerometer is

Fig. 2 Data visualization of the data collected by accelerometer

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Fig. 3 Mapping of frequency of each activity

normalized. Then, the time-sliced representation is done of the processed data, and this goes to be stored in data-frame. All these data are in text file format. The deep neural network (DNN) can only work with numerical values, and thus, we add encoded result set for each activity. The below graphs are few representations of data records captured by accelerometer of few activities (Figs. 4, 5 and 6):

Fig. 4 Standing

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Fig. 5 Walking

Fig. 6 Jogging

C. Training and Test Set The idea is to train the neural network, and thus, he needs to learn from the test data and predict the movement of the unknown data set which is totally new to him. To utilize the data set, further we split them into two part, i.e. training and

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Fig. 7 LSTM architecture

testing in 80/20 parts. Once this is done, we again normalize this data so that it can be fed further into the neural network. D. Reshaping of Data and prepare it for ML model All the collected data are formatted in order to feed them to the neural network. The dimensions are as follows: I. One record’s time periods. II. Sensors used: It is three here. III. Count of nodes for the o/p layer. E Training and building the model Our approach would be to design models: CNN and LSTM with similar data sets. The first one approach would be to entail convolution layer which is then followed by pooling layer (max) and another layer (convolution). This gives us the layer which is well established and would be associated to ‘Softmax’ layer (Fig. 7).

5 Result Now, we proceed to towards training both models with the data (training) that was earlier prepared. For the training, the hyperparameter used is: A batch size of 300 records and the model will be trained for 50 epochs. We will be using the earlier mentioned splitting of 80/20 split to separate validation and training data. Next we plot the learning curve for both the models. The figure below shows both the plotting, the Fig. 8 corresponds to the CNN model, it faced issues while in the testing phase. The loss in test seems to take a rise after 21 epochs, while the accuracy

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Fig. 8 Learning curve (CNN)

of its test consistency is maintained till 50 epochs. The important and noteworthy things are that if a model is more accurate its test loss curve should be having a downward curve which is the case in CNN model. If we look at the Fig. 9, the LSTM model we can easily make out that this model’s test loss curve is forming a downward curve with the epochs increasing with time, and thus, its learning capability is pretty well, and also, its accuracy curve is increasing at the start and then acquires a consistent course. Comparing both the models considering various parameters like consistency, learning curves its very evident that LSTM model is more accurate than the CNN model. Refer to the confusion matrix to check the prediction of these models (CNN and LSTM). CNN model’s prediction accuracy is represented in Fig. 10, and it turns out to be Fig. 9 Learning curve (LSTM)

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Fig. 10 Confusion matrix (CNN)

87%. This model struggles in identification of activities like standing and upstairs movement. When referring to the confusion matrix of LSTM model in Fig. 11, it turns out that it is accurate in predicting activity like walking but struggled in identifying few activities like upstairs and standing. If we look at its diagonal matrix, the accuracy turns out to be 92%. Both of them (CNN and LSTM) struggled a bit in identifying the same class of activities. Fig. 11 Confusion matrix (LSTM)

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6 Conclusion Finally, the conclusion came out to be that the LSTM model is more accurate than the CNN model (LSTM accuracy rate: 92%, CNN accuracy rate: 87%). What we see around is that in the real-world every individual movements/gestures have unique data in it which can be used to train and feed into these models so as to help develop efficient system that can contribute towards helping the society. Our aim was to study these models and compare them on some informational set of data, it comes out that LSTM is more accurate but both of them can if tuned more vigorously yield more accurate data and perform better. For future, there is a plan of creating a hybrid model combining two or more DNN so that it can help in increasing the abstraction level which would help in better understanding and functioning of the systems. Altogether, it can revolutionize the boundaries of human resources to tackle situation that were not addressed to its utmost potential.

References 1. Huynh, T.G.: Human Activity Recognition with Wearable Sensors. Technische Universität Darmstadt (2008) 2. Lawrence, C., Sax, K. F.N., Qiao, M.: Interactive games to improve quality of life for the elderly: Towards integration into a WSN monitoring system. In: 2010 Second International Conference on 112. 3. Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensorbased activity recognition. IEEE Trans. Syst., Man, Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012) 4. Lasecki, W.S., Song, Y.C., Kautz, H., Bigham, J.P.: Real-time crowd labeling for deployable activity recognition. In: Proceedings of the 2013 conference on 1203 5. Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey (2017). arXiv preprint arXiv:1707.03502 6. Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386–403 (2017) 7. Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., Marrocco, G.: Rfid technology for iot-based personal healthcare in smart spaces. IEEE Internet Things J. 1(2), 144–152 (2014) 8. Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes (2012) 9. Jalal, A., Uddin, Z., Kim, J.T., Kim, T.: Recognition of human home activities via depth silhouettes and â transformation for smart homes, pp. 467–475 (2011) 10. Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Trans. Cybern. 43(5), 1318–1334 (2013) 11. Ryoo, M.S.: Human activity prediction: early recognition of ongoing activities from streaming videos. In: 2011 Iccv, pp. 1036–1043 (2011) 12. Lange, C.-Y., Chang, E., Suma, B., Newman, A.S.R., Bolas, M.: Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. In: Conference on Proceedings IEEE Engineering in Medicine and Biology Society, vol. 2011, pp. 1831–1834 (2011) 13. Yoshimitsu, K., Muragaki, Y., Maruyama, T., Yamato, M., Iseki, H.: Development and initial clinical testing of ‘OPECT’: an innovative device for fully intangible control of the intraoperative image-displaying monitor by the surgeon. Neurosurgery 10 (2014)

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14. Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily living activity recognition based on statistical feature quality group selection. Expert Syst. Appl. 39(9), 8013–8021 (2012) 15. Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Vis. Comput. 60, 4–21 (2017) 16. Scholz, M., Sigg, S., Schmidtke, H.R., Beigl, M.: Challenges for device-free radio-based activity recognition. In: Workshop on Context Systems, Design, Evaluation and Optimisation, Conference Proceedings (2011) 17. Wang, S., Zhou, G.: A review on radio based activity recognition. Dig. Commun. Netw. 1(1), 20–29 (2015) 18. Vrigkas, M., Nikou, C., Kakadiaris, I.A.: A review of human activity recognition methods. Front. Robot. AI 2, 28 (2015) 19. Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimed. 19(2), 4–10 (2012)

Lora-Based Smart Garbbage Alert Monitoring System Using ATMEGA 328, 2560, 128 Anzar Ahmad and Shashi Shekhar

1 Introduction The flooding of the trash canisters is exceptionally regular in India, yet this will affect our general public, our environmental factors. It will harm the natural qualities that lead to cause the contamination alongside the medical problems for human and different creatures too. We proposed an IOT-based cost profitable rubbish watching structure which will screen and prepared when the garbage level crosses the cutoff level of the refuse container. This strategy will be finished with the help of sensors, microcontroller, and ESP8266. It will similarly offer them to get to our structure from a noteworthy separation by using a WiFi repeat nearby the advised texts, Facebook alert, email caution to. This will reduce the human undertakings, moreover decreases the fuel use. We are living during a period where assignments and structures are joining with the power of IOT to have an inexorably capable plan of working and to execute occupations quickly! With all the power promptly accessible, this is what we have composed. The Internet of things (IoT) will have the alternative to join direct and immaculately a tremendous number of different structures, while offering data to a large number of people to use and endorse. Building a general structure for the IoT is hence an amazing undertaking, mainly because of the inconceivably tremendous collection of contraptions, interface layer advances, and organizations that may be related with such a system. One of the rule stresses with our condition has been solid waste organization which impacts the prosperity and state of our overall population. The disclosure, checking, and the officials of wastes are one of the basic issues of A. Ahmad (B) · S. Shekhar Graphic Era Deemed To Be University, Dehradun, Uttrakhand, India e-mail: [email protected] S. Shekhar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_3

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the present time. The standard strategy for truly watching the misfortunes in waste containers is a massive system and uses progressively human effort, time and cost which can without a lot of a stretch be kept up a key good ways from with our present advances. This is our answer, a procedure where waste the board is modernized. This is our IoT garbage monitoring structure, a creative way that will help with keeping the urban zones perfect and sound. Follow on to see how you could have an impact to help clean your area, home or even ecological variables, making us a step increasingly like an unrivaled technique for living:

2 Literature Review I.

Chaware et al. [1] present garbage monitoring framework, which screens the trash receptacles and illuminates about the degree of trash gathered in the trash canisters by means of a website page. Figure 1 shows the system architecture, wherein framework utilizes ultrasonic sensors set over the containers to recognize the trash level and contrast it, and the trash receptacles profundity. The proposed framework utilizes Arduino family microcontroller (the LPC2131/32/34//38 microcontrollers depend on a 16/32-piece ARM7TDMI-S CPU with constant imitating), LCD screen, WiFi modem(the ESP8266 underpins APSD for VoIP applications and Bluetooth concurrence interface) for sending information and a signal, GSM (used to send message to the trash warehouse if the garbage can surpasses the set edge level) ultrasonic (sensor conveys a high-recurrence sound heartbeat and afterward times to what extent it takes for the reverberation of the sound to reflect back). II. RFID innovation is utilized for assortment of information with respect to trash compartment. RFID label identified inside the recurrence extends and when any label goes to the scope of RFID peruser, it consequently peruses information from RFID peruser; at that point, channels gathered information and organizes it into explicit designed SMS. From that point forward, the information is sent Fig. 1 Overflowing of garbage bins

Lora-Based Smart Garbbage Alert Monitoring System …

23

to focal server and sends the data to the web server just as approved individual’s cell phone. III. This paper proposed a technique as follows. The degree of trash in the container is recognized by utilizing the ultrasonic sensor and imparts to control room utilizing GSM framework. Four IR sensors are utilized to distinguish the degree of the trash receptacle. At the point when the container is full, the yield of the fourth IR is dynamic low, and this yield is given to microcontroller to make an impression on control room through GSM. In this paper, ZigBee, GSM, and ARM7 controller are utilized to screen the trash receptacle level. At the point when trash container is full, this message of trash level is sentto ARM7 controller. At that point, ARM7 will send the SMS through GSM to power with regard to which receptacle is flooding and requires tidying up. IV. The paper proposed strategy as ultrasonic sensors are utilized to detect the degree of canister and burden cell is utilized as an optional sensor. In the event that the level sensors are falling flat; at that point, load cell can be utilized as a kind of perspective. At the point when the container is full, GSM sends the message to the server room. This message contains the arrangement of the container which is given by GPS module. The microcontroller gets the contribution from GSM and performs signal handling. Microcontroller conveys to GSM by utilizing UART. V. In the paper, the framework is planned so that it stays away from flood of the receptacle by sending an alarm. It utilizes Arduino Uno R3 as a microcontroller for perusing information from sensors. This innovation for the most part utilizes RFID peruser which is interfaced with a microcontroller for the confirmation process. When RFID label interferes with the RFID peruser, the sensor will check the status of the receptacle and sends it to the web server.

3 Existing System In the current framework, the trash is gathered by the region workers on the planned routine premise, for example, week by week or 2–3 times inside the months. As we see commonly that trash canisters are set in the open places in the urban areas are flooding because of increment in the waste regular. Because of this, the trash psychologists and produces the awful stench which will in general reason the air contamination and spread infections. That can make the damage human wellbeing. In this manner, cleaning is the huge issue. Additionally finding the way of trash canister is one of the undertaking extraordinarily for new driver. According to stay away from such conditions, we have planned the improved framework.

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4 Proposed System In our proposed framework, which is the IOT-based savvy trash checking framework alongside the ESP8266, there is the constant observing with cautioning office. Prior frameworks which were configuration were not cost proficient; likewise, they are cumbersome in size, as they were utilizing Raspberry Pi module, GSM module, additionally some utilizing GPS radio wire, and so forth. Here in our motivation framework, we have expelled all the equipment part to lessen the size of hardware and this will likewise diminish the expense of the framework and we are utilizing LORA sensor to make our hardware progressively solid. Also, we are utilizing solar board here for power flexibly with the battery reinforcement for shady circumstances.

5 Working IOT-based keen trash checking framework utilizing ESP8266 is straightforward and ongoing. Fundamentally, the procedure begins from the trash receptacle. IR sensors are fixed on the each degree of the trash container. Here we are taking the 5° of the trash receptacle for our undertaking exhibit. We are giving the novel ID to every trash canister. Additionally, we are choosing the edge level for cautioning reason. Trash level is detected by the IR sensors. When the trash in the trash canister crosses the limit level, the alarming instant message will get gave to the concerned individual or in the district office. This message contains the trash container ID alongside the WiFi module. This WiFi module will assist with finding that trash canister is full or void, and we are additionally to send the information to closest district branch. This is useful particularly for new drivers of that district vehicle (Fig. 2). Block diagram shows the working of the framework. Essentially there are five primary pieces of the entire framework. Force gracefully part, detecting part, handling part, transferring to the server/cloud, and the cautioning part. IR sensors detect the trash level and as needs be imparts the signs to the ATMEGA328 microcontroller. Likewise the GPS coordinates of the trash receptacle are given to the microcontroller.

Fig. 2 Working block diagram of garbage monitoring system

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Fig. 3 Garbage monitoring system

ATMEGA328 process the got signal and passed further to the ESP8266. ESP8266 is a WiFi module which is additionally filling in as a transmitter in our framework. ESP8266 assumes significant job in diminishing the equipment of the framework. It replaces the Raspberry Pi module. As our framework is IOT based, the alarming will get occurring with the assistance of IOT. Because of this, GPS module is expelled. The cautioning message with the WiFi module has no compelling reason to utilize the GPS radio wire since we can take care of the coordinates of the trash receptacle in the programming part as the situation of the trash container is fixed. Thus, when trash crosses the edge level, the cautioning message will get constantly send until the trash in the trash container is expelled by the concerned individual. Thus, our entire framework will work. For the force flexibly, we are utilizing the sun-oriented board here alongside the battery reinforcement (Fig. 3).

6 Cloud Database Cloud stages permit clients to buy virtual machine cases temporarily, and one can run a database on such virtual machines. Clients can either transfer their own machine picture with a database introduced on it or utilize instant machine pictures that as of now incorporate an enhanced establishment of a database. With a database as an assistance model, application proprietors do not need to introduce and keep up the database themselves. Rather, the database specialist organization assumes liability for introducing and keeping up the database, and application proprietors are charged by their use of the administration. • Most database administrations offer online consoles, which the end client can use to arrangement and design database occasions. • Database administrations comprise of a database-director segment, which controls the hidden database occasions utilizing an assistance API. The administration API is presented to the end client, and grants clients to perform upkeep and scaling procedure on their database occasions.

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Table 1 Simulated output

Time stamp

Key

Trash (high/low)

2020-03-12, 19:09:43

*******

High

2020-03-12, 20:09:44

*******

Low

2020-04-12, 05:09:46

*******

Low

2020-05-12, 12:09:52

*******

High

• Underlying programming stack ordinarily incorporates the working framework, the database and outsider programming used to deal with the database. The specialist co-op is liable for introducing, fixing, and refreshing the basic programming stack and guaranteeing the general wellbeing and execution of the database. • Scalability highlights contrast between merchants—some offer auto-scaling, others empower the client to scale up utilizing an API, yet do not scale consequently. • There is regularly a responsibility for a specific degree of high accessibility (e.g., 99.9% or 99.99%). This is accomplished by duplicating information and bombing examples over to other database cases.

7 Simulated Output of the System on the Cloud See Table 1.

8 Components Required Hardware Requirements • • • • • • • • • • • • •

ATMEGA 328,128, 2560 ESP8266 WiFi Module HC-SR04 Ultrasonic/IR Sensor Buzzer Crystal Oscillator Resistors Capacitors Transistors Cables and Connectors Diodes PCB and Breadboards LED Transformer/Adapter

Lora-Based Smart Garbbage Alert Monitoring System …

• • • •

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Push Buttons Switch IC IC Sockets.

Software Requirements • Arduino Compiler • IOT Gecko.

9 IR Sensor An infrared (IR) sensor is an electronic gadget that measures and distinguishes infrared radiation in its general condition. Infrared radiation was incidentally found by a stargazer named William Herchel in 1800. While estimating the temperature of each shade of light (isolated by a crystal), he saw that the temperature just past the red light was most noteworthy. IR is undetectable to the natural eye, as its frequency is longer than that of obvious light (however it is still on the equivalent electromagnetic range). Anything that discharges heat (everything that has a temperature above around five degrees Kelvin) emits infrared radiation. There are two sorts of infrared sensors: dynamic and inactive. Dynamic infrared sensors both emanate and distinguish infrared radiation. Dynamic IR sensors have two sections: a light transmitting diode (LED) and a recipient. At the point when an item approaches the sensor, the infrared light from the LED reflects off of the article and is identified by the recipient. Dynamic IR sensors go about as closeness sensors, and they are regularly utilized in deterrent recognition frameworks, (e.g., in robots). The IR sensor module comprises for the most part of the IR Transmitter and Receiver, Op-amp, Variable Resistor (Trimmer pot), yield LED in a nutshell. IR LED emanates light, in the scope of Infrared recurrence. IR light is imperceptible to us as its frequency (700 nm–1 mm) is a lot higher than the obvious light range (Fig. 4). Fig. 4 IR sensor specification

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10 LoRaWAN LoRa is a technique for transmitting radio signals that utilize a tweeted, multi-image configuration to encode data. It is an exclusive framework made by chip producer Semtech; its LoRa IP is likewise authorized to other chip makers. Basically, these chips are standard ISM band radio chips that can utilize LoRa (or other balance types like FSK) to change over radio recurrence to bits, with no compelling reason to compose code to actualize the radio framework. LoRa is a lower-level physical layer innovation that can be utilized in a wide range of uses outside of wide zone. LoRaWAN is a point-to-multipoint organizing convention that utilizes Semtech’s LoRa adjustment conspire. It is not just about the radio waves; it is about how the radio waves speak with LoRaWAN entryways to do things like encryption and recognizable proof. It additionally incorporates a cloud part, which various portals associate with. LoRaWAN is once in a while utilized for mechanical (private system) applications because of its confinements. LoRaWAN has three classes that work at the same time. Class An is absolutely offbeat, which is the thing that we call an unadulterated ALOHA framework. This implies the end hubs do not trust that a specific time will address the passage—they essentially transmit at whatever point they have to and lie torpid up to that point. In the event that you have an impeccably planned framework more than eight channels, you could occupy each schedule opening with a message. When one hub finishes its transmission, another beginnings right away. With no holes in correspondence, the hypothetical most extreme limit of an unadulterated salaam arrange is about 18.4% of this greatest. This is expected to a great extent to impacts, in such a case that one hub is transmitting and another awakens and chooses to transmit in a similar recurrence channel with a similar radio settings, they will impact. Class B taken into account messages to be sent down to battery-controlled hubs. At regular intervals, the door transmits a reference point. (See the availabilities over the highest point of the graph.) All LoRaWAN base stations transmit guide messages at precisely the same time, as they are slave to one heartbeat for every second (1PPS). This implies each gp satellite in circle transmits a message toward the start of consistently, permitting time to be synchronized the world over. All Class B hubs are alloted a schedule opening inside the 128 s cycle and are advised when to tune in. You can, for example, advise a hub to listen each tenth schedule opening, and when this comes up, it takes into consideration a downlink message to be transmitted (see above graph). Class C permits hubs to listen continually and a downlink message can be sent whenever. This is utilized fundamentally for AC-controlled applications, since it takes a great deal of vitality to keep a hub effectively conscious running the beneficiary consistently.

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11 Principle of Operation This task IOT garbage monitoring framework is a creative framework which will assist with keeping the urban areas clean. This framework screens the trash receptacles and educates about the degree of trash gathered in the trash containers by means of a site page. For this, the framework utilizes ultrasonic sensors put over the containers to identify the trash level and contrast it and the trash canisters profundity. The framework utilizes AVR family microcontroller, LCD screen, WiFi modem for sending information and a signal. The framework is controlled by a 12 V transformer. The LCD screen is utilized to show the status of the degree of trash gathered in the containers, though a site page is worked to demonstrate the status to the client observing it. The page gives a graphical perspective on the trash receptacles and features the trash gathered in shading so as to show the degree of trash gathered. The LCD screen shows the status of the trash level. The framework puts on the ringer when the degree of trash gathered crosses as far as possible. Along these lines, this framework assists with keeping the city clean by illuminating about the trash levels of the containers by giving graphical picture of the receptacles by means of IOT Gecko web advancement stage (Fig. 5).

Fig. 5 Circuit diagram to establish a system of garbage monitoring system

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12 Conclusion This paper introduced the IOT-based smart garbage monitoring system using ESP8266 with the GPS link. It will provide the improved efficient solution to the waste management issue over the previous systems. This will responsible to reduce the health-related issues and putted the best example for real-time garbage management system. In papers, we studied the various technologies for garbage collection and management process. Various technologies are LORA(long range), IoT, etc. This smart garbage monitoring System designs will be very beneficial to our societies, economics development as the fuel, cost, transport system will be reduced. The system is efficient as it reduced human effort.

References 1. Int. J. Res. Sci. Eng. 3(2) (2017). e-ISSN: 2394-8299 2. Zanella, A., Bui, N., Castellani, A., Vengelista, L., Zorzi, M.: Internate of things for smart cities. IEEE Internet Things J. 1(1) (2014) 3. Mahajan, K., Chitode, J.S.: Waste bin monitoring system using integrated technologies. Int. J. Innov. Res. Sci., Eng. Technol. (An ISO 3297: 2007 Certified Organization) 3(7) (2014) 4. Int. J. Recent Innov. Trends Comput. Commun. 5(2) (2017). ISSN: 2321-8169 5. Bloor, R.: What is a cloud database? Retrieved 25th Nov 2012 from https://www.algebraix data.com/wordpress/wp-content/uploads/2010/01/AlgebraixWP2011v06.pdf (2011) 6. Curino, C., Madden, S., et.al.: Relational Cloud: A Database as a Service for the Cloud. Retrieved 24th Nov 2012 from https://www.cidrdb.org/cidr2011/Papers/CIDR11_Paper33.pdf 7. Finley, K.: 7 Cloud-Based Database Services. Retrieved 23rd Nov 2012 from https://readwr ite.com/2011/01/12/7-cloud-based-database-service (2011) 8. Hacigumus, H., Iyer, B., Mehrotra, S.: Ensuring the Integrity of Encrypted Databases in the Database-as-a-Service Model. Retrieved 24th Nov 2012 from https://link.springer.com/cha pter/10.1007%2F1-4020-8070-0_5?LI=true (2004) 9. Santos, A., Macedo, J., Costa, A., Nicolau, M.J.: Web of things and keen items for M-wellbeing observing and control. Procedia Innov. 16, 1351–1360 ((2014)). Kumar, N.S., Vuayalakshmi, B., Prarthana, R.J., Shankar, A.: IOT based brilliant trash ready framework utilizing Arduino UNO. In: 2016 IEEE District 10 Meeting (TENCON) (pp. 1028–1034). IEEE (2016) 10. Sedra, Smith: Microelectronic circuits, 5th edn. New York (2004) 11. Ma, Y.-W., Chen, J.-L.: Toward intelligent agriculture service platform with lora-based wireless sensor network. In: Proceedings of the 4th IEEE International Conference on Applied System Innovation (ICASI), Chiba, Japan, 13–17 Apr 2018, pp. 204–207 12. Pies, M., Hajovsky, R.: Monitoring environmental variables through intelligent lamps. In: Mobile and Wireless Technologies. Springer: Singapore, pp. 148–156 (2018)

Pre-birth Prognostication of Education and Learning of a Fetus While in the Uterus of the Mother Using Machine Learning Harsh Nagesh Mehta and Jayshree Ghorpade Aher

1 Introduction Education is the process of achieving knowledge, values, skills, beliefs and moral habits. People must be ingrained with good-quality education to be able to match up with this competitive world. Education is axe for any country and it is understood how governments are applying different strategies for creating progressive modern society by building human capability and reducing inter-generational disadvantage. Career education and guidance play an important role in curriculum that supports student’s interests, strengths and aspirations thus helping students making informed decisions about their subject choices and pathways. Strategies have already been developed by the government to resolve these issues, but still newer ways to resolve these issues are studied. In recent epoch, prenatal education by laying pre-birth prognostication of fetus in the uterus of the mother has been provoked. Researches in recent time have shown how foundation of the future health, learning and behavior of a baby while in the uterus of the mother is laid through children’s experiences while in the womb with their family, community and early learning environments. The hierarchical process of development of fetus’s brain before birth is highly impacted by the child’s early experience through prenatal education. It has been observed that the grasping power of a fetus in mother’s uterus is superior compared to a born child. Researchers have found out the delicate stages in a human brain when maximum development of the brain is possible and have proved most receptive stages occur during the pregnancy, i.e., when the child is in the womb. These delicate sensitive spell of 9 months can be an opportunity to boost baby’s development and H. N. Mehta (B) · J. G. Aher SCET, MITWPU, Kothrud, Pune, Maharashtra 411038, India e-mail: [email protected] J. G. Aher e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_4

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their readiness for future school learning. All the three aspects, i.e., health, values and creativity, go in conjunction in developing the child’s mind. All the skills set, values and qualities which are acquired during pregnancy from the mother can be used to anticipate which education stream the child would be likely suitable to opt for in future. This prediction can be achieved with the help of decision tree classification algorithm of machine learning. Decision tree from the collected information can easily get the most predicted outcome given a particular input. Thus, prediction can be made upon what the possible future of a baby inside the womb will be using decision tree algorithm.

2 Why Subsidizing in Early Brain Development? 2.1 Literature Survey Humans are inbred to learning, and humans are observed to evolve through ceaseless learning right from its existence. Alongside to this, in current axe, transition has shifted to education and learning even before birth, and extensive emphasis is now paid on experimenting on prenatal education by providing pre-birth interventions for fetus inside the uterus of the mother [1]. How a mother reacts and interacts with the baby during her pregnancy may have implications on the baby’s development. It was found by University of Cambridge [2] that mothers who “connect” with their baby during pregnancy are more likely to interact in a more positive way with their infant after it is born. A good foundation for baby’s development, future education and learning can be provided by creating a couraging, enhancing, caring environment replenish with pacific and warm interaction. Shelina Bhamani [3] has addressed development of the baby’s brain in utero. It is predicted by scientists working in the field of child development that when a baby is in uterus of the mother, the baby is able to hear and remember certain sounds and he or she is easily able to recall it even after his/her birth [4]. Among the sounds of a pregnant mother’s beating heart, breathing and blood coursing through veins, the baby is able to hear scuffed noise from the external environment as well [5]. All sense organs of the fetus begin to grow up in prenatal period. Human ear starts growing after 10 weeks of pregnancy. Using doppler ultrasound signals sensitivity to maternal voice, Tastan and Hardalac [6] proved that fetus has a learning process with experience in uterus. According to the studies, it has been proved that the grasping power of a fetus in mother’s uterus is superior compared to a born child. Also, as the age rises, the development of the human brain gradually stops. Growth of the brain is most during the duration of pre-birth to 5 years with maximum being in fetus. Apart from the various movements of the mother, maternal age plays as significant role in the overall development of the fetus [7].

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Fig. 1 Three aspects of prenatal education

2.2 The Three Key Aspects What Baby Learns Prenatal development is the evolution of the human body before the birth, and it is also known as antenatal development. Prenatal education is the process in which the expectant parents undergo a series of activities in order to interact positively with the baby. While for baby’s prenatal education is the teaching the baby receives from their families that helps them to develop [8]. All these three aspects—health, values and creativity go in conjunction in developing the child’s mind as seen in Fig. 1. All the skills set, values and qualities which are acquired during pregnancy from the mother can be used to anticipate which education stream the child would be likely suitable to opt for in future. Values are the way in which one is distinguished on basis of things, people, action or maybe situation. A mother having values transmits them to the child through her actions, while health is defined as the overall well-being of a human, activities such as maintaining the diet, yoga, spa, etc. Creativity is doing things which turn your imaginative ideas into reality. Language, art, music, mathematics, leadership are the some creativity aspects that help baby’s development toward education and learning. For pre-birth prognostication of education and learning of a fetus, we need to consider the creativity aspect of development as it is directly involved in the technical brain development.

2.3 How Early Staged Career Guidance Can Shape Future Half of the college drop-outs globally are due to financial condition or due to academic disqualification. Academic disqualification occurs majorly when there is lack of interest toward the subject. Career education and guidance play an important role in curriculum that supports student’s interests, strengths and aspirations thus helping students making informed decisions about their subject choices and pathways [9]. Predicting what the baby can actually do with all the experiences he received can be used to predict what the future stream of education and learning of the baby will be. Different streams of education, i.e., science, commerce, arts, require different skill set which a baby can acquire from their mothers. Creative aspect of prenatal education can be viewed as set of basic skill sets required in the different fields of studies. As the baby receives these creative aspects and skills from the mother, we can keep track of it and thus help in providing a newer insight into the field of

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career guidance. This prediction can be achieved with the help of different prediction algorithm of machine learning. Machine learning technique like decision tree is most suitable for our pre-birth prognostication of future education and learning.

3 How the Future Learning Can Be Predicted In order to provide career guidance for the baby, creativity aspect needs to be taken into account. Creativity is doing things which turn your imaginative ideas into reality (Table 1). In order to build various creativity aspects, tasks focused on very individual aspect must be performed. If we analyse the career guidance predication strategy, it is keening observed that all the creativity aspects used in developing fetus brain are the key skills set required to predict the optimal education stream for the fetus, As most of the countries around the world follow three different streams of education, namely science, commerce and arts, the career guidance strategy in this paper also revolves around these streams for prediction. Each field requires a person to have different mindset and skills in order to opt for it. Person tending to satisfy those skills are likely to have a greater liking toward the stream and are likely to succeed in it too. Decision tree classification algorithm can prove useful for us in this scenario as its easy for interpretation and provides higher accuracy most of the times.

3.1 Applying Decision Tree to Predict Educational Stream Decision tree is a tree-like structure wherein the internal nodes of the tree structure are the test attributes and the leaf nodes are the class labels. By investigating the various Table 1 Demand of different skill set to succeed in the different field

Science

Commerce

Arts

Cognition

Mathematics

Cognition

Logical thinking

Imaginative thinking Language

Planning and problem solving

Social interaction

Stability of mind

Interpretation of data Music

Observation

Comprehension

Imaginative thinking

Researching skills

Evaluation skills

Creativity

Persistence

Self-confidence

Discipline

Mathematics

Persistence

Persistence

–

Leadership

–

Art

Pre-birth Prognostication of Education and Learning of a Fetus …

35

Table 2 Various attributes and class label for decision tree prediction for future education stream Attributes

Age, Cognition, Logical Thinking, Planning and Problem Solving, Stability of Mind, Observation, Researching Skills, Persistence, Mathematics, Imaginative thinking, Social Interaction, Interpretation of data, Comprehension, Evaluation Skills, Self-Confidence, Language, Art, Music, Creativity, Discipline

Class label

Likely Stream (Science, Commerce and Arts)

activities and tasks done by the approximately 100 women’s during the pregnancy, an overall data was collected. In this data keeping in mind the future learning of the baby, all the creativity aspects tasks and activities of the mother were noted down. After investigating the data, for the prediction purpose, it has been segregated into attributes and class label as can see be seen from Table 2. In order to find the predicted future education stream in this paper, decision tree algorithm was applied after splitting the data into train and test and the training data is passed to DecisionTree function which performs the construction of the model using.fit() function. In the model usage phase of the we make use of.predict() function on the training data of attributes and thus compare it with the training class label data to completed the model and come up with the accuracy. From Fig. 2, we can easily interpret how decision tree can be used to predict the future of the baby’s education and learning by investigating the train data and testing on the test data, thereby making prediction unseen randomly inputted data. Through decision tree model, accuracy of 93.33% was found out thus helping us to know the field the baby is currently intended toward by analyzing the creativity aspect during pregnancy.

3.2 Merits and Demerits of This Predication It has seen how decision tree model worked to predict the data. With the help of this model, we can conclude some benefits of the model • Predict the effect of various tasks and activities performed by the mother during pregnancy on the child’s brain. • Reduce the drop-outs of students from college because of the issue of academics disqualification by guiding the child for his future through prenatal career guidance. While this subject of study can be the future of career guidance, it comes along with some demerits; some of them being • One of the major demerits being that we cannot exactly predict what the child would want to do after few years as their learning interest can change. • Apart from this, there are many factors like older aged mother, premature birth, alcohol and smoking of mother which can affect the brain’s development even

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H. N. Mehta and J. G. Aher

Fig. 2 Results achieved from decision tree to predict the future stream

if they various activities are performed by mother and thus making it difficult to predict the future education stream.

4 Conclusion Thus, by understanding and analysis of what a mother does in 9 months of pregnancy, we can predict its effects on child’s brain and how it can be helped to predict the future education and learning of the child.

Pre-birth Prognostication of Education and Learning of a Fetus …

37

References 1. Kleindorfer, S., Robertson, J.: Learning before birth. Australasian Science 34(9), 27–32 (2013) 2. University of Cambridge: Mother’s attitude towards baby during pregnancy may have implications for child’s development (2018) 3. Bhamani, S.: Educating Before Birth via Talking to the Baby in the Womb. J. Educ. Educ. Dev. 4, 368. https://doi.org/10.22555/joeed.v4i2.1736 4. Alvarez-Buylla, A., et al.: Birth of projection neurons in the higher vocal center of the canary forebrain before, during, and after song learning. Proc. Natl. Acad. Sci. U.S.A. 85(22), 8722– 8726 (1988). https://doi.org/10.1073/pnas.85.22.8722 5. Reissland, N. et al.: Do facial expressions develop before birth? PloS One 6(8), e24081. https:// doi.org/10.1371/journal.pone.0024081(2011) 6. Tastan, A., Hardalac, N., Kavak, S.B., Hardalaç, F.: Detection of fetal reactions to maternal voice using doppler ultrasound signals. In: 2018 International Conference on Artificial Intelligence and Data Processing, 2(5), 99–110 (2016) 7. Standford Children Care—“Risks of Pregnancy Over Age 30” 8. Svensson, J. et al.: Effective antenatal education: strategies recommended by expectant and new parents. J. Perinatal Educ. 17(4), 33–42 (2008). https://doi.org/10.1624/105812408X364152 9. Angra, S., Ahuja, S.: Machine learning and its applications: a review. In: 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, pp. 57– 60 (2017). https://doi.org/10.1109/ICBDACI.2017.8070809

Performance Analysis of Single-Stage PV Connected Three-Phase Grid System Under Steady State and Dynamic Conditions V. Narasimhulu and K. Jithendra Gowd

1 Introduction Photovoltaic systems have become an energy generator for a wide range of applications. The applications could be standalone PV systems or grid-connected PV systems. A standalone PV system is used in isolated applications, where a PV system that is connected through a grid is used when a PV system injects the current directly into the grid itself. The advantage of the grid-connected system is the ability to sell excess of energy. In response to global concerns regarding the production and deliverance of electrical power, photovoltaic (PV) technologies are attracted toward continue and improving living standards without environmental effect. Conventionally, two-stage PV grid-connected systems to converter dc to ac power. Two stage PV systems required both boost converter and inverter for power conversion. It leads to cost and complexity of the system. To overcome this, single-stage PV conversion system is used, and the cost and complexity of the system are reduced by eliminating boost converter in this system. To extract maximum power from the PV system [1], a robust controller is required to ensure maximum power point tracking (MPPT) [1–3] and deliver it to the grid through the use of an inverter [4–6]. In a grid-connected PV system, control objectives are met by using a pulse width modulation (PWM) scheme based on two cascaded control loops [7]. The current loop is also responsible for maintaining power quality (PQ) and for current protection that has harmonic compensation. Linear controllers are widely used to operate PV systems at MPP [8– 13]; however, most of these controllers do not account for the uncertainties in the V. Narasimhulu (B) EEE Department, RGM College of Engineering and Technology (Autonomous), Nandyal, Andhra Pradesh, India e-mail: [email protected] K. Jithendra Gowd EEE Department, JNTUA CEA, Anantapuramu, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_5

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PV system. The voltage dynamics of the dc link capacitor include non-linearities due to the switching actions of the inverter. The inclusion of these nonlinearities will improve the accuracy of the PV system model; however, the grid-connected PV system will be partially rather than exactly linearized as shown in [14]. Although the approaches presented in [15–17] ensure the MPP operation of the PV system, they do not account for inherent uncertainties in the system as well as the dynamics of the output LCL filter. The M2C-based single-stage PV conversion system is proposed. The PV system and M2C operating principle are presented in Sect. 2. The simulation result for validation of proposed topology is presented in Sect. 3. Finally, the conclusions were made in Sect. 4.

2 Photovoltaic System There are two stages of power conversion in the two-stage PV system but only one stage conversion in single-stage PV system. Bck diagram of single-stage PV conversion system is shown in Fig. 1. It consists of PV panel, inverter, LCL filter, and PWM along with MPPT technique. The three-phase voltage signal is sent to the PLL block to track the frequency under different operating conditions. The grid voltage, current signals, and generated PV voltage and current signals are sent to the MPPT controller to track maximum power and to generate the reference signal for PWM controller. Here, sinusoidal PWM is used to generate the gate pulses for inverter due to its simplicity.

2.1 PV System The PV system is modeled using [18, 19] Eq. (1).

MPPT

PWM PLL DC-AC Converter

PV System

&

LCL Filter Fig. 1 Block diagram of single-stage PV system

3-Ф GRID

Performance Analysis of Single-Stage PV Connected Three-Phase …

      v R i Rse i pv vpv α pv + se pv − Is e Nse Np − Is i pv = NP IL − + Rp Nse Rp N P

41

(1)

where ipv and vpv are the output current and voltage of the PV system. The Rp and Rse are the parallel and series resistance of the PV panel. I L and I s are the sun light produced current and solar cell saturation current, respectively. N p and N se are the number of parallel and series connected cells.

2.2 Power Electronic Converter In this paper, the M2C is adopted to analyze the level of THD in output voltage due to less conduction losses and its simplicity in modeling. One possible structure of M2C [20, 21] is shown in Fig. 2. For higher power applications, i.e., commercial or industrial applications, a three-phase PV power conditioning system is preferable. Submodules of SM1, SM3, and SM5 are the upper or positive side connected modules. The SM2, SM4, and SM6 are the bottom or negative side connected modules. Submodules are used in this application to convert voltage from DC to AC. The type of inverter to be used in the power conditioning unit for this study was selected to be three-level modular multilevel inverter. The M2C is controlled in voltage mode using well known sinusoidal pulse width modulated (SPWM) switching technique. PWM is generated using sine triangle PWM. For simulation purposes, due to the high frequency of the carrier (5 kHz), a much higher sampling frequency is chosen to run the simulation which reduces the speed of execution badly. In sine triangle PWM, in order to produce the output voltage of desired magnitude waveform, phase shift, and frequency, the desired signal is compared with a carrier of higher frequency

Fig. 2 PV grid connected using M2C

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V. Narasimhulu and K. Jithendra Gowd

to generate appropriate switching signals. The output voltage of the VSI does not have the shape of the desired signal, but switching harmonics can be filtered out by the series LCL low pass filter, to retrieve the 50 Hz fundamental sine wave. The DQ method is employed to extract the reference currents for PWM.

2.3 MPPT Method For maximum power transfer, the load should be matched to the resistance of the PV panel at MPP. Therefore, to operate the PV panels at its MPP, the system should be able to match the load automatically and also change the orientation of the PV panel to track the sun if possible. A control system that controls the voltage or current to achieve maximum power which is needed. This is achieved using a MPPT algorithm to track the maximum power. The incremental conductance method is implemented in this work to track maximum power. It uses the advantage that the derivate of the power with respect to the voltage at the maximum power point is zero. Phase locked loops (PLL) are employed in order to track the angular frequency and phase shift of the three-phase voltages for synchronization.

3 Simulation Result Analysis To estimate the concert of the three-phase grid-connected PV system with the proposed topology, a PV array with a total output voltage of 850 V is used. The grid voltage is 660 V with grid frequency of 50 Hz. The inverter switching frequency is considered to be 5 kHz. The capacitor value of 470µF is used for dc link voltage. The LCL filter includes an inductor of 5mH and a condenser of 2.2µF. Various operating conditions have been considered in order to verify the concert of the proposed topology. The performance of the proposed topology is corroborated under standard and changing atmospheric conditions. In case 1, normal solar irradiation (1 kW-2) and ambient temperature (298 K) values are considered. The three-phase grid-connected PV system is achieved the unity power factor which is shown in Fig. 3. In the case 2, it is considered that the PV unit operates under standard atmospheric conditions until 0.5 s. At t = 0.5 s, the atmospheric condition changes in such a way that the solar irradiation of the PV unit reduces to 50% from the standard value. Figure 4 shows that the PV unit operates under standard atmospheric conditions up to 0.5 s and changes in atmospheric conditions up to 0.6 s. After that, it operates under standard conditions, and the system maintains the operation at unity power factor. The dc link voltage balanced is achieved using PI controller in all the cases.

Performance Analysis of Single-Stage PV Connected Three-Phase …

43

Irradiation (w/m2)

1000 800 600 400 200 0 0

0.1

0.2

0.3

0.4

0.5 Time (s)

0.6

0.7

0.8

0.9

1

(a) Irradiation

DC Voltage (V)

850

0 0

0.4

0.2

Time (s)

0.8

0.6

1

(b) DC link voltage Voltage

Current

Voltage (V), Current (*5A)

933 500 0 -500 -933 0.45

0.5

0.6

0.55

0.65

0.7

0.8

1

Time (s)

(c) Displacement of Voltage and Current

Magnitude

1

0.5

0 0

0.2

0.4

0.6 Time (s)

(d) Power Factor

Fig. 3 Responses at standard atmospheric conditions

IRADIATION (W/m²)

44

V. Narasimhulu and K. Jithendra Gowd 1000

500

0 0

0.1

0.3

0.2

0.4

0.5 Time (s)

0.6

0.7

0.8

0.9

1

DC Voltage (V)

(a) Irradiation 850

0 0

0.6

0.4

0.2

0.8

1

Time (s)

(b) DC link Voltage Grid Voltage

Grid Current

Voltage (V), Current (*5A)

933 500 0 -500 -933 0.45

0.5

0.55

Time (s)

0.6

0.65

0.7

(c) Displacement of voltage and current

Magnitude

1 0.975

0

0.2

0.4

0.6 Time (s)

(d) Power Factor Fig. 4 Grid voltage and current under changing atmospheric conditions

0.8

1

Performance Analysis of Single-Stage PV Connected Three-Phase …

45

4 Conclusions In this paper, three-phase M2C-based single-stage PV grid-connected system is adopted and operated the system at unity power factor. The proposed topology has performed well under standard and changing atmospheric conditions. The DQ method has performed well to track the PWM reference currents and to synchronize the grid and PV panel. The sinusoidal PWM is implemented and controlled in systematic manner under all conditions. Single-stage PV conversion effectively performed in the conversion of dc-ac power. The PI controller effectively controlled the dc link voltage under all conditions.

References 1. Jain, S., Agarwal, V.: A single-stage grid connected inverter topology for solar PV systems with maximum power point tracking. IEEE Trans. Power Electron. 22(5), 1928–1940 (2007) 2. Zimmermann, U., Edoff, M.: A maximum power point tracker for long-term logging of PV module performance. IEEE J. Photovolt. 2(1), 47–55 (2012) 3. Koutroulis, E., Blaabjerg, F.: A new technique for tracking the global maximum power point of PV arrays operating under partial shading conditions. IEEE J. Photovolt. 2(2), 184–190 (2012) 4. Kjaer, B., Pedersen, J.K., Blaabjerg, F.: A review of single-phase grid-connected inverters for photovoltaic modules. IEEE Trans. Ind. Appl. 41(5), 1292–1306 (2005) 5. Esram, T., Chapman, P.L.: Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans. Energy Convers. 22(2), 439–449 (2007) 6. Houssamo, I., Locment, F., Sechilariu, M.: Maximum power point tracking for photovoltaic power system: Development and experimental comparison of two algorithms. Renew. Energy 35(10), 2381–2387 (2010) 7. Blaabjerg, F., Teodorescu, R., Liserre, M., Timbus, A.V.: Overview of control and grid synchronization for distributed power generation systems. IEEE Trans. Ind. Electron. 53(5), 1398–1409 (2006) 8. Kotsopoulos, A., Darte, J.L., Hendrix, M.A.M.: Predictive DC voltage control of single-phase pv inverters with small dc link capacitance. In: Proceedings of IEEE International Symposium on Industrial Electronics, pp. 793–797 (2003) 9. Meza, C., Negroni, J.J., Biel, D., Guinjoan, F.: Energy-balance modeling and discrete control for single-phase grid-connected PV central inverters. IEEE Trans. Ind. Electron. 55(7), 2734–2743 (2008) 10. Kadri, R., Gaubert, J.P., Champenois, G.: An improved maximum power point tracking for photovoltaic grid-connected inverter based on voltage-oriented control. IEEE Trans. Ind. Electron. 58(1), 66–75 (2011) 11. Selvaraj, J., Rahim, N.A.: Multilevel inverter for grid-connected PV system employing digital PI controller. IEEE Trans. Ind. Electron. 56(1), 149–158 (2009) 12. Rahim, N.A., Selvaraj, J., Krismadinata, C.C.: Hysteresis current control and sensorless MPPT for grid-connected photovoltaic systems. In: Proceedings of IEEE International Symposium on Industrial Electronics, pp. 572–577 (2007) 13. Kotsopoulos, A., Duarte, J.L., Hendrix, M.A.M.: A predictive control scheme for DC voltage and AC current in grid-connected photovoltaic inverters with minimum DC link capacitance. In: Proceedings of 27th Annual Conference on IEEE Industrial and. Electronic Society, pp. 1994– 1999 (2001)

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14. Zue, O., Chandra, A.: State feedback linearization control of a grid connected photovoltaic interface with MPPT. Presented at the IEEE Electronic Power Energy Conference, Montreal, QC, Canada, (2009) 15. Lalili, D., Mellit, A., Lourci, N., Medjahed, B., Berkouk, E.M.: Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter. Renew. Energy 36(12), 3282–3291 (2011) 16. Mahmud, M.A., Pota, H.R., Hossain, M.J.: Dynamic stability of three-phase grid-connected photovoltaic system using zero dynamic design approach. IEEE J. Photovolt. 2(4), 564–571 (2012) 17. Kaura, V., Blasko, V.: Operation of a phase locked loop system under distorted utility conditions. IEEE Trans. Ind. Appl. 33(1), 58–63 (1997) 18. Mahmud, M.A., Pota, H.R., Hossain, M.J., Roy, N.K.: Robust partial feedback linearizing stabilization scheme for three-phase grid-connected photovoltaic systems. IEEE J. Photovolt. 4(1) (2014). 19. Mahmud, M.A., Hossain, M.J., Pota, H.R., Roy, N.K.: Robust nonlinear controller design for three-phase grid-connected photovoltaic systems under structured uncertainties. IEEE Trans. Power Deliv. 29(3) (2014) 20. Debnath, S., Qin, J., Bahrani, B., Saeedifard, M., Barbosa, P.: Operation, control, and applications of the modular multilevel converter: a review. IEEE Trans. Power Electron. 30(1) (2015) 21. Acharya, A.B., Ricco, M., Sera, D., Teoderscu, R., Norum, L.E.: Performance analysis of medium-voltage grid integration of PV plant using modular multilevel converter. IEEE Trans. Energy Conv. 34(4) (2019)

Delay Feedback H∞ Control for Neutral Stochastic Fuzzy Systems with Time Delays T. Senthilkumar

1 Introduction Time delays emerge in different practical issues in the dynamic systems and source of instability some times and degradation in the control performance, which are encountered in various engineering systems, such as hydraulic, electronics, chemical, and communication biological systems [2, 3, 10]. In recent years, the study of time delay systems has received much more attention, and various stability analysis and the H∞ control methods have been discussed [3, 10] and and reference therein. On the other hand, the neutral time delays are often come across such as population ecology, heat exchanges, and lossless transmission lines. Thus, the neutral type with time delay systems in both stochastic and deterministic models is considered in [1, 3, 10, 11] and reference. In recent years, a great number of results on the stability analysis, stabilization, and H∞ control for stochastic systems with or without neutral systems have been widely studied in the literature, such as [1, 4–6, 9, 11] and references therein. Since the mathematical model prescribed by the author Takagi and Sugeno in [8], Takagi–Sugeno (T-S) fuzzy method approaches to establish effective method to good representation of complex nonlinear systems by some simple local linear dynamic systems over the past two decades. In [7, 12], the stabilization for time delay problem and H∞ control for state and input delay problems for stochastic systems with fuzzy model are developed. The fuzzy model approaches for delay feedback H∞ control method for neutral stochastic systems with time delay have not been considered in the past to the author’s knowledge. Motivated by the above discussion, the problem of delay feedback H∞ control for neutral stochastic fuzzy with time delay systems is considered in this paper. By T. Senthilkumar (B) Department of Mathematics, National Institute of Technology Puducherry, Karaikal, Puducherry 609609, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_6

47

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T. Senthilkumar

the Lyapunov stability theory and the linear matrix inequality (LMI) approach, the required delay feedback fuzzy controllers are designed in both drift and diffusion parts which satisfy such that the closed-loop system H∞ control for neutral stochastic system is stochastically stable and satisfies a prescribed level γ . Finally, a numerical example is given to show the effectiveness and feasibility of the developed theoretical method. Throughout this paper, notations are quite standard.

2 Problem Description Consider a class of neutral stochastic T-S fuzzy system with time delay as follows Plant rule i : IF υ1 (t) is ωi1 , υ2 (t) is ωi2 , · · · · · · and υ p (t) is ωi p , THEN     d x(t) − Ni x(t − h) = Ai x(t) + Adi x(t − h) + B1i u(t) + Avi v(t) dt   + E i x(t) + E di x(t − h) + B2i u(t) dw(t), (1) z(t) = Ci x(t) + Cdi x(t − h) + B3i u(t),

(2)

x(t) = ϕ(t),

(3)

t ∈ [−h, 0],

where i = 1, 2, . . . , r . r is the number of IF-THEN rules. ωi j is the fuzzy set, υ(t) = [υ1 (t) υ2 (t) . . . υ p (t)]T is the premise variable, x(t) ∈ Rn denotes the state vector, v(t) ∈ R p is the disturbance input defined on L2 [0 ∞). u(t) ∈ Rm and z(t) ∈ Rq is control input and controlled output, respectively. ω(t) is the standard Brownian motion defined on the complete probability space (Ω, F , {Ft }t≥0 , P) and satisfies E {dw(t)} = 0, E {dw(t)2 } = dt. ϕ(t) is real-valued continuous, h is positive scalar. ρ(Di ) < 1, and it denotes the spectral radius of Di . Utilizing center average defuzzifier, product inferences, and singleton fuzzifier, dynamic T-S fuzzy model (1)–(3) is expressed as follows: d x(t) =

r 

h i (υ(t))



 Ai x(t) + Adi x(t − h) + B1i u(t) + Avi v(t) dt

i=1

   +Ni d x(t − h) + E i x(t) + E di x(t − h) + B2i u(t) dw(t) ,

z(t) =

r 

  h i (υ(t)) Ci x(t) + Cdi x(t − h) + B3i u(t) ,

(4)

(5)

i=1

x(t) = ϕ(t), t ∈ [−h, 0],

(6)

where h i (υ(t)) = rνi (υ(t)) , νi (υ(t)) = j=1 ωi j (υ j (t)), and ωi j (υ j (t)) denoting i=1 νi (υ(t)) the grade of membership of υ j (t) in ωi j . It is easy to see that νi (υ(t)) ≥ 0 and p

Delay Feedback H∞ Control for Neutral Stochastic …

49

r

r > 0 for all t. Hence, h i (υ(t)) ≥ 0 and i=1 h i (υ(t)) = 1, ∀ t. Briefly, h i can be used to represent h i (υ(t)). Employing parallel distributed compensation technique, the following rules for fuzzy-model-based control are used in this paper: Control rule i : IF υ1 (t) is ωi1 , υ2 (t) is ωi2 , . . . and υ p (t) is ωi p , then i=1 νi (υ(t))

u(t) = K 1i x(t) + K 2i x(t − h), i = 1, 2, . . . , r.

(7)

Eventually, delay feedback fuzzy control law is obtained as u(t) =

r 

h i (K 1i x(t) + K 2i x(t − h))

(8)

i=1

where the matrices K 1i , K 2i are the controller gains. Combining (8) and (4)–(6), the overall closed-loop system can be expressed by the following r    h i Ni x(t − h) = f (t)dt + g(t)dw(t), d x(t) −

z(t) =

i=1 r  r 

hi h j

   Ci + B3i K 1 j x(t) + Cdi + B3i K 2 j x(t − h) ,

(9) (10)

i=1 j=1

x(t) = ϕ(t), t ∈ [−h, 0]

(11)

    where f (t) = ri=1 rj=1 h i h j Ai + B1i K 1 j x(t) + Adi + B1i K 2 j x(t − h) +      Avi v(t) and g(t) = ri=1 rj=1 h i h j E i + B2i K 1 j x(t) + E di + B2i K 2 j  x(t − h) . Definition 1 In this paper, our aim is to design a state feedback controller such that a) the system (9)–(11) is stochastically stable in the sense of Definition 1 in [9]; and b) z(t)E2 < γ v(t)2 for all nonzero v(t) ∈ L 2 [0, ∞) with zero initial condition x(0) = 0.

3 Main Results The sufficient condition for the solvability of the H∞ control for neutral stochastic fuzzy system with time delay is given as follows Theorem 1 Consider the closed-loop stochastic fuzzy system, (9)–(11) is stochastically stabilizable with a disturbance attenuation level γ > 0, if there exist matrices, Q¯ > 0, R¯ > 0, X > 0, Y1 j , Y2 j , (1 ≤ i ≤ j ≤ r ), and h > 0 such that the following LMIs are satisfied:

50

T. Senthilkumar

Ω ii Ω +Ω ij

ji

< 0, 1 ≤ i ≤ r

(12)

< 0, 1 ≤ i < j ≤ r

(13)

where ⎡

ij

Ω11 Adi j 0 Avi ⎢ 0 ⎢ ∗ − Q¯ 0 ⎢ ¯ 0 ⎢ ∗ ∗ − R ⎢ ⎢ ∗ ∗ −γ 2 I Ωi j = ⎢ ∗ ⎢ ∗ ∗ ∗ ∗ ⎢ ⎢ ∗ ∗ ∗ ∗ ⎢ ⎣ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗

⎤ AiTj E iTj CiTj 0 T CT X N T ⎥ ATdi j E di j di j i ⎥ ⎥ 0 0 0 0 ⎥ ⎥ ATvi 0 0 0 ⎥ ⎥, −X 0 0 0 ⎥ ⎥ ∗ −X 0 0 ⎥ ⎥ ∗ ∗ −I 0 ⎦ ∗ ∗ ∗ −X

with ij ¯ Ai j = Ai X + B1i Y1 j , E i j = E i X + B2i Y1 j , Ω11 = Ai j + AiTj + Q¯ + h 2 R,

Adi j = Adi X + B1i Y2 j , E di j = E di X + B2i Y2 j , Ci j = Ci X + B3i Y1 j , Cdi j = Cdi X + B3i Y2 j .

Then, the desired delay state feedback controller (8) can be realized by K 1 j = Y1 j X −1 , K 2 j = Y2 j X −1 , 1 ≤ j ≤ r.

(14)

Proof Consider the Lyapunov–Krasovskii functional: r r T      h i Ni x(t − h) P x(t) − h i Ni x(t − h) V (x(t), t) = x(t) − i=1

i=1

t x T (s)Qx(s)ds

+ t−h

0  t x T (s)Rx(s)dsdθ

+h

(15)

−h t+θ

where P > 0, Q > 0 and R > 0 are symmetric matrices with appropriate dimensions. By Ito’s ˆ Formula [5], stochastic derivative of V (x(t), t) is obtained as r  T  d V (x(t), t) = L V (x(t), t)dt + 2 x(t) − h i Ni x(t − h) Pg(t)dw(t). (16) i=1

Delay Feedback H∞ Control for Neutral Stochastic …

51

Using Lemma 1 in [11], it can be seen that −2x T (t − h)

r 

r r   h i NiT P f (t) ≤ x T (t − h) h i NiT P h i Ni x(t − h)

i=1

i=1

i=1

+ f T (t)P f (t). (17) From Lemma 2.3 in [6], we can obtain that −h

t

 t T  t x T (s)Rx(s)ds ≤ − x(s)ds R x(s)ds .

t−h

t−h

(18)

t−h

Then, from (17)–(18) and using Schur complement lemma, easily, we obtain, LV (x(t), t) ≤

r r  

h i h j ζ T (t)Ωˆ i j ζ (t),

(19)

i=1 j=1

where 

ζ (t) = x (t) x (t − h) T

T

T

 t x(s)ds

T

 v(t) , Ωˆ i j = Ω˜ i j + A˜ iTj P A˜ i j + E˜ iTj P E˜ i j

t−h

with ⎡

⎤ sym(P(Ai + B1i K 1 j )) + Q + h 2 R P(Adi + B1i K 2 j ) 0 P Avi T ∗ −Q + Ni P Ni 0 0 ⎥ ⎥, ∗ ∗ −R 0 ⎦ ∗ ∗ ∗ 0 T   T A˜ iTj = Ai + B1i K 1 j Adi + B1i K 2 j 0 Avi , E˜ iTj = E i + B2i K 1 j E di + B2i K 2 j 0 0 .

⎢ Ω˜ i j = ⎢ ⎣

Note that z T (t)z(t) ≤

r  r 

h i h j ζ T (t)C˘iTj C˘i j ζ (t)

(20)

i=1 j=1

 T where C˘iTj = Ci + B3i K 1 j Cdi + B3i K 2 j 0 0 . Now, we set J (t) = E

 t  0

  z T (s)z(s) − γ 2 vT (s)v(s) ds

(21)

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where t > 0. Under the zero initial condition for t ∈ [−h, 0], and it follows that J (t) = E

 t 

    z T (s)z(s) − γ 2 vT (s)v(s) + L V (x(s), s) ds − E V (x(t), t)

0

≤E

 t 

  z T (s)z(s) − γ 2 vT (s)v(s) + L V (x(s), s) ds

0

≤E

 t

 ζ T (s)Ωˇ i j ζ (s)ds ,

(22)

0

where Ωˇ i j = Ωˆ i j + C˘iTj C˘i j + diag(0 0 0 − γ 2 I ). If Ωˇ ii < 0, and (Ωˇ i j + Ωˇ ji ) < 0 holds for any (1 ≤ i <  j ≤ r ), equivalently to yield J (t) < 0. Pre- and post ij ˇ multiplying Ω by diag X, X, X, I and its transpose, respectively, defining the new variables X = P −1 , Q¯ = X Q X, R¯ = X R X and performing some simple algebraic manipulations,  we know that  the condition (12)–(13) holds, and the Schur complement ensures E LV (x(t), t) < 0. By Definition 1 and [5], the closed-loop stochastic fuzzy system (9)–(11) is stochastically stabilizable with disturbance a attenuation level γ . Remark 1 In the system (9) with v(t) = 0, in the above theorem easily, we can obtain the stabilization problem for the delayed feedback neutral stochastic fuzzy system with time delays. Remark 2 In [1, 11] have discussed the H∞ control method for uncertain neutral stochastic time delay system without fuzzy approach. In this paper, the H∞ control for neutral stochastic fuzzy systems with delay feedback is considered as special case.

4 Numerical Example Consider a delay feedback H∞ control for neutral stochastic fuzzy system (9)–(11) with the following parameters:

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53

      −0.3 0.2 −0.92 0.49 −0.1 −0.5 A1 = , A2 = , Ad1 = , 0.1 −0.4 0.15 0.51 0.5 0.01       −0.15 0.21 −0.3 0.2 −0.23 −0.4 Ad2 = , B11 = , B12 = , −0.1 −0.3 0.12 −0.2 0.3 −0.4       −0.2 0.3 −0.2 0.15 0.1 0 , Av2 = , N1 = N2 = , Av1 = −0.02 0.1 0.15 −0.33 0 0.1       −0.1 −0.2 −0.1 0.1 −0.3 0.1 , E2 = , E d1 = , E1 = 0.1 −0.4 0.1 −0.2 0.1 −0.2       −0.3 0.2 −0.1 0.1 −0.3 0.1 , B21 = , B22 = , E d2 = 0.1 −0.19 0.3 0.1 0.10 −0.27       −0.1 0.1 −0.1 0.11 −0.12 0.3 , C2 = , Cd1 = , C1 = 0 −0.1 0 −0.12 0.22 −0.13       −0.1 0.1 0.13 −0.25 −0.12 0.1 Cd2 = , B31 = , B32 = . 0 −0.1 0.35 −0.31 −0.035 −0.1 By utilizing Theorem 1, a delay feedback fuzzy controller such that the closed-loop above system is stochastically stabilizable with prescribed level γ can obtained. Consider the minimum γ = 17 and the maximum upper bound for the delay h = 7.3225 by using Matlab LMI Control Toolbox to solve the LMI (12)–(13), we obtain the feasible solution as follows  X=

     0.0392 0.0038 0.0154 −0.0073 0.3141 −0.1406 , Q¯ = , R¯ = ∗ 10−7 . 0.0038 0.0262 −0.0073 0.0131 −0.1406 0.4432

Thus, by using Theorem 1, the controller gains can be obtained as follows :  K 11 =  K 21 =

   0.7131 1.3557 0.4064 −1.3250 , K 12 = , 0.3526 2.1322 0.3751 1.6662

   −0.7859 0.1537 0.4577 0.5300 , K 22 = . 0.1919 1.0364 0.4058 −1.0820

5 Conclusion In this paper, the delay feedback H∞ control for neutral stochastic fuzzy system with time delay is investigated. By the Lyapunov stability theory and LMI approach, the aim is to design an delay feedback fuzzy controller which satisfies that the closedloop system is stable in the mean square and also satisfied H∞ performance criteria. An illustrated example is provided to examine the effectiveness and feasibility of the proposed approach.

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References 1. Chen, W., Ma, Q., Wang, L.: Xu, H,: Stabilisation and H∞ control of neutral stochastic delay Markovian jump systems. Int. J. Syst. Sci. 49, 58–67 (2018) 2. Fridman, E., Shaked, U.: Delay-dependent stability and H∞ control: constant and time-varying delays. Int. J. Control 76, 48–60 (2003) 3. Karimi, H.R.: Robust delay-dependent H∞ control of uncertain time-delay systems with mixed neutral, discrete and distributed time-delays and Markovian switching parameters. IEEE Trans. Circ. Syst. I(58), 1910–1923 (2011) 4. Li, B., Yang, G.: Robust stabilization and H∞ control of uncertain stochastic time-delay systems with nonlinear perturbation. Int. J. Robust Nonlinear Control 26, 3274–3291 (2016) 5. Mao, X.: Stochastic Differential Equations and Their Applications. Horwood, Chichester (1997) 6. Senthilkumar, T., Balasubramaniam, P.: Delay-dependent robust stabilization and H∞ control for nonlinear stochastic systems with Markovian jump parameters and interval time-varying delays. J. Optim. Theory Appl. 151, 100–120 (2011) 7. Senthilkumar, T., Balasubramaniam, P.: Delay-dependent robust H∞ control for uncertain stochastic T-S fuzzy systems with time-varying state and input delays. Int. J. Syst. Sci. 42, 877–887 (2011) 8. Takagi, T., Sugeno, M.: Fuzzy identification systems and it’s application to modeling and control. IEEE Trans. Syst., Man, Cybern. 15, 116–132 (1985) 9. Xu, S., Chen, T.: Robust H∞ control for uncertain stochastic systems with state delay. IEEE Trans. Automat. Control 47, 2089–2094 (2002) 10. Xu, S., Lam, J., Chen, B.: Robust H∞ control for uncertain fuzzy neutral delay systems. Eur. J. Control 10, 365–380 (2004) 11. Xu, S., Shi, P., Chu, Y., Zou, Y.: Robust stochastic stabilization and H∞ control of uncertain neutral stochastic time-delay systems. J. Math. Anal. Appl. 314, 1–16 (2006) 12. Zhang, B., Xu, S., Zong, G., Zou, Y.: Delay-dependent stabilization for stochastic fuzzy systems with time delays. Fuzzy Sets Syst. 158, 2238–2250 (2007)

Modeling Crosstalk of Tau and ROS Implicated in Parkinson’s Disease Using Biochemical Systems Theory Hemalatha Sasidharakurup, Parvathy Devi Babulekshmanan, Sreehari Sathianarayanan, and Shyam Diwakar

1 Introduction Computational and mathematical modeling of complex biological systems help to understand complex interactions between biomolecules inside a cell and how disruption in their connections can lead to complex diseases. In order to study complex diseases, instead of studying its responsible protein/gene alone, all the complex reactions leading to the emergent properties of the entire system also must be studied [1]. Mathematical modeling has been used to solve questions related to the complexity of living systems such as the brain, due to the difficulties in doing experiments on humans or other organisms [2]. Computational systems models provide better understanding of the integrated functioning of large-scale distributed brain networks and show how disruptions in brain function and connectivity impact proper functioning. Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, affecting approximately six million people worldwide. Although many medications are there to control the symptoms, there is no complete cure for this disease due to its complexity. The major goal of this study was to model two major biochemical sub-pathways involved in PD, tau and ROS using BST and kinetic equations, where disturbance in these pathways lead to death of dopamine producing cells inside the brain. Insoluble tau aggregates form a structure called neurofibrillary tangles, which are characteristic of neurodegeneration in Alzheimer’s disease and PD [3]. Recent studies have shown that DJ-1 and LAMP2A, two proteins have important roles in abnormal protein aggregation in the brain that leads to PD conditions [4, 5]. Studies also have proved that calcium homeostasis and inflammatory cytokines stimulate tau hyperphosphorylation causing production of neurofibrillary tangles (NFT) inside the H. Sasidharakurup · P. D. Babulekshmanan · S. Sathianarayanan · S. Diwakar (B) Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri campus, Kollam, Kerala 690525, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_7

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brain, also leading to PD conditions [6]. Although our previous models had explained the role of tau and ROS along with other factors causing conditions related to cell death in PD, the interconnection between them have not been well studied [7]. In this model, the interconnection between DJ-1 and calcium ions in regulating ROS and tau pathways in PD have been focused [5]. The model also explores the relation between LAMP2A protein and DJ-1 and how mutations in these proteins develop PD [4]. Some of the other factors which trigger ROS production and tau hyperphosphorylation such as inflammatory cytokines, calcium homeostasis, mitochondrial dysfunction and glutamate have been also discussed in this study. The positive feedback loop between mitochondrial dysfunction and NFT production has also been studied. In addition, the model also discusses the possible usage of oxidized DJ-1 as a biomarker for PD.

2 Methods In this study, systems theory and kinetic equations to reconstruct the biochemical pathways in PD. BST employed time-dependent ordinary differential equations to represent different types of reactions in a biochemical pathway network to analyze the rate of individual reactions over time and how they together are responsible for the emergent properties of the system. Initial concentration values for both diseased and normal conditions and their rate constants were extracted from the previous experimental studies. The values were normalized and produced by mimicking the percentage variations in concentration between control and diseased condition as observed in experimental studies. The pathway modeling tool, CellDesigner, has been used to model and simulate interactions (see Fig. 1). A reaction was created by connecting the reactant and product using a straight-line arrow using the GUI. Other modifications such as adding another reactant can also be created similarly. Individual structures inside a cell such as mitochondria, nucleus, etc., can be represented as compartments in a model. The biomolecules inside cells and their complex interactions were modeled using kinetics laws including reversible and irreversible Michaelis Menten, convenience kinetic equations, Generalized mass equation, Hill’s equation, Zeroth order forward and reverse kinetics, etc. Some of the equations are described below:

2.1 Generalized Mass Action Kinetics x˙i =

Ni  j=1

ai j

d  k=1

g

xk i jk

Modeling Crosstalk of Tau and ROS Implicated in Parkinson’s Disease …

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Fig. 1 Pathway segment showing implication of excess calcium influx on tau phosphorylation

where i = (1, …, d). Each variable x i represents the concentration of a reactant, and x˙ i denotes the time derivative of x i . The parameters aij are known as rate constants, whereas the parameters gijk are kinetic orders.

2.2 Michaelis–Menten’s Kinetics Michaelis–Menten’s kinetics includes both reversible and irreversible reactions. v=

vmax [s] d[ p] = dt km + [s]

where V max is the maximum rate achieved by the system, at saturating substrate concentration in relation of reaction rate v to [S], where [s] is the concentration of a substrate S. K m is Michealis constant is the rate constant. And the rate constant K m (Michaelis constant) is equal to [S] when v is half of V max .

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2.3 Hill Equation Gene regulation was modeled in the pathway using the Hill equation. θ=

[L]n 1 [L]n =  n n = KA K d + [L] (K A )n + [L]n +1 [L]

where θ is the fraction of the receptor protein concentration; [L] is the concentration of unbound ligand; k d , dissociation constant and n is the hill coefficient. In this way, by comparing both normal and diseased conditions, one can observe the important biomolecules where the concentration changes affect the cell homeostasis and predict the behavioral changes of the system.

3 Results Major biomolecules interacting with tau and ROS pathways have been modeled in both normal and diseased states and some of the important protein mutations and changes involved in this system that could develop diseased conditions have been analyzed.

3.1 Increased Concentration Levels of ROS, Alpha Synuclein Aggregation and Oxidized DJ-1 in Diseased Conditions In control, concentration levels of ROS and DJ-1 production have been noticed comparatively less compared to diseased state (see Fig. 2a). Increased production of ROS lead to oxidative stress and caspase activation that also lead to cell death. High concentration of oxidized DJ-1 production was observed in diseased condition compared to control (See Fig. 2b). In the diseased state, presence of oxidized DJ1 prevented its anti-oxidizing properties and led to increased oxidative stress and cell death. Results showed that DJ-1 triggered the production of LAMP2A and the presence of less DJ-1 led to decreased LAMP2A production. The elevations in alpha synuclein aggregation was also noticed as a leading cause of cell death during the diseased state.

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Fig. 2 a Low concentration level of ROS and DJ-1 in control. b Increased concentration levels of ROS and oxidized DJ-1 and related cell death. c No MPTP formation, mtDNA damage or Complex 1 in control. d Increased levels of tau, neurofibrillary tangles, oxidative stress in diseased state

3.2 Mitochondrial Dysfunction Leads to NFT Production in Diseased Conditions Simulation shows that in control conditions, tau protein and its phosphorylation decreased with time. Neurofibrillary tangles formation slightly increased in the beginning but decreased shortly and became stable along with phosphorylated tau. Formation of MPTP, damage in mtDNA or Complex 1 in mitochondria has not been observed in control condition (see Fig. 2c). In diseased conditions, the result shows an increase in calcium influx into neurons and mitochondria. A sudden increase in calcium level inside mitochondria led to increased ROS production. An increase in mtDNA damage and complex-1 damage was also observed. The result shows that MPTP formation has been rapidly increasing with time. Decrease in phosphorylated tau has also been observed with increase in neurofibrillary tangle formation which again leads to mitochondrial dysfunction. This loop continues eventually leading to cell death (see Fig. 2d).

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4 Discussion The main goal of this study was to understand the major proteins and their interactions involved in tau and ROS pathways that lead to PD using BST and mathematical equations. From the model, it has been observed that the ROS and tau played a major role in initiating cell death factors that lead to dopaminergic cell death in PD. The results indicated that ROS production and oxidative stress were directly linked to other abnormal processes such as alpha synuclein aggregation, Lewy bodies, tau phosphorylation, etc., that can be observed in PD condition. The results have shown an elevation in the DJ-1, alpha synuclein aggregation and neurofibrillary tangles during excess production of ROS in diseased state as compared to control. This suggests that ROS and tau are interconnected, and the interplay correlates with progression of PD condition. The model suggested that oxidative stress due to increase in ROS production was a major factor leading to cell death in PD, since it led to activation of several caspases and JNK pathways. Only disturbances in some of interconnections were involved in determining the emergent properties of the systems leading to disease condition.

5 Conclusion A computational model to analyze the importance of ROS and tau pathway has been modeled to understand how simple reactions in these pathways make complex interactions regulating the emergent properties of the system. The model predicts biomarkers of oxidative stress, mitochondrial dysfunction, DJ-1 oxidation and calcium homeostasis related to PD. The model shows how dysfunction in some of the factors in normal conditions lead to diseased conditions. The predictions from this model can be further tested in animal models and human subjects extrapolating existing experimental data. Acknowledgements This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. This study was partially supported by the Department of Science and Technology Grant DST/CSRI/2017/31, Government of India and Embracing the World Research-for-a-Cause initiative.

References 1. Fischer, H.P.: Mathematical modeling of complex biological systems: from parts lists to understanding systems behavior. Alcohol Res. Health. 31, 49–59 (2008) 2. Ji, Z., Yan, K., Li, W., Hu, H., Zhu, X.: Mathematical and computational modeling in complex biological systems. Biomed Res. Int. 2017, 1–16 (2017). https://doi.org/10.1155/2017/5958321

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3. Braak, H., Braak, E., Yilmazer, D., De Vos, R.A.I., Jansen, E.N.H., Bohl, J.: Neurofibrillary tangles and neuropil threads as a cause of dementia in Parkinson’s disease. J. Neural Trans. 49–55 (1997) 4. Issa, A.R., Sun, J., Petitgas, C., Mesquita, A., Dulac, A., Robin, M., Mollereau, B., Jenny, A., Chérif-Zahar, B., Birman, S.: The lysosomal membrane protein LAMP2A promotes autophagic flux and prevents SNCA-induced Parkinson disease-like symptoms in the Drosophila brain. Autophagy 14, 1898–1910 (2018). https://doi.org/10.1080/15548627.2018.1491489 5. Xu, X.M., Lin, H., Maple, J., Björkblom, B., Alves, G., Larsen, J.P., Møller, S.G.: The Arabidopsis DJ-1a protein confers stress protection through cytosolic SOD activation. J. Cell Sci. 123, 1644–1651 (2010). https://doi.org/10.1242/jcs.063222 6. Guo, T., Noble, W., Hanger, D.P.: Roles of tau protein in health and disease. www.alz.co.uk/res earch/world-report-2016 (2017) 7. Sasidharakurup, H., Melethadathil, N., Nair, B., Diwakar, S.: A systems model of parkinson. Disease 21, 454–464 (2017). https://doi.org/10.1089/omi.2017.0056

IoT-Based Patient Vital Measuring System Ashwini R. Hirekodi, Bhagyashri R. Pandurangi, Uttam U. Deshpande, and Ashok P. Magadum

1 Introduction IoT was first proposed by Kevin Ashton in 1999 [1]. This is the physical communication network where billions of data is collected from various devices we use and transforms them into usable information [2]. By 2020, unprecedented growth in the Internet of things (IoT) technologies will make it possible to talk about 50 billion connected devices through the Internet [3]. IoT can be used in the medical field so that doctors can monitor patients from anyplace at any time. This system can be used for patients who need continuous monitoring of their health. A systematic review of various mobile healthcare approaches was carried out by [4, 5]. IoT establishes a bridge between the ‘digital world (Internet)’ and the ‘real world (physical device).’ The devices are connected to the cloud-based services and create unique identification over the Internet [6, 7]. The Raspberry Pi acts as an aggregator of data collected from different sensors and provides a communication channel to external entities such as Web browsers running on devices over Wi-Fi/cellular data network for transferring aggregated sensors’ data. The REST APIs and asynchronous notification services are used for transferring the sensors’ data and triggering measurement activities. Since the pulse or ECG sensors used in the proposed platform emit analog signals, a 16-bit analog to digital converter ADS1115 is used for sampling. The digital samples are collected by the Python application running on Raspberry Pi for further processing and storing of aggregated data to a local database. The other Web server-based applications coordinate with the Python application and

A. R. Hirekodi (B) · B. R. Pandurangi · U. U. Deshpande KLS, Gogte Institute of Technology, Belagavi, India e-mail: [email protected] A. P. Magadum Osteos India, Pvt. Ltd, Belagavi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_8

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Fig. 1 High-level architecture for vital signs integration

transfer the sensor data for the respective patient being checked for vital signs as depicted in Fig. 1. Figure 1 shows the high-level architecture of the different entities involved in the overall flow of sensor data to NoSQL cloud-based database where patient record is being stored. The MySQL is used as an intermittent local storage for storing the aggregated sensor digital samples read from ADC chip. The overall coordination of sensor data collection and transfer of aggregated data along with ID of a patient being administered for vital signs to the cloud database happens via Web server and asynchronous notification service. The medical staff checking one of the vital signs such as pulse reading of a patient initiates the measurement by plugging a pulse sensor on patient’s finger and clicking a ‘START’ button in the electronic medical record (EMR) module UI. The standard pulse reading procedure is carried out and sensor data samples are collected as per the standards set by the medical organization.

2 Literature Survey Researchers have worked on this subject for a long time. Here is a brief summary of the related work.

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Rani et al. [8] proposed a system for the gathering of the readings of various important indications of the patients and sending the readings to the doctor or the individual about the health condition. In this project, MQTT communication was used to send the data in the form of pictorial representation to the cloud platform. Tastan [9] proposed a wearable sensor-based tracking system to record the patient’s heartbeat and the blood level. The patient who is in a critical health situation can be continuously monitored using this technique. If there is a fluctuation in the patient’s health levels, then the information/details of his health conditions are sent to the family members or the doctor through mail or Twitter notifications. The purpose of this project is to give medical treatment as soon as possible in case of heart diseases, so that survival chances of a patient are increased. Misbahuddin et al. [10] proposed the system for the victims of mass disasters and emergencies with MEDTOC, a real-time component used for the holistic solution. The proposed system sends real-time details of the affected victims to the doctor or to the central database about their health condition even before the arrival of the patient. But this project can only be useful if the disaster area has a cellular network. If cellular networks are having issues, then alternate connectivity such as Wi-Fi can be utilized in the future.

3 Hardware and Software Platform This section has a detailed view of the hardware and the software implementation of the project. A. Hardware Platform Figure 2 shows the hardware implementation of pulse sensor [11]. The three wires of the sensors are for signal(S), Vcc (3–5 V), and GND. In this project, the sensor is powered by 3.3 V pin and the signal pin will be connected to Raspberry Pi through the ADS1115 ADC module because Raspberry Pi by default cannot read analog voltage. Pulse sensor and ECG are used for checking the heartbeat of the person. The connections shown in the figure are done using jumping wires, where ECG is connected to channel 2 and pulse sensor is connected to channel 1. The consumption of power is less. B. Software Platform (i)

Python Code: The software used in this project is Linux, which uses Python code to run sensors. For checking of the pulse, a code is written specifying all the required parameters that are necessary. For getting analog output of the pulse sensor, the ADC module is interfaced via I2C communication. The upper peak and lower peak of the pulse is to be found. Then, the difference between the peak points is taken to convert it into BPM. The raw analog output is sent and BPM is sent to the serial port which is read from processing IDE for further process.

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Fig. 2 Hardware implementation for pulse sensor

(ii) MySQL Database: MySQL is an open-source relational database management system, which serves as a temporary storage of digital samples received from sensors in real time before presenting their aggregated reading to patient EMR front-end module, and eventually, the aggregated values will be stored in the cloud database (MongoDB) for patient medical history and continuous monitoring purposes. (iii) Web server: Web server used in this system is NodeJS which is distributed SaaS-based cloud application interacting with front-end patient medical record system and it is an open-source communicating server. NodeJS is used when staff calls for patient’s details, all the updated readings are displayed on the screen. Algorithmic code for NodeJS (https server): Step 1: Declare http and spawn. Step 2: Assign child process model i.e. python application to spawn variable and run using spawn method. Step 3: Create an http server using imported http and assign an unused port. Step 4: Http server receives patient id as request from EMR application.

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Step 5: The ID will be sent to python pulse sensor/ECG child application. Step 6: Pulse and ECG readings will be received from python application. Step 7: Received readings will be sent as Http response to EMR application.

4 Result and Performance Analysis The objective of the project is to assist the patients to keep track of their health condition on a regular basis, so that whenever there are any issues, they can contact their doctors as soon as possible. Even the doctors can get the reports from the MongoDB database by giving the patient’s ID which is stored when the patient comes for check-up. When the patient ID is entered, the EMR triggers and readings are updated in the WebSocket. As compared to other devices, the proposed system can get readings to the doctor faster and is more accurate. Every time the patient comes for a check-up, data is acquired and is stored for almost a year. Figure 3 shows the reading of a pulse sensor that is displayed on Raspberry Pi which is stored on a private cloud. Whenever a beat is found, the pulse is measured and when there is no response from the person then ‘no beat found’ is displayed. The patient’s details are stored under the patient’s ID. The name, age, weight, and cellular number are mentioned in excel form. Medical information can only be accessed if the doctor or medical staff knows the password which is given. Right now, only medical staff and doctor who is in charge of patient is able to go through the medical history. Every time the patient comes for a check-up data is acquired and is stored as long as it is required. The storage of the cloud is for 1 to 10 GB.

Fig. 3 Pulse-rate recording on the cloud

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5 Security Compliance This digital platform conforms to the safety and security regulations defined by the standard bodies such as CDASH and EHR standards. The implementation of this platform considers all the security measures to preserve the patient personal details and the front-end Web-based integrated EMR system binds with premium SSL certificate and all the front-end Web UI requests to the backend will be carried over secured HTTP connection. The platform also makes use of encryption feature provided by the NoSQL database out of the box. So, with all these security features incorporated in the digital platform along with backup and high availability features of cloud database server, it is ensured that the critical sections of the patient personal data and any other platform data critical to end users are preserved safely all the time.

6 Conclusion and Future Work This application enables the communication between patients and doctors, allowing tracking of the patient’s health as well. The stored data can be accessed easily by doctors and nurses only by entering the patient’s ID. The expected performance should be as close to the manual meters used for reading pulse and ECG of patients in a clinical setting with a marginal error in the range of 2–5%. Another aspect of expected performance is centered around the consistency of the readings from pulse and ECG sensors as more and more patients are screened in mass screening events such as camps conducted by the doctors and specialists.

References 1. Ashton, K.: That ‘internet of things’ thing. RFID J. 22(7), 97–114 (2009) 2. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): A vision architectural elements, and future directions. Fut. Gener. Comput. Syst. 29(7), 1645–1660 (2013) 3. Fernandez, F., Pallis, G.C.: Opportunities and challenges of the internet of things for healthcare: systems engineering perspective. In: International Conference on Wireless Mobile Communication and Healthcare, pp. 263–266 (2014) 4. Jersak, L.C., da Costa, A.C., Callegari, D.A.: A systematic review on mobile health care. Technical Report 073, Faculdade de Informática PUCRS—Brazil, May 2013 5. Fong, E.-M., Chung, W.-Y.: Mobile cloud-computing-based healthcare service by noncontact ECG monitoring. Sensors 13(12), 16451–16473 (2013) 6. Deshpande, U.U., Kulkarni, M.A.: Iot based real time ecg monitoring system using cypress wiced. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 6(2) (2017) 7. Deshpande, U.U., Kulkarni, V.R.: Wireless ECG monitoring system with remote data logging using PSoC and CyFi. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2(6): 2770–2778 (2013) 8. Rani, S.U., Ignations, A., Hari, B.V., Balavishnu, V.J.: IoT patient health monitoring system. Indian J. Health Res. Dev. 1330–1334 (2017)

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9. Tastan, M.: IoT based wearable smart health monitoring system. Celal Bayar Univ. J. Sci. 343–350 (2018) 10. Misbahuddin, S., Zubairi, J.A., Alahdul, A.R., Malik, M.A.: IoT-based ambulatory vital signs data transfer system. Hindawi J. Comput. Netw. Commun., 8 p (2018) 11. IoT based heartbeat monitoring system using Raspberry pi website (2019). [Online]. Available https://iotdesignpro.com/projects/iot-heartbeat-monitoring-system-using-raspberry-pi

IoT-Enabled Logistics for E-waste Management and Sustainability P. S. Anusree and P. Balasubramanian

1 Introduction The Organization for Economic Cooperation and Development (OECD) defines electronic waste or e-waste as “any appliance using an electric power supply that has reached its end-of-life” [1]. Electrical and electronic equipment (EEE) such as refrigerators, washing machines, computers, television sets, laptops and smartphones that reach the useful end is considered electronic waste or e-waste [2] and is known as waste electrical and electronic equipment (WEEE). E-waste is a constantly growing [3] and complex waste form [4], and its management is extremely challenging [5]. E-waste toxins cause environmental imbalances [6] including global warming [7] and climate change [8] apart from health implications [9]. The processing of humongous quantities of e-waste [10] is a major setback for economies. This also results in illegal trading [11] and mismanagement of e-waste [12]. Sustainable Development Goals proposed by the United Nations in 2015 strive for global environmental sustainability [13, 14]. E-waste management has an important role in this context. Digital revolution has led to rapid growth in e-waste generation, but today, technologically oriented solutions can ensure e-waste management. Modern computing and smart environment have evolved a sophisticated and dynamic network of connectivity and mobility [15]. Pervasive computing infrastructure enables a ubiquitous environment that the user can sense and control at any point [16]. The novel paradigm of Internet of things (IoT) network embedded devices [17] such as mobile phones, sensors, tags, actuators and radio-frequency identification (RFID) [18]. Electronic devices embedded with sensors would revolutionize the industry by enabling smart management of end-of-life devices [17]. Enabling information accessibility P. S. Anusree (B) · P. Balasubramanian Department of Commerce and Management, School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_9

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between the stakeholders through the pervasive smart network will transform the e-waste management scenario. The current study attempts to develop a sustainable and systematized e-waste management procedure that can be implemented by a well-framed network between different sectors, stakeholders and countries.

2 Literature Review The Internet network has transformed control over objects that surround us [19]. Pervasive smart environment enables embedded devices to work collaboratively and adapt as per needs [17, 20]. Close to 60% of the IoT market captures the industrial domain in India and the remaining by the consumer devices [19]. In waste management scenario, significant contribution can be formed with the paradigm of IoT [21]enabled identification, positioning, tracking, monitoring and management systems [22, 23]. Economies that lack state-of-the-art infrastructure for e-waste management activities such as collection, disposal and recycling can benefit with an integrated global network that facilitates these functions. The best-of-two-worlds (Bo2W) concept developed by the StEP Initiative (Solving the E-waste Problem), United Nations [24, 25], is a collaborative approach between developed and developing counties. This would encourage global recycling in e-waste [26], which involves recycling infrastructure accessibility to countries that lack scientific recycling mechanism. Obsolete devices can thus be recycled to ensure a circular economy [27] through the sustainability of resources and environment. A study in Malaysia developed a smart e-waste collection box for the households with sensors that promoted e-waste collectors for timely pickup [28]. A study in Italy recommended a collaborative robot model to disassemble scrap components and optimize recycling [29]. A study in India proposed a collaborative e-waste management system for the stakeholders using smart contracts and blockchain technology ensuring accountability of devices and management activities including collection, transportation and recycling [30]. In another study, the concept of virtualization, i.e., efficient server management technique was proposed to reduce the hazards of electronic waste [31]. A study in Sweden developed the WEEE ID project in-built with sensors that enable sorting as well as grading of waste mobile phones for treatment processes [32]. The philosophy of best-of-2-worlds fosters environmental, economic and social aspects in e-waste management by integrating geographically distributed technologies and making these accessible to developed and developing nations [33]. The approach was successfully tested by conducting informal initial processing of e-waste at units in India and the end processing or smelting undertaken by European recycling EMPA [33]. The Bo2W approach or international recycling increases the rate of recovery of metals, reduces toxic emissions and also generates social and economic opportunities; however, it can lead to difficulties in case of unmonitored hazardous trade [34]. Hence, we attempt to propose a novel approach integrating stakeholders and ensuring transparency throughout the procedure.

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3 E-waste Management Network We propose a systematic procedure to integrate the activities of collection, disposal, recycling and related e-waste management activities enabled through a smart environment. Smart pervasive environment that connects all the stakeholders such as the manufacturer, collection agent, recycler, customer, financing agent and government on a common platform makes communication and processing transparent. Here, the devices introduced in the market by the producer would have unique identification numbers that will serve tracking throughout the life of the product. At the sales point, the customer details are captured using the Aadhaar or PAN identification [35]. IoTenabled application would permit real-time information accessibility to stakeholders in the network (Fig. 1). 1.

2.

3.

E-waste information—IoT-enabled sensors that integrate electronic devices through their unique numbers make information available on established application. Consumers can easily inform device pick to the collecting vendor through the application as in e-commerce portals in China [36]. The integrated application makes it possible for all stakeholders including producer, consumer, collection agent, recycler and government authority to track the movement of particular devices. In detail information regarding the availability of processes depending on the e-waste product, routes of the necessary processes should be available, if not

Fig. 1 IoT application flowchart representing the information flow between the stakeholders

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Fig. 2 Logistics integrated model representing the logistical view of the proposed model

4. 5.

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logistics information regarding location and package of consignments should be made available to the producers, retailers and government. Specific window for government authorities to access conformity with applicable rules and regulations for given devices. Finance and tax-related information within this system in not included in the current study; however, with such massive project integration, monetary flows at various stages of the operations are crucial. Considering the operations within district, state, country and global scales, the system needs to be audited by authorized professionals. Additional employment will be generated at this level too (Fig. 2). Through the application, consumers (individual and bulk) can at the touch of a button inform pickup status of particular electronic device. The collection agent and the transport facility are also included in the integrated model. The informal networks of e-waste scavengers can thus have an improved work. The workers collect the devices from the household or workspace and deliver to the common point or a collection hub. Smart collection hubs can be developed with time as per the infrastructural capabilities of the district or state concerned. The collected devices can then be transferred to dismantling and/or recycling networks within state or country depending on the availability of facilities and infrastructure. Retrieved materials and resources can be applied for further processing into the markets.

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For complex advanced treatments and processing, e-waste can be contracted with international facilities for global recycling [34]. Thus, plant capacities that remain unused in many parts of the world due to shortage of e-waste stock can be utilized efficiently. Metals, different materials and substances recovered through the processes can be introduced to markets.

The model explains the logistic view of e-waste scrap in the integrated system. The objective with such a model is to make sure that the existing e-waste industry is transformed to a professional structure; however, doubts regarding employment of lacks of workers in the informal sector arise here. Dedicated efforts from the stakeholders and authorities can uplift the work-life scenarios of the workers by integrating them in the network. The knowledge and experience of the scavengers and the scrap workers can be transformed profitably with proper training and guidance. Futuristically, the traditional methods of e-waste treatment can be completely eradicated to protect workers from occupational health hazards in dumpsites [37]. Pay-related shortcomings persist in the model; however, user pay alone is insufficient for management companies as far as solid household waste is concerned [38]. Overlooking the existing gaps in the model, it can be ascertained that integrating stakeholders in the capacities is built around digital services and IoT; improvements can be attained in the area of e-waste management. Smart environment thus created can lead to efficient use of resources and sustainability.

4 Conclusion The paper attempted to present an integrated view of Internet of things (IoT) enabled into a combination of formal–informal e-waste scenario. With the linkage of Internet services through smart environment and best-of-2-worlds philosophy, the paper proposed a model specifying IoT application and logistics view of this system. Gaps in the model including financial flows and administrative paths are the limitations in the study. However, to make sustainable efforts in e-waste management, a perspective has been proposed through the model which would ultimately benefit in creating employment, industry development and welfare of the society as well as environmental sustainability.

References 1. OECD: Extended Producer Responsibility; A Guidance Manual for Governments. Organization for Economic Cooperation and Development (OECD) (2001) 2. Kumar, A., Sharma, L.: A study of e-waste management on the subject of awareness of college students. Foreword by Secretary, 41

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3. Heacock, M., Kelly, C.B., Asante, K.A., Birnbaum, L.S., Bergman, Å.L., Bruné, M.N.,Kamel, M., et al.: E-waste and harm to vulnerable populations: a growing global problem. Environ. Health Perspect. 124(5):550–555 (2016) 4. Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., Böni, H.: Global perspectives on e-waste. Environ. Impact Assess. Rev. 25(5), 436–458 (2005) 5. Bazargan, A., Lam, K.F., & McKay, G.: Challenges and opportunities of e-waste management. Nova Science (2012) 6. Awasthi, A.K., Wang, M., Wang, Z., Awasthi, M.K., Li, J.: E-waste management in India: a mini-review. Waste Manage. Res. 36(5), 408–414 (2018) 7. Devika, S.: Environmental impact of improper disposal of electronic waste. In: Recent Advances in Space Technology Services and Climate Change 2010 (RSTS & CC-2010), pp. 29–31. IEEE, Nov 2010 8. McAllister, L., Magee, A., Hale, B.: Women, e-waste, and technological solutions to climate change. Health Hum. Rights J. 16(1), 166–178 (2014) 9. Seeberger, J., Grandhi, R., Kim, S.S., Mase, W.A., Reponen, T., Ho, S.M., Chen, A.: Special report: E-waste management in the United States and public health implications. J. Environ. Health 79(3), 8–17 (2016) 10. Tansel, B.: From electronic consumer products to e-wastes: Global outlook, waste quantities, recycling challenges. Environ. Int. 98, 35–45 (2017) 11. Bisschop, L.: Is it all going to waste? Illegal transports of e-waste in a European trade hub. Crime, Law Soc. Change 58(3), 221–249 (2012) 12. Awasthi, A.K., Zeng, X., Li, J.: Environmental pollution of electronic waste recycling in India: a critical review. Environ. Pollut. 211, 259–270 (2016) 13. UN: Transforming our world: the 2030 agenda for sustainable development, United Nations (2015). Available at https://sustainabledevelopment.un.org/content/documents/21252030% 20Agenda%20for%20Sustainable%20Development%20web.pdf. Accessed 12 Nov 2016. 14. Annan-Diab, F., Molinari, C.: Interdisciplinarity: practical approach to advancing education for sustainability and for the sustainable development goals. Int. J. Manag. Educ. 15(2), 73–83 (2017) 15. Malatras, A.: State-of-the-art survey on P2P overlay networks in pervasive computing environments. J. Netw. Comput. Appl. 55, 1–23 (2015) 16. Satyanarayanan, M.: Pervasive computing: vision and challenges. IEEE Pers. Commun. 8(4), 10–17 (2001) 17. Mukhopadhyay, S.C., Suryadevara, N.K.: Internet of things: challenges and opportunities. In: Internet of Things. Springer, Cham, pp. 1–17 (2014) 18. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010) 19. Thapliyal, R., Patel, R.K., Yadav, A.K., Singh, A.: Internet of things for smart environment and integrated ecosystem. Int. J. Eng. Technol. 7(3.12), 1219–1221 (2018) 20. Ahmed, E., Yaqoob, I., Gani, A., Imran, M., Guizani, M.: Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel. Commun. 23(5), 10–16 (2016) 21. Saha, H.N., Auddy, S., Pal, S., Kumar, S., Pandey, S., Singh, R., Saha, S.: Waste management using Internet of Things (IoT). In: 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), pp. 359–363. IEEE, Aug 2017 22. Ashton, K.: That ‘Internet of Things’ thing. RFid J. 97–114 (2009) 23. Vimal Jerald, A., Rabara, S.A., Bai, T.D.P.: Internet of things (IoT) based smart environment integrating various business applications. Int. J. Comput. Appl. 128(8), 32–37 (2015) 24. Wang, F., Huisman, J., Meskers, C.E., Schluep, M., Stevels, A., Hagelüken, C.: The Bestof-2-Worlds philosophy: developing local dismantling and global infrastructure network for sustainable e-waste treatment in emerging economies. Waste Manag. 32(11), 2134–2146. In: Krüger, C. (ed.) E-Waste Recycling in India–Bridging the Gap Between the Informal and Formal Sector (2012)

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25. Buchert, M., Manhart, A., Mehlhart, G., Degreif, S., Bleher, D., Schleicher, T. Kummer, T.: Transition to sound recycling of e-waste and car waste in developing countries (2016) 26. Kuehr, R., Wang, F.: Rich and poor nations can link up to recycle e-waste (web). Retrieved 30 Jan 2010, from https://www.scidev.net/ 27. Park, J., Sarkis, J., Wu, Z.: Creating integrated business and environmental value within the context of China’s circular economy and ecological modernization. J. Clean. Prod. 18(15), 1494–1501 (2010) 28. Kang, K.D., Kang, H., Ilankoon, I.M.S.K., Chong, C.Y.: Electronic waste collection systems using Internet of Things (IoT): household electronic waste management in Malaysia. J. Clean. Prod. 252, 119801 (2020) 29. Alvarez-de-los-Mozos, E., Renteria, A.: Collaborative robots in e-waste management. Procedia Manuf. 11, 55–62 (2017) 30. Gupta, N., Bedi, P.: E-waste management using blockchain based smart contracts. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 915–921. IEEE Sept 2018 31. Krishnan, S.S.R., Balasubramanian, K., Mudireddy, S.R.: Design/implementation of a novel technique in virtualization to reduce e-waste. Int. J. Adv. Res. Comput. Commun. Eng 2, 12 (2013) 32. Barletta, I., Johansson, B., Cullbrand, K., Björkman, M., Reimers, J.: Fostering sustainable electronic waste management through intelligent sorting equipment. In: 2015 IEEE International Conference on Automation Science and Engineering (CASE), pp. 459–461. IEEE, Aug 2015 33. Wang, F., Huisman, J., Meskers, C.E., Schluep, M., Stevels, A., Hagelüken, C.: The Bestof-2-Worlds philosophy: developing local dismantling and global infrastructure network for sustainable e-waste treatment in emerging economies. Waste Manage. 32(11), 2134–2146 (2012) 34. Manhart, A.: International cooperation for metal recycling from waste electrical and electronic equipment: an assessment of the “Best-of-Two-Worlds” approach. J. Ind. Ecol. 15(1), 13–30 (2011) 35. Tiwar, D., Raghupathy, L., Khan, A.S.: Evolving and e-waste tracking system for India to mitigate the problems of e-waste management. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 6(1), 1865–1873 (2018) 36. Zhang, B., Du, Z., Wang, B., Wang, Z.: Motivation and challenges for e-commerce in e-waste recycling under “Big data” context: a perspective from household willingness in China. Technol. Forecast. Soc. Chang. 144, 436–444 (2019) 37. Annamalai, J.: Occupational health hazards related to informal recycling of e-waste in India: an overview. Indian J. Occupat. Environ. Med. 19(1), 61 (2015) 38. Boateng, K.S., Agyei-Baffour, P., Boateng, D., Rockson, G.N.K., Mensah, K.A., Edusei, A.K.: Household willingness-to-pay for improved solid waste management services in four major metropolitan cities in Ghana. J. Environ. Publ. Health (2019)

Navigation Through Proxy Measurement of Location by Surface Detection G. Savitha, Adarsh, Aditya Raj, Gaurav Gupta, and Ashik A. Jain

1 Introduction Several recent advancements have been observed in the field of mobile robots and autonomous vehicles. Most of these systems rely on external sources like global positioning system (GPS) for navigation. This increases the costs as well as complexity of such systems. Moreover, the moving robots quite often lost its balance and fall down which results in damage of their parts leading to an increment in the maintenance expenditures. To deal with all such issues, an attempt is made by us to develop a moving system which is capable of detecting the type of surface on which it is moving. The detected surface type can be used as a substitute for measurement of location. Since the surface is detected by inertial measurements, the robots will get an optimum range of values for acceleration and speed by which it can navigate safely without falling down. The system mainly comprises of a raspberry pi processing device, an IMU sensor and a moving cart. In the further sections, the details of the approaches used for the development are explained in detailed manner.

2 Related Works Sebastian Thrun et al. [1], proposed a system for indoor navigation of mobile robots. The system used grid-based maps learned using artificial neural networks (ANNs) and Bayesian integration along with topological maps built on top of it. Feder et al. [2] presented a technique of adaptive mobile robot navigation and mapping which G. Savitha · Adarsh (B) · A. Raj · G. Gupta · A. A. Jain Department of Computer Science and Engineering, B.N.M.I.T. (Affiliated To VTU), Bengaluru, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_10

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explains how to perform concurrent mapping and localization using sonar. Benn et al. [3] presented a monocular image processing-based algorithm for detecting and avoiding obstacles. The authors used colored segmentation technique against the selected plane of the floor to detect the collision. Balch et al. [4] presented an avoiding past strategy which is implemented using a spatial memory within a schema-based motor control memory which produced promising results in simulation and with mobile robots. Conner et al. [5] proposed a methodology for composition of local potential functions for global control and navigation such that the resulting composition completely solves the navigation and control problem for the given system operating in a constrained environment. Walas et al. [6] proposed a walking robot controller for negotiation of terrains of terrains with different traction characteristics. The paper presented the discrete event systems (DES) for the specification of the robot controller. Kertesz et al. [7] proposed a technique to fuse four different types of sensors to distinguish six indoor surface types. Random forest (RF) was used for analyzing machine learning aspects. Rimon et al. [8] proposed a technique for exact robot motion planning and control using artificial potential function that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. Brooks et al. [9] proposed an approach to detect geometric hazards by using self-supervised classification. The accuracy was also assessed for different data sets with traditional trained classification. Lomio et al. [10] proposed a methodology for indoor surface detection by mobile robots using inertial measurements. The paper presented several time series classification techniques for classifying the floor types based on the IMU sensor dataset. Feature extraction techniques proposed by Savitha et al. [11] provides efficient ways for selecting required features from a given dataset for offline signature verification. The work describes use of singular value decomposition for feature selection. Savitha et al. [12] proposed multimodal biometric fusion methodology which offers face and fingerprint as biometric traits as an input for sanctuary purpose that are not unique to each other of the human body. The system proposed a linear discriminant regression classification (LDRC) algorithm. Savitha et al. [13] presented a system which focus on developing a multimodal biometric authentication system, in order to improve the recognition process.

3 Methodology The work presented here involves four main stages. These stages are data collection, data preprocessing, feature extraction, model selection, and performance evaluation. Each of these stages are covered in upcoming sections.

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3.1 Data Collection The initial step is to collect the time series data of inertial measurements from the inertial measurement units (IMU) sensors. An IMU is an electronic device that measures and reports orientation, velocity, and gravitational forces through the use of accelerometers and gyroscopes and often magnetometers. There are several variants of IMU sensor available is market but for the work here, MPU 6050 is used. The sensor is interfaced with raspberry pi which is kept on a moving wheeled robot. Again there are several variants of raspberry pi available, and for the work described here, model 3B+ is used. The robot is then moved on different kinds of floors to collect the dataset. Here, data is collected for 3 different surfaces comprising of mosaic surface (which can be considered as smooth surface and resembles texture of other similar surfaces like wood, tiles, and marbles), pavement and moderate rough surfaces like concrete floor as shown in Fig. 1. The sensor collects data over acceleration, velocity and orientation along x-axis, y-axis, and z-axis providing total six degrees of freedom. For interfacing IMU sensor with raspberry pi, I2C communication protocol must be enabled first in raspberry pi. This can be achieved by typing the command raspiconfig in the raspberry pi terminal following by navigating to ‘Advanced Options’ from where I2C can be enabled. Once the protocol has been enabled, IMU can be interfaced with raspberry pi board with the pin connection as shown in Fig. 2.

Fig. 1 Types of surfaces

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Fig. 2 Interfacing MPU 6050 with raspberry pi

Fig. 3 Data rows of different surfaces

Once the proper connection has been established, the raspberry pi can be coded to collect data from the sensor over the surfaces described earlier. Data collected from sensor will be stored in.csv extension file for further preprocessing. For the work here, over 1000 data rows are collected for each surface for better training of model as shown in Fig. 3.

3.2 Data Preprocessing Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format. Each of the feature row is first mapped to a target value having the name of the floor. This is followed by encoding the values of target. Since target value is the name of the type of floor which is a non-numeric categorical data, it need to be encoded into numeric form so that it can be used for performing the prediction. Checks over dataset are performed for any missing values. If any missing

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Fig. 4 Correlation matrix visualization

value is present, it will be substituted with mean of values in the case of features data and median in case of categorical target values. Hence, a preprocessed set of data which is suitable for performing data analysis was obtained. This data is further used for building the classification model.

3.3 Feature Extraction Feature extraction starts from an initial set of measured data and builds derived values intended to be informative and non-redundant, facilitating the subsequent learning steps, and in some cases leading to better human interpretations. The features which are useful for training the model are selected, and the remaining ones are eliminated. The correlation matrix is constructed for the set of features and the target values. The features having high correlation between them are considered, and among those features, the one having low correlation with the target variable are eliminated. The correlation matrix is constructed using pandas library. The orientation variables of all axes are eliminated in this process and the remaining ones are selected. Figure 4 shows the visual representation of the correlation matrix.

3.4 Model Selection There are several algorithms which can be used for classifying a given set of data. The process of selecting the best algorithm and developing the model which best fits

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the data set is termed here as model selection. The train test split technique is used to divide the dataset into two parts namely the training set and test set. The model is trained on the training set and validated against the test set. The other cross validation strategy used to select the model is K-Fold cross validation technique. In this methodology, the K value is fetched to the system which is the total number of divisions in the data set. One among such divisions is taken as test set and remaining as the training set. This process is iterated till all of the set is taken as test set. In this way, various set of accuracy is obtained for each set of iteration. Hence, the mean accuracy value of all of the iteration is calculated and is considered as the performance of the model.

3.5 Performance Evaluation The algorithms used to build the classification model are random forest classification and K nearest neighbors classifier. Random forest consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest fetches a class prediction and the class with the most votes becomes the model’s prediction. The K nearest neighbors works on the principle that similar data points exist close to each other. The K-nearest data points to the test data point are considered for prediction of the class. Random forest model has performed better than the K nearest neighbors model. The accuracy of KNN is around 50% in case of K-Fold cross validation technique and is around 58% in case of train test split. In contrast to that, the accuracy of random forest is around 66% in case of train test split CV and 50% in case of K-Fold CV. Classification report for K-nearest neighbors is precision

recall

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0.57 0.69 0.52

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micro avg 0.60 macro avg 0.59 weighted avg 0.59

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precision 0 1 2

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0.65 0.82 0.54

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micro avg 0.66 macro avg 0.67 weighted avg 0.67

0.66 0.67 0.66

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The accuracy of KNN in case of train test split is 58% and in case of K-Fold cross validation is 49.20%. The model is developed for feeding it into the raspberry pi for performing floor detection. The RAM available in raspberry pi is 1 GB SRAM and also has relatively low processing power as compared to PCs or other computing devices. Since the raspberry pi has limited processing power, it is not feasible to dump a complex model like random forest having a high level of complexity to perform the classification. The time complexity for building a complete unpruned decision tree is O(V * nlog(n)) where v is the number of variables or features and n is the number of data points. A random forest consists of several such decision trees. In contrast to this, the time complexity of KNN algorithm is O(m * n) where n is the number of training examples and m is the number of dimensions in the training set. For simplicity, assuming n  m, the complexity of the brute-force nearest neighbor search is O(n). Hence, both K nearest neighbors and random forest are suitable as the model for performing the classification of floors based on the data collected by the IMU sensors. The system is thus able to detect the floor quite accurately and efficiently.

4 Conclusion and Future Enhancements The work presented here suggests an ambient method to make the robots more intelligent by providing them the information about the environment particularly about the type of floor on which it is moving. The floor detection can be used for navigation in indoor as well as outdoor surfaces. Moreover, since the type of floor is depending upon the inertial measurements, the robots can be enhanced by embedding actuators which will adjust its speed and acceleration based on the type of floor. This will ameliorate the performance of mobile robots and protect them from damage due to falling down during navigation. Further, having an idea about the type of floor also help the robots for a safe navigation and protect them from external threats. The proposed system uses a raspberry pi computing device which is compact and suitable for smaller mobile robots. On the contrary, the earlier technologies used large sized trolley along with desktop computer system for the data collection. This has significantly improved the mobility of the device. Moreover, the accuracy of classification is also nearly same, i.e., 66% in case of our proposed model and 68% in

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case of previous systems. Further, the computational requirements have also reduced to a great extent in our system. Instead of performing edge computing in the mobile system, cloud-based computing can also be used to further enhance the accuracy of the prediction. A state-of-art classification model can be used to predict the floor type by running the model on a high processing device like GPUs, and the result can be fed to the system using WiFi technology available with the raspberry pi. Thus, the input data and the predicted result can be shared between the system and cloud in a much better way. Although this technique will increase the cost along with some delay due to data passing, the accuracy of the results will get enhanced to a great extent.

References 1. Sebastian Thrun Computer Science Department and Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, US. In: Proceedings of Elsevier Artificial Intelligence on Learning Metric-Topological Maps for Indoor Mobile Robot Navigation (1998) 2. Feder, H.J.S., Leonard, J.J., Smith, C.M.: Proceedings of the International Journal of Robotics Research for Adaptive Mobile Robot Navigation and Mapping. Massachusetts Institute of Technology, 77 Mass Ave., Cambridge, MA021392 Charles Stark Draper Laboratory, 555 Technology Square, Cambridge, MA02139 (1999) 3. Benn, W., Lauria, S.: Robot Navigation Control Based on Monocular Images: An Image Processing Algorithm for Obstacle Avoidance Decisions. In: Proceedings of Hindawi Publishing Corporation, Mathematical Problems in Engineering, vol. 2012, Article ID 240476, 14 p. Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UKA (2012), https://doi.org/10.1155/2012/240476 4. Balch, T., Arkin, R.: Proceedings of IEEE on Avoiding the Past: A Simple But Effective Strategy for Reactive Navigation. Mobile Robot Laboratory, College of Computing, Georgia Institute of Technology Atlanta, Georgia USA (1993) 5. Conner, D.C., Rizzi, A.A., Choset, H.: Proceedings of IEEE 2003 Conference on Intelligent Robots and Systems on Composition of Local Potential Functions for Global Robot Control and Navigation. Carnegie Mellon University, Pittsburgh, US (2003) 6. Walas, K.: Terrain Classification and Negotiation with a Walking Robot. Springer Science + Business Media, Dordrech, vol. 78, pp. 401–423 (2015) 7. Kertesz, C.: Rigidity-Based Surface Recognition for a Domestic Legged Robot. IEEE Robot. Autom. Lett. (2016) 8. Elon Rimon University of California Daniel E. Koditschek University of Pennsylvania, [email protected]. In: Proceedings of Departmental Papers. Department of Electrical and Systems Engineering on Exact Robot Navigation Using Artificial Potential Functions (1992) 9. Christopher, A.: Brooks and Karl lagnemma self-supervised terrain classification for planetary surface exploration rovers. J. Field Robot. 29(3), 445–468 (2012) 10. Lomio, F., Skenderi, E., Mohamadi, D., Collin, J., Ghabcheloo, R., Huttunen, H.: In: Proceedings of Arxiv on Surface Type Classification for Autonomous Robot Indoor Navigation. Tampere University, Finland (2019) 11. Savitha, G., Vibha, L.: Textural and Singular Value Decomposition Feature Extraction Technique for Offline Signature Verification. In: Proceedings of International Journal of Information Processing, vol. 8(3), pp. 95–105 (2014). Department of Computer Science and Engineering, BNMIT, Bangalore-560070, India , Venugopal K R, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore 560 001, India , L M Patnaik Honorary Professor, Indian Institute of Science, Bangalore, India. ISSN: 0973-8215

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12. Savitha, G., Vibha, L., Venugopal, K.R.: Multimodal biometric authentication system using LDR based on selective small reconstruction error. J. Theoret. Appl. Inf. Technol. 92(1) (2016). ISSN: 1992-8645 13. Savitha, G., Vibha, L., Venugopal, K.R.: Multimodal cumulative class specific linear discriminant regression for cloud security. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 15(2) (2017). ISSN 1947-5500

Unsupervised Learning Algorithms for Hydropower’s Sensor Data Ajeet Rai

1 Introduction The maintenance of different machines in hydropower plant is one of the crucial tasks for the engineers. It is very difficult to monitor each machine manually. Such types of problems cause the application of data science in the energy sector. Instead of monitoring manually, we can develop a system that uses data which helps to keep tracking the performance of the machines. The data generated from a machine’s sensor behaves the same if there are no issues with machines but if sensors generate abnormal data that shows engineers have to check the machines if it’s working well. There are many techniques to solve these problems using data and one of them is anomaly detection. The abnormal data can be treated as outliers, and the system should be able to detect these outliers after training on the past data. So, anomaly detection techniques are well suited for such kind of problems. The terms anomalies and outliers are used interchangeably. Mathematically, data points that are far away from the mean or median of data can be treated as outliers or anomalies. Sometimes human error, instrument error, sampling error, novelties, etc., can also be a reason for anomalies in data. Machine learning approaches like supervised and unsupervised learning are used to detect anomalies. Supervised algorithms such as support vector machine, k-nearest neighbors, Bayesian networks, decision trees, etc. whereas unsupervised algorithm such as self-organizing maps (SOM), K-means, C-means, expectation–maximization (EM), one-class support vector machine, etc. used for anomaly detection. Methods based on statistics, clustering, distance, and density are also suggested by researchers. Statistical methods for anomaly has been categorized in the distribution-based method, and depth-based methods are also very

A. Rai (B) iServeU Technology, Bhubaneswar, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_11

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effective. Also, parametric Z-score and non-parametric proximity-based models as well have been suggested for anomaly detection.

2 Literature Review In literature, the depth-based method has been implemented by Ruts and Rousseeuw [1]. Different clustering methods such as DBSCAN, K-means, etc. have been implemented for anomaly detection [2]. Various statistical, machine learning, and deep learning methods have been proposed to detect anomalies in data [3, 4]. Netflix used principal component analysis (PCA) for anomaly detection [5]. There are many other deep learning models such as LSTMs, RNNs, and DNNs that are implemented for anomaly detection [6]. Another perspective of detecting anomalies is in time-series data. Data containing time variables are treated differently like other methods. Statistical methods such as seasonal and trend decomposition using Loess (STL) along with interquartile range (IQR) and method based on generalized extreme studentized deviate test (GESD) also used to detect anomalies in time-series data. In particular for the application in hydropower plants, Ahmed et al. used the minimum spanning tree approximated distance measures as an anomaly detection method [7]. Also, Liu et al. used SCADA data mining technique for anomaly detection in wind turbines [8]. CRISP-DM data mining used by Galloway et al. for the condition monitoring of tidal turbines [9].

3 Methodology Our objective is to build a model such that the model should be able to detect anomalies in data. We tried with many techniques which we have discussed above, and we are presenting few techniques here. Machine learning models such as one class support vector machine (SVM) and isolation forest have been discussed here. For our study, the dataset which has been used is taken from the thermal power plant’s turbine machine. The data was collected from a turbine through a sensor. We got very messy and noisy data, so the first data cleansing step was performed. Features timestamp and corresponding values of sensors extracted from data with 35,040 observations. Before implementing, model data scaling was necessary, so we used standardization for scaling. However, there is a lot of sensor data in the dataset but only one sensor will be used for this paper because whatever result will be valid for all the sensors. The depiction of the plot shown below is the behavior of a sensor over the period of one year (Fig. 1).

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Fig. 1 Time series plot for the turbine’s sensor data

3.1 One Class Support Vector Machine Researchers have developed many machine learning models such as tree-based, linear, neural network and kernel models, etc. Depending on the data, different models can be used in different scenarios. One such kernel-based model is support vector machine (SVM) which is useful when data is not linear. SVM models can outperform the other models when there are no clear decision boundaries in the class label. There are two kernel-based method one is support vector machine (SVM) used for classification problems and support vector regression (SVR) used for regression problems. The modified version of SVM is known as one class SVM which is used for solving unsupervised problems. In classification problems, if data is not balanced and ratio is very large between the class labels, then the anomaly detection method is most suitable. One class SVM model is trained on normal data only such that when unseen data is very different from normal data points, it is classified as anomalies. Consider the feature matrix X and label class Y such that having pairs, (X, Y ) = {(x1 , y1 ), (x2 , y2 ), . . . , (xn , yn )} Where xi is input data and yi is output data, yi ∈ {0, 1} We define hyperplane,

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Fig. 2 Anomalies in turbine’s sensor data using one class SVM

wT x + b = 0 Where w T is weight that belongs to higher dimension, x is data points, b∈R Such that the margin between two classes should be maximum. That means, we have a function that can be minimized using any optimization method. Hyperplane separates the class in such a way that there should be homogeneous data points in each region. That means, we fixed the position of the hyperplane such that the margin is maximum by solving objective function. Then, we need a kernel function to change the position of the data point into the higher dimensions. The data points which are abnormal are sent to the higher dimension and identified as anomalies. During practical implementation first, we build a model with default parameters kernel, gamma, nu, etc. After optimizing the parameters model classified 3388 data points as anomalies and remaining 30,880 data points as normal (Fig. 2).

3.2 Isolation Forest Isolation forest is another unsupervised machine learning algorithm for anomaly detection. Most of the anomaly detection techniques focused on data points which are normal and the data points which are not similar to normal one, treated as outliers as we have seen in one-class SVM. But isolation forest algorithm focused on abnormal data points and others treated as normal data points. Algorithms consider that anomalies are very few and they are different from normal data points. This makes algorithms easy to isolate those data points which are abnormal as compared to normal data points. In isolation forest, data points divided recursively in partitions. These partitions can be considered as trees. The number of partitions required to isolate data points can be treated as length of depth. The anomalous data points take less partitions whereas normal data points more. This makes this algorithm faster than many

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Fig. 3 Anomalies in turbine’s sensor data using isolation forest

other techniques. In isolation forest below the equation is used to make decisions whether the data points are anomalous or not. E(h(x)) c(n) Where, h(x) is length of depth to the data points,

s(x, n) = 2 −

c(n) is the average length of depth of unsuccessful search, And n is the number of external nodes.

(3)

If the above equation gave a score close to 1 that indicates that point is anomalous whereas much smaller than 0.5 indication for normal data points. If the score is close to 0.5 that means the entire data does not seem to have outliers. We performed the same data preparation steps as we did for one-class SVM. After optimizing the model, we got 10,132 anomalies data points (Fig. 3).

4 Conclusion Decisions about anomalies are subject to the domain. Different domains have different meanings for anomalies. Plots have shown that one-class SVM and isolation forest are able to classify anomalies subject to parameter tuning. Anomalies data points which are marked with red color, correctly identified as anomalies in both the methods. But, one-class SVM has less false alarm than Isolation Forest. Isolation forest identified more normal data points as anomalies than one-class SVM. So, we conclude one-class SVM is best suited for our work. In particular, for the application in hydropower, these methods will help engineers to monitor the performance of sensors in the turbine. Not only the turbine, but the same technique can be used for different machines as well.

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References 1. Ruts, I., Rousseeuw, P.: Computing depth contours of bivariate point clouds. Comput. Stat. Data Anal. 23, 153–168 (1996). https://doi.org/10.1016/S0167-9473(96)00027-8 2. Hardin, J., Rocke, D.: Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. Comput. Stat. Data Anal. 44, 625–638 (2004). https://doi.org/10. 1016/S0167-9473(02)00280-3 3. The Science of Anomaly Detection. Numenta, Redwood City (2015) 4. Das, K.: Detecting patterns of anomalies, 174 (2009) 5. RAD—Outlier Detection on Big Data, 10 Feb 2015. Available https://medium.com/netflix-tec hblog/rad-outlier-detection-on-big-data-d 6. Vallis, O.S., Hochenbaum, J., Kejariwal, A.: A novel technique for long-term anomaly detection in the cloud. Twitter Inc. (2014) 7. Ahmed, I., Dagnino, A., Ding, Y.: Unsupervised anomaly detection based on minimum spanning tree approximated distance measures and its application to hydropower turbines. IEEE Trans. Automat. Sci. Eng. 1–14 (2018). https://doi.org/10.1109/TASE.2018.2848198 8. Liu, X., Lu, S., Ren, Y., Wu, Z.: Wind turbine anomaly detection based on SCADA data mining. Electronics 9, 751 (2020). https://doi.org/10.3390/electronics9050751 9. Galloway, G.S., Catterson, V.M., Love, C., Robb, A.: Anomaly detection techniques for the condition monitoring of tidal turbines. In: PHM 2014—Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014, pp. 713–724 (2014)

Feature Construction Through Inductive Transfer Learning in Computer Vision Suman Roy and S. Saravana Kumar

1 Introduction The concept of deep learning was ideated by Rina Dechter in 1986 [1], and Igor Aizenberg and colleagues in 2000, which was for Boolean threshold neurons. In 1971, a paper described the deep network having eight layers being trained by the group method of data handling algorithm [2]. Deep learning which is designed for computer vision began with the Neocognitron introduced by Kunihiko Fukushima in 1980 [3]. In the year 1989, Yann LeCun et al. by using standard backpropagation algorithm to deep neural network to find out handwritten area codes on mail, the algorithm took 3 days to train [4]. Currently, transfer learning is being evolved as one of the main research areas among the researchers, where the use of the neural networks in 1996 [5]. This paper is organized in following way, Sect. 2 talks about deep learning, Sect. 3 talks about transfer learning and the types of transfer learning like inductive, transductive, and unsupervised transfer learning. Section 4 talks target dataset which is PLACE database as having very large collection of data which is used as the base dataset for other places dataset with different characteristics similarly ImageNet, and its other scaled down version was discussed. Finally, Sect. 5 Overall Approach, it is observed that the overall goal is to transfer the model in the field of transfer learning which is one of the fast evolving field for researchers in the area of machine

S. Roy Department of Computer Science, CMR University, Bangalore, India e-mail: [email protected] S. Saravana Kumar (B) Professor, Department of Computer Science and Engineering, CMR University / iNurture IT Vertical, Bangalore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_12

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learning. Section 6 Results in the experiment, sigmoid function is used as final classifier. Section 7 Conclusions and Future Scope and concludes the effects of data size, layers, and its impact on transfer learning.

2 Deep Learning Supervised learning is that the machine learning task of learning a function that maps an input to an output supported example of input–output pairs from labeled training data comprises as described in the foundation of machine learning [6]. In supervised learning, each example maybe a pair consisting of input data and therefore the desired output value. The details of the neural networks [7] and how the use and application of neural network for intelligent system play a role to determine the learning-based systems. In the field of deep learning, we have Convolutional Neural Network [8] (CNN), Artificial Neural Networks (ANN) [9], Recurrent Neural Network (RNN) [10], Deep Belief Network (DBN) [11], Autoencoder [12, 13], Generative Adversarial Networks (GANs) [14], Self-Organizing Map (SOM) [15], and Boltzmann machine [16].

3 Transfer Learning Transfer learning is one type of research topic in machine learning which looks at storing knowledge obtained by solving a problem and applying it to a different but related domain. Building a model from scratch is time consuming and costly and depending on the availability of hardware and software. By using transfer learning, one can see significant improvement in the performance and computation accuracy. Transfer learning [17, 18] is the improvement of learning during a new task through the transfer of data from a related task that has already been learned. While most machine learning algorithms are designed to deal with single tasks, the event of algorithms that facilitate transfer learning may be a topic of ongoing interest within the machine learning community. There are three type of transfer learning which are inductive [19], transductive [20], and unsupervised [20] transfer learning. Inductive transfer learning setting is as follows for this paper, • Labeled data in source domain not available, but in target domain, it is available— this is known as self-taught learning. • Labeled data in source and target domain—Are available, so source and target tasks are learned simultaneously, this is known as multi-task learning.

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4 Target Dataset—Place Dataset Places with 205 [21] scene categories having 2.5 million of images with a category label. Using neural network, one can learn deep scene recognition tasks and establish new state-of-the-art performances on scene-centric benchmarks. In the study, it is further looked into Places365—Standard will be considered for the experiment. To construct the Places database, one has to follow these steps by querying and then downloading the images followed by labeling the images with respect to categories and then scaling up the dataset by using a classifier and the further separating based on the same class categories. Once images are downloaded, one has to categorize them with respect to either places or scenes or image level pixels, then labeling of images either in positive image set or negative image set by cleaning them with their categories; the category here is a deciding factor in choosing a particular image set.

5 Overall Approach In this paper, we have used the pre-trained weights of ImageNet [22]. After that, the base dataset and target dataset sizes were varied to check how transfer learning affects the overall performance. If the target dataset is different from the base dataset, then training the whole neural network would be time consuming, but if a pre-trained is being used which will certainly reduce the overall timing. For inductive transfer learning, we have four different approaches which are (1) Instance transfer, (2) Feature representation transfer, (3) Model transfer, (4) Relational knowledge transfer. For our experiment, we are using only model transfer for INCEPTION V3, VGG16, and RESNET50.

5.1 Data Pre-processing As part of data pre-processing following subset of Places205 databases [21] with 2.5 million images from 205 scene categories, 100 images per category for validation set and 200 images for test set are used for base dataset; Places365 [23] are the target dataset to be used for target dataset. In this paper by looking into the Places 205 dataset, ImageNet, and then further work on the subset of data rather than the full dataset, we have used different subsets of base and target datasets.

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5.2 Model Transfer By creating a model from which will be used to transfer the features, first train the neural network on the source dataset; the weights and parameters of the source model are stored in either in Tensorflow [24] and Keras [25]. The approach is in the beginning to get the baseline; transfer learning will not be used as the model itself is now being trained on the selected training set, once the model is tuned enough, and then, using all the features of the model to the source task were attributes play a major role, and we will continue with backpropagation on the newly created features. Inception V3 has 48 Layers, whereas RESNET50 has 50, and VGG16 has 16 layers. The transferring of the features is completed one layer at a time. Within the process, parameters are not updated by gradient descent as our goal is to transfer the model by looking into the total parameter, trainable, and non-trainable parameters.

5.3 Training the Datasets To train the model, we are using Tensorflow and Keras framework [25] in Anaconda [25] environment. To train the MODEL, a good computing power is essential as the training is time intensive and based on the selection of CPU and GPU. Tensorflow uses computational graph abstraction, and it provides the Tensor board for visualization. Thus, the ratio which is being used in these models remains consistent. Finally, to determine for how many iterations one should train the models, it is found in the model transfer that even if we increase the size of the dataset the parameters which are affected remains same which signifies that in model transfer data size does not play a major role. To train the dataset at dense layer 1, “sigmoid” and at 512-layer “relu” activation function are used. To compile the mode, binary cross-entropy is being used. To fine tune, the pre-trained model in VGG at 6-layer, layer names are block2_pool, block3_conv1, and block3_conv2, is used. In INCEPTION V3 model at 299-layer, layer names conv2d_94, batch_normalization_86, and activation_88 whereas in RESNET 50 model at 155-layer, layer names res5b_branch2a, bn5b_branch2a, activation_44 is used for the training the dataset.

5.4 Figures and Tables In this experiment, we are using Places205 [21] dataset along with Places365 [23] and then carried out the model in Inception V3 [26], VGG16 [27], and RESNET50 [22] models by varying the sizes of base dataset with respect to target dataset. For the experiment, the batch size is taken as 32 and 3 epochs are used. For small size dataset, set the time consumed as also mentioned in Table 1. We have found in this

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Table 1 Data with INCEPTION V3, RESNET50, and VGG16: 1 class Model/base-image places 205, target-places 365

Time (s)

Per/steps (in s)

Epochs

Train loss

Train accuracy

Val loss

Val accuracy

INCEPTION V3

613

1000

1

0.0015

0.9989

1.192

1.0000

INCEPTION V3

519

1000

2

1.2129

1.0000

1.1921

1.0000

INCEPTION V3

242

513

3

1.2191

1.0000

1.9221

1.0000

INCEPTION V3

275

585

1

0.0814

0.9850

3.0821

1.0000

INCEPTION V3

262

557

2

3.2524

1.0000

1.2752

1.0000

INCEPTION V3

276

586

3

6.4921

1.0000

1.1921

1.0000

RESNET50

548

1000

1

0.0019

0.9990

1.1921

1.0000

RESNET50

572

1000

2

1.1921

1.0000

1.1921

1.0000

RESNET50

634

1000

3

1.1921

1.0000

1.1921

1.0000

RESNET50

796

2000

1

0.0072

0.9972

1.8958

1.0000

RESNET50

747

2000

2

1.5318

1.0000

1.2361

1.0000

RESNET50

777

2000

3

1.2639

1.0000

1.2212

1.0000

VGG16

678

1000

1

0.59

0.9991

1.1921

1.0000

VGG16

703

1000

2

1.1921

1.0000

1.1921

1.0000

VGG16

668

1000

3

1.1921

1.0000

1.1921

1.0000

VGG16

1265

3000

1

0.0171

0.9969

1.1951

1.0000

VGG16

1296

3000

2

1.2116

1.0000

1.1921

1.0000

VGG16

1295

3000

3

1.1943

1.0000

1.1921

1.0000

experiment that even though we increase the data size the models take longer during the predict the accuracy and value loss but virtually size has no impact which is shown in the various tables below. The first experiment is carried out for Inception V3 model, where base dataset is “ImagePlaces205” and target dataset is “Places365.” The bases dataset is having 15,100 images, and target dataset is having 5000 images. The total parameters are 21,802,784 in which trainable parameters are 0 and non-trainable parameters are 21,802,784 [12]. Then, after using “sigmoid” function, the total parameters are 23,115,041 in which trainable parameters are 1,312,257 and non-trainable parameters are 21,802,784 [13]. Then, we add the “fully connected layer” for which total parameters are 23,115,041 in which trainable parameters are 1,705,985 and non-trainable parameters are 21,409,056 (Fig. 1). The second experiment is carried out with RESNET50, where the base dataset is ImagePlaces205 and target dataset is Places365. The bases dataset is having 15,100 images, and target dataset is having 5000 images. The total parameters are 23,587,712 in which trainable parameters are 0 and non-trainable parameters are 23,587,712. Then, after using “sigmoid” function, the total parameters are 40,628,609 in which trainable parameters are 17,040,897 and non-trainable parameters are 23,587,712. Then, we add the “fully connected layer” for which total parameters are 40,628,609

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Fig. 1 Result in graphs, with INCEPTION V3 and 3 epochs and 1 class

in which trainable parameters are 25,972,225 and non-trainable parameters are 14,656,384 (Fig. 2). The third experiment is carried out with VGG16 model, where the base dataset is ImagePlaces205 and target dataset is Places365. The bases dataset is having 15,100 images, and target dataset is having 5000 images and 1 class. The total parameters are 14,714,688 in which trainable parameters are 0 and non-trainable parameters are 14,714,688. Then, after using “sigmoid” function, the total parameters are

Fig. 2 Result details. The model is used as RESNET50 with 3 epochs and 1 class

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Fig. 3 Result details. The model is used as VGG16 with 3 epochs and 1 class

17,337,665 in which trainable parameters are 2,622,977 and non-trainable parameters are 14,714,688. Then, we add the “fully connected layer” for which total parameters are 17,337,665 in which trainable parameters are 17,077,505 and non-trainable parameters are 260,160 (Fig. 3). The experiment is then repeated with 2 classes for Inception v3, where the base dataset is ImagePlaces205 and target dataset is Places365. The bases dataset is having 30,200 images, and target dataset is having 5000 images with 2 classes. The total parameters are 21,802,784 in which trainable parameters are 0 and non-trainable parameters are 21,802,784. Then, after using “sigmoid” function, the total parameters are 23,115,041 in which trainable parameters are 1,312,257 and non-trainable parameters are 21,802,784. Then, we add the “fully connected layer” for which total parameters are 23,115,041 in which trainable parameters are 1,705,985 and non-trainable parameters are 1,705,985 (Fig. 4). The experiment is then repeated with 2 classes for RESNET50, where the base dataset is ImagePlaces205 and target dataset is Places365. The bases dataset is having 30,200 images, and target dataset is having 5000 images with 2 classes. The total parameters are 23,587,712 in which trainable parameters are 0 and non-trainable parameters are 23,587,712. Then, after using “sigmoid” function, the total parameters are 40,628,609 in which trainable parameters are 17,040,897 and non-trainable parameters are 23,587,712. Then, we add the “fully connected layer” for which total parameters are 40,628,609 in which trainable parameters are 25,972,225 and non-trainable parameters are 14,656,384 (Fig. 5). The experiment is then repeated with 2 classes for VGG16, where the base dataset is ImagePlaces205 and target dataset is Places365. The bases dataset is having 30,200

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Fig. 4 Result details. The model is used as INCEPTION V3 with 3 epochs and 2 classes

Fig. 5 Result details. The model is used as RESNET50 with 3 epochs and 2 classes

images, and target dataset is having 5000 images with 2 classes. The total parameters are 14,714,688 in which trainable parameters are 0 and non-trainable parameters are 14,714,688. Then, after using “sigmoid” function the total parameters are 17,337,665 in which trainable parameters are 2,622,977 and non-trainable parameters are 14,714,688. Then, we add the “fully connected layer” for which total parameters are 17,337,665 in which trainable parameters are 17,077,505 and non-trainable parameters are 260,160 (Figs. 6, 7, 8, 9, and 10; Table 2) [28].

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Fig. 6 Result details. The model is used as VGG16 with 3 epochs and 2 classes

Fig. 7 Result in graphs with value and training accuracy

6 Result In this paper, we have carried out the experiment how the feature construction through inductive transfer learning in computer vision works for model transfer in INCEPTION V3, RESNET50, and VGG16. We have shown the results how a pre-trained model as feature extractor works on different layers and then combining the layer and using ImageNet weights in our model to avoid re-training the data which is very much time consuming. We have also observed the total no of parameters which are either trainable or non-trainable. We have also observed that the no. of epochs does

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Fig. 8 Result in graphs with value and training loss

Fig. 9 Result in graphs with value and training accuracy

not play a role in reaching the accuracy of the model. In the experiment, sigmoid function is used as final classifier. In the end, we can say that model transfer one of the significant approaches for inductive transfer learning, where saving the time and getting a high accurate model can be achieved in a very less time, where if we need to train our model from scratch, then it will be very cumbersome and humongous task which will not only time consuming but also will be costly and getting a good fast result is the need of the hour.

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Fig. 10 Result in graphs with value and training loss Table 2 Data with INCEPTION V3, RESNET50, and VGG16 and 2 classes Model/base-image-places Time Per/steps Epochs Train 205, target-places 365 (s) (in s) loss

Train Val accuracy loss

Val accuracy

INCEPTION V3

452

480

1

7.2134 0.9995

1.1921 1.0000

INCEPTION V3

486

515

2

1.1953 1.0000

1.1921 1.0000

INCEPTION V3

481

510

3

1.1952 1.0000

1.1921 1.0000

INCEPTION V3

920

976

1

0.0261 0.9991

1.1925 1.0000

INCEPTION V3

826

876

2

5.5760 1.0000

1.1921 1.0000

INCEPTION V3

622

660

3

1.7187 1.0000

1.1921 1.0000

RESNET50

1396

1000

1

0.0014 0.9992

1.1921 1.0000

RESNET50

1139

1000

2

1.1921 1.0000

1.1921 1.0000

RESNET50

1204

1000

3

1.1921 1.0000

1.1921 1.0000

RESNET50

1827

2000

1

0.0010 0.9997

1.2775 1.0000

RESNET50

1816

2000

2

1.2803 1.0000

1.2775 1.0000

RESNET50

1852

2000

3

1.2869 1.0000

1.2024 1.0000

VGG16

1187

1000

1

0.0064 0.9994

1.1921 1.0000

VGG16

1290

1000

2

1.1921 1.0000

1.1921 1.0000

VGG16

1197

1000

3

1.1921 1.0000

1.1921 1.0000

VGG16

2416

3000

1

0.0237 0.9956

1.1921 1.0000

VGG16

2406

3000

2

1.7744 1.0000

1.1921 1.0000

VGG16

2407

3000

3

1.1921 1.0000

1.1921 1.0000

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7 Conclusion and Future Work In this paper, it is observed that the overall goal is to transfer the model in the field of transfer learning which is one of the fast evolving field for researchers in the area of machine learning with a pre-trained network with the necessary adjustment and to the new network which is fast and time and cost saving. We have looked into INCEPTION V3, VGG16, and RESNET50 as each of having different layers of its own and see how each layer’s shapes while training the base and target training dataset. Further research can be carried out for instance transfer, feature representation transfer, and relational knowledge transfer in either same of even can be looked into domain to domain transfer. Acknowledgements I along with my guide would like to acknowledge that the kind of inputs and information available is keep increasing but it is also important to use the available pre-trained models which not only helps to understand the various work being carried out in this field, but also huge scope exists for many future work in the field of transfer learning.

References 1. Dechter, R.: Learning While Searching in Constraint-Satisfaction Problems. University of California, Computer Science Department, Cognitive Systems Laboratory (1986) 2. Ivakhnenko, A.: Polynomial theory of complex systems. IEEE Transact. Syst. Man Cybern. 1(4), 364–378 (1971) 3. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980) 4. LeCun, Y. et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1:541–551 (1989) 5. Pratt, L.: Special issue: Reuse of neural networks through transfer. Connect. Sci. Retrieved 2017-08-10 (1996) 6. Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. The MIT Press. ISBN 9780262018258 (2012) 7. Jordan, M.I., Bishop, C.M.: Neural networks. In: Tucker, A.B. (ed) Computer Science Handbook, 2nd edn. (Section VII: Intelligent Systems). Chapman & Hall/CRC Press LLC, Boca Raton. ISBN 1-58488-360-X (2004) 8. Bengio, Y., Goodfellow, I.J., Courville, A.: Deep Learning, pp. 183–200 (2015) 9. van Gerven, M., Bohte, S.: Artificial Neural Networks as Models of Neural Information Processing. Front. Res. Topic (2017) 10. Den Bakker, I.: Python Deep Learning Cookbook, pp. 173–189. Packt Publishing (2017). ISBN 978-1-78712-519-3 (2017) 11. Bengio, Y., Goodfellow, I.J., Courville, A.: Deep Learning, pp. 382–384 (2015) 12. Zaccone, G., Rezaul Karim, M., Menshawy, A.: Deep Learning with Tensorflow, p. 98. ISBN 978-1-78646-978-6 (2017) 13. Sammut, C., Geoffrey, I.: Encyclopedia of Machine Learning, p. 99 (2017) 14. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, J.: Generative Adversarial Networks (2014) 15. Kohonen, T., Honkela, T.: Kohonen network. Scholarpedia https://www.scholarpedia.org/art icle/Kohonen_network (2007)

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16. Sammut, C., Geoffrey, I.: Encyclopedia of Machine Learning, pp. 159–162 (2017) 17. Olivas, E.S., Guerrero, J.D.M., Sober, M.M., Benedito, J.R.M., López, AJS.: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques,(2009), pp. 242–264. Information Science Reference. ISBN 978-1-60566-767-6 18. Torrey, L, Shavlik, J.: Transfer Learning. University of Wisconsin, Madison, WI (2009) 19. Olivas, E.S., Guerrero, J.D.M., Sober, M.M., Benedito, J.R.M., López, A.J.S.: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, pp. 245–246. Information Science Reference. ISBN 978-1-60566-767-6 (2009) 20. Arnold, A., Nallapati, R., Cohen, W.W.; A comparative study of methods for transductive transfer learning. In: Proceedings of the 7th IEEE International Conference on Data Mining Workshops. IEEE Computer Society, Washington, DC, pp. 77–82 (2007) 21. Place 205 Dataset. https://places.csail.mit.edu/user/download.php 22. ResNet50. https://www.kaggle.com/dansbecker/transfer-learning/data 23. Places365. https://places2.csail.mit.edu/download.html 24. https://www.tensorflow.org/ 25. www.keras.io 26. InceptionV3 Model in Kaggle. https://www.kaggle.com/google-brain/inception-v3 27. VGG16 Model in Kaggle. https://www.kaggle.com/keras/vgg16 28. Pan, S.J., Yang, Q.: A survey of transfer learning (2009). https://ieeexplore.ieee.org/document/ 5288526/

Decoding Motor Behavior Biosignatures of Arm Movement Tasks Using Electroencephalography Rakhi Radhamani, Alna Anil, Gautham Manoj, Gouri Babu Ambily, Praveen Raveendran, Vishnu Hari, and Shyam Diwakar

1 Introduction Human movements are usually volitional in nature for adaptation with the real-world for activities of everyday life [1]. Emerging trends in brain–computer interface (BCI) for decoding brain signals lightened a realistic technical possibility for connecting machines to the human brain [2]. Many countries have been actively designing and developing wearable assistive devices for rehabilitation such as robotic prosthetic hands and exoskeletal orthotic hands to regain functionality, thereby improving the quality of daily life activities and other social activities [3]. According to World Health Organization (WHO) report on disabled population in India, 2.21% of Indian population has one or the other kind of disability, among which 69% of the overall disabled Indian population lives in rural areas, due to road, rail, and agricultural injuries, with an over all of 20% of individuals reported with locomotion disabilities. In this current situation, it was highly recommended to develop low-cost prosthetic devices to meet needs of amputee population (https://wecapable.com/disabled-pop ulation-india-data/). Neuroscience community have been relying on low-cost surface-based EEG portable sensors for elucidating the neural mechanisms underlying cognitive processes such as motor movement, motor imagery, attention, memory and visual perception on a millisecond-range [4]. It has been shown that sensory motor cortex generates mu rhythm (7–13 Hz) and beta rhythm (15–30 Hz) with event related desynchronization (ERD) of mu rhythms in motor imagery tasks. Mapping neurologically relevant signatures for squeeze tasks with EEG reported frontal symmetry as regions for motor planning and motor execution characterized by predominant R. Radhamani · A. Anil · G. Manoj · G. B. Ambily · P. Raveendran · V. Hari · S. Diwakar (B) Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, Kerala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_13

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mu and beta oscillations [5]. Recent study on decoding reach-and-grasp actions in humans using EEG showed central motor cortex (Cz) during movement onset on contralateral side (C1) than on the ipsilateral side (C2). Previous EEG-based studies on left- and right-hand movement imagery indicated sensorimotor areas (C3, C4) as functional areas which were qualitatively the same with imagination and execution processes [6]. Studies on fast finger movement and index finger movement demonstrated the presence of the alpha and beta bands over premotor and primary sensorimotor cortex and variations in beta power during preparation and execution of complex grasping movements [7, 8]. Studies on temporal organization of neural grasping patterns indicated different movement covariates in grasping tasks, as centro-parietal lower beta frequency band in pre-shaping stage and contralateral parietal EEG in the mu frequency band in the holding stage with muscle activity [9, 10]. Research initiative is still necessary to progress the brain mapping studies for classification of motor tasks in more realistic situations. This paper highlights the use of low-cost EEG device for understanding behavioral changes in brain rhythms (mainly theta and gamma) attributed in cognitive motor performance in real motor tasks, motor imagery, and visual imagery processes. The present study intended to explore cortical activity measures as biosignature for arm movement in diverse populations of study subjects and to characterize EEG spatial pattern variations as an attribute for linear and complex movement-related task applicable in activities of daily life.

2 Methods 2.1 Subject Summary and Data Collection Sixteen able-bodied volunteering participants (6 males and 10 females, 4 left-handed subjects and 12 right-handed subjects; age range of 18–40 years; mean age 25.4 years) without diagnosis of neurological impairment or motor dysfunctions voluntarily participated in this study. Vividness and motor imagery questionnaire (VMIQ) was employed for testing visual and kinesthetic imagery of motor tasks. Physiological data were also measured, and subjects were duly informed about the purpose of the research. An open consent was collected from all the participants. The data collection and methodology were approved by ethics review board of the university.

2.2 Design of Experiment and Execution Steps As a reference to analyze motor control, subjects were asked to perform steady hand game with minimum errors as possible. The experiment consisted of three categories:

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Fig. 1 Illustration of linear and complex movement patterns on a marble board game

motor execution as in motor performance, motor imagery, and visual imagery, for upper left/right arm movement. A marble board game (40 cm * 40 cm, 9 g) was selected for the study, as it involves precise motor in daily life activities of holding and moving objects. Participants were seated comfortably with both forearms resting on the chair’s arm, and a computer screen was in front of them. In a linear movement, using pincer grasp, subjects must move the marble ball from left to right and vice-versa with both left and right hands. In complex movements, subjects were asked to move marble in clockwise and anticlockwise according to the visual cue provided (Fig. 1).The linear task involved both lateral and medial rotation, partial flexion of the forearm and partial flexion of the arm. The complex task involves a series of movements, movement from O to A involved arm flexion, forearm extension, A to B involved gradual lateral rotation, partial flexion of the forearm and acute flexion of the arm, in B to C arm remained in anatomical position and the forearm gets completely flexed, C to D have gradual medial rotation, acute arm flexion and partial flexion of the forearm, whereas in D to A, forearm is extended and the arm is flexed.

2.3 Design of Experiment and Execution Steps EEG signals were recorded using 14 + 2 electrode system according to the 10–20 international electrode location system, at a sampling frequency of 128 Hz. The linear task lasted for 25 s and complex task lasted for 40 s in time for each anticlockwise and clockwise movement pattern (Fig. 2). Offline signal processing of EEG data was performed using EEGLAB tool. EEG data were filtered between 0.01 and 60 Hz using filtering techniques. Power spectrum density (PSD) for specific frequency distribution (Delta, 0.01–3 Hz, Theta, 4–7 Hz, Alpha, 8–12 Hz, Beta, 13–30 Hz, and Gamma, 31–60 Hz) at brain lobes was computationally estimated.

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Fig. 2 EEG recording protocol with time periods among movement tasks

3 Results 3.1 Increased Θ and γ Activity in Frontal and Temporal Regions in Motor Planning Phase of Cognitive Tasks In cortical mapping of specific time bins of planning phase of the motor execution (ME) tasks, it was observed that averaged θ wave oscillations were higher in frontal regions (AF3, AF4, F3, and F4) and γ wave activations were observed in temporal regions (T6, T5). In motor imagery tasks, θ, and γ wave activations were found in temporal (T4, T6), frontal (F7, F8, F3, F5) regions, respectively. Motor planning phase of visual imagery showed γ and θ wave in the frontal (F7, FC5, F3, F8) regions (Fig. 3). As in any cognitive performances, beta (β), delta (δ), and alpha (α) brain rhythms were also noted at different brain lobes.

3.2 Behavioral Variations in Θ and γ Rhythms in Frontal, Temporal and Parietal Lobes in Motor Execution Phase of Cognitive Motor Performances During motor execution time bin, θ and γ rhythms activity were observed in the anterior frontal region (AF4). In motor imagery tasks, no significant power spectrum distribution variations in θ rhythms were observed, while γ rhythms were visualized in the frontal region (F8). In visual imagery task, θ rhythms were observed in the frontal and temporal (AF4, F8, T4) regions and δ rhythms were visible in frontal (AF3, F3, and AF4) regions (Fig. 4). β, δ, and α were also noted at different brain lobes.

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Fig. 3 Power spectral density plot indicating cortical activation differences during the motor planning phase of different cognitive tasks

3.3 Activity Related Increase of Theta and Gamma Waves in Frontal and Temporal Regions in Linear and Complex Motor Actions In clockwise movement, during motor execution phase, γ and θ rhythms were observed in frontal regions (AF3, AF4, F8, F7), lobes in real motor action. In anticlockwise movement pattern, θ rhythms were observed in frontal (F8, AF3, AF4) regions, γ rhythms were observed in the temporal regions (T4, T6). In anticlockwise movement of MI task, higher spectral power of θ was observed in frontal (AF4) and occipital (O2) regions and no significant spectral power variations in γ rhythms. In clockwise movement of θ and γ waves were higher in frontal (AF3, AF4, F8, F7) regions. In VI anticlockwise movement pattern, it has been observed that θ and γ rhythms were higher in the frontal regions (AF3 and AF4) of brain (Fig. 5). Lefthanded and right-handed subjects showed theta and gamma spectral variability as biosignatures for specific arm movement tasks (data not presented due to limited number of left-handed subjects for validation).

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Fig. 4 Heatmaps showing activation of EEG rhythms during motor execution phase of varying cognitive tasks

4 Discussion Toward understanding relevant neural circuitry patterns of motor movements, the present study focused on understanding cortical activity and associated neural dynamics for analyzing specific biosignature in an upper limb movement task. Our methodology depicted attempts to decode neural correlates of real-time motor tasks, imagined and visually imagined linear and complex patterns of movement that was in parallel with activities of daily life. Higher intensity of θ rhythms in anterior frontal regions and γ rhythms in temporal lobe during the motor planning indicated substantial information about movement initiation and execution. Higher spectral power density of γ rhythms in the frontal region of VI tasks indicated the salient changes of functional activation in motor cortex areas associated with different cognitive tasks. Activation of θ waves in the frontal regions in motor execution phase of real motor tasks indicated the activation of the sensorimotor cortex with the stimuli. During motor imagery and visual imagery tasks, θ and γ waves have no significant variations indicating lack of substantial stimuli extraction for movement execution process. Moreover, motor execution and motor imagery showed similarity patterns

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Fig. 5 Topographical plots of spectral power density showing brain rhythm pattern variations related to direction-related patterns of motor tasks

in γ wave activation indicated that motor imagery tasks follow similar neurological patterns as in the actual movement scenario. During motor execution, brain rhythms were predominant in ipsilateral regions, whereas imagery tasks have contralateral activation of brain regions. Initial cortical mapping studies on varying patterns of brain rhythms in left- and right-handed subjects for complex movement indicated handedness as a factor which influences sensorimotor rhythm distribution during motor performances. The pattern of activity of the linear and complex movement patterns indicated kinaesthetic variations during voluntary actions. Data need to be validated with support vector machine classifier to predict accuracy.

5 Conclusion This study with ME, MI, and VI task mapped cortical activity of the brain related to initiation and execution of motor activity among diverse populations. The results could be applied for feature extraction in classifying patterns of activation in brain rhythms for different motor tasks using low-cost EEG. Acknowledgements This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. This work is partially funded by Embracing the World Research-for-a-Cause initiative.

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References 1. Cordella, F., Ciancio, A.L., Sacchetti, R., Davalli, A., Cutti, A.G., Guglielmelli, E., Zollo, L.: Literature review on needs of upper limb prosthesis users. Front. Neurosci. 10, 1–14 (2016). https://doi.org/10.3389/fnins.2016.00209 2. Schwartz, A.B., Cui, X.T., Weber, D.J.J., Moran, D.W.: Brain-controlled interfaces: Movement restoration with neural prosthetics. Neuron 52, 205–220 (2006). https://doi.org/10.1016/j.neu ron.2006.09.019 3. Ou, Y.-K., Wang, Y.-L., Chang, H.-C., Chen, C.-C.: Design and development of a wearable exoskeleton system for stroke rehabilitation. Healthcare 8, 18 (2020). https://doi.org/10.3390/ healthcare8010018 4. Alazrai, R., Alwanni, H., Baslan, Y., Alnuman, N., Daoud, M.I.: EEG-based brain-computer interface for decoding motor imagery tasks within the same hand using Choi-Williams timefrequency distribution. Sensors (Switzerland) 17, 1–27 (2017). https://doi.org/10.3390/s17 091937 5. Krishnan, M., Edison, L., Radhamani, R., Nizar, N., Kumar, D., Nair, M., Nair, B., Diwakar, S.: Experimental recording and computational analysis of EEG signals for a squeeze task: Assessments and impacts for applications. In: International Conference on Advance Computing, Communications and Informatics, ICACCI 2018, pp. 1523–1527 (2018). https://doi.org/10. 1109/ICACCI.2018.8554913 6. Bodda, S., Chandranpillai, H., Viswam, P., Krishna, S., Nair, B., Diwakar, S.: Categorizing imagined right and left motor imagery BCI tasks for low-cost robotic neuroprosthesis. Int. Conf. Electrical and Electronical Optimization Technologies, ICEEOT 2016, pp. 3670–3673 (2016). https://doi.org/10.1109/ICEEOT.2016.7755394 7. Schwarz, A., Ofner, P., Pereira, J., Sburlea, A.I., Müller-Putz, G.R.: Decoding natural reachand-grasp actions from human EEG. J. Neural Eng. 15, 1–15 (2018). https://doi.org/10.1088/ 1741-2552/aa8911 8. Gudiño-mendoza, B., Sanchez-ante, G., Antelis, J.M.: Detecting the Intention to move upper limbs from electroencephalographic brain signals. Comput. Math. Methods Med. 2016, 1–11 (2016). https://doi.org/10.1155/2016/3195373 9. Sburlea, A.I., Müller-Putz, G.R.: Exploring representations of human grasping in neural, muscle and kinematic signals. Sci. Rep. 8, 1–14 (2018). https://doi.org/10.1038/s41598-018-35018-x 10. Cisotto, G., Guglielmi, A.V., Badia, L., Zanella, A.: Classification of grasping tasks based on EEG-EMG coherence. In: 2018 IEEE 20th International Conference on e-Health Networking, Application Services, pp. 6–11 (2018). https://doi.org/10.1109/HealthCom.2018.8531140

Emergency Robot Shubham V. Ranbhare, Mayur M. Pawar, Shree G. Mane, and Nikhil B. Sardar

1 Introduction Nowadays, terrorism and other security issues have grown to a peak which causes headache for many nations including India. In order to encounter these issues, India has developed its own defense method, i.e., commando operation. In this operation, soldiers die while performing rescue or encounter operation due to lack of equipment. Commandoes need advanced rescue tools for successful rescue operations. This project work shows how a remote-controlled robot can be used to carry the operation of commandoes. The main application of this robot is to search and rescue. Most of the applications are related to environment and security basis; to prevent the critical situation and risking life, these bots are good in assisting during military operations [1]. The main objective of this robot is to ensure the safety of the workers and provide the necessary equipment. The work performed on the initial stage of sensing and controlling is shown in this paper. Related research is also provided using the references [2, 3]. The tests performed during the build of the robot are also described. The overall design is described using the appropriate structure and data. This robot can detect: 1.

Presence of terrorists hiding in buildings.

S. V. Ranbhare (B) · M. M. Pawar · S. G. Mane · N. B. Sardar MIT Academy of Engineering, Alandi, Pune, Maharashtra 412105, India e-mail: [email protected] M. M. Pawar e-mail: [email protected] S. G. Mane e-mail: [email protected] N. B. Sardar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_14

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Presence of metallic weapon or explosives. Presence of poisonous or inflammable gasses.

These techniques are based on processing speed and accuracy that provides various algorithms for threat detection. Further, we discuss a few challenges in the detection process which gives a conclusion.

1.1 Literature Survey At the initial stage, the robot was designed by following the rules of Isaac Assimo. This law says that the robots should not harm any human being and the Robot has to follow the instructions given by an instructor. Lots of soldiers were killed in the war, so nations started using technologies. As per the data available on “all of robots: a robotics history,” the robot used in war was designed by German and the design changes in the robotics, changed the principal of Assimo in the nineteenth century. Tanks have undergone some modifications which were done by replacing some equipment with electronic devices. By considering the environmental conditions, the tanks were designed, in such that, they could be controlled from a distance of 500–1500 m away.

2 Description of Vehicle Section The robot works on RF Tx/Rx [3], controller, robotic sensors, LCD display, and power supply. The initial work or the model is required for detecting, sensing, and controlling a robotic system. In this section discuss the two sensors and two different devices to control the robot.

2.1 Gas detector This type of device is used widely in the chemical industry and can be found in locations, such as bunkers and gas companies. Also, use in gas leak and measurement of the explosion [4].

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2.2 Metal Detector The main objective of the metal detector is to identify the devices which are hidden in complex location or detection of landmines that come in the track of military men during any operation. These metal detectors work on the principles of electromagnetism [5].

2.3 Web Camera To make this robot more effective in all dimensions, we mounted the webcam; it gives live footage of the area, and this can guide us to the perfect information in less time and is more versatile to use. This camera uses infrared light to cove the information at night or less light [6].

2.4 Gun Control It is used to detect the target provided by leaser. Using those sensors and electronics devices, the system allows us to control the robot more effectively over speed and operations. The robot can be featured with the collision avoidance sensors like ultrasonic and more efficient suspensions and a drone carrying facility.

3 System Implementation 3.1 Work Flow of the Model • • • • • • • •

Initialization of accelerometer with ground as reference. Setting potentiometer with reference as control signal. Transmitting signal using RF transmitter communication. Collecting information at the receiver side (Robot) and transmitting it to the controller After processing the signal, send to motor drivers to perform actions. At receiver side (Robot), if any threat gets detected, then transmit to RF transmitter on robot. This signal will be received by the RF. Received RF signal is send to controller and then to LCD display to process.

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The flow chart shows the running process of program and a short description of the actual project. By interfacing all the components with 8051 controller and debugging will get the required outcome. At the very first stage, the robot is null and initialized. When any command is asked to perform, the instruction is first compared, and then, the process of actual movement is started. The operation includes roaming in surrounding and acquiring an actual position. If any threat is detected by the robot, then it immediately intimates the user, about the detection of the actual threat. It can be gas detection, metal detection, or any terrorist. The indication is given by beeping on the controller side and by the actual image captured by the camera. This helps the team to understand the situation and get information (Fig. 1).

3.2 Troubleshooting It is very common that accurate output does not get at the very first stage; the same case is with me. Setting up with the communication channel was the major problem. In that case, you just set up a wired communication. In short, you connect the Dout (Data Out) pin in the encoder IC directly to the Din (Data In) pin of the decoder. Then, you check whether the address of both the encoder and decoder is the same, and later, you should go for any VCC and GND connections. If any successful communication link is not established, then change resistance as they are responsible for the change in frequency. If any successful communication link is not established, then you have to change the encoder and decoder IC. Once the communication is established, you can connect the RF module.

4 Proposed System The proposed system is of transmission on both the sides [7], i.e., robotic transmitter and human transmitter. The robotic transmitter is used to inform the controller about the present situation and the detection of metal, gas, and the presence of terrorists. The human transmitter is a controlling device; this provides the instruction to the robot (Fig. 2).

4.1 Controlling Section The proposed system is of reception of data on both side, i.e., human receiver and robotic receiver. The human receiver collects the data send by the robot. The data can be a detection signal (Figs. 3 and 4).

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Fig. 1 Flow chart

Start

Initialize and if Robo side=1&0

Signal Transmission from user side

Signal Receiving from Robo side

Sensor detecting & webcam controlling

If Received= OK

Survive area & send signal back to user side

Beep buzzer & LED ON receiver side

Stop

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Fig. 2 Block diagram of human transmission/reception

The robotic receiver is a decoding section which decodes the transmitted signal by the transmitter and act according to the instructions programmed.

Emergency Robot Fig. 3 Block diagram of receiver section

Fig. 4 Block diagram of robotic transmitter and receiver section

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Fig. 5 Robotics transmitter

5 Hardware Implementation 5.1 Circuit Diagram Figure 5 is a transmitting section from a robot which transmits signals when it detects the threat. Figure 6 is a receiver section of robot. When the signal from the user is transmitted, this section is responsible for the reception of signal and performs actions accordingly. Actions involved are front movement, back movement, right turn, left turn, camera rotation.

Fig. 6 Robotics receiver

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Figure 7, section which transmits the controlling signals of the robot. The signals are generated from the accelerometer and compared with reference. This provides variety of combinations and is able to control the robot accordingly. Figure 8 is simply a display circuit. The threat detected by the robot is received here and displayed on the LCD display.

Fig. 7 Human transmitter

Fig. 8 Human receiver

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Fig. 9 Working motor

6 Result Figure 9 shows the working of the robotic motors used for traveling and taking accurate position. Figure 10 shows the initial condition of all sensors, where the robot has not detected any object or gas. Figure 11 shows the result, where metal detector has detected a threat of landmines or grenade. Figure 12 shows the result, where gas detector has detected a threat of poisonous gas or gas leak. Figure 13 shows the actual model and the size of the robot after mounting the equipment and sensors. Figure 14 shows the mounting of RF communication and the gun triggering mechanism. Figure 15 shows the gun controlling direction and the guidance provided by camera to perform any action.

7 Conclusion This emergency robot can easily move to any location by detecting the threats and giving clear information about the situation. Due to camera implementation, the image can be transmitted to a specific receiver for more details. The robot can be used on the war field or in a situation, where humans cannot enter. The robot has the

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Fig. 10 Indicating display

Fig. 11 Metal detected

capability to move in rocky and slope regions. This robot can also be used in disaster situations such as floods and emergency service providers. This robot has the ability to detect a gas leak and detect underground metal can help military men to make or find the path. The robot is light in weight and can be carried to any location. It is

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Fig. 12 Gas detected Fig. 13 Actual size

Fig. 14 RF module

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Fig. 15 Mounted gun

easy to disassemble and reassemble the robot at any location. It has the capability to be carried by a drone and can support any military applications.

8 Future Scope The robot can be improved by using advance processors, thermal sensors, rotational cameras, and advancement in robot location by Global Positioning System (GPS); also drone of appropriate capacity can be used to shift the robot from one location to another.

References 1. Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B.: The benefits of interactions with physically present robots over video-displayed agents. Int. J. Soc. Robot. 3(1), 41–52 (2011) 2. Gao, G., Clare, A.A., Macbeth, J.C., Cummings, M.L.: Modeling the impact of operator trust on performance in multiple robot control. In: 2013 AAAI Spring Symposium Series (2013) 3. Cardozo, S., Mendes, S., Salkar, A., Helawar, S.: Wireless communication using RF module and interfacing with 8051 microcontroller. IJSTE—Int. J. Sci. Technol. Eng. 3(07) (2017). ISSN (online): 2349-784X 4. Parasuraman, R., Miller, C.A.: Trust and etiquette in high criticality automated systems. Commun. ACM 47(4), 51–55 (2004)

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5. Nováˇcek, P., Ripka, P., Pribula, O., Fischer, J.: Metal detector, in particular mine detector. G. Kellermann. 09/04; 2005/01/08). US Patent 7265551, 2007 6. Mehta, L., Sharma, P.: Spy night vision robot with moving wireless video camera. Int. J. Res. Eng. Technol. Manag. (IJRETM) (2014) 7. Sample, A., Smith, J.: Experimental results with two wireless power transfer systems. In: Radio and Wireless Symposium 2009. RWS ‘09. IEEE, pp. 16–18 (2009)

Type Inference in Java: Characteristics and Limitations Neha Kumari and Rajeev Kumar

1 Introduction Java is a static-typed language. This implies that the type of a variable is known before compilation. The type of variable can be assigned in two ways; one is the explicit way where the programmer mentions the type of variable in code, and the other is the implicit way where compiler automatically deduces the type based on code information. This automatic deduction of type reduces verbose codes and simplifies code writing and readability. Type inference was added in Java 5 to reduce the burden of explicit declaration for generic methods. The Java compiler was enhanced to infer the type of generic methods using context information. Since then, the Java type inference algorithm has evolved in its several editions. Earlier type inference was limited to generic methods whereas Java 10 onwards type inference for local variables is also included. Such enhancements in the type inference algorithm has improved its scope in languages. However, there are several situations where the compiler is unable to infer a type despite the context. One such case is to infer wildcard as a return type of generic method. Java’s support for backward compatibility and complex type system is some of the reasons for this. A type inference algorithm should be sound. An inference algorithm is sound when it always results in well-typed for the inference variables. Java type inference does not always infer a proper type. In contemporary time, several sound type inference algorithms have been proposed which are based on machine learning techniques [1, 6, 12]. These algorithms use a corpus of program code to train the learning models (e.g., RNNs, GNNs), and then, these trained models are used to predict the type. Such N. Kumari (B) · R. Kumar School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India e-mail: [email protected] R. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_15

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learning-based algorithms can enhance Java static type inference to become sound. In this paper, we observe incremental changes in the Java type inference algorithm and discuss its limitations. Our aim is to exhibit the limitations and pitfalls of Java type inference mechanism. Thus, this study can help to enhance the inference algorithm in different contexts. This paper is organized as follows: Sect. 2 discusses evolution of type inference algorithm in mainstream Java. Section 3 elaborates a few limitations and pitfalls of Java type inference algorithm. Section 4 discusses various aspects of type inference algorithms. Finally, we conclude this paper with future work.

2 Type Inference in Java Java type inference algorithm is local scoped. Therefore, it searches for constraints or type information in same expression or statement. The following three steps are the basis of Java type inference: reduction, incorporation, and resolution [5]. • Reduction is a process where constraints reduce to a set of bounds on inference variables. • Incorporation ensures a set of bounds on inference variables, which are consistent when new bounds are added. • Resolution examines the bounds and determines an instantiation that is compatible with those bounds. In Java, type inference algorithm evolves from its several editions. Due to backward compatibility and upward upgradation principal, the Java type inference evolved in a restricted manner. Here, we discuss such incremental changes in inference algorithm of Java. We include only those versions of Java where major changes occurred in type inference algorithm.

2.1 Java 5 In Java 5, type inference was introduced for generic methods. Without type inference, a generic method must specify type argument explicitly for each method invocation. public static void sample(T t) {} //Generic method invocation with explicit type Object.sample(10);

In the above example, T is explicitly initialized. The type inference algorithm enables a compiler to automatically infer the type parameter based on the target type. Consider the following example:

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public static void sample(T t) {} //Generic method invocation without explicit type Object.sample(10);

2.2 Java 7 The diamond operator, introduced in Java 7, enables type inference for instance creation. The generic class type parameter infers the type using assignment context. Consider the following example: //Before Java 7 SampleClass Obj = new SampleClass(); //Java 7 Onwards SampleClass Obj = new SampleClass();

In this example, the compiler infers the type “Integer” for the formal type parameter of the generic class SampleClass.

2.3 Java 8 In Java 8, type inference mechanism expanded to method invocation context. //assignment context List list1 = new ArrayList(); // method invocation context List list2 = SampleClass.m(new ArrayList());

In the first code example as above, the missing constructor parameter is inferred from the left-hand side of the assignment, whereas in second code, the diamond operator appears in the method invocation context. Prior to Java 8, the type parameter inference within method invocation context was not valid [2].

2.4 Java 10 The local variable type inference in Java 10 extends type inference for local variables [4]. It introduced a reserved type “var” which infers the type of a variable through initializer. var x = new ArrayList();//infer ArrayList

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Unlike earlier versions of type inference in Java, it can infer all kinds of local variables with initializers and is not only limited to generic types and lambda formals. This variable type inference has eased code writing and also improved readability by removing redundant code. However, there are several risks associated while using it, for example, use of var without initializers, improper use of var type, etc. Therefore, a programmer needs to be careful with the use of local type inference.

3 Limitations In the above section, we discussed how type inference is evolved in Java and reduces verbosity and boiler codes. It also improves code writing. However, there are several restrictions that make type inference algorithm less expressive and underutilized. For example, Var type cannot be used as a method return type, argument type, field type, lambda expressions, etc. Also, there are several situations where inference causes error only due to the sophisticated structure of the type system. In the following sub-sections, we mention some such cases where type inference can be achieved but the sophisticated type system restricts to infer.

3.1 Wildcard as Return Type The return type of a method can be inferred based on assignment context, but the type inference algorithm fails to infer return type if there is a wildcard. List method(List list){ ....return list;} List str = new ArrayList(); //error: incompatible types List result= obj.method(str);

The above code works if the wildcard gets replaced by a type variable. Consider the following example: List method(List list) { ....return list;} List str = new ArrayList(); List result= obj.method(str); //inferred.

The lack of wildcard inference fails to type check and allows explicit cast for an incompatible type. In following code, the illegal cast of a list of string type is accepted for a list of integers. Due to this, String values can be added to an Integer list.

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List method(List list){ ....return list;} List integer = new ArrayList(); integer.add(10); List result=(List)obj.method(integer); result.add("string"); System.out.println(integer); // Output: [10,string]

3.2 Chained Method Call Java type inference uses assignment context to infer generic methods. However, the assignment context fails to infer when generic method invocations are chained together. class Sample Sample m1(){ Sample t=new Sample(); return t;}; Z m2(Z z){ return z;} //works for single method invocation Sample s = Sample.m1(); //error: Object cannot be converted to String String s1 = Sample.m1().m3("str");

The above code will work when the programmer explicitly mentions the type of generic method as follow. String s2 = Sample.m1().m3("str"); // Valid

3.3 Local Variable Inference As mentioned in Sect. 2.4, the “var” keyword is used to infer local variables only. It cannot infer those variables that can appear explicitly in class files, for example, field type, array type, method parameters, and return types. Also, it cannot infer variables without an initializer as the choice of type depends on such expression only. Apart from these limitations, this local type inference requires proper guidelines as mentioned in [4] to avoid risks associated with it, for example, uses of primitive literals. The var type cannot differentiate among numeric values, and this infers all numeric values as integer. var x=127; byte y=x; //error:lossy conversion from int to byte var y=(byte)x; //need to cast explicitly

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Here, x is within the range of byte type, but var infers it as int type. Therefore, we get incompatible type error when assigned it to a byte type.

4 Discussion Type inference is a crucial feature of several programming languages. Functional programming languages like ML, Ocaml, and Haskell support complete type inference. These languages follow Damas and Milner’s globally scoped inference algorithm, which infers type based on the functionality of values [3], whereas the static objectoriented programming languages (OOPLs), like Java, Scala, and C# follow a local type inference approach which is based on the declaration of local information. Both type inference systems have their limitations. For example, Milner’s type inference system does not support subtyping and the local type inference system is incapable to infer global variables. Complete type inference is challenging for OOPLs like Java. There are several situations where explicit type annotation is necessary, such as subtyping. Moreover, the local type inference system in Java suffers from some limitations, and there are some issues, for example, uncertain inference while performing join operations on wildcards, inconsistency with wildcard capture conversion, etc. These issues of Java type inference have been discussed in [9, 10]. The restricted Java type system and its support for backward compatibility are some of the reasons for type inference issues. Several approaches have been suggested for the improvement of unsound Java type inference algorithm [8, 10, 11], and to avail complete solution for Java type inference system [7].

5 Conclusion The Java type inference algorithm is evolving. As of now, it can infer the type of local variables also. This feature of Java compiler has reduced loads of verbose and boilerplate codes and enhanced code readability. However, Java type inference algorithm is limited in use. There are many situations when types are not inferable instead of the availability of required type information. Moreover, the type inference algorithm does not guarantee to infer a proper type. Java type inference algorithm needs to enhance such that it can infer more types and ensures sound result. In future, we aim to use machine learning techniques to develop a sound type inference system for Java. In contemporary time, several machine learning-based type inference algorithms have been proposed for a variety of programming languages [1, 6]. These machine learning models understand which types naturally occur in certain contexts, and based on this, machine learning techniques provide type suggestions. This can be verified by a type checker. The machine learning approaches ensure safety that can help to develop a robust type inference algorithm for Java. There are various aspects of Java program from where we can get information about

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a variable type, for example, identifier name, associated comments, code usage patterns, etc. Such data helps to train a learning model that can precisely predict the type of a variable.

References 1. Boone, C., de Bruin, N., Langerak, A., Stelmach, F.: DLTPy: Deep learning type inference of Python function signatures using natural language context. arXiv preprint arXiv:1912.00680 (2020) 2. Cimadamore, M.: JEP101: Generalized target-type inference. Last accessed: 2020. https:// openjdk.java.net/jeps/101 3. Damas, L., Milner, R.: Principal type-schemes for functional programs. In: Proceedings of 9th POPL, pp. 207–212, New York. ACM (1982) 4. Goetz, B.: JEP 286: Local-Variable Type Inference. Last accessed: 2020. https://openjdk.java. net/jeps/286 5. Gosling, J., Joy, B., Steele, G., Bracha, G., Buckley, A.: The Java Language Specification (Java SE 8 edition) (2015) 6. Hellendoorn, V.J., Bird, C., Barr, E.T., Allamanis, M.: Deep learning type inference. In: Proceedings of 26th ESEC/FSE, pp. 152–162, New York. ACM (2018) 7. Plümicke, M.: More type inference in Java 8. In: Proceedings Perspectives of System Informatics, pp. 248–256, Berlin, Heidelberg. Springer (2014) 8. Smith, D.: Designing Type Inference for Typed Object-Oriented Languages. Ph.D. thesis, Rice University, USA (2010) 9. Smith, D., Cartwright, R.: Java type inference is broken: can we fix it? In: Proceedings of 23rd OOPSLA, pp. 505–524, New York, USA. ACM (2008) 10. Tate, R., Leung, A., Lerner, S.: Taming wildcards in Java’s type system. In: Proceedings of 32nd PLDI, pp. 614–627, New York, USA. ACM (2011) 11. Torgersen, M., Plesner Hansen, C., Ernst, E., von der Ahé, P., Bracha, G., Gafter, N.: Adding wildcards to the Java programming language. In: Proceeding of 19th SAC, pp. 1289–1296, New York, USA. ACM (2004) 12. Wei, J., Goyal, M., Durrett, G., Dillig, I.: LambdaNet: Probabilistic Type Inference Using Graph Neural Networks. arXiv preprint arXiv:2005.02161 (2020)

Detection and Correction of Potholes Using Machine Learning Ashish Sahu, Aadityanand Singh, Sahil Pandita, Varun Walimbe, and Shubhangi Kharche

1 Introduction Traffic congestion has been increasing worldwide as a result of increased motorization, urbanization, population growth and changes in population density. Congestion reduces utilization of the transportation and infrastructure and increases travel time, air pollution, fuel consumption and most significantly, traffic accidents. There is an exponential increase in the population of Mumbai. As people live in far places from their offices, they believe in commuting through road transport or trains. This has led to faster corroding of the roads. These roads are left attended because of huge traffic, delays and accidents which can cause mental strain and in some cases can be fatal. At present, there are various ways to detect the potholes either manually or automated. The main aim of our project is to detect the pothole automated and also repair it without us being present there. Along with that our system is quite cheaper than other proposed systems. To reduce the potholes, we have decided on a mechanism where it could simultaneously detect and store the data of the pothole in the database. For transmission of data, we use LoRaWAN module which can send data of the pothole in the database. Then it would send a robot without any manual assistance to that place to correct it. This reduction of manual labour will also lead less in time consumption for the correction of pothole. In our system, image processing is an integral part of the detection of potholes. Plus, for the transmission of the images and data, we are using LoRa instead of Zigbee, as LoRa can transmit data over longer ranges, i.e. ~10 km. Also the project deals with the filling of potholes which has not been attempted earlier on, here instead of asphalt we are using chip filling which can greatly reduce the recurring of the filling. Also by using chip filling techniques, we A. Sahu (B) · A. Singh · S. Pandita · V. Walimbe · S. Kharche Department of Electronics and Telecommunications, SIES Graduate School of Technology, Navi Mumbai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_16

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can reduce the carbon emission by the production of asphalt resulting in a greener world and environment. The project will improve the efficiency of road maintenance and reduce the labour requirement for the same.

2 Literature Survey We know that the potholes are a nuisance to the society and well-being of the humans. Several measures have been taken in reducing the number of potholes or detecting it to make the driver of the potholes aware of it beforehand. In one such case, a camera was used to detect any erosion on the roads by using various image processing techniques like Canny edge detector, dilation and contouring. It was quite portable as there was a reduction in non-portable objects as computers were used. Since image processing was used, it was relatively faster than any other previous processes. The system produced an average accuracy, sensitivity and specificity within the expected output, and the success rate of sending reports of detection was found to have no errors. The value of the accuracy achieved during the testing showed that the system was excellent in terms of the overall performance [1]. Another idea was presented with an integrated approach to road distress identification. In this the most important thing was a set of multiple algorithms working synergistically for a common goal. By adjusting and changing various functions like pixel, sub-image or object and the number of signals to be processed, they were able to achieve robust performance and computational tractability [2]. Another paper suggested the use of 2D LiDAR and camera for a pothole detection system. It used specifically two LiDARs so as to scan the wide road more accurately. After that they developed the system algorithm including filtering clustering, line extraction and gradient of data function. Error rate of pothole detection system shows the performance of developed system. One of the novel things was that 3D pothole detection can be performed using 2D LiDAR. Pothole detection using video data is combined with that of 2D LiDAR, and combined data gives more accurate pothole detection performance [3]. In another paper, the problem was tackled by designing a Wi-Fi-based infrastructure enabling application data transfer to the vehicles moving on the roads. In this method, the driver is given early notifications on the roads condition so as to assist the driver in making strategic and real-time tactical decisions in varied environments. The architectural design and system support for the pothole detection and warning system ensured that the driver gets information about potholes well in advance and has sufficient time to take decision according to the prevailing road conditions [4]. In another paper, it countered the problem by using an ultrasonic sensor for the pothole detection and Zigbee module pair for communication. The proposed model uses NXP LPC1768 microcontroller for taking decisions about controlling the speed of the vehicle. Its main aim was to achieve proper and efficient detection of pothole and communication between multiple vehicles. Zigbee was used for multi-vehicular communication establishment. Pothole detection is an important feature of the autonomous vehicles, and this idea can be extended to detect vehicles in the vicinity and any type

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of obstacles on the road [5]. Another method describes accelerometer data-based pothole detection algorithms for deployment on devices with limited hardware and software resources and their evaluation on real-world data acquired using different Android OS-based smart phones. The evaluation test resulted in optimal set-up for each selected algorithm, and the performance analysis in context of different road irregularity classes shows true positive rates as high as 90%. Thus, we have seen all these papers that image processing is an integral part of the detection of potholes. Plus, for the transmission of the images and data, we are using LoRa instead of Zigbee, as LoRa can transmit data over longer ranges, i.e. ~10 km. Also the project deals with the filling of potholes which has not been attempted earlier on, here instead of asphalt we are using chip filling which can greatly reduce the recurring of the filling. Also by using chip filling techniques, we can reduce the carbon emission by the production of asphalt resulting in a greener world and environment. The project will improve the efficiency of road maintenance and reduce the labour requirement for the same (Table 1).

3 System Architecture Figure 1 shows the system architecture which is explained stepwise as follows: 1.

2.

3.

4. 5.

6. 7.

8.

CCTV footage: A CCTV takes the footage of the surrounding area, i.e. a specific part of the road. It sends images of the section of the road in specific intervals to the server via LoRA. Server: The main server where the pothole detection model is, so basically it gets the video from the CCTV and runs it in the SVM model to see if there is a pothole in the sent video. Pothole detection: It is done using trained SVM model. Once the pothole is detected, it is updated in the real-time database so that the bot can go to the desired location. Database: This is the real-time database of the whole system. It gets updated with the location and size of the pothole by the server. Camera module: The camera module on the bot is used to detect the pothole at the exact location when the bot is on the field. It is used for distance calculation and depth calculation. GPS module: It is installed in the bot to get the general location of the bot, also so that the bot finds its way to the exact location of the pothole. Ultrasonic sensor: It is used for collision detection and depth detection. Ultrasonic sensors are cheap and can be used fairly easily for collision detection. For depth detection like of a pothole, which has an irregular surface, LiDAR can be used but it is an expensive alternative. Raspberry Pi: It is the heart and brain of the system. It is the main controller. It runs the bot and has the image processing model in it for pothole detection. Also, it helps in the manoeuvering of the bot from one place to another, i.e.

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collision detection and moving the bot to exact coordinates. It also calculates the distance of the pothole from the bot. It also has the amount of material needed for filling the hole and the depth of the hole. 9. Motors: It is used for the movement of the bot. These are high power, high RPM, high torque motor so that the bot can move around with its weight at a fair speed. 10. Burners: These are used to heat up the thermosetting plastic and heat, and the flame is regulated by the Raspberry Pi. 11. Servomotor: These are used for controlling the valve of the material container so that the exact amount of material can be used.

4 Support Vector Machine The image of a pothole is very complicated. Roads are usually dark grey in colour or almost similar to black. It is very difficult to track a black object in a blackcoloured road by image processing algorithms. So basically in short, there are many outliers in a pothole image, and data is nonlinear. SVM is robust and is not much impacted by data with noise and outliers. Due to this, SVM excels among all the other machine learning algorithms. It is very accurate compared to other algorithms. The predictions results using this model are very promising. Pothole detection is a binary classification problem. SVM excels in binary classification. For detecting a pothole accurately on the road, the data set for training should be huge. SVM runs efficiently on large and expensive data sets. For the classification and detection of protocols, we use SVM. SVM is a model which can do linear classification and regression. It is based on concept called hyperplane which draws boundary between data instances plotted in the multidimensional feature space. SVM algorithm builds an N-dimensional hyperplane model that assigns future instances onto one of the two possible output classes. SVM is perfectly meant for binary classification. It is robust, i.e. not much impacted by data or outliers. The prediction results using this model are very promising. SVM Algorithm. Step 1 Step 2 Step 3 Step 4

Selection of two classes on which classification has to be done. Boundary plane is drawn between the two classes (hyperplane). Find the optimal hyperplane. Data is classified using the correct hyperplanes and input training data (Fig. 2).

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Fig. 1 System architecture Fig. 2 Working of SVM

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4.1 Working of SVM 1. Hyperplane and margin: For an N-dimensional feature space, hyperplane is a flat subspace of dimension (N-1) that separates and classifies a set of data. For example if we consider a two-dimensional feature space, then a hyperplane will be a one-dimensional subspace or straight line. Mathematically, in a two-dimensional space hyperplane can be defined by the equation which is given by: c0 + c1X1 + c2X2 = 0 which is nothing but an equation of straight line This concept is used for binary classification. 2. Kernel Trick: SVM has a technique called kernel trick to deal with nonlinearly separable data. These are functions which can transform lower-dimensional input space to higher-dimensional space. In the process, it converts linearly nonseparable data to a linearly separable data. These functions are called kernels. There are mainly three main types of kernels:

I. Linear Kernel: it is in the form K(xi, xj) = xi.xj II. Polynomial Kernel: It is in the form K(xi, xj) = (xi.xj+ 1)ˆd III. Sigmoid Kernel: It is in the form K(xi, xj) = tanh(kxi.xj − epsilon).

4.2 Working of Pothole Detection Robot Step 1: The bot monitors the area around it in search of potholes with the help of camera mounted at the top of servomotor. Step 2: The frames from the camera are captured real time using Open CV image processing library. These images are passed through SVM model. As soon as an image of pothole is passed, the model predicts it accurately and gives the feedback that the image is of pothole. If the image is not pothole, then again the bot starts monitoring. Step 3: After the image is detected, then the distance of the pothole from the bot is calculated using simple mathematics and image processing operations. After that the bot approaches the pothole. Step 4: After reaching the spot, bot calculates the depth of pothole using ultrasonic sensor. After that it fills the pothole up to its depth and process is complete. This whole process is repeated when another pothole is detected.

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5 Results and Discussions The result would be based on the accuracy we have achieved from different types of feature extraction techniques. For training, we have used a total of 698 images, 343 potholes (we call it positive images) and 355 plain images (we call it negative images). For testing, we have used a total of 16 images, eight potholes and eight plain images.

5.1 Normal Support Vector Classifier (SVC) Where no Features Are Used Gives 62.5% Accuracy. 1. Positive image accuracy is 75% accuracy. b. Negative image accuracy is 50% accuracy. Inference: Figure 3 suggests that the model identifies a pothole as a pothole 75% times but identifies a normal plain road as pothole image 50% of times. Thus, a total accuracy of 62.5% is achieved.

Fig. 3 Normal SVC results

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Fig. 4 SVC with corner results

5.2 SVC with Corner Detection Gives 62.5% Accuracy. 1. Positive image accuracy is 75% accuracy. b. Negative image accuracy is 50% accuracy. Inference: Figure 4 depicts that the model identifies a pothole as a pothole 75% times but identifies a normal plain road as a pothole image 50% of times. Thus, a total accuracy of 62.5% is achieved.

5.3 SVC with Canny Edge Detection is 75% Accuracy. a. Positive image accuracy is 50% accuracy. b. Negative image accuracy is 100% accuracy. Inference: Figure 5 depicts that the model identifies a pothole as a pothole 50% times but identifies a normal plain road as a pothole image 0% of times. Thus, a total accuracy of 75% is achieved.

5.4 SVC with Canny Edge Detection and Corner Detection 68.75% Accuracy. 1. Positive image accuracy is 62.5% accuracy. 2. Negative image accuracy is 75% accuracy.

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Fig. 5 SVC with Canny edge results

Inference: Figure 6 suggests that the model identifies a pothole as a pothole 62.5% times but identifies a normal plain road as a pothole image 25% of times of times. Thus, a total accuracy of 68.75% is achieved. Seeing the main accuracy graph Fig. 7, we can say that Canny edge detection gives the best output overall output, which is 75% overall accuracy. But seeing the graph of only positive images that is the model identifies a pothole image as a pothole, a normal SVC without any feature extraction or an SVC with

Fig. 6 SVC with corner and Canny edge results

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Fig. 7 Final comparison

corner detection gives us the best result that is 75% accuracy. This is shown in the graph in Fig. 8. Now the case comes with the graphs of negative images, which means a plain road image is identified correctly and not as a pothole. We see SVC with Canny edge detection gives the optimum output of 100% accuracy. It is shown in the graph in Fig. 9. With this, we can say that Canny edge detection gives the best output overall

Fig. 8 Positive images comparison

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Fig. 9 Negative images comparison

output. It can also be inferred as follows: The robot can successfully detect the pothole by image processing. So we can say SVM has been successfully trained. Robot can successfully calculate the distance of the pothole from it by mathematical equations and image processing. Robot can successfully repair the pothole in a fast and accurate manner. Robot successfully monitors the area by an angle of 360° in 3D space, so at any angle pothole is located, and the bot will be able to find and repair it. Robot works in fully autonomous mode. That means there is no human interference while robot is operating. Robot spots the pothole goes to it and then repairs it. Robot operates in four steps: 1. 2. 3. 4.

Robot identifies the pothole. Robot calculates the distance of the pothole and goes near to it. Robot calculates the depth of the pothole using LiDAR sensor. Robot then starts filling the pothole until it is completely filled up to its depth.

Then robot starts moving randomly in an autonomous manner in order to look for other potholes. It also has the ability to randomly stop moving sometimes and keep scanning in order to accurately identify a pothole.

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6 Future Scope So if we see the after effects of using this model, we could clearly see a dip in carbon emission in the atmosphere, thereby reducing the greenhouse effect. Also by reducing the number of potholes, we see that there is a decrease in the number of accidents, reduces the commuters’ anxiety, reaching on time will be a thing then and also minimizing the cost taken by the municipality. Since we are already using CCTV camera, accidents on the road can be identified, and prevention from road rage and locating criminal bound vehicles will be easier. The data can be further used to analyse traffic patterns and lay new roads. The data can be used to understand the wear of roads and plan a total renewal of roads.

7 Conclusion The proposed system is a completely autonomous vehicle capable of traversing through streets; it can detect road signs and can detect obstacles in its path LiDARs and react accordingly. Irregularities on the road, i.e. the potholes are detected, and cold lay asphalt material is dispensed on the affected area, thus making the road smooth and pothole free. This whole process is fully automatic. This will also result in a decrease of heavy machinery used for repairing and will also reduce the expenditure. Table 1 Literature survey in concise Author/year

Applications used

Merits

Garcillanosa [1]

RPi, image processing, cloud storage

Image processing is very fast, very portable and efficient

Gill (1997)

Detectors, line trackers, Hough transform

Integrated approach to road distress identification, robust performance and computational tractability

Choi [2]

2D LiDAR, camera, OpenCV

Wide area of the road scanned efficiently, more accurate pothole detection performance

Rode [3]

Wi-Fi-based architecture, GPS module

Assist in making strategic and real-time tactical decisions

Artis Mednis, Girts Stradznis/2011

Android OS, accelerometer sensors, GPS module

Detects different road irregularity classes show true positive rates as high as 90%

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References 1. Garcillanosa, M.M.: Smart detection and reporting of potholes via image-processing using Raspberry-Pi microcontroller. In: Conference: 2018 10th International Conference on Knowledge and Smart Technology (KST). https://doi.org/10.1109/KST.2018.8426203 2. Choi, S.-i.: Pothole detection system using 2D LiDAR and camera. INSPEC Accession Number: 17063558. https://doi.org/10.1109/ICUFN.2017.7993890 3. Rode, S.S.: Pothole detection and warning system: infrastructure support and system design. In: 2009 International Conference on Electronic Computer Technology. INSPEC Accession Number: 10479675. https://doi.org/10.1109/ICECT.2009.152 4. Hegde, S.: Pothole detection and inter vehicular communication. In: 2014 IEEE International Conference on Vehicular Electronics and Safety. INSPEC Accession Number: 15001142. https:// doi.org/10.1109/ICVES.2014.7063729 5. Salavo, L.: Real time pothole detection using android smartphones with accelerometers. In: IEEE XploreConference: 2011 7th IEEE International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS). https://doi.org/10.1109/DCOSS.2011.5982206 · Source

Detecting COVID-19 Using Convolution Neural Networks Nihar Patel, Deep Patel, Dhruvil Shah, Foram Patel, and Vibha Patel

1 Introduction Coronavirus disease 2019 or COVID-19 is the newest virus in the category of coronaviruses that caused the earlier epidemics of severe acute respiratory syndrome (SARS-CoV) in 2002 and Middle East respiratory syndrome (MERS-CoV) in 2012. COVID-19 outbreak is believed to have started from the Huanan seafood market of Wuhan city in Hubei province in People’s Republic of China in late 2019, most probably from a bat to a pangolin and finally to humans. It is a zoonotic disease, which is caused by a pathogen and transmits from non-human animals (usually vertebrate) to human beings. Other deadly zoonotic diseases include Spanish flu (1918), HIV (1980), bird flu or H5N1 (2006), swine flu or H1N1 (2009) and Ebola virus (2013). The first case and its transmission are reported to have begun in December 2019 as confirmed by the WHO and China. As a result of the severity of the virus, the World Health Organization (WHO) declared the COVID-19 outbreak a public health emergency of international concern (PHEIC) on January 30, 2020 and a pandemic on March 11, 2020. As of 1 July 2020, more than 10.4 million cases have been reported across 188 countries and territories, resulting in more than 511,000 deaths. The USA N. Patel (B) · D. Patel · D. Shah · F. Patel · V. Patel Vishwakarma Government Engineering College, Ahmedabad, Gujarat 382424, India e-mail: [email protected] D. Patel e-mail: [email protected] D. Shah e-mail: [email protected] F. Patel e-mail: [email protected] V. Patel e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_17

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remains the most affected country with nearly 2.5 million cases. As studied by Johns Hopkins University, the global death-to-case ratio is 4.8% in USA as of 1st July, but the number varies as regions. This is a huge threat affecting mankind, and it is need of the hour to contribute collectively to eliminate it. COVID-19 is a respiratory disease, and so, it majorly affects lungs, though some cases also had multiple organ failures. Chest X-ray can be useful to detect the disease at early stages. Also if this process could be automated using deep learning, it would be beneficial for the doctors and could save their time. In symptomatic patients, the most observed symptoms are fever, cough, nasal congestion, fatigue and other signs of upper respiratory tract infections. The infection can progress to severe disease with dyspnoea and severe chest symptoms corresponding to pneumonia in approximately 75% of patients, as seen by computed tomography on admission of the patient [11]. Pneumonia mostly occurs in the second or third week of a symptomatic infection. Prominent signs of viral pneumonia include decreased oxygen saturation, blood gas deviations, changes visible through chest X-rays and other imaging techniques, with ground glass abnormalities, patchy consolidation, alveolar exudates and interlobular involvement, eventually indicating deterioration [9]. Recent findings have revealed that the key imaging techniques used in the diagnostic test of COVID-19 disease are the chest X-rays and computed tomography (CT) scans. Hence, CNN can be used efficiently to detect COVID-19 in patients as the patients’ chest X-ray shows certain anomalies in radiography. The aim of this paper is to test various deep learning models available, both standard and customized for detecting COVID-19 at an early stage through X-ray images.

2 Related Work A detailed discussion on diagnosing the COVID-19 disease with the help of X-rays is given by Apostolopoulos et al. [2]. The author experimented various CNN models like VGG19, MobileNet v2, Inception, Xception and ResNet v2 using transfer learning and achieved accuracy of 96.78%. Li et al. [8] developed a CNN model COVNet to identify COVID-19 from other community acquired pneumonia using chest CT scans. COVNet is a 3D deep neural model with ResNet50 as its backbone. A series of computed tomography slices are given as input to COVNet, and it generates features for each slice. After combining these features by max pooling operation, the final feature map is then given to the fully connected dense layer and softmax layer for the prediction of COVID-19 disease. Abbas et al. [1] proposed a novel approach based on class decomposition for the classification of COVID-19 chest X-ray images. This research presents a Decompose, Transfer, and Compose (DeTraC) model by applying a class decomposition layer to a pre-trained ResNet18 architecture to detect COVID19 from normal and severe acute respiratory syndrome (SARS) images. The accuracy of this model was 95.12 with 97.91% sensitivity and 91.87% specificity. The study described in [3] introduces a three-phase approach to fine-tune a pre-trained ResNet50 architecture to enhance the efficiency of the COVID-ResNet model for the image

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classification of four different classes normal, bacterial, viral pneumonia and COVID19. In this work, input images are gradually resized in three phases, and the model is finely tuned at each phase.

3 Proposed Approach The proposed approach diagnoses COVID-19 patients using the X-ray data of the patient. The X-ray image needs to be provided to the pre-trained deep neural network, and hence, an accurate prediction of whether a patient has been infected with COVID19 or viral pneumonia can be obtained. By training our model on a dataset of 956 X-rays images along with the labels, we could cause our model to accurately predict from the three specified labels.

3.1 Data Preprocessing The dataset needed for training was prepared by using the COVID-19 images from github repository of IEEE8023, and the images for normal and viral pneumonia class were downloaded from Kaggle. The dataset included 256 X-ray images of COVID19-infected patients, 350 X-ray images of patients diagnosed with viral pneumonia and 350 X-ray images of normal people. 10% of the data was used for validation, and a further 10% was used for model testing. The images were preprocessed before feeding to the deep learning model. The size of the images was set to (224, 224, 3), and the pixel values were normalized.

3.2 Implementation The experimental evaluation of several custom CNN models as well as standard CNN models is presented in this paper. All these models were finely tuned by adjusting different hyperparameters like learning rates, momentum, various batch sizes and optimizers. The best model that fits the data with appropriate hyperparameter tuning has been identified for the given task. 100 epochs are used as a standard for all models. For training, Google Colab was used as it provides GPU-accelerated computing. For the task of image classification, many standard deep neural network architectures are available. AlexNet [6] was a major breakthrough in the image classification domain, and it significantly outperformed all the prior competitors eventually winning the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 by reducing the top-5 error from 26 to 15.3%. The network had a very similar architecture as LeNet [7] but was deeper, with more filters per layer, and with stacked convolutional layers. Inception v1 or GoogleNet [10] was introduced in 2014 by Google, the

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main intuition being creating “wider” models rather than “deeper” models. The other variants include Inception v2 and Inception v3 which were modifications of their predecessors. ResNet or residual network [4] introduced in 2015 was a great improvement for image classification. The core idea of ResNet is introducing a so-called identity shortcut connection that skips one or more layers and hence eliminating the vanishing gradient problem. There are many variants of ResNet which are modifications in the number of layers. ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-110 and ResNet-152 are some of the variants. Other approach to eliminate vanishing gradients was introduced in DenseNet or dense convolution networks [5] in 2017. In DenseNet, each layer obtains additional inputs from all preceding layers and passes on its own feature maps to all subsequent layers. Hence, each layer is receiving a “collective knowledge” from all preceding layers. Different variants of DenseNet include DenseNet-121, DenseNet-169, DenseNet-201 and DenseNet-264. These models are standardized by the deep learning community, and hence, they are preferable to use. Also, the models could be used with pre-trained weights using transfer learning. For our study, all the standard architectures except Inception v3 are fully trained on the dataset. For Inception v3 network, we applied transfer learning by loading the pre-trained weights of the ImageNet dataset to re-train the network for our dataset. Along with standard architectures, we customized the ResNet architecture by trying number of different arrangements for residual blocks keeping its configuration same as that of standard architecture. ResNet uses four modules made up of residual blocks which has convolution operations with skip connections. These modules have basic residual blocks arranged sequentially. The ResNet-18 model has four residual blocks, each comprising of two convolution layers in [2, 2, 2, 2] sequence which sums to 16 and the additional input convolution and output softmax equals 18. The custom models including ResNet-10, ResNet-12, ResNet-14, ResNet-18 (customized) and ResNet-20 were implemented, fine-tuned with a batch size of 32 and Nesterov accelerated gradient descent as the optimizer. Each module of ResNet-10 contains residual blocks in order [1, 1, 1, 1]. Similarly, ResNet-12 [2, 1, 1, 1], ResNet-14 [1, 2, 2, 1], ResNet-18 [3, 2, 2, 1] and ResNet-20 [2, 2, 2, 3] were formed and implemented. Here, the number of filters for each module is fixed that is 64, 128, 256 and 512 filters, respectively. The last block is followed by the softmax layer with three units as we have three classes to predict. Furthermore, a custom CNN model was also created. Figure 1 shows the architecture of the CNN model. Model consisted of three pairs of convolution layers followed by a max pooling layer and then a fully connected layer of 128 hidden units. The dense layer with 128 units and activation as rectified linear unit (ReLU) is then connected with the softmax layer that gives the probability of each of the three classes using the softmax activation function. For this model, the RMSProp was used as an optimizer with the learning rate of 0.001, and categorical cross-entropy was used as a loss function.

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Fig. 1 Custom CNN3 architecture

4 Results and Discussions Table 1 summarizes the results of different architectures implemented. Inception v3 gave the best results among the standard CNN architectures with the training accuracy of 99.22% and validation accuracy of 97.89%. Inception v3 network has a number of salient features to help enhance network performance on our dataset. It provides factorized convolutions (reduces computational complexity) and an additional technique of regularization called label smoothing that prevents data overfitting. Another main factor behind such an exemplary performance of Inception v3 is that the network is wider, rather than deeper, in order to solve the information loss problem that often occurs in very deep neural networks. In Inception v3, the batch-normalized auxiliary classifiers also take care of the problem of the vanishing gradients. Figure 2a, b shows the plots for loss and accuracy for training and validation of Inception v3. For custom models, the best performance was given by the CNN3 model with training accuracy of 96.61% and validation accuracy of 97.89%. Figure 3a, b shows the plots for loss and accuracy for training and validation of CNN3. One thing to

Table 1 Loss and accuracy of implemented models Type Model Loss Train Validation Standard

Customized

AlexNet ResNet-18 ResNet-34 ResNet-50 DenseNet-121 Inception v3 ResNet-10 ResNet-12 ResNet-14 ResNet-18 ResNet-20 CNN3

0.2524 0.1858 0.1407 0.2473 0.1974 0.0250 0.1843 0.1511 0.2265 0.1208 0.1432 0.0860

0.1521 0.084 0.0904 0.1228 0.1384 0.0691 0.1227 0.102 0.1317 0.105 0.0754 0.0819

Accuracy (%) Train Validation 91.91 93.6 96.74 92.95 93.61 99.22 94.91 94.13 90.99 93.73 95.56 96.61

90.53 95.79 92.63 91.58 92.63 97.89 96.84 89.47 92.63 94.74 94.74 97.89

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(a) Loss

(b) Accuracy

(a) Loss

(b) Accuracy

Fig. 2 Inception v3

Fig. 3 CNN3

notice here is that the plots of the CNN3 model contain many spikes during the training process as compared to those of Inception v3 model. CNN3 is a smaller model as compared to Inception v3 but has similar validation accuracy. The reason for this is X-Ray images are in gray-scale and thus have less features than multicolored images and so smaller networks can also predict true classes with promising accuracy. For promising results, we want to select a model that has smooth curves for accuracy and loss. Here, using the Inception v3 model for the X-ray image classification task would be more preferable than using the CNN3 model. After training the Inception v3 and CNN3 models, the models were tested for the X-ray images new to them. They were tested on 95 X-ray images including 25 images of COVID-19, 35 images of viral pneumonia and 35 images of normal conditions. The confusion matrix depicts the measurement of performance of the machine learning or deep learning model in classification purposes. The diagonal of the confusion matrix represents the number of true positives of the results. The confusion matrix for both the models is shown in Fig. 4a, b. The Inception v3 model had specificity and sensitivity (recall) for all three classes COVID-19, viral pneumonia and normal as (98.5% and 100%), (96.66% and 94.2%) and (98.3% and 94.2%), respectively. Similarly, for CNN3 model, the specificity and sensitivity for all three classes COVID-19, viral pneumonia and normal were (100% and 100%), (100% and 94.2%) and (96.66% and 100%), respectively.

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(b) CNN 3

Fig. 4 Confusion matrix

5 Conclusion Using deep learning and machine learning in medical science domain has given extraordinary results in the past decade. Seeing the current situation, the task of detecting COVID-19 at an early stage with the help of Deep Learning would be helpful to the medical community and could save their precious time. Though too much reliance on automatic detection poses a threat, small errors in results can be hazardous in real life. It could be eliminated by increasing the dataset for better model. Also, a two-step validation must be there to reduce errors from prediction. The current work suggests that use of CNN for COVID-19 detection shows promising results. The Inception v3 and CNN3 were found to be better than others with similar validation accuracy. More and better models could be developed based on the existing models for better prediction. Hence, this paper discusses at length, number of standard and customized deep learning-based COVID-19 detection models which could be helpful to mankind in this hour of world crisis.

References 1. Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of Covid-19 in chest x-ray images using detrac deep convolutional neural network. arXiv preprint arXiv:2003.13815 (2020) 2. Apostolopoulos, I.D., Mpesiana, T.A.: Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med., p. 1 (2020) 3. Farooq, M., Hafeez, A.: Covid-resnet: a deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395 (2020) 4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) 5. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017) 6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

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7. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998) 8. Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al.: Artificial intelligence distinguishes Covid-19 from community acquired pneumonia on chest ct. Radiology, p. 200905 (2020) 9. Rothan, H.A., Byrareddy, S.N.: The epidemiology and pathogenesis of coronavirus disease (Covid-19) outbreak. J. Autoimmunity, 102433 (2020) 10. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016) 11. Velavan, T.P., Meyer, C.G.: The Covid-19 epidemic. Trop. Med. Int. Health 25(3), 278 (2020)

Electroencephalography Measurements and Analysis of Cortical Activations Among Musicians and Non-musicians for Happy and Sad Indian Classical Music Nijin Nizar, Akhil Chittathuparambil Aravind, Rupanjana Biswas, Anjali Suresh Nair, Sukriti Nirayilatt Venu, and Shyam Diwakar

1 Introduction Recent advances in neuroscientific research on music perception and cognition have provided biosignature-based evidences for connecting brain plasticity and musical activity [1]. Music, a perceptual entity, has been controlled by auditory mechanisms influencing cognitive behaviours of humans such as memory and attention, language processing and perception [2]. With the changing life styles in this era, listening to music has remarkable influence in promoting physical rehabilitation managing stress, improving communication, increasing stimulation and enhancing other cognitive skills [3]. With the ubiquitous nature of music, it has thought to evoke and enhance a wide range of emotions, with sadness and happiness as most frequent ones. Experimental studies with neuroimaging techniques on influence of musical parameters such as tempo, rhythms and tunes indicated listening to a familiar music awoken attention or aurosal in neurologically deficit patients [4]. The brain areas responsible for music perception involved superior temporal gyrus of the temporal lobe and lateral sulcus and the transverse temporal gyri and the sound processing regions, parietal and frontal areas of human cerebral cortex that was responsible for elucidating mental consciousness [5]. Robust research using different modalities and experimental designs focussed on identifying the neural correlates for familiar and unfamiliar musical excerpts among diverse populations under different geographical conditions [6, 7]. Neuroimaging studies on frequency tagging techniques have mapped neural dynamics of auditory cues in music listeners and performers [8]. Previous studies integrated with biosignal analysis reported the hypothetical approach for understanding the influence of music on brain waves and N. Nizar · A. C. Aravind · R. Biswas · A. S. Nair · S. N. Venu · S. Diwakar (B) Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri campus, Kollam, Kerala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_18

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functional neural networks augmenting multimodal sensory and motor information, auditory, visual-spatial, auditory-spatial and memory skills among musicians and non-musicians [9]. Perceptual activation of frontal midline θ rhythm (Fm theta) during music listening or music training over a fixed period indicated improved cognitive performance in mental calculation, working memory and learning process. β rhythm activation have thought to be associated with increased alertness and cognitive performances, α power synchronization in left and right hemisphere of the brain regions in music listening and perceptions accounted for internal processing and creative thinking [10]. Neuroscience community has been dwelling to understand impact of Indian classical music on brain behaviour among different study population [11]. Research studied reported the effect of Indian classical music and rock music on shifting of alpha rhythm to beta rhythm with the switching of techno to classical music type during music perception [12]. A study on Indian Bhupali raga in university students indicated significant effect on attention and concentration of digit span task when compared to the scores with pop music [13]. Even though the influence of music on human brain and cognition was well documented, research on the effects of sad and happy music on human cognition remains elusive. The present study focuses on understanding the effect of Indian classical ragas Reethigowla raga (a song as an example of happy music) and Shivaranjini raga (as an example of sad music) on brain activity using low-cost non-invasive EEG signal analysis technique. The objective was to understand the neural correlates associated with different auditory stimuli (happy music, sad music placebo and sham condition) and to computationally explore spatiotemporal characterization of functional circuits among musicians and non-musicians. The study could be further extended to investigate biosignal markers elicited in response to human emotion and cognition, memory retention, visual perception and information processing.

2 Methods 2.1 Subject Selection and Screening 20 healthy university students (16 females and 4 males), age group of 18–23 years, without any hearing or neurological impairment were recruited for the study. Participants were categorized into two groups, musicians (N = 10) who had musical training for more than 3 years and non-musicians (N = 10) having no prior musical training experience. Physiological parameters and the mental state of the study participants were assessed using cognitive batteries. An informed consent was collected from the participants prior to experiment process.

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Fig. 1 Schematic illustration of experiment protocol

2.2 Auditory Stimuli Two ragas from Indian Carnatic music with emotional behaviour happy and sad were the choice of music stimuli for this study. Reethigowla raga was selected as happy music stimulus, Shivaranjini raga as sad music stimulus, as it has well documented evidences on both physiological and psychological aspects. For comparing the neural networks underlying different auditory stimuli, a placebo condition a familiar voice (speech of a famous person) and Sham condition with a stressor noise (traffic noise) were included as auditory stimuli. The length of each auditory stimulus was 6 min (Fig. 1).

2.3 Experiment Design The subjects were randomly assigned to have one of the two music interventions (either happy or sad raga). Among the set of 10 musicians, participants were divided into two subsets; 5 were subjected to listen to happy stimuli; and remaining 5 subjects have to listen to sad stimuli. Similar random selection was done for non-musician’s groups with two subsets. Real-time EEG data collection was performed for different auditory cues; happy music, sad music, placebo and sham condition. For analyzing the effect of ragas on brain activity and cognitive skills memory, attention and concentration, subjects were advised to listen to the respective music stimuli once daily for 6 days, during the experiment paradigm. Placebo and sham stimuli were tested on consecutive days of course of the study.

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2.4 EEG Data Acquisition and Computational Analysis The participants were seated in a comfortable position in a soundproof dimly lit laboratory condition. The recording was carried in an eye closed state in order to avoid the possible visual artefacts. Data acquisition was performed using a low-cost surface-based non-invasive EEG device having 14 + 2 electrodes, and sampling rate was set at 128 Hz. The collected biosignal data were computationally converted to numeric values using MATLAB embedded with EEGLAB tool. Artefact-free EEG data were obtained by filtering methods like band pass filtering and Independent Component Analysis (ICA). Topographical plots representing functional regions of brain for different auditory stimulus were computationally analyzed [14].

3 Results 3.1 Brain Rhythms Shifts from Gamma-Alpha in Happy Music Perception and Alpha–Beta-Gamma for Other Auditory Cues in Musicians and Non-musicians In non-musicians, the normalized topographical plots of alpha and gamma rhythms of auditory cues (pre-silence, stimuli and post stimuli) showed a shift of gamma rhythms in fronto-parietal and temporal lobes (F4, P8, T8) in pre-silence condition to alpha rhythms in parietal and occipital lobes (P8 and O2) with happy music stimuli. It was also observed that similar pattern of alpha rhythm intensity was retained in post-stimulus condition. In musicians, it was seen that gamma rhythm intensity was higher in the temporal and parietal lobes (T8, P8) during silence which was shifted to alpha waves in frontal, parietal and occipital (F4, P8, O2) in Reethigowla music at its highest peak. Post-stimulus silence condition shows alpha wave frequency in right fronto-parietal and occipital regions as same to that of auditory cue. In placebo condition, non-musicians showed higher intensity of alpha rhythm in frontal lobes (AF4, AF3, F4, F3) in pre-silence period, and the alpha rhythm was shifted to parietal and occipital lobes (P8/O2) during the cue. After placebo cue, the alpha rhythms were shifted to gamma rhythms with higher intensity in occipital lobes (O2). Pattern of activity of alpha rhythms in frontal and parietal lobes was similar in case of presilence and auditory cue in musicians for placebo stimuli, which was shifted to gamma rhythms in frontal and parietal lobes (F4/P8) post-auditory stimuli. In sham condition, for non-musicians, it was observed that alpha rhythm activity during presilence in frontal and occipital lobes (F4/O2) was shifted to beta rhythms in frontal and temporal lobes (F3/T7) during the auditory stimuli. The activity pattern was then shifted to gamma rhythms in frontal regions (F4) post-stress condition. In musicians, for sham condition, shifting of alpha rhythm activity in frontal and temporal lobes (F4/T8) before stress condition to beta activity in frontal lobes (F4) during stress

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Fig. 2 Differential cortical activation of brain rhythms in musicians and non-musicians to a happy music cue, placebo and sham stimuli

condition was observed. The pattern was then shifted to gamma activity in frontal lobes (F4) post-stress silence condition (Fig. 2).

3.2 Behavioural Pattern Variations of Brain Rhythms for Sad Music Stimuli and Other Auditory Cues in Musicians and Non-musicians Cortical mapping of alpha of Shivaranjini raga (before music, during and after music) showed higher alpha rhythm activity in all time bins. It was observed that in nonmusicians, frontal (AF4, F4) alpha rhythm activity in pre-silence condition was shifted to temporal (T8) and parietal (P8) regions during and post sad stimuli. In musicians, pre-stimuli condition showed higher beta activity intensity in frontal lobes (F4 and F8), which was shifted to dominant alpha activity in occipital lobes (O2) during and post sad stimuli. In placebo condition, among non-musicians, alpha in frontal and occipital lobes (F4, O2) were shifted to beta activity in frontal and temporal regions from silence condition to auditory cue. Beta activity in frontal (F3) and temporal (T7) regions were shifted to gamma activity in frontal regions (F4/F8) post-auditory stimuli. In musicians, alpha activity in frontal lobes (F4, F8) in silence condition is shifted to beta activity in frontal lobes (F4) with auditory stimuli, which were then shifted to gamma activity in frontal lobes (F4/F8) post-auditory cue silence condition. In sham condition, for non-musicians, no shifts of brain rhythms were observed in experimental condition. Gamma rhythm activity was higher in anterior frontal (AF3, AF4) and frontal lobes (F7, F8, F3, F4) during pre-silence, auditory

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Fig. 3 Cortical differentiation of brain rhythms to sad stimuli placebo and sham stimuli

stimuli and post-silence condition. In musicians, higher theta activity at frontal lobe (F4) was observed before stress which was shifted to gamma activity in frontal lobes (AF4, F4) with stress and gamma activity in temporal region (T8) post-stress silence condition (Fig. 3).

4 Discussion The focus of the study was to functionally map cortical activity patterns of brain rhythms among musicians and non-musicians under different auditory stimuli. Higher intensity of gamma rhythms in silence state of non-musicians may be correlated with the preparation phase of cognitive task execution. Spectral analysis of cortical EEG rhythms indicated shifting of gamma from fronto-parietal and temporal lobes to alpha in parietal and occipital lobes during happy stimuli indicated music were synchronizing the brain signals to alpha waves, and the subjects were experiencing positive emotions with the auditory cue. Retaining of alpha rhythms after happy stimuli indicated the subjects was at relaxed and calm state. Musicians also showed gamma to alpha shift indicating processing of joyful emotions with Rajagowla raga. Differential activation patterns of brain lobes for similar music cue among musicians and non-musicians indicated variations in information pathways and language processing pathways at similar time bins. Musicians and nonmusicians have shown higher intensity of alpha rhythm for a famous voice clip as auditory cue throughout the experiment time period, indicating subject in an alert condition and switching of intensity of alpha rhythms to different lobes indicated activation of visual and auditory pathways at particular time bins. In stress condition, among musicians and non-musicians, shifting of alpha to beta rhythms indicated

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motor preparatory processes for sound synchronization and the activation of pathways associated with auditory–motor interaction. Beta to gamma shift post-stress stimuli indicated affective processing in the brain which could be related to induced stress condition. Patterns of variation of brain rhythms at different lobes indicated activation areas of auditory stimuli and evoked visual responses to select events. The spectral plot analysis of EEG signals of non-musicians for Shivaranjini raga showed alpha wave dominance in anterior frontal lobe, temporal, parietal regions of brain indicated activating the centres responsible for attention, auditory pathway and language processing. Shifting of beta rhythm in frontal regions to alpha rhythms in occipital regions of musicians indicated the visual perception of the music score and the memory retention. For a placebo stimulus in non-musicians and musicians, switching of alpha, beta and gamma among frontal and temporal lobes indicated the alertness of the subjects to the familiar voice and its perception. The gamma wave dominance in anterio-frontal lobes of brain in non-musicians to traffic noise cue indicated the attaining of stressor state. The switching of theta to gamma rhythms in anterio-frontal and temporal regions of musicians indicated the changing of alertness state to stressor state while listening to sham cue. Behavioural studies of different auditory cues in musician and non-musicians evidenced the interconnection of neural networks of musical activity and brain plasticity.

5 Conclusion There has been a potential interest for understanding how different music stimuli can evoke and enhance emotional responses. The selected Indian ragas in this study have musical composition that has helped to create emotional states. The study on happy music and sad music on musicians, and non-musicians may help researchers to moderately correlate varying EEG frequency measures for different auditory cues. Further investigations based on cognitive performance tasks need to be implemented for testing impact of music perception in music therapy, especially for stress management. Acknowledgements This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. This work was partially funded by Embracing the World Research-for-a-Cause initiative.

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3. Rentfrow, P.J.: The role of music in everyday life: current directions in the social psychology of music. Soc. Personal. Psychol. Compass. 6, 402–416 (2012). https://doi.org/10.1111/j.17519004.2012.00434.x 4. Hurless, N., Mekic, A., Peña, S., Humphries, E., Gentry, H., Nichols, D.F.: Music genre preference and tempo alter alpha and beta waves in human non-musicians. Impuls. Prem. Undergrad. Neurosci. J. 22, 1–11 (2013) 5. Singh, N.C., Balasubramanian, H.: The brain on music 299–308 (2018) 6. Kumagai, Y., Matsui, R., Tanaka, T.: Music familiarity affects EEG entrainment when little attention is paid. 12, 1–11 (2018). https://doi.org/10.3389/fnhum.2018.00444 7. Freitas, C., Manzato, E., Burini, A., Taylor, M.J., Lerch, J.P., Anagnostou, E.: Neural correlates of familiarity in music listening: A systematic review and a neuroimaging meta-analysis. Front. Neurosci. 12, 1–14 (2018). https://doi.org/10.3389/fnins.2018.00686 8. Nozaradan, S.: Exploring how musical rhythm entrains brain activity with electroencephalogram frequency-tagging. Philos. Trans. R. Soc. B Biol. Sci. 369, 1–11 (2014). https://doi.org/ 10.1098/rstb.2013.0393 9. Gaser, C., Schlaug, G.: Brain structures differ between musicians and non-musicians. J. Neurosci. 23, 9240–9245 (2003) 10. Lin, Y.P., Duann, J.R., Feng, W., Chen, J.H., Jung, T.P.: Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis. J. Neuroeng. Rehabil. 11, 1–11 (2014). https://doi.org/10.1186/1743-0003-11-18 11. Santhosh, A.K., Sangilirajan, M., Nizar, N., Radhamani, R., Kumar, D., Bodda, S., Diwakar, S.: Computational exploration of neural dynamics underlying music cues among trained and amateur subjects. Procedia Comput. Sci. 171, 1839–1847 (2020). https://doi.org/10.1016/j. procs.2020.04.197 12. NS, D.S.K.: A Study on Effect of Indian Classical Music on Brain Activity Using EEG Signals. J. Med. Sci. Clin. Res. 05, 21702–21706 (2017). https://doi.org/https://doi.org/10.18535/jmscr/ v5i5.76 13. Nagarajan, K., Srinivasan, T., Ramarao, N.: Immediate effect of listening to Indian raga on attention and concentration in healthy college students: a comparative study. J. Heal. Res. Rev. 2, 103 (2015). https://doi.org/10.4103/2394-2010.168367 14. Krishnan, M., Edison, L., Radhamani, R., Nizar, N., Kumar, D., Nair, M., Nair, B., Diwakar, S.: Experimental recording and computational analysis of EEG signals for a squeeze task: assessments and impacts for applications. In: 2018 International Conference on Advances Computing Communication Informatics, ICACCI 2018, pp. 1523–1527 (2018). https://doi. org/10.1109/ICACCI.2018.8554913

Signal Processing in Yoga-Related Neural Circuits and Implications of Stretching and Sitting Asana on Brain Function Dhanush Kumar, Akshara Chelora Puthanveedu, Krishna Mohan, Lekshmi Aji Priya, Anjali Rajeev, Athira Cheruvathery Harisudhan, Asha Vijayan, Sandeep Bodda, and Shyam Diwakar

1 Introduction In today’s world, people are also facing a pandemic of lifestyle disorders mainly attributed to lifestyle and habits including the lack of sufficient physical activity, and this may have led to problems related to psychosomatic and mental health [1]. Several researches have showed that lifestyle factors and psychological issues like stress, anxiety and depression have adverse effects on memory, attention and other cognitive skills which leads to neurological disorders like Parkinson’s disease, Alzheimer’s Disease, multiple sclerosis, epilepsy [2]. Medications are readily available, but due to their adverse effects, researchers seek non-pharmacological and non-invasive treatments for these disorders. Evidence have demonstrated that regular yoga practices can be an complementary solution for improving memory, attention and visual perception [3] and also improve psychophysiological measurements associated with anxiety, depression and stress. Studies have shown an regular hatha yoga practices improved working memory and attention in healthy older adults [4]. Yoga focuses on improving one’s self through physical and mental practices that involve more mindful elements that are absent in other forms of the exercises [5]. Yoga comprises of physical postures, controlled breathing exercises, deep meditation practices and mantras [6] and improves health by increasing physical stamina, flexibility, balance and relaxation [7]. Physical and cognitive benefits of yoga is related to the increased activation of gray matter volume in amygdala, increased body perception, activation of parasympathetic nervous system, stronger functional connectivity within the basal ganglia [8, 9] and cerebellar circuits [10]. D. Kumar · A. C. Puthanveedu · K. Mohan · L. A. Priya · A. Rajeev · A. C. Harisudhan · A. Vijayan · S. Bodda · S. Diwakar (B) Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Clappana po, Kollam, Kerala 690525, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_19

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For understanding the spatial, temporal and spectral characteristics of the cortical regions associated with various cognitive tasks such as memory, attention, motor coordination, visual and auditory perception, non-invasive neuroimaging technique like electroencephalogram (EEG) can be used. The current study focuses on the effects of various yoga-based practices in the brain using computational and statistical analysis on EEG signals. Datasets were compared to address the different aspects of a yoga practice such as postures and movement contributed differently to cortical function. The main objective of the paper was to compare stretching and sitting asanas based on its spatiotemporal changes in the brain which is associated with improved working memory, attention and visual perception.

2 Methods 2.1 Experimental Protocol and Characteristics The study was conducted among 70 healthy volunteering subjects of mean age 21 years. Subjects were randomized into three groups as control group and two experimental groups. Each experimental group was further divided into two subgroups, among which a subgroup was trained to practice sitting yoga (Swastikasana and Vajrasana) and the other subgroup practiced stretching yoga (Suryanamaskar) for 6 weeks. Experimental group 1 consisted of 20 female subjects, among where ten subjects were asked to perform stretching asanas and the remaining ten subjects were asked to perform sitting asanas or static yoga. Experimental group 2 consisted of 20 male subjects, whose yoga practices were same as experimental group 1. Control group consisted of 30 subjects who neither had any prior training in sitting or stretching yoga, nor were allowed to perform yoga during the session. An open consent was collected from all the participants prior to the data collection which was approved by an institutional ethics review board. Sitting yoga group and stretching yoga group performed their asanas for 10 min followed by shavasana. EEG signals after pre- and post-yoga sessions were recorded in an eyes-opened state for 2 m 40 s. After post-yoga recordings, three different tasks were assigned to the participants, digit letter substitution task, word memory task [11] and Gollin incomplete figure tests. The experimental protocol is summarized in Fig. 1. Computational analysis of the raw EEG signals was done with MATLAB, and the artifacts (eye blink and muscle movements) were removed using a basic FIR filter which included the filtering of raw data between 1 and 50 Hz using EEGLAB. Fast Fourier transform (FFT) was used to convert time domain signals to frequency domain, and the distribution of δ, θ, α, β, γ rhythms was computationally estimated at different brain regions. Statistical test like t-test and ANOVA was also carried out on the task scores.

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Fig. 1 Experiment protocol

3 Results 3.1 Increased Changes Attributed to Memory and Attention Observed in Practitioners From the plot analysis, it was observed that there was a decrease in beta rhythm in the left anterior frontal (AF3) and frontal (F7) regions for the static group when compared to pre-yoga recordings which reflect a decrease in semantic processing and an increase in memory performance (Fig. 4) [12]. A decrease in alpha rhythm was also observed in the right temporal (T8), motor cortex (FC6) and frontal region (F4, F8) which reflect a gradual increase in attention. In the case of dynamic group, it was observed that there was a beta activation in the temporal (T7, T8) and parietal (P7, P8) which are known to increase the accuracy of decision making [13]. The right motor cortex region (FC6) was also found to be activated after dynamic yoga (Fig. 2). Gender-based comparisons showed a decrease in beta rhythm in the left anterior frontal (AF3) and frontal (F7) regions when compared to pre-yoga recordings which also reflects a decrease in semantic processing and an increase in memory performance [12] (Fig. 3). In female subjects, there was significant activation in the right frontal lobe (F4, F8 AF4, FC6) which focuses on the emotional state [14], whereas in male subject, an alpha activation was observed in the frontal regions suggesting an increase in momentary memory storage and other cognitive functions [15].

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Fig. 2 Bar graphs shows channel-wise activity of alpha and beta rhythms for static verses dynamic yoga practitioners

Fig. 3 Bar graphs shows channel-wise activity of alpha and beta rhythms for male verses female yoga practitioners

Spectral map comparisons revealed that there was a profound alpha activation in the parietal (P7, P8) and occipital (O1, O2) regions in yoga practitioners when compared to control suggesting an increase in ability to associate stimuli to its corresponding responses and an increase in attention (Fig. 4). The raw data was preprocessed and analyzed on a MATLAB (MathWorks, USA) platform in Intel i3 CPU @ 2.00 GHz, with 4 GB RAM and 64-bit Operating System,

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Fig. 4 Cortical maps comparison between static and dynamic yoga practitioners for α rhythms

× 64-based processor. Time complexity for the analysis was calculated, and it was observed that the control data took lesser time to be processed than the other groups (Table 1). Time taken for static group of ~11.9 s and the control was ~3.4 s. As the number of samples increases, the computational time also increases. Table 1 Computational time for processing the data

Groups Control Static (Pre and post)

Time (s) 3.359035 11.711336

Dynamic (Pre and post)

9.69801

Male static (Pre and post)

8.53879

Female static (Pre and post)

6.861729

Male dynamic (Pre and post)

6.629959

Female dynamic (Pre and post)

9.941373

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Fig. 5 Graphical comparison between experimental groups in DLST and WM tasks

3.2 Statistical Analysis on the Task Scores Shows Homogenous Performance Between the Genders A t-test was computed to compare the means of both experimental group 1 (female subjects) and experimental group 2 (male subjects). The p values for DLST task and WM were 0.86 and 0.89, respectively. At 0.05 level of significance, the p value suggests that there was no gender-based differences when the two tasks were conducted (Fig. 5).

3.3 Statistical Analysis on the Task Scores Shows There is Significant Difference Between the Groups The scores for both DLST and WM tasks among the dynamic, static and control groups were compared for significant differences using single factor analysis of variance (ANOVA). It showed a significant difference between the dynamic, static and control groups with a p value of 0.001 and 0.021 for DLST and WM tasks, respectively, at 0.05 level of significance. This correlates to a significantly higher attentional skill and memory among the yoga group when compared to the control group (Fig. 6).

4 Discussion The current study focuses on understanding the neural correlates of cognitive tasks, such as working memory, internal attention and visual perception by analyzing yogabased practices with brain activity mapping using EEG and percentage power spectral density analysis. As an initial study for understanding the functional neural dynamics associated with different cognitive functions of brain, brain topography mapping of inter-subjects (between and among the group) was analyzed. Time complexity for

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Fig. 6 Graphical comparison between control and yoga groups in DLST and WM tasks

the computations was calculated and was found to be less than 12 s for the current dataset and increases with an increase in the number of samples. Cortical mapping of alpha rhythms on both practitioners showed an activation in the parietal (P, P8) and occipital (O1, O2) regions which can suggest that yoga practice increased attention and stimulus–response associations. An increase of alpha activity in frontal regions could suggest the regulatory functions related to emotional challenges and attentional engagements. An increase in alpha and beta activity in the right posterior parietal lobe could have implied an increase in the working memory. A decrease in beta activity in the left frontal regions also suggests there may be a significant change in memory performance. The preliminary results also suggest similar dynamic changes which need to be validated with greater number of subjects. As yoga-based practices are shown to have an impact on the anatomical changes of brain which mainly includes frontal cortex, hippocampus, anterior cingulate cortex and insula, these imaging techniques like EEG can be used to study mind–body interventions. These mapping and EEG dynamics potentially suggest the need for further exploration of memory and time-based attenuation attributed to the practice and possible implications of yoga having been used by generations of communities as sustainable wellness practices and for mental and physical well-being.

5 Conclusion Understanding neural circuits and its functions associated with yoga-based practices helps us to preserve and nurture traditional beliefs. This initial study has allowed mapping functional activity associated with various cognitive tasks and in identifying variations in cortical mapping related to the integration of yoga in their daily-life activities. Acknowledgements This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. Authors thank staffs and students of Amrita Vishwa Vidyapeetham, Amritapuri for their role as volunteering subjects. This work was funded

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partially by Amrita School of Biotechnology and Embracing the World Research-for-a-Cause initiative.

References 1. Singh, S.: Yoga: An Answer To Lifestyle Disorders, vol. 5, pp. 27–34 (2016) 2. Deshpande, R.C.: A healthy way to handle work place stress through Yoga. Meditation Soothing Humor 2, 2143–2154 (2012) 3. Akshayaa, L., Jothi Priya, A., Gayatri Devi, R.: Effects of yoga on health status and physical fitness an ecological approach—a survey. Drug Invent. Today. 12, 923–925 (2019) 4. Gothe, N.P., Kramer, A.F., Mcauley, E.: The Effects of an 8-Week Hatha Yoga Intervention on Executive Function in Older Adults, vol. 69, pp. 1109–1116 (2014) 5. Bhatia, T., Mazumdar, S., Nn, M., Re, G., Rc, G., Vl, N., Mishra, N., Raquel, E., Gur, R.C.: Protocol to evaluate the impact of yoga supplementation on cognitive function in schizophrenia : a randomised controlled trial (2014) 6. Rocha, K.K.F., Ribeiro, A.M., Sousa, M.B.C., Albuquerque, F.S.: Improvement in physiological and psychological parameters after 6 months of yoga practice. Conscious. Cogn. 21, 843–850 (2012) 7. Tran, M.D., Holly, R.G., Lashbrook, J.: Effects of Hatha Yoga practice on the health-related aspects of physical fitness. Prev. Cardiol. 4, 165–170 (2001) 8. Monchi, O., Petrides, M., Strafella, A.P., Worsley, K.J.: Functional Role of the Basal Ganglia in the Planning and Execution of Actions, pp. 257–264 (2006) 9. Roman-gonzalez, A., Processing, S., Human, A., Systems, C.: EEG Signal Processing for BCI Applications Avid Roman-Gonzalez To cite this version : HAL Id : hal-00742211 (2012) 10. Fisher, S.D., Reynolds, J.N.J.: The Intralaminar Thalamus—An Expressway Linking Visual Stimuli to Circuits Determining Agency and Action Selection, vol. 8, 1–7 (2014) 11. Radhamani, R., Nizar, N., Kumar, D., Pillai, G.S., Prasad, L.S., Jitha, S.S., Vannathi Kuniyil, M.K., Sekhar, A.A., Kumar, V.S., Pillai, S., Diwakar, S.: Computational analysis of cortical EEG biosignals and neural dynamics underlying an integrated mind-body relaxation technique. Procedia Comput. Sci. 171, 341–349 (2020) 12. Hanslmayr, S., Matuschek, J., Fellner, M.C.: Entrainment of prefrontal beta oscillations induces an endogenous echo and impairs memory formation. Curr. Biol. 24, 904–909 (2014) 13. Park, J., Kim, H., Sohn, J.W., Choi, J.R., Kim, S.P.: EEG beta oscillations in the temporoparietal area related to the accuracy in estimating others’ preference. Front. Hum. Neurosci. 12 (2018) 14. Palmiero, M., Piccardi, L.: Frontal EEG Asymmetry of Mood: A Mini-Review (2017) 15. Fundamentals of Cognitive Neuroscience (2013)

Automation of Answer Scripts Evaluation-A Review M. Ravikumar, S. Sampath Kumar, and G. Shivakumar

1 Introduction To evaluate the performance of students at various levels (like primary level, high school level, undergraduate level, and also at post-graduation level), examination process will be conducted, which plays an important role in preparation and formulation of question papers. To evaluate the answer booklet written by the students is very difficult task because handwritten varies from person to person because of the style, font, size, orientation, etc. In particular, the question paper pattern of primary and high school level will consist of fill in the blanks, match the following, true or false, one-word answers, the odd man out, and pick out the odd word. All these patterns will be answered by the students in their booklets. The questions in the booklets are printed type and the answers that were written by the students is handwritten type. Evaluation of the answer booklets particularly written by the primary level students is very challenging task for the teachers. Handwriting of some students may be in cursive form which is connected by ligatures as joining strokes and some may contain disjointed words and an apparent mixture of uppercase and lowercase letters. For student development and to learn, evaluation can be greatly helpful. Handwriting is still a necessary skill in our society to achieve a consistent evaluation. Answers written by students must be clearly understood for the teachers.

M. Ravikumar (B) · S. Sampath Kumar · G. Shivakumar DOS in Computer Science, Kuvempu University, Shimoga, Karnataka, India e-mail: [email protected] S. Sampath Kumar e-mail: [email protected] G. Shivakumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_20

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The teachers have to look after the answers properly to grade the marks, and it is not possible to understand every student handwritten answer written in a booklet easily by viewing the books and grading the marks. Handwritten recognition became most challenging research area in document image analysis. The manual system of evaluation for technical subjects is difficult to do for the evaluators. Answers contain various parameters for evaluation, such as questionspecific content and writing style. Evaluating answers may vary along with the perception of person because checking hundreds of answer scripts contain same answers that can be a boring task for evaluators. To overcome all these possible problems and for faster evaluation, automation of answer scripts is needed. Hence, in order to automate the evaluation of answer books, it is necessary to overcome all these issues. The strategic approach of automated evaluation process provides a framework for evaluation. From the survey, in Karnataka, there are around 23,640 government primary schools and 8216 high schools (excluding aided and unaided). If the answer scripts are evaluated automatically, the process of declaring the result will become easy and faster. Every year, the education institution must conduct two examinations and by taking overall students answer scripts for evaluation, it is very complex task. The automation framework gives many benefits such as reuse of resources, reduction in time, and less paper work, and human errors can be controlled during the evaluation process. Automation of answer scripts evaluation helps teaching faculties to reduce the work load. The paper is organized in the following sections: Sect. 2 contains the review of related work. Section 3 gives brief idea of classification techniques used. Section 4 contains challenges and Sect. 5 explains the conclusions of this research work.

2 Related Work In this review paper [1], an algorithm, i.e., single sentence descriptive answer is proposed. The main intension is to represent the answer in the form of graph and comparing it with predefined answer. Aforementioned problem is solved in the proposed method; precisely the proposed system will solve grammatically incorrect sentences. The answer which belongs to a particular subject written by the students and the standard predefined answer is converted into graphical form in order to compare the similarities such as string match and WordNet to calculate the similarity score. The proposed model briefs an explanation for automation of subjective answer evaluation process. For subjective answer assessment, a method called as subjective answer evaluation system is proposed [2], which is based on four modules, login module, information extraction module, weighting module, and score generation module. The proposed model works at word level, where the answers written by students are compared with predefined keyword, and the grading for the students is performed based on the

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highest matching score. There are three main steps that are key words and synonyms extraction, matching of keywords, weighting keyword and generating score. Different examination pattern where one-word answer, true or false, and multiplechoice question are evaluated using a method called blooms taxonomy is proposed [3]. The blooms taxonomy is represented in the form of triangle which is divided into four different steps, for above-mentioned problem. The stated method can also be adopted for online evaluation system. Answer evaluation for one word is implemented by using the concept of text pattern recognition, for recognition of similar pronunciation of English words. To assess single sentence and one-word answer, a cognitive and computationalbased algorithm is proposed [4], where the pattern from the answer are extracted for the purpose of comparing with model answer. The proposed algorithm is implemented using NLTK Python toolkit. The proposed method mainly focuses on inference process which is required for the development to asses one-word and one sentence-based answer. To evaluate the descriptive answer, a pattern matching algorithm is proposed [5]. The main intension is to represent student answer and teacher predefined answer in the form of graph to compare each other, to apply some of similarity measures like [string match, partial string match, full string match, WordNet] for allocation of marks. The proposed method represents an approach to check the degree of student learning knowledge, by evaluating their descriptive exam answer sheets. To evaluate multiple sentence descriptive answers, a pattern matching technique algorithm is proposed [6], and the proposed system caters a Web application for checking descriptive-type answer, learner descriptive answer, and predefined answer. To convert and to apply similarity measures, the major step in the proposed algorithm is string match, WordNet, and spreading process. These steps help in analysis of similarity matching in assessment of evaluation process. A non-optical test scoring of grid answer on projection profile method is discussed [7], where the algorithm is used for scoring non-optical traditional grid answer sheets. Projection profile and thresholding methods are used for experimentation percentage of correctness that was measured. Answer sheets are divided into three types for testing, total 16,500 questions were tested, and average accuracy result is detected. For evaluation of subjective answer, a method is proposed [8], and machine learning and NLP are proposed to solve evaluation of subjective answer problem. The algorithm explains semantic meaning of the context, by performing tasks like words tokenizing and sentence. For experimentation, Python flask Web application is used and also developed Android app for the results. Automatic OMR answer sheet evaluation, a new technique is proposed [9], using computer and scanner a software-based approach is developed, for generating scores multiple-choice test is used. Opencv is implemented to match accurate option that is already been saved in database. Thus, designed software is used for decoding answer sheets. By using the proposed system, it can be executed in microlevel in administration of government sectors. A field programmable gate array (FPGA) for implementation of OMR answer sheet scanning using sensors is proposed [10]. A finite state machine is designed

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for FPGA. IR sensors are used to scan the answers. Pre- and post-algorithms are used for computing stored data. Automatic document feeder is used for scanning the OMR answer sheets. An efficient alternative method is proposed for optical mark recognition technique over complex images. Natural language processing that has been used for evaluating student descriptive scripts is proposed [11], and the techniques like statistical, information extraction, and full natural language processing are used for automatic marking of free text. Computer-aided assessment (CAA) has been implemented for evaluation of descriptive answer. Collective meaning of multiple sentences is considered and proposed in the existing system. The proposed system tries to check grammatical and spelling mistakes made by the student. A Python tool that has been used for evaluation of subjective answers is proposed [12], and a Python tool for evaluation of subjective answers (APTESA) is a tool developed for automated evaluation. The tool is divided into two types for better evaluation: First is the semi-automated mode and second is complete automated mode. The development of APTESA is by using pyqt, Python, and its modules. Thus, comparatively it is established that semi-automated mode yields better results than the complete automated mode. A comparative study of techniques for automatic evaluation of free text [13], the techniques like LSA, BLEU, and NLP, has been used for automatic evaluation of free text. LSA technique can extract hidden meaning in a text. Bilingual evaluation understudy (BLEU) is to measure the translation closeness between candidates. Translation NLP is used as a statistical means to disambiguate word or multiple parse of same sentence. The above-mentioned techniques review the different methods for automatic evaluation based on free text. Automatic grading of multiple-choice answer sheets is proposed [14]. The main process of the system is divided into three parts, i.e., checking the number of problem series, identifying examiners id, and checking the answer part. Correlation coefficient is applied to system for checking the answer from solution sheet images. The proposed system supports with any pencil or pen. Finally, the accuracy was 99.57% for poorly erased marking. In the developed system, 71.05% time is saved when compared with manual checking of answers. A novel approach for descriptive answer script is proposed [15]. ITS has been designed for models and for different algorithms. The ITS system provides a content materials and important test sessions. Descriptive answer evaluation works best for simple sentences. Third party tool has been used for grammar checking and spellchecking module for better accuracy. Thus, proposed system represents assembled approach with number of essential features. Descriptive examination and assessment system by comparison-based approach is proposed [16], and descriptive answers which are stored on the server machine are compared with standard descriptive answer by using the approach text mining technique, which involves matching keywords and sequence. Thus, the proposed algorithm provides an automation of descriptive answer evaluation.

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Soft computing techniques are used to evaluate students answer script that is proposed [17], and weights of the attributes have been generated for automatic evaluation. To evaluate students, answer scripts in a more flexible way, adjustment quantity is normalized to ensure the fairness of the adjustment in each inference result. By introducing a new fuzzy evaluation system, the system provides a more quick and valid evaluation. A performance evaluation based on template matching technique is proposed [18], and frequently asked question (FAQ) answering system that provides prestored answers to user question, an automated approach is adopted. Question answering system is divided into two systems, i.e., closed domain and open domain. Three main techniques are used for answering system based on template matching which are random classification templates, similarity-based classification of templates, and weighting template words. On experimentation, it is showed that similarity-based clustering method with weighted templates would be better choice according to results. Different approaches for automated question answering are proposed [19], and natural language processing (NLP) information retrieval and question templates are used to compare question answering approaches. For difficulties in quality of answer, NLP is applied. For extraction of facts from text, IR QA is used. An automatic answering system to solve close domain problems is proposed [20], to perform matching template technique is used. To conquer mistakes that may happen because of spelling botches, a strategy is created. The framework is developed with the goal of answering questions asked through phones and messages to understand the SMS language comprehend to English. In the next section, summary of algorithms and classification techniques is discussed.

3 Challenges In this section, we mentioned some of challenging issues related to evaluation of answer scripts, they are as follows. 1. 2. 3. 4.

5.

Present system evaluates only in English language. In future, it can be extended to other languages. Grammar checking tool has to be implemented for accurate assessment of Marks of a student. A framework can be created to assess answers with diagrams and formulas. A technique for assessment of handwritten paper by converting it to soft copy using descriptive examination system and voice recognition system can be implemented. To minimize the gap between human and computer assessor, an appropriate algorithm has to be implemented.

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For same pattern of answer sheet, the algorithm can be improved in many angles for some repetitive task like line and grid determination.

4 Conclusion This survey paper gives an immense idea of different approaches and techniques used for evaluation of answer booklet. Subjective and descriptive type of evaluation is explained, and machine learning and many more techniques are vastly used in automating the evaluation process. Thus, it is observed that from this research concept, and this work provides a better basic knowledge for automating the answer booklet evaluation (Table 1). Table 1 Summary of algorithms and classification techniques is discussed Year

Authors

Database

Segmentation

Classification

Accuracy (%)

2016

Dr. Vimal and P.Parmar

KAGLE

MCQ and one-word answer

KNN

70.2

2012

Ashmita Dhokrat

Database from 10 candidates

VPP

Question-based answer Situation-based answer

80

2013

Nutchanat Sattayakawee

Database from 16,500 Question by Thailand

Image projection

KNN

99.09

2018

Piyush Patil

Real-time dataset created on their own

Averaging thresholding

Bayes theorem–nearest neighbor

90

2014

Ms. Shwetha M.Patil

IAM statistical technique

Information extraction technique

LINEAR

70

2017

Dharma Reddy Tetali

Sample of 120 students

aptesa

SVM

60

2014

Ms. Paden Rinchan

Sample of 120 students written answer

LSA BLEU NLP

KNN

81

2017

Divyapatel Shaikhji Zaid

Human-marked data from documents

NLP

SVM

99

2008

K.Ntirogiannis

70 K FONT

Document image binarization

SVM

70

2018

Nilima Sandip Gite

IAM

Aptesa. Text mining

KNN

74

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References 1. Praveen, S.: An approach to evaluate subjective questions for online examination system. Int. J. Innov. Res. Comput. Commun. Eng. 2, 6410–6413 (2014) 2. Tulaskar, A., Thengal, A., Koyande, K.: Subjective answer evaluation system. Int. J. Eng. Sci. Comput. 7(4), 10457–10459 (2017) 3. Parmar, V.P., Kumbharana, C.K.: Analysis of different examination patterns having question answer formulation, evaluation techniques and comparison of MCQ type with one word answer for automated online examination. Int. J. Sci. Res. Publ. 6(3), 459–463 (2016) 4. Dhokrat, A., Hanumant, G., Mahender, N.: Automated answering for subjective examination. Int. J. Comput. Appl. 56, 14–17 (2012) 5. Nikam, P., Shinde, M., Mahajan, R., Kadam, S.: Automatic evaluation of descriptive answer using pattern matching algorithm. Int. J. Comput. Sci. Eng., 69–70 (2015) 6. Sattayakawee, N.: Test scoring for non-optical grid answer sheet based on projection profile method. Int. J. Inf. Educ. Technol. 3(2), 273–277 (2013) 7. Patil, P., Patil, S., Miniyar, V., Bandal, A.: Subjective answer evaluation using machine learning. Int. J. Pure Appl. Math., 01–13 (2018) 8. Kulkarni, D., Thakur, A., Kshirsagar, J., Ravi Raju, Y.: Automatic OMR answer sheet evaluation using efficient reliable OCR system. Int. J. Adv. Res. Comput. Commun. Eng. 6(3), 688–690 (2017) 9. Patil, A., Naik, M., Ghare, P.H.: FPGA implementation for OMR answer sheet scanning using state machine and Ir sensors. Int. J. Electr. Electron. Data Commun. 4(11), 15–20 (2016) 10. Patil, S.M., Patil, S.: Evaluating student descriptive answers using natural language processing. Int. J. Eng. Res. Technol. (IJERT) 3(3), 1716–1718 (2014) 11. Reddy Tetali, D., Kiran Kumar, G., Ramana, L.: Python tool for evaluation of subjective answers (APTESA). Int. J. Mech. Eng. Technol. (IJMET) 8, 247–255 (2017) 12. Rinchen, P.: Comparative study of techniques used for automatic evaluation of free text answer. Int. J. Recent Innov. Trends Comput. Commun. 3839–3842 (2014) 13. Patel, D., Zaid, S.: Efficient system for evaluation of Omr sheet—a survey. Int. J. Adv. Res. Eng. Sci. Manag., 01–08 (2017) 14. Ntirogiannis, K., Gatos, B., Pratikakis, I.: An Objective Evaluation Methodology for Handwritten Image Document Binarization Techniques, 217–224 (2008) 15. Gite, N.S.: Implementation of descriptive examination and assessment system. Int. J. Adv. Res. Sci. Eng. 07, 252–257 (2018) 16. Malpe, V., Bhatia, S.: Evaluation of students’ answer scripts using soft computing techniques. Int. J. Mod. Eng. Res. (IJMER) 2(3), 1280–1289 (2012) 17. Gunawardena, T., Pathirana, N., Lokuhetti, M., Ragel, R., Deegalla, S.: Performance evaluation techniques for an automatic question answering system. Int. J. Mach. Learn. Comput. 5(4), 294–300 (2015) 18. Andrenucci, A., Sneiders, E.: Automated question answering: review of the main approaches. In: Proceedings of the Third International Conference on Information Technology and Applications, pp. 514–519 (2005) 19. Gunawardena, T., Lokuhetti, M., Pathirana, N., Ragel, R., Deegalla, S.: An Automatic Answering System with Template Matching for Natural Language Questions. IEEE, 353–358 (2010) 20. Singh, P., Sheorain, S., Tomar, S., Sharma, S., Bansode, N.K.: Descriptive answer evaluation. Int. Res. J. Eng. Technol. 05, 2709–2712 (2018)

Diabetes Mellitus Detection and Diagnosis Using AI Classifier L. Priyadarshini and Lakshmi Shrinivasan

1 Introduction According to International Diabetes Federation (IDF), there are no signs of diabetes epidemic relenting. More than 463 million adults worldwide struggle with diabetes according to 9th edition of IDF Diabetes Atlas. Diabetes is responsible for 4.2 million deaths each year and can result in severe complications, disability and reduced quality of life, which to a large extent could be prevented with proper diagnosis and access to medical care. Diabetes mellitus (DM) is a chronic disease that develops when the pancreas is no longer capable of producing insulin or the condition when the body is unable to use the insulin produced. It contributes to elevated blood glucose levels known as hyperglycaemia. High glucose levels cause damage to body by failure of various organs and tissues. Diabetes requires ongoing medical treatment and patient self-management knowledge to avoid severe complications in order to decrease the likelihood of long-term problems. Development in insulin sensitivity or reduction in the generation of hepatic glucose will resolve the hyperglycaemia condition. In order to achieve this, continuous and accurate monitoring of insulin and glucose level are required. With advancement in computer technology, there is a huge demand for production of knowledge-based and intelligent systems specially in medical diagnosis. This has led to interaction between doctors and engineers almost in all interdisciplinary fields. With the implementation of computer-based technologies, CDSS was designed to provide assistance to medical practitioners [1], the results obtained are faster and more accurate when compared to that of conventional technologies. However, there exists uncertainty in medical data which leads to difference in diagnosis. Introduction of artificial intelligence in medical domain has provided solutions for such conditions L. Priyadarshini (B) · L. Shrinivasan Ramaiah Institute of Technology, Bangalore, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_21

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with expert systems such as fuzzy systems and neural network. In [2, 3], it was shown that fuzzy logic is a solution to improve these uncertainties providing powerful decision-making support with improved reasoning capabilities. In [4], a fuzzy expert system was proposed to manage dynamics of diagnosis and medication of type 1 diabetes in an individual by calculating probability of diabetes. Output is semantically arranged in terms of fuzzy numbers like very low, low, medium, high and very high diabetes. Based on these, insulin dosage was recommended. Similarly, in [5], a fuzzy system was designed to determine risk percentage of getting diagnosed with diabetes wherein clinicians were assisted through a GUI making it a user-friendly layout and saving diagnosis time. Joshi and Borse [6] makes use of artificial neural network (ANN) using back propagation as an accurate method to diagnose DM in an individual. In [7, 8], ANFIS was proposed which incorporates the features of both fuzzy control interpolation and neural network (NN) with back propagation for adaptability. The results show that classification accuracy is higher than other methods. In [9], pre-emptive diagnosis was attempted to identify features and classify data using machine learning. Experimental results showed that ANN outperformed SVM, Naïve Bayes and K-nearest neighbour techniques with testing accuracy of 77.5%. Attempts have been made in the present work to apply ANFIS to PIMA Indian Diabetes Database (PIDD) to obtain better prediction and classification accuracy with which the inherent uncertainty degree can be accessed.

2 Proposed Methodology The proposed work as shown in Fig. 1 presents a simple fuzzy system for diagnosing DM. PIDD parameters glucose level, insulin level, body mass index (BMI), diabetes pedigree function (DPF) and age were fed as input attributes and probability of diabetes as output. The membership functions were computed using fuzzy logic toolbox in MATLAB, with which the fuzzy inference system (FIS) tracks inputs and output

Fig. 1 Proposed fuzzy-based expert system design

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Fig. 2 Classification of PIMA datasets depicting probability of diabetes

data. FIS was constructed by fuzzifying crisp input values contained in the knowledge base using Mamdani approach. If–then rules were created to form rule base for decision making and aggregated into fuzzy single output set. Thereafter, defuzzification process was carried out with centroid method which provides crisp output as shown in Fig. 2. Proposed fuzzy system was robust in nature and capable of classifying diagnosis for a large dataset of various attributes with both objective and subjective knowledge coordinated in logical way. However, to increase accuracy further, the number of rules was increased. This slowed down system time response which indicated system had poor adjusting capability during a learning process. ANFIS is an integrated NN and fuzzy inference system exploiting the benefits of both. ANFIS forms a class of adaptive networks where in degree of membership are modified either by back-propagation algorithm or by using hybrid algorithm. Supervised algorithm such as the back-propagation algorithm minimises difference between actual and desired output by gradient descent method thus optimising premise parameters whereas with hybrid algorithm, both premise and consequent parameters are optimised, the latter by least squares estimate (LSE) method thus converging faster. Figure 3 shows structure of the proposed ANFIS model which comprises of two fixed nodes and three adjustable nodes which are interconnected equivalent with first order Takagi–Sugeno-type system. The proposed ANFIS model had five layers of five input attributes and one output. Initially, 80% of the dataset was loaded as training data. FIS was generated with trapezoidal membership function. Both hybrid and back-propagation algorithm were applied for optimization and compared at different epochs. Sugeno-based fuzzy model was considered for its efficiency and adaptability to extract appropriate knowledge from database. Input attributes were fuzzified in layer1 where every node being adaptive in nature was associated with a linguistic label which defines degree of membership set for each input and forms premise parameters of ANFIS model. Fixed nodes in layer 2 performs AND operation to multiply inputs indicating simple multiplier with each node representing firing strength of each rule. Each node in layer 3 was fixed and plays the role of firing strength normalisation. Nodes in layer 4 are adaptive and computes product of normalised firing strength obtained from previous layer and first order polynomial forming consequent parameters. Layer 5 had just one fixed node which computes the total output by adding up all received signals, i.e. process of defuzzification. Outputs were classified based on weighted mean (WM) method. Once training was complete, remaining 20% of dataset was fed to the system for testing. 243 if–then fuzzy rules were created. Further, performance

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Fig. 3 Proposed structure of ANFIS model

of the model was verified by calculating root mean square error (RMSE) and mean squared error (MSE) during training and testing by using subsequent equations: 1

p 2 1 |ti − oi |] RMSE = [ p i=1

MSE = [

p 1 |ti − oi |] p i=1

(1)

(2)

with ‘p’ indicating number of data points, ‘t’ representing target value and ‘o’ representing output value.

3 Results and Discussion The model was trained and tested by using fuzzy logic toolbox in MATLAB. Accuracy of the model during training and testing using both hybrid algorithm and backpropagation algorithm was substantiated through the statistical indices mentioned in Eqs. 1 and 2 in the previous section. The statistics obtained from the training data is presented below in Table 1. As observed from the performance table, in both cases, RMSE and MSE values were marginal for the training data, thus negligible. The same was depicted in the graphs below in Fig. 4. RMSE values obtained by implementing hybrid algorithm

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Table 1 Performance of ANFIS model with hybrid algorithm Epoch

10

20

50

100

150

0.21964

0.21964

0.21964

0.21964

0.21964

Hybrid

RMSE MSE

0.04843

0.04824

0.04824

0.04824

0.04824

Back propagation

RMSE

0.49394

0.48819

0.48242

0.47891

0.47676

MSE

0.24398

0.23833

0.23272

0.23272

0.22731

Fig. 4 Performance evaluation of ANFIS model for hybrid and back-propagation algorithms

were found to be lower than those obtained with back-propagation algorithm. Thus, hybrid algorithm proves to provide better performance accuracy and thereby more efficient in training the ANFIS model. Validation is an essential phase in evaluating ability of the model. ANFIS model was compared with different classifiers of machine learning techniques as tabulated in Table 2 in terms of statistical parameters such as accuracy, specificity and sensitivity obtained from the confusion matrix. A fivefold cross-validation of SVM, K-nearest neighbour and Naïve Bayes are presented in the table. Also, an optimised recursive general regression neural network (R-GRNN) oracle was applied on the same PIMA diabetes database in previous work [10]. Accuracy results of all these models were compared and observed that most accurate results were obtained from the ANFIS method of classification. Table 2 Performance validation of classification models

Classification model

Accuracy (%)

Sensitivity (%)

Specificity (%)

ANFIS

86.48

92.78

73.08

SVM

79.61

81.18

71.69

K-nearest neighbour

77.1

79.32

70.40

Naïve Bayes

78.4

82.48

68.90

R-GRNN oracle

81.14

63.80

89.14

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4 Conclusion The need for an efficient expert system to be used to diagnose and analyse medical data has been in demand by CDSS in the recent years. ANFIS model was designed with hybrid of NN and fuzzy system to achieve medical diagnosis in a timely and concise manner. ANFIS has the advantage over limitation of fuzzy logic in adjusting to changes and requirement of lesser number of epochs, thus reducing computation time. Results exhibit proposed ANFIS model to have a good classification accuracy of 86.48% with minimal error when compared with other algorithms. In future, the model is to be tested for a larger dataset and compared with deep neural network to provide competent diagnosis and support CDSS in the best way.

References 1. Sutton, R.T., Pincock, D., Baumgart, D.C., Sadowski, D.C., Fedorak, R.N., Kroeker, K.I.: An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ. Digital Med. 3(1), 1–10 (2020) 2. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985). https://doi.org/10.1016/b978-14832-1450-4.50045-6 3. Sikchi, S.S., Sikchi, S., Ali, M.S.: Design of fuzzy expert system for diagnosis of cardiac diseases. Int. J. Med. Sci. Publ. Health 2(1), 56–61 (2013). doi.org/https://doi.org/10.5455/ijm sph.2013.2.56-61 4. Lalka, N., Jain, S.: Fuzzy based expert system for diabetes diagnosis and insulin dosage control. In: International Conference on Computing, Communication & Automation. IEEE (2015). doi.org/https://doi.org/10.1109/ccaa.2015.7148385 5. Abdullah, A.A., Fadil, N.S., Khairunizam, W.: Development of fuzzy expert system for diagnosis of diabetes. In: 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) IEEE (2018). doi.org/https://doi.org/10.1109/ icassda.2018.8477635 6. Joshi, S., Borse, M.: Detection and prediction of diabetes mellitus using Back-propagation neural network. In: 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE). IEEE (2016). doi.org/https://doi.org/10.1109/icmete.201 6.11 7. Kalaiselvi, C., Nasira, G.M.: A new approach for diagnosis of diabetes and prediction of cancer using ANFIS. In: 2014 World Congress on Computing and Communication Technologies. IEEE (2014). doi.org/https://doi.org/10.1109/wccct.2014.66 8. Saraswati, G.W., Choo, Y.-H., Jaya Kumar, Y.: Developing diabetes ketoacidosis prediction using ANFIS model. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS). IEEE (2017). doi.org/https://doi.org/10.1109/icoras.2017.8308066 9. Alassaf, R.A., et al.: Preemptive diagnosis of diabetes mellitus using machine learning. In: 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE (2018). doi.org/https://doi.org/10.1109/ncg.2018.8593201 10. Kirisci, M., Yılmaz, H., Ubeydullah Saka, M.: An ANFIS perspective for the diagnosis of type II diabetes. Ann. Fuzzy Math. Inform. 17(2), 101–113 (2019). doi.org/https://doi.org/10. 30948/afmi.2019.17.2.101

Review on Unit Selection-Based Concatenation Approach in Text to Speech Synthesis System Priyanka Gujarathi and Sandip Raosaheb Patil

1 Introduction The text-to-speech system converts raw or input text in any natural language in to its corresponding spoken waveform. Speech signal is used in many applications in human computer interactive systems. In India, wide variety of societies, religions are present. Great linguistic diversity and majority of Indian states have different languages that is natively written and spoken. Nowadays, people are more interested in their native language. A TTS synthesis system mainly consists of two components: the natural language processing (NLP) and the signal processing. Recent state-ofthe-art corpus-based text-to-speech systems generate synthesized speech by concatenating phonetically labeled speech segments which are selected from a large speech database. Database must contains various combinations of labeled speech segments. TTS systems using a corpus-based concatenative synthesis method produce more natural and higher quality speech as the size of the recorded database becomes larger. A speech corpus is digitally recorded and stored, and then, the speech segments are marked manually with visualization tools or automatically with segmentation algorithms. But manual segmentation is very tedious and time consuming task. Manual segmentation is done through many softwares, e.g., audacity, wave surfer, etc. Speech segments are selected to minimize discontinuity problems caused by their concatenation. Generally, a combination of diphone, half-syllables, syllables, and triphones P. Gujarathi (B) E & TC Engineering Department, JSPM Rajarshi Shahu College of Engineering, Pune, Maharashtra, India e-mail: [email protected] S. R. Patil E & TC Engineering Department, Bharati Vidyapeeth’s College of Engineering for Women, Pune, Maharashtra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_22

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are chosen as speech segments because they involve most of co-articulations and transitions.

2 The Process of Speech Production Speech signals are fundamentally composed of a sequence of sounds. Transition between sounds provides a symbolic representation of information. The arrangement of these sounds information is governed by the rules of language. These rules of languages and their implementation is called linguists domain. Study and classification of these sounds speech signal is called as phonetics. The concept of speech signal processing is to enhance or extract information to get more detail information about structure of the signal means how the information is encoded in the signal [1]. Basic components of TTS system are (i) Text pre-processing, (ii) Text to phoneticprosodic translation. (i)

Text pre-processing: In this step, text string of characters are translated into a new string with ambiguities resolved, example of this is the translation of “Sr” into either “Senior” or “Serial” or “Sir,” depending on the linguistic context. (ii) Text to phonetic-prosodic translation: Parsing is done on processed text to determine its semantic structure. The sequence of words and their derived structure then used to generate prosodic information and sound units like phonemes, diphone, syllables, and polysyllables. The generation of sound is usually a more complex task than searching words in dictionary because the pronunciation of words is highly dependent on context. Some prosodic rules determine the quantities such as pitch, amplitude, and duration for each of these sound units. (iii) After sound unit labeling, pitch, duration, amplitude, spectrum modification the signal processing component generates speech signal [2].

2.1 Basic TTS System TTS system is mainly divided into two parts: Front end and back end (Fig. 1). A. (i)

Front end: (i) Text Normalization, (ii) Phonetic Transcription, (iii) Prosody information. Text Normalization: In text pre-processing input text containing symbols, numbers, abbreviations are converted into equivalent words. This is also called as text tokenization. Text analysis can be generally divided into several stages, such as labeling, word segmentation, syntactic parsing, semantic interpretation, etc. Lexical ambiguity should be minimized before processing any speech signal. A text Normalization plays a major role in TTS systems, because the segmentations and part of speech (POS) of the sentence will directly influence

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Fig. 1 Basic TTS system

the prosody of speech such as pitch contour, the duration of syllable or pause, stress, and many more parameters [3]. (ii) Phonetic Transcription: It is also called as text to phoneme conversion. In this, each word is assigned to its phonetic transcription. (iii) Prosody information: Basically, there are three specific features related to prosody are: intonation, segmental duration, and energy. In linguistics prosody means the rhythm, stress, and intonation of speech waveform prosody may reflect the various features of the speaker, their utterances. For example: It may represent the emotional state of speaker, prosody may also reflect if it is statements or questions or command. Intonation is the variation of pitch period information for word. It is used in all languages to shape sentence and indicates its structure. It is used in non-tonal languages to add attributes to words, and it is also used to differentiate between questions (like yes or no), declarative statements, commands, requests, etc. Fluctuations in pitch either give rising pitch information or falling pitch information. Different intonations are: rising intonation (Pitch of the voice increases with time), falling intonation (Pitch of the voice decreases with time), dipping intonation (Pitch falls and then rises), peaking intonation (Pitch rises and then falls.).Pitch can also indicate attributes and has four different levels: Low pitch is used at the end of utterances. Normal conversion uses middle or high pitch. High pitch occurs at the end of questions (yes/no). Very high pitch is used for strong emotions. Mostly, intonation modeling is considered to play a key role to produce natural speech synthesis systems. Phonetic transcription and prosodic information together will give symbolic linguistic representations of signal. A.

(i)

Back end: The symbolic linguistic representation of speech signal is applied to synthesizer block to get sound signal using signal processing. It also includes computation of output prosody such as pitch contour, durations, spectral information, cepstrum information, phoneme, etc., to get natural sounding speech output. Backend is very crucial and important component when intelligibility and quality of speech signal in TTS is considered. Unit length selection is an important task in concatenative speech synthesis. A shorter unit length of sound requires less space but sample collecting and labeling becomes more difficult and complex.

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(ii) A longer unit length of sound requires more memory space but gives more naturalness, better co-articulation effect, and less concatenation points. (iii) Choices of unit for TTS are phonemes, diphones, triphones, demi syllables, poly syllables, syllables, and words.

3 Different Synthesizer Technologies The essential qualities required for speech synthesis system are intelligibility and naturalness. Intelligibility means the ease with which output is understood and how comprehensible speech is in given conditions. Naturalness describes how closely the output is similar to human speech. Ideal speech synthesizer must be both natural and intelligible. Different speech synthesis technologies are articulatory synthesis, LPC synthesis, formant synthesis, concatenative synthesis, HMM-based synthesis, sine wave synthesis, source filter synthesis.

3.1 Concatenative Synthesis Basically, concatenation means stringing together different speech segments from pre-recorded speech database. Generally, concatenative synthesis can generate natural sounding speech signal. In this first speech, database is created and from database speech segments are extracted by using different semiautomatic or automatic segmentation algorithms. Then, these segments (Sound unit: Phoneme, syllables, Polysyllables) are joined together to get synthesized speech signal, but some audible glitches are observed in the output, and it should be minimum to get natural sounding speech output. There are different subtypes of concatenative synthesis: 1. Unit selection synthesis 2. Diphone synthesis 3. Domain specific synthesis. 4. Phoneme-based synthesis.

3.2 Unit Selection Synthesis In unit-selection synthesis, large database of recorded speech is used. First database is created after that each recorded utterance is segmented into individual phones, diphones, halfphones, syllables, polysyllables, words, phrases, etc., according to speech unit is selected. The division of the units in the speech database into segments is made based on segmentation and different acoustic parameters for each segment such as pitch, position, duration in the syllable, and the context for each segment is stored. Then, each segment is indexed for easy recovery. In synthesis, desired output utterance is created by determining the best combination of segments from speech database accessed using a proper index.

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3.3 Unit Selection and Specification Process Many text-to-speech synthesizers for Indian languages have used synthesis techniques that require prosodic models but due to unavailability of properly annotated databases for Indian languages, prosodic models for these synthesizers have still not been developed properly. Syllable-like speech unit is suitable for concatenative speech synthesis do not require extensive prosodic models [4]. The general format of an Indian language syllable is CVC, CV, CCV, etc., where C is a consonant, V is a vowel. Syllable is again divided into onset, rime, coda. During the selection process, the phonetic and prosodic constraints are applied. Indian languages are syllable centered, and pronunciations are based on these syllables. Syllable units can capture co-articulation better than phones.

4 Comparison of Different Synthesis Methods Prosodic information generation is the important parameter. Prosodic parameters include pitch contour, energy level, initial duration, final duration, pause duration. Recurrent fuzzy neural network (RFNN) is a multilayer recurrent neural network technique for a Chinese TTS system with a Chinese database based on the timedomain pitch synchronous overlap adds (TD-PSOLA) method [3]. For formant estimation, pole analysis procedure is used. Different methods of spectral and formants smoothing are explored. Spectral smoothing is achieved at segment boundaries by interpolating the LP autocorrelation vectors. Formant smoothing methods involves direct modification of the formant frequencies. Spectral continuity at concatenative boundaries and across periodic groups in the sentence is observed [5]. Non-predictive analysis-by-synthesis scheme for speaker-dependent parameter estimation is implemented to get a high compression ratio. The spectral coefficients are quantized by using a memory less split vector quantization (VQ) approach. Non-productive and predictive type methods are combined to improve the coding efficiency in TTS system [6]. Accurate phone boundaries are essential for acoustic–phonetic analysis in automatic speech recognition and speech synthesis systems. However, the process of manually determining phonetic transcriptions and segmentations is laborious, expensive. It requires expert knowledge and very time consuming. In addition, exact positions in time and some phone or syllable boundaries are detected accurately. The cost and effort required for this process are important for large databases, so there is need for automatic segmentations and is mainly motivated by the need for large speech databases used to train and evaluate to build concatenative text-to-speech (TTS) systems. Mel Frequency cepstrum coefficients (MFCC) spectral features are used. The phone boundaries are automatically detected at the maximum spectral transition positions and manual detection of phone boundaries in the training part of the TIMIT database [7]. There are two types of costs used in concatenation, target costs and concatenation costs. The target cost is calculated by adding the effort required in

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finding the relevant unit in database. The concatenation cost is estimated in joining the speech units [8]. Speech segments or units can be of various sizes such as phones, diphones, syllables, and even words. Selection of each of the sound units has their own advantages and disadvantages. The selection of sound units depends on basic characteristics of the language. Indian languages have a well-defined syllable structure. Syllable can preserve better co-articulation effect as compared to phones and diphones. Hence, syllables are selected as the basic units for synthesis. Unit selection process is carried out based on two cost functions: concatenation cost and target cost. Unit selection cost functions (target cost and concatenation cost) should ensure that the selected optimal unit sequence should closely match with the target unit specification and with other adjacent units in the sequence [9]. For modifying speech prosody, a time-domain algorithm is used which requires less work for computation. The pitch synchronization overlap and add algorithm (PSOLA) modifies the pitch and duration of speech signals [10]. But PSOLA has no control over formant features. Modifying some prosodic features such as line formants and pitch, we can get natural speech synthesis [11]. Another method involves concatenation of pre-recorded speech audio files by selecting the most appropriate unit from a speech corpus database [12]. Unit selection algorithm to select the best annotated speech unit from the database [13]. Speech synthesis approach is statistical parameters HMM for generating intelligible speech [14]. The advantages of HMM and unit selection can be place together to generate better speech quality [15]. A unit-selection algorithm has been used for developing the speech synthesizer [16]. The basic genetic algorithm searches in the large search space by searching at multiple locations than at just one location [17]. Implementation of GA in speech synthesizers based upon unit selection [18]. The approach taken for implementation of GA is based on the concept of reducing join cost [8]. Unit selection speech synthesis used to solve some of the problems of unnaturalness introduced by the signal processing techniques [19]. Adoption of deep neural networks for acoustic modeling has further enhanced the prosodic naturalness and intelligibility of the synthetic speech [20]. In addition, the ongoing emergence of neural network waveform generation models (neural vocoders). Neural vocoders has nearly closed the quality gap between natural and synthetic speech. Speech synthesis systems raw waveform generation neural models WaveNet and GlotNet are presented [21]. WaveNets can be used for text-to-speech (TTS) synthesis in state-of-the-art concatenative and statistical parametric TTS systems [22]. Source-filter vocoder STRAIGHT used in parametric TTS [23]. Unit selection methods require improvements to prosodic modeling and that HMM-based methods require improvements to spectral modeling for emotional speech signal. The main drawback of statistical parametric speech synthesis is that the spectra and prosody generated from HMMs can be over-smooth and lacking the detail present in natural spectral and prosodic patterns because of the averaging in the statistical method [24]. The main drawback of concatenative methods such as unit selection is that the technique requires a large speech database genetic algorithm in solving unit-selection problem [25]. As compared to diphone-based TTS

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systems, unit selection TTS synthesis minimizes the number of artificial concatenation points and reduces the need for prosodic modification at synthesis time [26]. At current stage, unit selection and waveform concatenation synthesis [3] and HMMbased parametric synthesis [1] are two main speech synthesis methods. Each of these two methods has its own advantages. For unit selection and waveform concatenation method, the original waveforms are preserved and better naturalness can be obtained especially given a large database. On the other hand, HMM-based parametric synthesis provides better smoothness, robustness, flexibility, and automation in system building [27]. Although statistical parametric speech synthesis offers various advantages over concatenative speech synthesis, the synthetic speech quality is still not as good as that of concatenative speech synthesis or the quality of natural speech [28]. In Table 1, comparison of different synthesis methods, prosodic features, and performance evaluation for TTS systems are given from different papers. Table 1 Comparison of TTS systems Author

Synthesis method Unit

Text Prosodic analysis features

Performance evaluation

Date

Chin-Teng Lin et al.

1. Recurrent fuzzy neural network, 2.TD-PSOLA

Monosyllable

Yes

Pitch, Subjective maximum listening test energy levels, syllable duration, and pause duration

2004

Phuq Hui Low et al.

Spectral and formants smoothing

Phoneme

No

Formant, bandwidths, spectrum shape

MOS

2003

Xian-Jun Xia et al.

Hidden markov model (HMM) Log likelihood ratios (LLR)

phone-sized unit

Yes

Spectral and F0 features and the phone duration

Listening test

2014

Chang-Heon Lee et al.

Corpus-based Phonemes, concatenative Syllables synthesizer, Correlation-based pattern matching method

No

Pitch-pulse codebook, Spectral information, LPC or LSP coefficients

Objective, 2007 subject, computational evaluation

Sorin Dusan et al.

Spectral transition measure: Peak picking, post-processing method

Phoneme

NO

Mel-frequency – cepstrum coefficients (MFCC)

Isabelle Y. et al.

PHONEME segmentation method

Phonemes

No

Pitch

–

1974

(continued)

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Table 1 (continued) Author

Synthesis method Unit

Text Prosodic analysis features

Performance evaluation

Date

Hideki Kasuya et al.

Speaker independent algorithm, linear prediction analysis

Syllables

No

Spectral parameters

–

Nutthacha Median Jittiwarangkul smoothing, et al. Moving average smoothing, Energy normalization

Syllables

Yes

Energy Accuracy contour, absolute energy, the root mean square energy, the square energy

1998

Jakkapan Jitsup et al.

1-D stationary Syllables wavelet transform endpoint detection analysis

No

Energy contour

–

2007

Chih-Hsun Chou et al.

Decision-based neural network, syllable endpoint detection

Syllables

NO

MFCC

–

2008

Tian Li et al.

Root means square energy (RMSE) Zero-crossing rate (ZCR)

Syllables

No

Power, pitch, features from time and frequency domain

F-measure

2015

Samuel Thomas et al.

Group delay-based segmentation algorithm, unit-selection procedure

Monosyllables Yes Bisyllables

–

MOS

Francisco Campillo Dı´az et al.

Unit selection method, Viterbi algorithm

Accented word,Syllable

-

Accent, pitch, duration

Objective and 2006 subjective tests

Xin Wang et al.

HMM, Syllables unit-selection concatenative speech, decision tree-based context clustering

Yes

MFCC, logF0

–

2012

Roberto Unit selection Barra-Chicote and HMM-based et al. synthesis

Words, Allophones

No

Spectral components

Objective and 2010 perceptual tests

Yee Chea Lim Genetic et al. algorithm (GA)

Phoneme

No

MFCCs and Euclidean distance

MOS

2012

(continued)

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Table 1 (continued) Author

Synthesis method Unit

Francesc Alı´as et al.

Interactive genetic algorithms (aiGAs)

Diphone and triphone

Mukta Gahlawat et al.

A genetic algorithm

Diphones

Zhen-Hua Ling et al.

Minimum unit selection error (MUSE) for HMM-based unit-selection speech

Phone-sized unit

Robert A.J. Clark et al.

Unit selection for Diphones the Festival

Lauri Juvela et al.

Statistical parametric speech synthesis GlotNet, WaveNet, neural network components, neural vocoder model

Shinnosuke Takamichi et al.

Modulation spectrum statistical parametric speech synthesis

Text Prosodic analysis features

Performance evaluation

Date

Weight tuning

MOS

2011

Yes

HRTF functions

MOS

2015

No

Mel-cepstrum parameters

Subjective evaluation

2008

Yes

Segment duration, f0

MOS

2007

Phoneme, – Syllable, word

Acoustic features, glottal excitation, fundamental frequency, spectrum

Listening test

2019

Phoneme

Power spectrum

Subjective evaluation

2016

–

5 Conclusion For text-to-speech conversion, the unit-selection speech synthesis is the simplest method. The sound unit plays important role. Different speech units are available like phoneme, diphone, triphone, syllable, mono syllable, poly syllable, words, etc. These speech units are synthesized by different speech synthesis technologies. However, unit-selection synthesizers are usually limited to one speaker and one voice. But by selecting longer speech units high naturalness, concatenation is achieved [29]. Available speech synthesis technologies are having both advantages and disadvantages. Using different prosodic features, we can get natural speech synthesis output. But prosody and naturalness are still an issue and research is going to improve and to get intelligible speech.

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References 1. Rabiner, L.R., Schafer, R.W.: Digital signal processing of speech signals 2. Xydas, G., Kouroupetroglou, G.: Tone-Group F0 selection for modeling focus prominence in small-footprint speech synthesis. Speech Communication, Greece (2006) 3. Lin, C.-T., Wu, R.-C., Chang, J.-Y., Liang, S.-F.: A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34(1) (2004) 4. Thomas, S.: Natural Sounding Text-to-Speech Synthesis Based on Syllable-Like Units. Indian Institute of Technology Madras (2007) 5. Low, P.H., Ho, C.-H., Vaseghi, S.: Using Estimated Formants Tracks for Formants Smoothing in Text to Speech (TTS) Synthesis. Brunel University, London, UB8 3PH, UK. LEEE (2003) 6. Lee, C.-H., Jung, S.-K., Kang, H.-G.: Applying a speaker-dependent speech compression technique to concatenative TTS synthesizers. IEEE Trans. Audio Speech Lang. Proces. 15(2) (2007) 7. Dusan, Rabiner, L.: On the relation between maximum spectral transition positions and phone boundaries. Sorin Center for Advanced Information Processing Rutgers University, Piscataway, New Jersey, USA 8. Gahlawat, M., Malik, A., Bansal, P.: Integrating Human Emotions with Spatial Speech Using Optimized Selection of Acoustic Phonetic Units. Computer Science & Engineering Department, DCRUST, Murthal, India (2015) 9. Narendra, N.P., Sreenivasa Rao, K.: Optimal Weight Tuning Method for Unit Selection Cost Functions in Syllable Based Text-to-Speech Synthesis. School of Information Technology, Indian Institute of Technology Kharagpur, Bengal, India (2012) 10. Moulines, E., Charpentier, F.: Pitch-synchronous waveform processing techniques for text-tospeech synthesis using diphones. Speech Commun. 9, 453–467 (1990) 11. Erro, D., Navas, E., Hernáez, I., Saratxaga, I.: Emotion on version based on prosodic unit selection. IEEE Trans. Audio Speech Lang. Proces. 18, 974–983 (2010) 12. Iida, A., Campbell, N., Higuchi, F., Yasumura, M.: A corpus-based speech synthesis system with emotion. J. Speech Commun. 40, 161–187 (2003) 13. Black, A.W.: Unit selection and emotional speech. In: Paper Presented at the Eurospeech, Geneva, Switzerland (2003) 14. Zen, H., Tokuda, K., Black, A.W.: Statistical parametric speech synthesis. Speech Commun. 51, 1039–1064 (2009) 15. Taylor, P.: Unifying unit selection and hidden Markov model speech synthesis. In: Paper Presented at the Interspeech (2006) 16. Hunt, A.J., Black, A.W.: Unit selection in a concatenative speech synthesis system using a large speech database. In: Paper Presented at the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (1996) 17. Hue, X.: Genetic Algorithms for Optimization. University of Edinburgh, Edinburgh (1997) 18. Kumar, R.: A Genetic Algorithm for Unit Selection Based Speech Synthesis. InterSpeechICSLP, Korea (2004) 19. Black, A.W., Campbell, N.: Optimising selection of units from speech databases for concatenative synthesis. In: Proceedings of the Eurospeech’95, Madrid, Spain (1995) 20. Zen, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (May 2013) 21. Juvela, L., Bollepalli, B., Tsiaras, V., Alku, P.: GlotNet—A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis. IEEE/ACM Trans. Audio Speech Lang. Proces. (2019)

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22. van den Oord, A., et al.: WaveNet: A Generative Model for Raw Audio (2016). Available: https://arxiv.org/abs/1609.03499 23. Kawahara, H., Estill, J., Fujimura, O.: Aperiodicity extraction and control using mixed mode excitation and group delay manipulation for a high quality speech analysis, modification and synthesis system STRAIGHT. In: Proceedings of MAVEBA (2001) 24. Barra-Chicote, R., Yamagishi, J., King, S., Montero, J.M.: Analysis of Statistical Parametric and Unit Selection Speech Synthesis Systems Applied to Emotional Speech, Javier Macias-Guarasa (2010) 25. Lim, Y.C., Tan, T.S., Salleh, S.H.S., Ling, D.K.: Application of Genetic Algorithm in Unit Selection for Malay Speech Synthesis System (2012) 26. Clark, R., Richmond, K., King, S.: Multisyn: open-domain unit selection for the festival speech synthesis system. Speech Commun. 49, 317–330 (2007) 27. Ling, Z.-H., Wang, R.-H.: Minimum Unit Selection Error Training for Hmm-Based Unit Selection Speech Synthesis System. iFlytek Speech Laboratory (2008) 28. Takamichi, S., Toda, T., Black, A.W., Neubig, G., Sakti, S., Nakamura, S.: Postfilters to modify the modulation spectrum for statistical parametric speech synthesis. IEEE/ACM Trans. Audio Speech Lang. Proces 24, 755–767 (2016) 29. Kayte, S., Mundada, M., Kayte, C.: A review of unit selection speech synthesis. Int. J. Adv. Res. Comput. Sci. Softw. Eng. (2015) 30. Li, T., Shen, F.: Automatic Segmentation of Chinese Mandarin Speech into Syllable-Like. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 31. Liberman, I.Y., Shankweiler, D., William Fischer, F., Carter, B.: Explicit syllable and phoneme segmentation in the young child. Child Psychol. 18, 201–212 (1074) 32. Gold, B., Morgan, N.: Speech and Audio Signal Processing. Wiley India Edition, New Delhi 33. Narendra, N.P., Sreenivasa Rao, K.: Syllable Specific Unit Selection Cost Functions for Textto-Speech Synthesis. Indian Institute of Technology Kharagpur 34. Xia, X.-J., Ling, Z.-H., Jiang, Y., Dai, L.-R.: HMM-Based Unit Selection Speech Synthesis Using Log Likelihood Ratios Derived from Perceptual Data. National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China 35. Dusan, S., Rabiner, L.: On the Relation Between Maximum Spectral Transition Positions and Phone Boundaries. Center for Advanced Information Processing Rutgers University, Piscataway, New Jersey, USA (2006) 36. Kasuya, H., Wakita, H.: Automatic Detection of Syllable Nuclei as Applied to Segmentation of Speech. Speech Communications Research Laboratory, Inc. 37. Jittiwarangkul, N., Jitapunkul, S., Luksaneeyanawin, S., Ahkuputra, V., Wutiwiwatchai, C.: Thai Syllable Segmentation for Connected Speech Based on Energy. Digital Signal Processing Research Laboratory Bangkok, Thailand 38. Jitsup, J., Sritheeravirojana, U.-T., Udomhunsakul, S.: Syllable Segmentation of Thai Human Speech Using Stationary Wavelet Transform. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand (2007) 39. Chou, C.-H., Liu, P.-H., Cai, B.: On the Studies of Syllable Segmentation and Improving MFCCs for Automatic Birdsong Recognition. In: IEEE Asia-Pacific Services Computing Conference, Department of Computer Science and Information Engineering, Taiwan (2008) 40. Thomas, S., Nageshwara Rao, M., Murthy, H.A., Ramalingam, C.S.: Natural Sounding TTS Based on Syllable-Like Units. Indian Institute of Technology Madras 41. Díaz, F.C., Banga, E.R.: A Method for Combining Intonation Modelling and Speech Unit Selection in Corpus-Based Speech Synthesis Systems. Campus Universitario, 36200 Vigo, Spain (2006) 42. Wang, X.: An HMM-Based Cantonese Speech Synthesis System. Joint Research Center for Media Sciences, Technologies and Systems, China. IEEE Global High Tech Congress on Electronics (2012)

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43. Alías, F., Formiga, L., Llora, X.: Efficient and Reliable Perceptual Weight Tuning for UnitSelection Text-to-Speech Synthesis Based on Active Interactive Genetic Algorithms (2011) 44. Clark, R.A.J., Richmond, K., King, S.: Multisyn: Open-Domain Unit Selection for the Festival Speech Synthesis System CSTR. The University of Edinburgh, 2 Buccleuch Place, Edinburgh UK (2007) 45. van Santen, J., Kain, A., Klabbers, E., Mishra, T.: Synthesis of prosody using multi-level unit sequences. Speech Commun. 46, 365–375 (2005)

Enhancing the Security of Confidential Data Using Video Steganography Praptiba Parmar and Disha Sanghani

1 Introduction Over the past few decades, the internet has become very common utilization medium for transmitting information from sender to the receiver. It has opened the new door for the attacker to attack on that information and easily take the information of the users. There is much confidential information like military’s secrets, law enforcement’s secrets, etc. Terrorists can also use data hiding as well as cryptography techniques for securing their data. Cryptography is the science of using mathematics to encrypt and decrypt the data [1]. Steganography is an art and science of hiding the secret data into another media. The advantages of video steganography are: • Large space for hiding confidential data. • Multiple numbers of frames for hiding the data. So, it is hard for the intruder to detect the data. • Increase the security. The rest of the paper is organized as follows. In Sect. 2, steganography and cryptography is defined. In Sect. 3, related work is defined about this research work. After Sect. 4 defined frame selection approach for selecting frames from cover video. In Sect. 5, parametric matrixes are calculated. Section 6 is defined results and discussion. Sections 7 and 8 represent conclusion and references.

P. Parmar (B) · D. Sanghani Shantilal Shah Engineering College, Bhavnagar, India e-mail: [email protected] D. Sanghani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_23

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2 Steganography and Cryptography 2.1 Steganography Steganography is an art and science of hiding the data behind any media. It can be categorized in four types based on cover medium (Fig. 1). Information is hidden behind any media like audio, text, image, or video. The data will be hiding behind audio then it is calledaudio steganography. Information is hiding behind the text is called text steganography and so on. There are two types of data in steganography which are secret data and carrier data. Confidential data which is hidden called secret data. This data cannot be seen from human visual eye. The cover medium is used for hiding the data is called carrier data. This cover medium is seen by human visual eye. In Fig. 2, the confidential information is in text format. This text data is encrypted by any cryptographic algorithm. Encrypted data is hidden behind any cover medium. Here, it uses image as a cover medium. After it creates stego image. In above Fig. 3, stego image is extracted using any extracted algorithm. Then, the encrypted data is extracted and going for decryption. Here, use cryptographic decryption algorithm and achieve original data. There are different algorithms of steganography like; (1) least significant bit (LSB), (2) bit-plane complexity segmentation (BPCS), (3) DCT-based techniques, (4) DWT-based techniques. LSB technique is used for embedding the data into least significant bit of cover medium. Data is divided into 3D partitioning and after that it embedded behind any medium called BPCS techniques. DWT and DCT technique are used for compressing the data, and after compression, it embedded into low, medium, and high frequency band. Fig. 1 Types of steganography [2]

Fig. 2 Steganography process at sender side [3] Text

Encry ption

Text hide behind image

Stego Image

Enhancing the Security of Confidential Data … Fig. 3 Steganography process at receiver side [3]

Stego Image

205

Extract

Decry ption

Origin al text

2.2 Cryptography Cryptography is the mathematical technique related to information security such as confidentiality, integrity, authentication, etc. The widely use of cryptographic technique is unauthorized uses from the user into communication channel. There are two types of cryptography techniques based on the key. 1. Symmetric key cryptography and 2. Asymmetric key cryptography. Sender and receiver use the same key for encryption and decryption of the message is called symmetric key cryptography. Sender and receiver use different key such as public and private key for encryption, and decryption is called asymmetric key cryptography.

3 Related Work The following section discusses the steganography and cryptography algorithms that have been used together for providing high security. There are many techniques implemented in this field. In recent years, if the attacker analyzing the video sequences, then it easily detect the stego video and fetch the original data. Yadav and Bhogal [4] present video steganography in spatial and discrete wavelet transform. They had used DWT and BPCS methods for embedding the data behind any video. The main work of this research is to increase the capacity in video data hiding within DWT technique. 3D SPIHT BPCS steganography uses decomposition of bitmaps. When one video file has been selected after this extracted frame and the frame decomposed into the bitmaps, we can get a dualistic frame for each bit-plane [4]. At the corresponding approach, Mstafa et al. [5] presents a research on DWT and DCT domains based on multiple object tracking and error correcting code. Multiple object tracking is used for tracking motion-based objects from cover video. That objects are used for embedding the data into video. They had propose a novel approach of multiple object tracking (MOT) with the help of DWT and DCT coefficient. Ramandeep Kaur et al. [6] present a hybrid approach of steganography methods and cryptography methods. Multiple use of both algorithms, it gives better security to embedded data. 4LSB and identical match algorithms are used for embedding data and how to arrange the data into cover medium for embedding purpose, respectively. Canny edge detector helps to embedded data behind edges of that cover video frames because edges have large amount of sharp area that no one can easily detect

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that embedded data. RSA encryption algorithm is asymmetric encryption algorithm. Sender and receiver use different keys for encrypting and decrypting the data. In [6], RSA gives high security for data securing. Jangid and Sharma [7] propose multilevel clustering algorithm with integer wavelet transform (IWT). Multi-clustering algorithm use K-Mean clustering for cluster the Cover Frame. Data is partition by K-Mean clustering.

4 Frame Selection Approach Some attacker attacks on data using sequential analysis of the video frame. They can easily detect data which are hidden behind any medium. So, frame selection approach using mathematical function helps to avoid this analysis. Step 1: Calculate total number of frames (Total_No_Frames) of cover video; Step 2: Take an Alpha for constant value; Step 3: Calculate slab value; Slab_value = seed_value + (Total_No_Frames/Alpha); Step 4: Calculate Floor (Slab_value). And store this value to s1; Step 5: s1 is the list of selected frames. Alpha is a constant value. If user wants to increase the number of selected frames, then value of alpha is also increased, and if user wants to decrease the selected frames, then alpha value is also decrease. Value of alpha is totally depends on data size and user’s requirements.

5 Methodology Sequential methods give better security. Least significant bit is used for performing video steganography and AES encryption algorithm used for performing encryption. When we used both together, then it gives confidentiality and integrity of data (Fig. 4). Algorithm Steps: Step 1: User has to take cover video. Step 2: Extract the video frames and select frames from video using arithmetic function. Step 3: Take a secret text data from user. Step 4: Perform encryption algorithm.

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Cover Video

Other Frames

Secret data

Selected frames using arithmetic formula

Encryption Video Steganography

Stego Frames

Stego video

Extract Stego video frames

Extraction Data

Decryption

Original Data

Fig. 4 Proposed work flow

Step 5: After that encrypted data perform video steganography and embedded behind cover video. Step 6: Stego video is generated. Step 7: After extraction of selected video frames. Step 8: Extraction of encrypted data from video. Step 9: Decryption of data and take original data.

6 Parameter Metrices Evaluation of results can be defined by peak-signal-to-noise ratio (PSNR) and mean square error (MSE). Both are quality measurement evaluation methods. 1. Mean square error (MSE)

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The mean square error (MSE) represents the cumulative squared error between the stego image and the original image, whereas PSNR represents a measure of the peak error [4].  MSE =

[I1 (m, n) − I2 (m, n)]2

M,N

M∗N

M and N are the number of rows and columns in the input images. 2. Peak-signal-to-noise ratio (PSNR) The PSNR block computes the peak-signal-to-noise ratio, in decibels, between two images. This ratio is used as a quality measurement between the original and a compressed image. The higher the PSNR, the better is the quality of the compressed or reconstructed image [8]. PSNR = 10 log 10(R 2 /MSE)

7 Results and Discussion Table 1 shows the value of PSNR and MSE of average ratio of selected frames from cover video. If we take PSNR average value of whole value, then it gives 100% results and it was wrong. Because here, we had used frame selection approach and we had to take only selected frames of PSNR average value. Different size video with different data size gives the best comparison results. Test.mp4 is original video and used for performing steganography process. The size of this video is 1.08mb, and after performing extraction of video frames from video, then it gives 132 frames. We had used frame selection approach using arithmetic formulas for selection of frames from cover video (Figs. 5 and 6).

8 Conclusion Cryptography and steganography both are very useful algorithms for protecting the data to be transmitted. It gives high multiple securities to data because cryptography encrypts the secret data and steganography keep hide the existence of that data. This paper gives high PSNR ratio and low MSE value with LSB method and AES encryption algorithm along with frame selection approach. Frame selection approach helps for selecting video frames from original video; so, attacker cannot easily detect secret data using sequential analysis of video frame. The main objective behind using LSB method is it implemented on any type of video format.

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Fig. 5 Original cover video

Fig. 6 Stego video

Table 1 Various parameter metrics value with different video frames Video

Video size

Data (bytes)

MSE

PSNR (db)

Test.mp4

1.08 mb

128 bytes

0.00023

89.059

Test.mp4

1.08 mb

2048(2 kb)

0.00037

81.025

Animation1.mp4

752 kb

128 bytes

0.00045

84.40

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References 1. Wang, H.: Cyber warfare steganography vs. steganalysis. In: Communications of the ACM, vol. 47 no. 10 (2004) 2. Wadekar, H., Babu, A., Bharvadia, V., Tatwadarshi, P.N.: A new approach to video steganography using pixel pattern matching and key segmentation. In: 2017 International Conference on Innovations in information Embedded and Communication Systems (ICIIECS) (2017) 3. Prashanti, G., Jyothirmai, B.V., Sai, K.: Data confidentiality using steganography and cryptographic techniques. In: 2017 International Conference on Circuits Power and Computing Technologies [ICCPCT].s (2017) 4. Yadav, S.K., Bhogal, R.K.: An video steganography in spatial, discrete wavelet transform and integer wavelet domain. In: 2018 International Conference on Intelligent Circuits and Systems (2018) 5. Mstafa, R.J., Elleithy, K.M., Abdelfattah, E.: A robust and secure video steganography method in DWT-DCT domains based on multiple object tracking and ECC. IEEE. https://doi.org/10. 1109/ACCESS.2017.2691581 6. Ramandeep Kaur, Pooja, Varsha: A hybrid approach for video steganography using edge detection and identical match techniques. In: IEEE WiSPNET 2016 Conference 7. Jangid, S., Sharma, S.: High PSNR based video steganography by MLC (multi-level clustering) algorithm. In: International Conference on Intelligent Computing and Control Systems ICICCS (2017) 8. https://in.mathworks.com/help/vision/ref/psnr.html

Data Mining and Analysis of Reddit User Data Archit Aggarwal, Bhavya Gola, and Tushar Sankla

1 Introduction Reddit is a popular user-driven Website that consists of several communities devoted to debating a predefined subject, called subreddits. Users can submit original content or links and have discussions with other users. This is a unique Web platform, because the focus is not on a single user but on the community [13]. The optimal sentiment analysis method will be to analyse a series of search results for a given object, to produce a list of product characteristics (quality, features, etc.) and to aggregate opinions on every one of these (negative, neutral, positive). Even so, the concept has often been defined more widely recently in order to incorporate several different forms of review of the content of the assessment. In general, opinions may be conveyed on something, e.g. a commodity, a facility, a subject, a person, an entity or a case. The general term object is used to represent the person to which comments have been given. Our objective through this study is to create our own corpus into a Reddit program. The system is precisely trained to take inputs which are as status updates from the corpus, ignoring the updates sans words or emojis. During the testing phase, the capability of the system is judged by its capacity to categorize the polarity of opinion per status update. Curiskis et al. [3] research on opinion mining or sentiment analysis began with the identification of words bearing opinion (or feeling), e.g. excellent, beautiful, marvelous, terrible and worst. Several scholars focused on mining these words and A. Aggarwal (B) · B. Gola · T. Sankla Bharati Vidyapeeth’s College of Engineering, New Delhi, India e-mail: [email protected] B. Gola e-mail: [email protected] T. Sankla e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_24

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Fig. 1 Steps in opinion mining

defining their polarization assessment (i.e. positive, negative and neutral) or semantic orientations. The authors have identified numerous language features which can be exploited from a large corpus to identify sentiment words and their orientations (Fig. 1).

2 Related Works There have been many past works that show the analysis of Reddit data such as comments using neural networks. Earlier, the Reddit submissions were evaluated on the basis of the title of the submission, but the results showed how the various factors such as the title, submission times and the community choices of image submission determine the effect of the content by checking on the resubmitted images on Reddit. Using the language model that included bad and good words, speech tagging, title length, sentiment analysis; it was concluded that the success of the submission was governed by the title. Except the language model, the content quality, time of submission and the community contributed immensely to the post’s success. Broy [1] there is similar analysis for Websites like Facebook, Twitter, Google+, etc. [4]. Many research has been done on sentiment analysis for blogs and product reviews as well [11]. Researchers have also studied the impact of microblogging on sentiment analysis [8]. Various studies have pointed out on the significance of machine learning in text classification [10]. The research showed how sentiment analysis was performed as a pyramid scheme in which the text was first categorized as containing sentiment, and then as positive and negative. The analysis was performed by machine learning algorithms. Work done on labelling emoticons as positive and sentiment is quite relevant to Twitter as users have emoticons in their tweets. The various levels of natural language processing tasks are document level, sentence level and phrase level. And sentiment analysis is performed on all these levels [9]. This research focuses on the application of SVMs in sentiment analysis having a diverse

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information source [14]. Here, unlike the previous research, the text is firstly classified as polar or neutral and then as positive or negative. While in [6], the algorithm identified with a large number of adjectives which were each assigned a score of polarity. The main reason for such scoring was that the authors believed that the online texts consist of very less neutrality. They focus on positive and negative words by going through the synonyms and antonyms from the WordNet. The recursive search connects words from the groups quickly which made taking the preventive steps important. Like, assigning weights which decrement exponentially as the number of hops increases. They showed that algorithm was accurate when compared with the ones manually picked from the list. Kreger et al. [7] the paper focused on the data that opinion mining uses, ML and sentiment analysis tasks for text classification. The research concluded that to perform classification fixed syntactic patterns that are used to express opinions are used.

3 Research Methodology On Reddit, only the final scores resulting from the difference between upvotes and downvotes are displayed, and the total number of votes a post has received is hidden. Thus, posts that have been heavily downvoted may still have positive overall scores. In order to identify these posts, Reddit provides a ‘controversial post’ flag. Posts which, in turn, have received significant negative feedback and have negative overall scores can become hidden in the discussion once their score falls below a certain threshold. The person posting categorizes all posts on Reddit into whichever topic they think the post belongs to also known as a subreddit [2]. The subreddits are accessible to every user to share his opinion through comments or posts. Also, users can comment on someone else’s comment on this platform, thus creating a nested comment structure while a discussion is going on. We are involved in examining the polarity of opinion in responses to news in of any article. We need comments to train our systems which contain an point of view about the article. For this purpose, we review the gathered data manually to see if there are views on the topic itself in the nested discussions. We notice that these nested comments in certain cases deviate from the topic being discussed in the post. Most of the times they contain personal attacks or sarcastic comments. Firstly, a large amount of data is collected from Reddit and is analysed. Crawler collects data from popular sections on Reddit where the user is more active, and then, it crawls through comments, likes and number of votes [5]. Data is divided on the basis of date of posting, popularity, highest scoring in a time period. Different users can post topics in different subreddit so we need to list all subreddits relevant to the topic. Subreddits’ Metadata contains all details except comments on the topic. We have taken top 250 posts which constitute around 1% of data available on Reddit. After Metadata of the list, the comments, submission and the difference of up and down scores are collected. As a crawler, we have used PRAW library which gives way

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to share objects of Python across the network. All crawlers were executed on virtual machines, and all of them have access to shared storage. Data of distinct pages is stored. One of the difficulties faced in the project is to fight back against the blocking system of Reddit. Since many crawlers want to collect data from the Reddit, due to this, huge number of requests is received by Reddit system, and the system cannot handle a large number of bot requests at time that is why it starts blocking the crawler. If the rate of request of crawlers becomes greater than threshold, it will be blocked for some time by anti-crawling techniques. The IP address of crawler will be black listed. This is a very common technique used by this type of Web platform. For this, the crawling speed decreases, and the crawler sends only one request in one minute [12]. Reddit normally anticipates creeping in various ways. Like a non-confirmed session cannot peruse the subreddit requested by date. A confirmed client can peruse a subreddit by date, yet the separation you can peruse back is topped. In this manner, it is innately unrealistic to get an entire creep of the site. The other problem is that the crawlers make many attempts to crawl badly or down URLs of Reddit because crawlers were unable to find difference between down and bad URLs. It was also noted that sometimes systems get restarted because of load and controlling reliability of shared resources. Comments in Reddit have a tree-like structure, and it can be seen like a directed acyclic graph or more particularly, a tree with the root that accommodates itself. While each Reddit’s remark tree is distinct, numerous properties appear to recur. For instance, the higher score a comment has, the more likely it is to have many replies. Many variables can be examined including the tree height, the number of leaves, normal traverse and many more. These parameters are then compared with other socio-political topics. Using them, we can plot graphs, classify data and clustering for further calculation. The normal comment on Reddit had less than 10 replies, with 95% of all entries having fewer than 100. It has seen more than 5000 comments on a post. Different properties including greatest width and height were found to have comparative appropriations to the general tree size. These qualities show direct association with the dissemination of the tree. All subreddits relevant to the topic need to be listed. Due to the strong impact on the sentiment polarity due to low comment frequency, we anticipate our sentiment prediction method to get even less output on the subreddit forums with a significant amount of single comment posts. In a posting for our data, we set a requirement of two or more to the minimum number of comments; we discard this data because we require a broad range of samples to address a variety of topics. Now, the model has not reported a worse classifier performance with the same problem. Therefore, as the sentiment is predicted for different users with such categorized subreddit choices, we expect that to have less variability in problems. We have also done Subreddit Visualization using R. First we extracted the Reddit dataset using Google BigQuery and the user information to create network graphs. The graph includes weight, source and destination and calculates the weight by counting the user participants in the two subreddits above 5, by using the comment section (Fig. 2).

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Fig. 2 Methodology of our experiment

4 Result and Discussion By conducting this research, we first analysed the edge connection between various subreddits. This helped us analyse the interconnection between them and can visualize which subreddit can be recommended to a user.

In the above graphs, we can see that if a user regularly follows r/worldnews, then he would most likely also like r/politics and r/television, and hence, we can recommend these subreddits to him. Also, further in our study, we extracted top 1000 posts in r/India subreddit in the year of 2020 and their respective 100 comments. Then, we applied sentiment analysis using NLTK library to these comments to figure out:

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2. Sentiment Analysis on a Specific Topic

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These graphs represent the sentiments of users towards the aforementioned topics. Based on user interaction with certain posts, we can recommend new subreddits. We can use this data to better understand our user base, and companies can use this data to identify their potential customers and reach out to them with their products and services.

5 Conclusion This project provides us with a dataset on r/India and an overview of the comment structure of Reddit. We have identified complex correlations and Web platform properties among subreddits. This task has shown that using NLTK for sentiment analysis on Reddit post and comments is appropriate and works fine. This project can provide us with insights into the current trends on a specific subject, so that people’s feelings can be realized. The use of such methods may be used for marketing, evaluating guests and making operational improvements or capital expenditure. Discussions in online forums are very rich and complex, both in terms of the content and dynamics of conversations and the characteristics of the underlying platform. Our proposed

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archetypes link these important elements and give us insights into the relationship between feelings, topics and user actions.

6 Future Scope Due to the use of APIs (PRAW), this project is limited to Reddit, but we will use the same algorithm and tools to evaluate other Websites like 4Chan, Facebook or Twitter. Due to the reliability issues of the algorithm, much less data was used for analysis. By improving algorithms, we can use this method to analyse large datasets for more accurate results. By using knowledge gained through the work on this paper, we suggest a simple concept for a predicting public sentiment in response to any news. Such a model will be focused on predicting the accuracy of user responses to various subjects in news items. Other characteristics based on the text of the news report, such as the number of responses to the report, may be predicted. We present the research effort to evaluate the influence of additional predictions used as features for sentiment prediction.

References 1. Broy, M.: Software engineering from auxiliary to key technology. In: Software Pioneers, pp. 10–13. Springer (2002) 2. Choi, D., Han, J., Chung, T., Ahn, Y.Y., Chun, B.G., Kwon, T.T.: Characterizing conversation patterns in Reddit: from the perspectives of content properties and user participation behaviors. In: Proceedings of the 2015 ACM on Conference on Online Social Networks, pp. 233–243 (2015) 3. Curiskis, S.A., Drake, B., Osborn, T.R., Kennedy, P.J.: An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit. Inf. Process. Manage. 57(2), 102,034 (2020) 4. Dod, J.: Effective substances. In: The Dictionary of Substances and Their Effects. Royal Society of Chemistry. Available via DIALOG. http://www.rsc.org/dose/titleofsubordinatedocument. Cited 15 (1999) 5. Glenski, M., Pennycuff, C., Weninger, T.: Consumers and curators: browsing and voting patterns on Reddit. IEEE Trans. Comput. Soc. Syst. 4(4), 196–206 (2017) 6. Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale sentiment analysis for news and blogs. ICWSM 7(21), 219–222 (2007) 7. Kreger, M., Brindis, C.D., Manuel, D.M., Sassoubre, L.: Lessons learned in systems change initiatives: benchmarks and indicators. Am. J. Commun. Psychol. 39(3–4), 301–320 (2007) 8. Manning, C.D., Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press (1999) 9. Mullen, T., Collier, N.: Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 412–418 (2004) 10. Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)

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11. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Tr. Inf. Retrieval 2(1–2), 1–135 (2008) 12. Stoddard, G.: Popularity and quality in social news aggregators: a study of Reddit and hacker news. In: Proceedings of the 24th International Conference on World Wide Web, pp. 815–818 (2015) 13. Weninger, T.: An exploration of submissions and discussions in social news: mining collective intelligence of Reddit. Soc. Netw. Anal. Min. 4(1), 173 (2014) 14. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)

Analysis of DNA Sequence Pattern Matching: A Brief Survey M. Ravikumar and M. C. Prashanth

1 Introduction DNA could be a super particle, which contains hereditary headings in working of all the amazing living life forms. The main role of deoxyribonucleic acid molecules is that the long storage of data. DNA is regularly contrasted with a lot of diagrams, since it contains the directions (proteins) expected to build different parts of cells, for example, proteins and RNA atoms. The deoxyribonucleic acid segments that carry this genetic info square measure referred to as genes. DNA includes two long polymers of fundamental units called nucleotides, ester securities with a blend of sugars and phosphate. The structure of deoxyribonucleic acid was first discovered by James D. Watson and Francis Crick. DNA is dealt with into long structures called chromosomes. These chromosomes square measure duplicated before cells divide, in a process called DNA replication. It comprises of four bases Adenine (A), Guanine (G), Cytosine (C), Thymine (T) all these make a DNA code. The cell of DNA contains hereditary data. This information is shared through chromosomes. There are 23 pair’s chromosomes. The nucleotides bond, A to T and C to G, between the two strands of the helix just like the rungs of a ladder or, better, the steps in a very spiral stairway. A pair of complementary nucleotides (or bases) A-T, G-C, T-A, or C-G is called a base pair (bp). DNA replication, that takes place in association with organic process, involves the separation of the 2 strands of the spiral and also the synthesis of a replacement strand of nucleotides complementary to each strand. DNA is the most accurate and exact way of identifying an individual because every human being has its unique individual mapping in every cell of a human body. M. Ravikumar · M. C. Prashanth (B) Department of Computer Science, Kuvempu University, Shimoga, Karnataka, India e-mail: [email protected] M. Ravikumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_25

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A person polymer contains information regarding their heritage and might generally reveal whether or not they square measure in danger sure diseases. Human being has around three billion DNA bases and more than of a present basis are the same in all people according to United States National Library of medicine. DNA sequencing in an exceedingly approach that permits researchers to work out the bases of order in a deoxyribonucleic acid nucleotides. DNA analysis has crystal rectifier to some attention grabbing and necessary findings within the last few decades. Recently, it has published in the study of journal science found that mistakes are random in DNA, but not in heredity or also in environmental factors of two third of career mutation in cells.

2 Literature Review In this review paper [1], which focus more on networks of Bayesian to deal the complication level of the real data level in DNA setting involved in evidence. Thus, networks considered as more efficient tool in the domain of Forensic Science. DNA profiling evidence is the main categories of evidence whose assessment been studied through Bayesian networks. The scope of this paper also includes Forensic DNA profiling such as DNA stain and stains with low quantity of DNA. An efficient pattern matching algorithm, derivative BoyerMoore (d-BM) for compressed DNA sequence is proposed [2]. Using this algorithm, both DNA sequences and also DNA pattern are compressed. Experimentation is carried out on both synthetic and also real data, which shows that as the pattern length increases, time taken to process the data decreases. It means that for short pattern, the algorithm is less efficient when compared with long patterns. Classification of DNA pattern is performed using a shape descriptor analysis is proposed [3]. For the purpose of classification KNN, back propagation and Naïve Bayes classifiers are used. Experimentation is carried out on the data sequence complete DNA genome of the E-coli bacteria. KNN classifier gives an average of 41.26% classification rate. Friedman statistical algorithm used for non-parametric statistical test, which works on a ranking-based classification of each dataset in the experiment. DNA recognition pattern by correlation canonical algorithm (CCA) has proposed [4], which used to find the required DNA genetic sequence code. The algorithm finds between correlation observations of two genomic dataset of hemoglobin coding sequences (HBB), which contains alpha and beta hemoglobin chains on the basis of the data simulated. To pattern recognition, CCA uses two cases, presumed model investigation for sequence of HBB for test case and other one for integration site for searching (training set). Unsupervised numerical tool CCA finds the function of correlated over sets of different variables and used in a DNA sequence. Finite automata (FA) model is used to analyze the DNA pattern to convert the patterns to the state of non-deterministic finite automata (NFA) and then to the state of deterministic finite automata (DFA) [5]. This purposed model will detect the

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change, alteration, or duplication in the obtained genetic information, which has the advantage of irrelevant mutation, sequencing errors and incomplete specifications, can be identified by using FA. The pattern analyzed by creating a transition table for conversion of NFA into DFA. Time complexity is reducing, and to analyze the DNA pattern efficiently, NFA converted into DFA. A sequence pattern matching fuzzy algorithm proposed for a data of sequence, where a similar match obtains by using the fuzzy function [6]. The algorithm proposed finds the most “similar” match by preprocessing, fuzzification and inference of sequences. Approximate match achieved from the reference pattern and used to identify the patterns with zinc finger domain proteins. The non-occurrences are not allowed by the proposed algorithm. The algorithm, faster string matching based on hashing, and bit parallelism proposed [7] to know the occurrences of the problem of length in a text of length. The proposed algorithm has phase of preprocessing and searching. Preprocessing is to pseudocode length pattern is larger than the bit cover. The searching phase combine each attempts of the 2q-grams ORing the bit vectors. The fast variation of improved hashq is by greedy strip and hashing. By the experiment results, as Fhash is faster than the other algorithms of size 4 DNA alphabets. A pattern matching context-sensitive algorithm proposed to detect the RNA forms in secondary structures [8]. User-interface of Java used for the RNA_spec language for RNA sequence to provide the spaces scan and to compare with a context free conventional grammar. The use of context-sensitive searching of a pseudoknot particular is an advantage to represent the RNA natural structures. This article [9] proposes a method for automated similarity evaluation for the given patterns of denaturing gradient gel electrophoresis (DGGE) image. The vertical data stripes in the each targeted image have many vertical stripes and have its own similarity information which is evaluated on the basis of stripe by stripe. When analyzing cross-correlation analysis with DP (depository participants described as an agent (law) of the depository) matching analysis, it shows the way of matching in DP performed better than the analysis by cross-correlation using FRR and FRA. In this paper, distinct natural numbers searching approach to find the occurrences in DNA given string over a transition matrix is proposed [10]. The algorithm has only O (n − 1) time complexity as the worst case for a length in given string for the length of DNA pattern to search. The numerical transition table checks frequency in the scheme of search and then turns the exact matching to the number of comparisons. A Zhu-takaoka Bayer-Moore-Horpool (ZTBMH) algorithm for fast pattern matching in biological sequences proposed [11], which gives a variation of Zhutakaoka (ZT) algorithm. It absorbs the idea of BMH algorithm, utilizes only heuristic bad character and reduces comparisons number. Nucleotide in genome and amino acid sequences data set compared with the algorithm and obtained fast matching result of 10.3 s. For small alphabet such as nucleotide sequences, the algorithm performance is faster. The pattern matching BerryRavindran fast search (BRFS) algorithm is proposed [12], which is a fast and exact matching algorithm on the basis of fast search (FS) algorithm, and it absorbs working of BerryRavindran (BR) algorithm. From BR

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algorithm, heuristic bad character exploits to get the maximal shift and reduces the number of comparison character. Shifted values used to calculate to store in a one dimensional array. Experimentation conducted on both BRFS and BR algorithms in which BRFS gives the best result than BR for short patterns. A character key location Pattern Matching with Wildcards-based algorithm proposed [13], where a valuable information from DNA sequences explored. The matching done with the constraints length and wildcards based on the key character location and subspace division to reduce the search time and the range of searching while the characters of patterns distributed differently. The searching efficiency increased by 40–60% of algorithm with SAIL comparison. An efficient pattern matching algorithm is presented [14] based on pattern of string preprocessing by considering the text of consecutive three characters. The pattern aligned follows the mismatch with the text character by sliding the both patterns in parallel until founding the first pattern. It gives the best performance by both side searching when compared with other algorithms. Composite BoyerMoore (BM) algorithm proposed for string matching [15], which uses the historical matching information and accelerates the speed of the pattern while matching effectively. Binary matching, the test made between BM and CBM algorithm, where the proposed CBM algorithm gives the efficiency of 84% when compared with BM algorithm. Comparison algorithm, using logical match technique proposed [16], which used to generate the number of matches and mismatches using fuzzy membership values. The logical match performed in Linux platform using CPP language for DNA sequences. The proposed algorithm generates the membership values to find the similarities between sequences with time complexity O(m + n). The unique method analyzed for DNA sequences from NCBI databank, and it is on both artificial and real datum (piece of information) for the computational time that depends on the length of the sequences. DNA sequences analysis using distributed computing system is developed [17] to detect disease in forensic criminal analysis and for protein analysis. The subsequence distributed identification algorithm used to detect the repeated patterns in computer investigation of DNA analysis using internet. The algorithm requires two patterns for searching and comparison with the broken DNA sequence. The broken DNA sequence gets the one segment of data by server (java-based sever with GUI), which is required to search and to identify the pattern of trinucleotide pattern. By the analysis, non-consecutive pattern identification is having the more complexity with non-consecutive searching algorithm. For cluster pattern matching in gene expression data, a hierarchical approach is proposed [18], which analyze the cluster of gene expression data without making a distinction among DNA sequences called “genes.” The proposed method is gradient simple base technique to remove the noisy gene from the bottom-up and clustering density-based approaches, by identifying the density estimation of the sub-clusters with large clusters proposed algorithm gives the best measure of z-score. To detect the codon by the Hash function from the large RNA sequences met and stop techniques are proposed [19], it search faster on any length of codons and find

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the gene sequence even when increasing the length of its string and the proposed techniques performs in less time. String matching in DNA performed using index-based shifting approach is proposed [20], which has preprocessing and searching phase. The number of character comparison are made in preprocessing phase, and in searching phase, as the first character of substring and pattern will be same, so the pattern second character matched with first substring second character from left to right. For the sequence of protein, the algorithms work faster than the English text with increasing the repetitions. For evaluating the DNA mutation pattern sequences are matched using distancebased approach is proposed [21]. To perform this, nucleotide pattern arrangement in DNA hamming technique modified to find the primary Hepatitis C Virus (HCV) pattern from experts. Experimentation compared with positively affected different age groups isolated 100 sample data. Experiment results the matching score with hamming value after including the normalization value. Compressed genomic pattern matching algorithm proposed [22], focuses on the textual data for compressing the complexities analyzing the experimentation, the sequences of DNA will reduced. It indicates that, the data compressed can easily analyzed with the benefits for the community of biological. To approximate circular pattern matching, the simple, fast, filter-based algorithm is purposed [23], which finds all the occurrences of rotations in a pattern of length in the test of length. The proposed algorithm practice will be much faster because of the reduction in huge in the search space by filtering. The comparisons of the proposed algorithm with ACSSMF- simple algorithm, runs twice faster than state of art. For approximation, the pattern maximal matching with constraints one-off and gaps a Nettree approach is proposed [24]. To perform this, along with Nettree, a heuristic search of an offline occurrence algorithm is used. Experimentation is conducted on a real-world biological data; the comparison of the performance of the proposed algorithm is made with SAIL algorithm. To evaluate the associated TF and TFBS patterns of DNA and to form the effective pipeline link up the associated unified, score patterns by summing, and normalization method is proposed [25]. The accurate rankings provide the method to identify the rules and binding cores with the excellent correlation sum scores. Protein data bank structures verifications matching ratio serves the highest correspond percentage. Two single pattern matching algorithms (called ILDM1 and ILDM2) are proposed [26], when each of which is composed by smallest suffix automation and forward finite automation. The proposed single pattern matching algorithms usually scan the text with the help of a window, whose size is equal to m. In LDM algorithm, when the window is shifted from previous window to the current window, useful information is produced through the forward automation is discarded. In order to use fully or conditionally, the useful information is produced by two single pattern matching algorithms. From the experimentation, it can be seen that average time complexities of two algorithms are less than that of RF and LDM for short patterns and that of BM for long patterns.

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An algorithm is proposed [27] for probe selection from large genomes, which is fast and accurate. Selection of good probes is based on the criteria of homogeneity and specify to find the probes, a set of experimental results based on a few genomes that have been widely used for testing purpose by the other probe design algorithms. Based on the proposed algorithm, optimal short (20 base) or long(50-70 bases) probes can be computed for large genomes. An online type of algorithm is proposed [28] for finding significant matches of position weight matrices in linear time. The proposed algorithm is based on classical multi-pattern matching filtrations and super alphabet approaches developed for exact and approximate key word matching. Some well-known base time algorithms (Naïve and PLS algorithms) as well six proposed algorithms, ACE, LF, ACLF, NS, MLF, and MALF are implemented to carry out the experimentation. Among all the above algorithms, ACE algorithm is theoretically optimal (search speed does not depend on the matrix length and which is competitive for short matrices). A low communication-overhead parallel algorithm for pattern identification in biological sequences is proposed [29], because it is essential in achieving the reasonable speedups of clusters in the interprocessor communication of latency is usually higher. Identifying genes by comparing their protein sequences to those already identified in databases providing a scalable parallel approximate pattern matching with predictable communication efficiency is of higher practical relevance. Using multiple Hash functions, a fast searching algorithm is proposed for biological sequences [30], which improves the performance of existing string matching algorithms when used for searching DNA sequences. The algorithm has two different stages like preprocessing and the searching phase, preprocessing phase consists in computing different shift values for all possible strings of length where a searching phase of the algorithm is based on a standard sliding window mechanism, where it reads the rightmost substrings of length q of the current window of the text to calculate the optimal shift advancement. The proposed algorithm serves a good basis for massive multiple long pattern search. A pattern matching algorithm is proposed for splicing (donor groups) junction to recognize the donor in genomic DNA sequences [31]. The pattern information is generated by using the motif models; it will be done by creating of 9-base sequence DNA data of two groups. It consists of training 250 positive donor group and a negative training group of 800 GT containing false donors. By analyzing with the motif score for both the groups, the score of minimum in the positive scoreboard is called lower positive bound (Lp), and the maximum negative scoreboard is called upper negative bound (Un). Based on the positive and negative donor groups found in motif algorithm, the donor detection algorithm of pattern matching works effectively and efficiently. The approach for visualizing to motify discovery in DNA sequences is proposed [32], which consists nucleotide sequence of strings represent the molecules of RNA and DNA. The DNA and RNA molecules stored in the databases are taken each preprocess of string by converting for numeric equivalent sequence and plotted in 3D space for multiple sequence alignment. This multiple sequence alignment aligns DNA sequences vertically to the similar regions and for the similar columns. The 3D

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plotted graph identifies the patterns by rectangles, and further, the mapping reverse is used to generate the symbolic DNA sequences. They will be stored in a matrix called matrix alignment, from which the profile matrix nucleotide computed to get the patterns or motif. The proposed algorithm gives the best performance to visualize the motif discovering in DNA sequences. Recognition of DNA sequences, using the acceptor sites algorithm is proposed [33], as the acceptor site is an important component present in the recognition of gene. The motif model is generated for site of donor to represent the features of degeneracy on the site of acceptor. The True and False scores of the acceptor site motify model exhibits the score of minimum in the score true board as true bound lower (Li) and false upper bound which is score of maximum in the board false score (Uf). Using this, the algorithm discovers greater degree match of the pattern with 88.5% acceptor true sites and 91.5% false sites of acceptor. Coefficient correlation of 0.8004 is obtained, which is the best in the gene structure detection. An algorithm to find DNA patterns, an improved pattern matching, is proposed [34], and the algorithm is of predictable theoretical which is to find a text window moving direction search. The 6 run-pairs were used by the proposed algorithm, where the right and left arrows indicates the text window direction. When algorithm is compared with the run-pair characters, the opposite direction gave the best performance result. DNA computing uses the proposed pattern matching algorithm to solve the engineering problems [35]. The parallel operation method is developed to recognize the DNA molecules; the model finds the position of a molecule as the one exist search image by having the classified features of molecule, then the features are applied to form a network by PCR and gel electrophoresis. The searching time of the image or pattern is persistent, because sorting and computation both simultaneously performed in each test. From the literature survey, we brief some of the challenges involved in DNA sequences pattern searching, pattern matching, and also compression of DNA sequences. Some of the challenging issues are summarized as follows. 1. To improve the efficiency of faster string searching algorithms for DNA sequences. 2. Analyzing the compressed genomic data in DNA sequence. 3. Approximation of maximal, circular pattern matching in DNA sequences. 4. Building efficient algorithm for DNA profile for recognition. 5. Finding the DNA probes in forensic applications. 6. Searching the compressed DNA sequence efficiently.

3 Conclusion In this review paper, the different algorithms have been discussed on DNA sequence pattern searching, matching and also compression, these matching algorithms may be used in some cases which have been diagnosed from the DNA sequences like the

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virus diseases HIV, malaria, dengue, H1N1, and also corona. This survey paper will help a lot for the new researchers those who are interested to take up their research problem in the domain of DNA pattern analysis.

References 1. Biedermann, A., Taroni, F.: Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature. Forensic Sci. Int. Genet. 6(2012), 147–157 (2012) 2. Chen, L., Lu, S., Ram, J.: Compressed pattern matching in DNA sequences. In: Proceedings of the 2004 IEEE 5 Computational Systems Bioinformatics Conference (CSB 2004) 3. Loya Larios, H.E., Montero, R.S., Hernández, D.A.G., Espinoza, L.E.M.: Shape descriptor analysis for DNA classification using digital image processing. In: (IJCSIS) International Journal of Computer Science and Information Security, vol. 15, no. 2, pp. 67–71 (February 2017) 4. Sarkar, B.K., Chakraborty, C.: DNA pattern recognition using canonical correlation algorithm. J. Biosci. 40(4), 709–719 (2015) 5. Qura-Tul-Ein, Saeed, Y., Naseem, S., Ahmad, F., Alyas, T., Tabassum, N.: DNA pattern analysis using finite automata. Int. Res. J. Comput. Sci. IJRCS 1–5 6. Chang, B.C.H., Halgamuge, S.K.: Fuzzy sequence pattern matching in zinc finger domain proteins. In: Proceedings Joint 9th IFSA World Congress and 20th Nafips International Conference (Cat. No. 01th8569). IEEE, pp. 1116–1120 (2001) 7. Al-Salami, A.M., Hassanmathkour: Faster string matching based on hashing and bitparallelism. Inform. Proces. Lett. 123, 51–55 (2017) 8. Sung, K.-Y.: Recognition and modeling of RNA pseudoknots using context-sensitive pattern matching. In: 2006 International Conference on Hybrid Information Technology (ICHIT’06). IEEE 9. Sugiyama, Y., Saito, H., Takei, H.: A Similarity Analysis of Dgge Images Using DP Matching. IEEE 10. Le, V.Q.: A natural number based linear time filtering approach to finding all occurrences of a DNA pattern. In: 2006 Fourth International Conference on Intelligent Sensing and Information Processing, IEEE 11. Huang, Y., Cai, G.: A fast pattern matching algorithm for biological sequences, 2008. In: 2nd International Conference on Bioinformatics and Biomedical Engineering 12. Huang, Y., Ping, L., Pan, X., Cai, G.: A fast exact pattern matching algorithm for biological sequences. In: 2008 International Conference on Biomedical Engineering and Informatics 13. Liu, Y., Wu, X., Hua, X., Gaoa, J., Wua, G.: Pattern matching with wildcards based on key character location. IEEE Iri 2009, July 10–12 2009, Las Vegas, Nevada, USA 14. Radhakrishna, V., Phaneendra, B., Sangeeth Kumar, V.: A two way pattern matching algorithm using sliding patterns. 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), pp. 666–670 15. Xiong, Z.: A composite Boyer-Moore algorithm for the string matching problem. The 11th international conference on parallel and distributed computing. In: Applications and Technologies, pp. 492–496 16. Sanil Shanker, K. P., Austin, J., Sherly, E.: An algorithm for alignment-free sequence comparison using logical match. In: 2010 The 2nd international conference on computer and automation engineering (ICCAE), vol. 3, pp. 536–538 17. Kumar, R., Kumar, A., Agarwal, S.: A distributed bioinformatics computing system for analysis of DNA sequences. In: Proceedings 2007 IEEE Southeast on, pp. 358–363 18. Hoque, S., Istyaq, S., Riaz, M.M.: A hierarchical approach for clustering and pattern matching of gene expression data. In: 2012 Sixth International Conference on Genetic and Evolutionary Computing, pp 413–416

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Sensor-Based Analysis of Gait and Assessment of Impacts of Backpack Load on Walking Chaitanya Nutakki, S. Varsha Nair, Nima A. Sujitha, Bhavita Kolagani, Indulekha P. Kailasan, Anil Gopika, and Shyam Diwakar

1 Introduction Gait analysis includes the systematic study of locomotion and the coordination between nervous and musculoskeletal systems [1]. The emergence of wearable (accelerometer, gyroscope) [2, 3] and non-wearable sensors used in gait analysis [4] helps to identify different factors that are influencing gait through biomechanics and kinematics [5]. The applications of low-cost wearable sensors used in gait analysis is not only limited to analyse the spatio-temporal parameters and gait events, but also helps to analyse the variability between normal and pathological gait [6]. Physiological factors that influence the gait variability are muscle activity, functional control between muscle and nervous systems [7]. Gait variability was studied using Froude number to understand the trends in gait patterns [8]. Mathematically, a walking limb is commonly modelled as an inverted pendulum, where the centre of mass goes through a circular arc cantered at the foot [8]. Also, quantifiable genderbased gait variability may have clinical and biomechanical importance [9], that can be analysed by using Froude numbers as a combination of velocity and acceleration. Speed of walking and posture balancing was analysed using shank and lower back mounted IMU sensors with different machine learning algorithms to understand the gait alterations in daily life [10]. To classify healthy and pathological gait, the spatio-temporal parameters were extracted from lower back using accelerometers from healthy and peripheral neuropathy subjects [11]. Biomechanical changes due to carrying a backpack may cause alterations in gait kinematic patterns and ground reaction forces which leads to postural changes and musculoskeletal injury [12]. The kinetic and kinematic changes of gait associated with usage of backpack C. Nutakki · S. V. Nair · N. A. Sujitha · B. Kolagani · I. P. Kailasan · A. Gopika · S. Diwakar (B) Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri campus, Kollam, Kerala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_26

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[13] remained the same during swing and stance, however, significant changes were also noticed during swing while carrying backpack [14]. This remains unclear to understand the changes due to carrying backpacks in young adults. In our previous studies [2, 3], gait kinematic data extracted using accelerometer sensors attached to different joints of the body were classified and analysed using machine learning approaches and inverse dynamic analysis. In this paper, we used mobile phone accelerometers to study the effects of backpack with different loads on gait and assessments to understand the lower trunk kinematic changes in young adults. Also, the gait similarities and gender variability between subject groups were identified using the Froude number of similar anthropometric characters.

2 Methods Gait kinematic analysis was performed using 11 triaxial mobile phone accelerometers, attached to the pelvis, left and right (knee, ankle, shoulder, elbow, wrist). A total of nine subjects with average age of 19–21 (male and female) were selected with no gait pathological disorders. A total of 3 trials with 2 gait cycles per trail was considered. Before the data analysis, kinematic data was low pass filtered using a Butterworth filter (cut-off frequency of 6 Hz) and data analysis was done using MATLAB (Mathworks, USA). Gait kinetic and postural differences have been analysed using Froude number (Eq. 1) as a combination of velocity and gravitational acceleration. After the collection of data, subjects were divided into three groups according to subject’s body weight. Subjects weighing between 40 and 50 kg considered as group 1, 50 and 60 kg considered as group 2 and individuals weighing between 60 and 70 kg considered as group 3. Froude number (Fr) (Eq. 1) was calculated from the given accelerations extracted from accelerometers and gravitational (Eq. 2) constant during walking. Fr = v 2 /gL , where v = velocity

(1)

g = gravitational acceleration

(2)

L = length of the joints

(3)

V =a∗t

(4)

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2.1 Effect of Backpack on Lower Body In this study, mobile phone accelerometers were attached to the right and left knee, pelvis (lumbar 5) region, right and left ankle to extract gait spatio-temporal parameters like walking distance, speed and pelvic movements. 6 healthy subjects who signed the consent forms before participating in the experiment were recruited. Each subject was asked to stand up straight, keeping left leg forward and right leg backward and walk along a straight line at their natural speed for 5 m. The backpack was positioned at the lumbar level 5. Each subject’s gait patterns, and pelvic tilt was evaluated with and without weights. After the collection of data, all the subjects were divided into two groups based on their body weight to check the effect of backpack on lower back with respect to weight of a person. Individuals weighing between 40 and 60 kg considered as group 1 and individuals weighing between 60 and 80 kg considered as group 2. Here, in the model, mean activity of each joint rotation Jθ (Eq. 5) during normal and controlled conditions were analysed. Also, the accelerations at each joint − → was also considered across the time S (Eq. 7). Jθ =

n 

θ

(5)

i=1

JDisplacement =

n 

S

(6)

i=1

− → S = [Accx, Accy, Accz]

(7)

3 Results 3.1 Measured Gait Variability Across Different Subject Groups Using Froude Analysis Gait kinematic data was collected and the mean Froude number (Fr) of each joint during stance and swing was measured. Fr amplitude of right shoulder in group 1&3 and left elbow in group 1&2 and group 2&3 were shown to be statistically significant (Fig. 1a). While in the lower joints, the Froude numbers of pelvis belonging to group 1&2 were significantly different. Also, the difference in Froude numbers of left ankle of group 1&2 and group 2&3 were significant (Fig. 1b). However, the variation of Froude numbers within the groups were not significant suggesting they belong to same group. Based on these observations, the significant differences among the groups could be employed to discriminate the gait patterns of different individuals who share the same anthropometric characteristics.

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Fig. 1 a Graphical comparisons of average Froude amplitudes for upper body joints (R shoulder—Right shoulder, R elbow—Right elbow, R wrist—Right wrist, L wrist—Left wrist, L elbow—Left elbow, L shoulder—Left shoulder b Average Froude amplitude for lower body joints among different subject groups (R Knee-Right Knee, R AnkleRight Ankle, Pelvis, L Ankle- Left ankle, L Knee-left knee)

3.2 Attribute Selection to Classify Gait We commenced analysis with the full attribute set and filtered the best attributes by selectively using WEKA attribute selector. The dataset contained the average Froude number of right and left (shoulder, wrist, knee, ankle) and the pelvis during static and dynamic walking. The WrapperSubsetEval with BestFirst search method was used to evaluate the attributes using learning classifiers (Naïve Bayes, SMO, J48). Off nine attributes, it was shown that the data belonging to pelvis, knee, shoulder and wrist was ranked as high. Correlation attribute eval and gain ratio attribute eval also showed that the pelvis, knee, shoulder and ankle was crucial for classification of static and dynamic gait movements.

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Fig. 2 Male and female gait spatio-temporal parameters associated with Froude numbers of shoulder, pelvis, knee and ankle during stance and swing

3.3 Gender-Based Classification with Respect to Fr Number. Gait spatio-temporal parameters between male and female subjects associated with Froude numbers were analysed. Joint motions in the pelvis, knee, shoulder and ankle from the sagittal plane during stance and swing phases were compared among the two groups. Males and females had unique gait patterns, although walking speed, cadence and step length remained the same. Average Fr number of males were higher compared to females in the shoulder and knee, whereas female gait data showed more activity in hip and ankle throughout the gait cycle (Fig. 2).

3.4 Effect of Backpack Load on Pelvis during Walking to Understand the Gait Pathological Condition Lower body joint kinematic changes in young adults while carrying backpack with different loads were assessed. The recruited subjects were categorised into two groups based on the subject’s weight. Subjects weighing between 40 and 60 kg considered as group 1 and subjects weighing between 60 and 80 kg considered as group 2. When compared with and without backpack load, for all the subjects, a significant increase in the pelvis rotation was noticed by adding 10% of body weight as a backpack load (Fig. 3). Error bars represent the standard deviation.

4 Discussion Froude analysis was effectively employed to identify dynamic gait similarities between subjects having similar anthropometric characteristics. The present study

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Fig. 3 Change in activity of pelvis in young adults during normal and loaded conditions

used the Froude number to identify kinematic differences between the subjects during walking. Though there are difference in gait speed and cadence, similar Froude number values were found for all the subjects in the group. The significant differences in average Fr amplitude of shoulder, wrist, elbow, knee and pelvis among the subject groups could help differentiate the subjects with similar anthropometric characters. This shows the gait reliability hinge on the skeletal maturity. Also, gait spatiotemporal parameters and variability in a population of typically males and females were analysed using Froude analysis. Joint angular motion for female subjects in the sagittal plane showed more activity in the ankle and pelvis, whereas male subjects showed more activity in knee and shoulder. Moreover, female subjects showed less knee extension during swing. Gender-related differences in identifying gait-related risk factors helps in understanding potential neuro-pathological conditions and allow mapping appropriate intervention design. Also, by using attribute selector filters, it was observed that pelvis, knee, shoulder elbow and wrist ranked high and can be used to abstract gait characteristics. The extracted patterns can be used in machine learning to develop low-cost models that can help clinicians to analyse pathological conditions. This might be a possible way for the proper detection of gait-related disorders. The purpose of the backpack-load study was to examine changes in pelvic movement and gait alterations while carrying different weights during walking. Carrying heavy bags is becoming a cause for musculoskeletal disorders, also affecting the kinematic behaviour of neck, shoulder, and lower trunk. Pelvis being the region that connects the lower and upper trunks is considered to balance and stabilise the body centre of mass during walking, sitting and balancing. Also, forward inclination of the pelvis and trunk while carrying backpack loads may be associated with abnormal muscle contractions that may lead to strain in the lower back and possibly, increased risk of lower back injury. It may be suggested that the backpack weight can be limited to a range predetermined from our methods relating trunk kinematic change. This study can further help in lowering risks in studying pre-clinic scenarios such as in

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telemedicine, although more simulations need to be performed to reconstruct gait from this data.

5 Conclusion With many other existing techniques to quantify gait have becoming expensive and lack translations to pre- or post-clinical scenarios, we see this study could help for low-cost studies. This analysis can be used for continuous monitoring can help reduce and reconstruct scenarios that may help avoid overcrowded hospital environments. Although there are 11 attributes that define gait, in this current study, we employed 5 major attributes to estimate the healthy gait and the study suggests that it is feasible to compute the gait similarities and gender variabilities across subject groups using Froude amplitudes that may help in identifying walking related conditions with attributions to age and weight. The developed models can be fine-tuned to a more economical, simpler and non-invasive sensor-app-based technique to analyse gaitrelated pathological conditions especially for those who live in rural and backward areas. Acknowledgements This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. This study was partially supported by Department of Science and Technology Grant DST/CSRI/2017/31, Government of India and Embracing the World Research-for-a-Cause initiative.

References 1. Whittle, M.: Gait Analysis : An Introduction. Butterworth-Heinemann (2007) 2. Nutakki, C., Narayanan, J., Anchuthengil, A.A., Nair, B., Diwakar, S.: Classifying gait features for stance and swing using machine learning. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 545–548. IEEE (2017) 3. Balachandran, A., Nutakki, C., Bodda, S., Nair, B., Diwakar, S.: Experimental recording and assessing gait phases using mobile phone sensors and EEG. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 1528–1532. IEEE (2018) 4. Leu, A., Ristic-Durrant, D., Graser, A.: A robust markerless vision-based human gait analysis system. In: SACI 2011—6th IEEE International Symposium on Applied Computational Intelligence and Informatics, Proceedings. pp. 415–420 (2011) 5. van Schooten, K.S., Pijnappels, M., van Dieën, J.H., Lord, S.R.: Quality of daily-life gait: Novel outcome for trials that focus on balance, mobility, and falls. Sensors (Switzerland). 19 (2019). https://doi.org/https://doi.org/10.3390/s19204388 6. Weiss, A., Herman, T., Giladi, N., Hausdorff, J.M.: Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One 9 (2014). https://doi. org/https://doi.org/10.1371/journal.pone.0096675 7. Herman, T., Giladi, N., Gurevich, T., Hausdorff, J.M.: Gait instability and fractal dynamics of older adults with a “cautious” gait: why do certain older adults walk fearfully? Gait Posture. 21, 178–185 (2005). https://doi.org/10.1016/j.gaitpost.2004.01.014

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8. Kramer, P.A., Sylvester, A.D.: Humans, geometric similarity and the Froude number: is “reasonably close” really close enough? Biol. Open. 2, 111–120 (2013). https://doi.org/10.1242/ bio.20122691 9. Smith, L.K., Lelas, J.L., Kerrigan, D.C.: Gender differences in pelvic motions and center of mass displacement during walking: stereotypes quantified. J. Women’s Heal. 11, 453–458 (2002). https://doi.org/10.1089/15246090260137626 10. Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors (Basel) 10, 1154–1175 (2010). https://doi.org/10.3390/ s100201154 11. Sejdic, E., Lowry, K.A., Bellanca, J., Redfern, M.S., Brach, J.S.: A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains. IEEE Trans. Neural Syst. Rehabil. Eng. 22, 603–612 (2014). https://doi.org/10.1109/TNSRE.2013.2265887 12. Orr, R.M., Johnston, V., Coyle, J., Pope, R.: Reported load carriage injuries of the Australian army soldier. J. Occup. Rehabil. 25, 316–322 (2015). https://doi.org/10.1007/s10926-0149540-7 13. Hong, Y., Cheung, C.K.: Gait and posture responses to backpack load during level walking in children. Gait Posture. 17, 28–33 (2003). https://doi.org/10.1016/S0966-6362(02)00050-4 14. Hong, Y., Brueggemann, G.P.: Changes in gait patterns in 10-year-old boys with increasing loads when walking on a treadmill. Gait Posture. 11, 254–259 (2000). https://doi.org/10.1016/ S0966-6362(00)00055-2

Wireless Battery Monitoring System for Electric Vehicle Renuka Modak, Vikramsinh Doke, Sayali Kawrkar, and Nikhil B. Sardar

1 Introduction Today, the electric vehicles are becoming popular as fuel prices are becoming more expensive day by day. Because of this scenario, many vehicle manufacturers are looking for alternatives from energy sources other than gas. The use of electricity sources can improve the environment as there is less pollution. In addition, EVs produce great advantages in terms of energy-saving and environmental protection. Overcharging the battery can not only significantly reduce battery life but can also cause serious safety incidents, such as a fire. Therefore, an electric vehicle battery monitoring system is required, which can communicate battery status to the user to avoid the indicated problem. Thanks to the advancement of the notification system design. Internet of things technology can be used to inform the manufacturer and users regarding battery condition. Cloud is used as an integration platform to acquire process and transmit data, as it is a generally excellent graphical programming condition for creating estimations, tests, and control frameworks. Objectives: 1. To design a prototype model for an electric vehicle. 2. Turn on the cooling circuit if the temperature is exceeded above the limit. R. Modak (B) · V. Doke · S. Kawrkar · N. B. Sardar MIT Academy of Engineering, Alandi(D), Pune, Maharashtra 412105, India e-mail: [email protected] V. Doke e-mail: [email protected] S. Kawrkar e-mail: [email protected] N. B. Sardar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_27

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2 Literature Survey The first research paper we referred to includes charging stations powered by sustainable combines both sustainable and EVs, which could be the key charging infrastructures. As the usage of EVs increases, there will be more recharging problems, and to overcome this situation, the charging station with battery storage system power dispatch for EVs, and this system is supported by the non-cooperative game achieves dynamically adjusting the charging power [1]. This research paper talks about the extreme concept idea in the traditional substation, which is a transfer station. Different locations have gained recognition as well as it has been developed. The idea can be implemented in the charging of electric vehicles, charging capacity improvement, and improves customer service [2]. In this section, they have talked about the advanced quality of the electric vehicle charging system, the business market, and structures a charging system method [3]. In this research, the paper investigates the metering and charging framework that can effectively show the charging cost of the EV on the remote of the charging road [4]. In this paper, the outcomes demonstrated that the proposed keen charging strategy decreased the advantages for owners of electric vehicles. This controller requires fundamental communication with the power organization to get the power value in signal every 60 min [5].

3 Methodology Batteries are utilized in EVs ought not to be overcharged or over-released to overlook injury to the battery, compress the battery life, and create fire or explosion. The BMS, with the end goal of battery demonstrating, battery state progression, battery adjusting, and so on, is one of the keys focuses to make sure about the battery and upgrade the use of the battery in EVs. A car battery plays a vital role in an electric car to keep it going on the road, thus the electric car battery pack needs to be protected from damage because of uneven temperature. Depending on the electrochemical used in the battery, the perfect scale is different, but the ideal temperature of the electric car battery is 45 °C in order to maintain life for the battery. The state of charge (SoC) is the level of charge of an electric battery relative to its capacity. State-of-charge determination: One attribute of the battery monitoring system is to monitor the condition of charge of the battery. The SoC could signal the user and limit the charging and discharging process. There are three methods of determining SOC, 1. Direct measurement: To measure the SoC directly, one could directly use a voltmeter because the battery voltage reduces more or slighter linearly through the discharging cycle of the battery. 2. During coulomb-counting: In the coulomb-counting technique, the current going into or coming out of a battery is combined to construct the respective value of its charge.

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3. Through the cooperation of two techniques: In addition, the two procedures could be combined. The voltmeter is used to detect the battery voltage. Meanwhile, the battery current could be merged to decide the particular charge going into and coming out of the battery. The SOC refers to the remaining capacity as a percentage of the highest available capacity.

4 System Block Diagram The design blocks in Fig. 1 gives an overview of the proposed BMS system, which is the combination of sensor network, Wi-Fi module, thermoelectric plate, and microcontroller.

4.1 Sensors A potential divider module DFR0051 has been chosen as a voltage sensor. The DFR0051 potential divider module made bolstered resistor divider standards. This may sense a voltage attainability up to 25 V. Current sensor (ACS712- 05B) supported Hall Effect guidelines. It delivers 185 mV/A (graciously at +5 V power) yieldingness and can determine current up to ±5A [6]. The inside and the surrounding temperature of the battery perform an important task which is choosing the battery execution since it can modify automatically with the help of temperature. During this exploration, the LM35 temperature sensor, a simple temperature sensor which is used for the remote battery checking framework, is operated on 5 V. The output of this sensor is relative to temperature. The output was estimated with one among the microcontroller inbuilt analog–digital converter, and an alignment formula given by the manufacturer was wont to change over the voltage sign to temperature, with an exactness of ±0.5 °C.

Fig. 1 System block diagram

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4.2 Wi-Fi Module The ESP8266 is easy to use, and it is a coffee cost tool and can supply an Internet connection to the project. The module can work both as an access point (make a hotspot) and as a station (associate with Wi-Fi); henceforth, it can without much of stretch get information and transfer it to the Web making IoT as simple as could reasonably be expected.

5 System Flow Chart The system flowchart in Fig. 2 illustrates the source code flow of the system. Once the system is switched ON, it will start to initialize the code. The sensor network of the system measures the voltage, current, and temperature of the EV battery. And these measured parameters then convey to the microcontroller ATMEGA16. The microcontroller passes it on the physical data to the LCD screen to display the realtime value of voltage, current, and temperature sensors. Then, the microcontroller checks the temperature of the EV battery. If it is less than the threshold value, then

Fig. 2 Wireless BMS flow chart

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it continues to pass the data to the LCD screen. But, if the temperature of the EV battery is more than the threshold value which is approx. above 40 °C, then the microcontroller turns ON the cooling circuit, which is basically a thermoelectric plate controlled by a relay. And the thermoelectric plate tries to maintain the temperature of the EV battery. Before the hardware implementation of this prototype, we visited the electric vehicle showroom, where we studied the specifications of the EV batteries. But we were unable to purchase the actual EV battery due to its high cost. So, we decided to design the EV battery on simulation, according to its specifications. After the successful output from simulation, we started to design the hardware circuit.

5.1 Electric Vehicle Battery Specification • • • • • •

Rated Voltage: 72 V Maximum voltage: 84 V Max. charging current: 20 A Capacity: 46 Ah Charging temp.: 0–50 °C Discharging temp.: − 20 to 60 °C.

The hardware implementation of the proposed system prototype consists of the electric vehicle battery and the IoT sensors network to monitor the different parameters of the battery. Atmega16 is the main component of the circuit. The IoT sensors network consists of different sensors. To the EV battery, the different sensors are connected, which are the current sensors, voltage sensors, and temperature sensors

Fig. 3 System hardware setup

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to get the physical data from the battery and to process this data, the sensor network passes it on to the Atmega16 microcontroller. Atmega16 takes the input data from the sensors and passes it toward the LCD display to show the real-time data while charging the EV battery. LED also glows to indicate this working process. Then there is the ESP8266 WIFI module, to store the physical data of EV battery on the cloud. To connect this ESP8266 WIFI module, we used the free available software ‘ThingSpeak’ where we get the free cloud storage. This system is designed for some special purpose, which is to track the data of EV batteries for maintenance purposes. The other circuit of the project is the temperature monitoring circuit. As we are not only controlling the parameters of the EV battery but also trying to control the temperature of the EV battery during its charging and deep discharging process. To control the temperature of the EV battery, we are using the thermoelectric plate to cool down the temperature. If the temperature of the EV battery exceeds the threshold level, the cooling circuit will turn ON and will try to control its temperature. The main purpose to design this controlling circuit is to protect the consumer from the explosion of the battery due to high temperature. It is a safety measure circuit. This is how the overall battery monitoring circuit system works to achieve its aim.

6 Result The executive performance of WBMS is user friendly and simple to observe parameters like current, voltage, and temperature of the battery through charging and discharging operation. A wireless battery monitoring system can be registered on the PC, as well as on smartphones [7]. Figure 4 introduces the temperature feature about the battery. It shows a battery temperature graph which is relatively fixed at about 30 °C Figure 5 displays current features of battery for EV on charging and discharging

Fig. 4 Temperature monitoring of WBMS

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Fig. 5 Current monitoring of WBMS

Fig. 6 Voltage monitoring of WBMS

procedure. In this exploration, a sustained current method is utilized for discharging around 1.8 A. Figure 6 displays Voltage characteristic of battery for EV during the charging and discharging process.

7 Software Preparation The wireless battery monitoring system checking framework was upgraded utilizing two programming stages, i.e., Arduino Incorporated Improvement for Ecological (IDE), AVR studio.

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7.1 Arduino IDE The Arduino IDE is an autonomous stage application that works on C and C++ languages. It is obtained from the IDE for the sorting out programming language [6].

7.2 AVR Studio AVR studio is an Integrated Development Environment (IDE) created by ATMEL for creating diverse implanted applications based on an 8-bit AVR microcontroller.

8 Conclusion and Future Scope The prototype system for wireless monitoring of electric vehicle batteries has been designed and implemented to increase the lifespan as well as to monitor the different parameters of the battery such as temperature, voltage, current, etc. In the future, a highly accurate battery monitoring device will be developed. By using this system, we can also control different battery parameters. Users can also monitor on its smartphones from anywhere using the Android app. At that time, it is expected that the wireless communication system presented in this paper will contribute to the realization of an advanced and efficient battery management system.

References 1. Zhang, J., Yuan, R., Yan, D., Li, T., Jiang, Z., Ma, C., Chen, T.: A non-cooperative game-based charging power dispatch in electric vehicle charging station and charging effect analysis. In: Published in 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (E12) https://doi.org/10.1109/E12.2018.8582178 2. Shuanglong, S., Zhe, Y., Shuaihua, L., Yun, C., Yuheng, X., Bo, L., Fengtao, J., Huan, X.: Study on group control charging system and cluster control technology of electric vehicle. In: Published in 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). https:// doi.org/10.1109/EI2.2018.8582178 3. Zhang, J., Yan, H., Ding, N., Zhang, J., Li, T., Su, S.: Electric vehicle charging network development characteristics and policy suggestions. In: Published in 2018 International Symposium on Computer, Consumer and Control (IS3C). https://doi.org/10.1109/IS3C.2018.00124 4. Danping, V.Z., Juan, L., Yuchun, C., Yuhang, L., Zhongjian, C.: Research on electric energy metering and charging system for dynamic wireless charging of electric vehicle. In: Published in 2019 4th International Conference on Intelligent Transportation Engineering (ICITE). Published in 2019 27th Iranian Conference on Electrical Engineering (ICEE). https://doi.org/10.1109/ ICITE.2019.8880214. 5. Chen, Z., Lu, J., Yang, Y., Xiong, R.: Research on the influence of battery aging an energy management economy for plug-in hybrid electric vehicles. In: IEEE, Issue no 978-1-53863524@2017 IEEE

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6. Juang, L.W.: Online battery monitoring for state-of-charge and power capability prediction. Master of Science thesis, University of Wisconsin- Madison, USA (2010) 7. Yang, Y., Chen, B., Su, L., Qin, D.: Research and development of hybrid electric vehicles CANbus data monitor and diagnostic system through OBD-II and Android-based smartphones. Adv. Mech. Eng. 2013(Article ID 741240), 9 p (2013)

Iris Recognition Using Selective Feature Set in Frequency Domain Using Deep Learning Perspective: FrDIrisNet Richa Gupta and Priti Sehgal

1 Introduction Iris biometric is a useful authenticating tool [1]. It is used as a tool to avoid frauds in traditional security systems like password hacking, presentation of fraudulent documents, etc. The traditional approaches prevalent in field of iris biometric work by using entire iris data for user authentication. This iris feature set is extracted using different techniques like Gabor filters, log Gabor filter, 2D wavelets [2, 3], Fourier transform [4, 5], etc. These approaches show high accuracy rate but they are an easy prey to several security attacks over the biometric system [6–10]. With the advancement in technology and splurge of data, research is going toward self-learning approaches. CNN have shown high accuracy with respect to iris recognition whilst requiring minimum human intervention [8, 11 12]. The iris recognition systems present in the literature work by inputting the segmented or normalized iris image to a CNN model and processing information from it. Additionally, the images provided to the model are in spatial domain. This is the widely used and wellinterpreted domain of representing an image. But its frequency counterpart has been found to be more effective in applying convolution operations over it [13]. This is advantageous as CNN model’s first layer is convolutional layer, which works directly on an input image. Frequency domain helps the CNN model to learn parameters efficiently. Further, it is well noted that high frequency components of the transformed image contain boundary and edge data while lower frequency components represent smooth regions of the image. Hence, the important information contained only in the high frequency components can be used for authentication. R. Gupta (B) · P. Sehgal University of Delhi, Delhi, India e-mail: [email protected] P. Sehgal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_28

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In this paper, we propose and present the technique of authenticating user with a reduced iris feature set using CNN model. The reduced feature set is derived using approach presented in [1] which works in spatial domain. This set is further transformed to frequency domain and higher frequency components are extracted from it. This refined data set is presented to the proposed CNN model, FrDIrisNet. With the help of experiments, we prove that the proposed system is capable of identifying user with greater accuracy as compared to model with information presented in spatial domain. Enhanced security of the proposed approach is shown by mitigating replay attack and database attack over iris recognition system. The experiments are conducted on CASIA-Iris-Interval v4 DB and IIT Delhi DB. The rest of the paper is organized as follows: Sect. 2 summarizes literature survey on iris recognition with reduced feature set, CNN-based model and security with respect to attacks over the system. Section 3 presents the proposed approach FrDIrisNet and steps involved in the process. Section 4 discusses the experimental results followed by conclusion in Sect. 5.

2 Literature Survey In the recent past, researchers have been exploring and proposing various techniques for biometric authentication and biometric security using deep learning. Menotti et al. [14] proposed the use of CNN model to handle spoofing attack on biometricbased authentication system. They proposed and presented the results on iris, face and fingerprint biometric with high accuracy in most of the cases. Pedro et al. [15] and Ramachandra et al. [16] presented a technique to detect liveness detection attack on iris biometric by revealing the presence of any contact lens. They achieved comparable results with other state-of-the-art techniques on IIIT Delhi Contact lens iris database and Notre Dame cosmetic contact lens database 2013. Liu et al. [17] projected the use of CNN on heterogeneous set of iris images. The experimental results show high accuracy on Q-FIRE and Casia cross sensor iris databases. Shervin et al. [18] used pre-trained CNN model for feature extraction on iris and applied principal component analysis (PCA) to reduce further the dimensionality for classification using support vector machine (SVM). The proposed approach provides promising results on IIT Delhi and Casia-1000 Iris Database. Similar work has been presented by Nguyen Kien et al. [19] who show performance of several pre-trained models (AlexNet, VGG, Inception etc.) on iris-based authentication. They achieved a high performance on LG2000 and Casia-Thousand Iris database Waisy et al. [20] proposed a multi-biometric fusion of left and right eye of iris of each user, using deep convolutional network followed by rank based fusion. They claim to achieve promising results on Casia-Iris-Interval v3, IIT Delhi database and SDUMLA-HMT database. Although, the idea of reduced feature set is not new, yet very limited work could be found in this area with respect to iris recognition [11, 12, 21–23]. Recently, Richa and Priti [1] proposed the concept of robust iris regions using local binary

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patterns. They presented the accuracy on CASIA and IIT Delhi database as 98.14% and 98.22%, respectively. In this paper, we propose a novel CNN model, FrDirisNet that uses reduced and relevant feature set in transform domain, for iris recognition and discuss the implicit advantage of this approach in mitigating replay attack and template attack simultaneously.

3 Proposed Methodology The working of the proposed methodology is presented in Fig. 1 and discussed in following subsections on two publicly available databases—CASIA-Iris-Interval v4 and IIT Delhi DB.

3.1 Image Augmentation The amount of data processed plays an important role when dealing with deep neural network models. Image augmentation is used to increase the size of the dataset by applying certain transformations over it. It is believed that smaller dataset at the training phase tends to over fit the classifier. This limitation can be overcome by use of artificially produced dataset derived from the original dataset. Deep neural

Fig. 1 Working of the proposed approach

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network models are trained over this augmented dataset to make them robust over small variations in the images. In our proposed approach, intensity transformation using gamma correction is applied over the images as proposed by Miki Yuma et al. [24], with Y values 0.65 and 1.5. This increased our dataset by the factor of 3. The gamma correction is applied on the database shown in Eq. 1:  y = Imax ∗

x Imax

 Y1 (1)

where x and y are the input and output pixels of an image, respectively. Imax is the maximum pixel value for input image. Let the captured eye image be represented by E i, j for ith user jth image. This image is used for augmenting database by applying intensity transformation over it. The image in the augmented database is represented by Ii, j .

3.2 Image Pre-Processing The iris image Ii, j for ith subject, jth eye is of size 320 × 280 pixels. This image is preprocessed to get the segmented and normalized iris image be represented by N i, j of size 512 × 64 pixels. This step is performed using OSIRIS software version 4.1 [25], which is a freely available linux-based tool.

3.3 Robust Region Determination The popular way of authenticating the biometric of a user involves verification of complete biometric data. This method posed a deterministic approach of authentication. A different non-deterministic approach has been proposed in previous work [1]. The pre-processed normalized image is partitioned into 64 non-overlapping regions or blocks N bi, j , ∀b = 1, 2, . . . , 64 of size 16 × 32. This value has been empirically derived by Richa and Priti [1], where it is proved that 64 partitions attained best results. Average Local Binary Pattern (ALBP) thresholding is applied to each block N bi, j . ALBP is an improvement over LBP codes [26] which works by averaging the image around center pixel to remove the sensitivity over the gray value of the center pixel. The uniform patterns in the form of histograms are further obtained as H b , ∀b = 1, 2, . . . 64 of size 1 × 243 for ALBP configuration of radius 4 and 16 neighbors. This implies that the features in the form of histogram for each image H i, j are of size 64 × 243. The feature vector H i, j is further used to obtain robust iris regions using ChiSquare distance method. Let the identified robust regions for each user be represented

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as R m i where 1 ≤ m ≤ 64. Subjects having m = 40 robust regions are selected for experimentation denoted by r i from R m i . The value of m has been empirically driven.

3.4 Transformation Layer This layer transforms the selective robust iris regions from the normalized image to the frequency domain. In proposed approach, frequency transform (FFT) is applied to transform this data. The higher frequency components from this data are extracted for all the regions. This supports the convolutional operation, which also works by extracting the detailed features of an image which increases the efficiency of the CNN model. The higher frequency components from this data are collated to get a new image and fed to the CNN model. For each selected robust region ri ri , FFT is applied on normalized image Ni,b j , where ri ∈ Rim and b ∈ ri Ni,b j , where ri ∈ Rim and b ∈ ri (refer Sect. 3.3) The transformed regions are filtered to get higher frequency components using Gaussian   high pass filter (GHPS). This data is collated to form an image denoted as Ii, j Ii, j of size 128 × 160 constituting of 40 regions each of size 16 × 32 and is represented as Eq. (2)      Ii, j = collate GHPS FFT Ni,b j

(2)

3.5 FrDIrisNet: Convolutional Neural Network Architecture FrDIrisNet consists of 5 convolution layers (CONV), 5 rectified linear units (ReLU) layers, 5 pooling layers, a fully connected layer. The classification is performed using Softmax layer. Each convolution layer is followed by batch normalization layer, ReLU layer, and MaxPool layer. This is depicted in Fig. 2. The input to FrDIrisNet is a transformed image of 128 × 160 × 1 pixels. This image is passed through first convolution layer conv1 of size 5 × 5, with filters f = 56, stride s = 1 and padding p = 0. The output of 10,83,264 neurons is given to maxpool 1 of size 2 × 2 with s = 2. The output of 2,70,816 neurons is passed to conv2 of size 5 × 5 with f = 112,s = 1 and p = 0. 4,80,704 neurons are passed to maxpool2 of size 2 × 2 with s = 2. The output is successively passed to conv3 of size 3 × 3 with f = 124, s = 1 and p = 0, maxpool3 of size 2 × 2 with s = 2, conv4 with parameters same as conv3, maxpool4 of size 2 × 2 with s = 2, conv5 of size 3 × 3 with f = 136, s = 1 and p = 0, maxpool5 of size 2 × 2 with s = 2. The output of 272 neurons is given to fully connected layer which is mapped to get final probability. The other important parameters involved while training the model are MaxEpochs as 30, InitialLearnRate of 0.001, L2Regularization of 0.0001, and MiniBatchSize of 40.

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Fig. 2 FrDIrisNet: The working CNN model for proposed approach

4 Experiments The experiments are conducted on two publicly available databases CASIA-IrisInterval v4 and IIT Delhi DB. The experiments are shown on 185 subjects with 1324 images and 330 subjects with 1702 images for CASIA-Iris-Interval v4 and IITD database, respectively, which is determined as explained in [1]. This dataset is augmented as described earlier in Sect. 3.1, to get the dataset, which is 3 times the original dataset. The CNN model FrDIrisNet is trained with 80% of the images while rest 20% images are used for testing.

4.1 Experimental Results The aim of this paper is to present a CNN model that authenticates the user using feature set which is reduced and transformed to its frequency counterpart. This is attained using self-trained model FrDIrisNet. The technique proposed here authenticates the user by selectively getting the robust feature set. It combines the idea of non-determinism with neural network. The non-deterministic approach makes use of a subset of available information for authenticating the user. The results show that the accuracy achieved with FrDIrisNet on augmented and non-augmented database is comparable but the FAR and FRR values for IITD DB show a steep decrease in case of augmented database as shown in Table 1. This implies that FrDIrisNet is capable to attain similar accuracy but at far lower false rates (FAR and FRR), which is crucial in improving the overall system performance. The FAR for IITD DB decreases from 6 × 10−3 to 8 × 10–4 while FRR decreased from 2.42

0.006

IITD

2.42

Accuracy (%) 99.78

99.18

CRR (%) 97.73

98.4 0.0008

0.002

FAR (%)

FRR (%) 1.62

FAR (%)

0.008

CASIA-Iris-Interval v4

Augmented database

Non-augmented database

0.3

0.54

FRR (%)

99.84

99.72

Accuracy (%)

99.71

99.49

CRR (%)

Table 1 Comparison of proposed approach on non-augmented vs augmented database on CASIA-Iris-Interval v4 and IITD database over FAR, FRR, accuracy, and correct recognition rate (CRR)

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to 0.3 for proposed approach. For CASIA-Iris-Interval v4 FAR decreases from 8 × 10−3 to 2 × 10–3 while FRR decreases from 1.62 to 0.54. The accuracy of the system is compared with existing state-of-the-art techniques and is presented in Table 2. The techniques chosen for comparison use optimal iris feature set for user authentication using traditional approach. Table 3 presents the comparison of proposed approach with other deep learning-based approaches which use full feature set. Clearly, in both the tables it can be seen that the proposed system not only achieves high accuracy but also a significant improvement in FAR and FRR is seen, which governs the false acceptance and rejection rate of the system. Table 2 Comparison of proposed approach with respect to traditional approaches using reduced feature set Database

Method

Accuracy

FAR

FRR

Description

CASIA-Iris-Database

Gu et al. [21]

99.14

0.63

0.23

Genetic algorithm to optimize features

Roy and Bhattacharya [22]

99.81

–

–

Multi-objective genetic function

Richa and Priti [1] 98.14

0.03

3.68

Local binary patterns with chi-square distance

Proposed IIT Delhi DB

0.002

0.54

Richa and Priti [1] 98.22

99.72

0.01

3.55

Proposed

0.0008

0.3

99.84

Local binary patterns with chi-square distance

Table 3 Comparison of proposed approach with respect to existing deep learning approaches using image in spatial domain with complete feature set Database

Method

Accuracy

FAR

FRR

Description

CASIA-Iris-Interval v4 DB

Waisy et al. [20]

100%

–

–

Use multimodal biometric on eye itself

Fei [27]

99.98%

–

–

Data-driven Gabor filter optimization

Proposed

99.72

0.002

0.54

Use of image transformed in frequency domain

Waisy et al. [20]

100%

–

–

Use multimodal biometric on eye itself

Proposed

99.84

0.0008

0.3

Use of image transformed in frequency domain

IIT Delhi DB

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Table 4 Comparison and importance of correct region ordering vs. incorrect region ordering Correct ordering

Incorrect ordering

FAR (%)

FRR (%)

FAR (%)

FRR (%)

CASIA

0.002

0.54

0.54

98.92

IITD

0.0008

0.3

0.3

99.7

4.1.1

Role in Mitigating Replay Attack

The proposed CNN model learns parameters from only partial, robust iris data, which is found to contain considerably important biometric information. The nondeterministic approach, of seeking the randomly ordered robust iris regions, plays a key role in mitigating replay attack. It provides flexibility to the system to choose a desired order of regions, such that any interception followed by a replay message is barred from accessing the system. The experiments were performed based on the approach as described in [2]. The selective determination of robust regions and using a random subset of these regions for data exchange between sensor and system helps to mitigate the replay attack. By tapping the interface, an impostor cannot certain the correct order of these regions for next authentication. The probability as determined earlier in [1] for getting the correct sequence of these regions is just 1/40!. Table 4 presents the results of incorrect ordered data being presented to FrDIrisNet and its implication on system which attains low system accuracy with high FRR rate.

4.1.2

Role in Mitigating Template Attack

The innate nature of CNN model to learn by itself and work using those parameters rule out the need to maintain a database of templates. This facilitates to mitigate template-based attack on the system, which had been one of the major security concerns in critical applications.

5 Conclusion In this paper, we presented a CNN-based approach to authenticate the user using reduced iris feature set. The proposed model FrDIrisNet, works by extracting the robust iris regions, transforming them using Fourier transform and extracting high frequency components. Data augmentation is applied to avoid the problem of overfitting the database and increasing the size of database to 3 times the original database. The approach is additionally shown to mitigate replay attack and database attack over the system. The experiments are carried out under different categories and comparison show excellent and competitive results with significant decline in false error rates on CASIA-Iris-Interval v4 and IITD database.

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References 1. Gupta, R., Sehgal, P.: Non-deterministic approach to allay replay attack on iris biometric. Pattern Anal. Appl. 1–13 2. Daugman, J.: How iris recognition works. In: Essential Guide to image Process, pp. 715–739. Academic Press (2009) 3. Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997). https://doi.org/10.1109/5.628669 4. Conti, V., Militello, C., Sorbello, F., Vitabile, S.: A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(4), 384–395 (2010). https://doi.org/10.1109/TSMCC.2010. 2045374 5. He, X., Lu, Y., Shi, P.: A fake iris detection method based on FFT and quality assessment. In: Chinese Conference Pattern Recognition, 2008. CCPR’08, pp. 1–4. IEEE (2008). https://doi. org/10.1109/CCPR.2008.68 6. Shelton, J., Roy, K., O’Connor, B., Dozier, G.V.: Mitigating iris-based replay attacks. Int. J. Mach. Learn. Comput. 4(3), 204–209 (2014). https://doi.org/10.7763/IJMLC.2014.V4.413 7. Smith, D.F., Wiliem, A., Lovell, B.C.: Face recognition on consumer devices: reflections on replay attack. IEEE Trans. Inf. Forensics Secur. 10(4), 736–745 (2015) 8. Rathgeb, C., Uhl, A.: Attacking iris recognition: an efficient hill-climbing technique. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1217–1220. IEEE (2010) 9. Rathgeb, C., Uhl, A.: Secure iris recognition based on local intensity variations. In: Image Analysis and Recognition, vol. 6112 LNCS, no. PART 2, pp. 266–275. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13775-4_27 10. Tomeo-reyes, I., Liu-jimenez, J., Fernandez-saavedra, B., Sanchez-reillo, R.: Iris recognition : threat analysis at the user interface level 11. Bolle, R.M., Pankanti, S., Connell, J.H., Ratha, N.K.: Iris individuality: a partial iris model. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 2, pp. 927–930. IEEE (2004). https://doi.org/10.1109/ICPR.2004.1334411 12. Hollingsworth, K.P.K.P., Bowyer, K.W.K.W., Flynn, P.J.P.J.: The best bits in an Iris code. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 964–973 (2009). https://doi.org/10.1109/TPAMI.200 8.185 13. Signal Processing. https://dsp.stackexchange.com/questions/38750/why-frequency-domainconversion-is-important-in-digital-image-processing. Accessed 11 Dec 2019 14. David, M., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015) 15. Pedro, S., Luz, E., Baeta, R., Pedrini, H., Falcao, A.X., Menotti, D.: An approach to iris contact lens detection based on deep image representations. In: 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, IEEE, pp. 157–164 (2015) 16. Ramachandra, R., Raja, K.B., Busch, C.: ContlensNet: robust iris contact lens detection using deep convolutional neural networks. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1160–1167 (2017) 17. Liu Nianfeng, T.T., Zhang, M., Li, H., Sun, Z.: DeepIris: learning pairwise filter bank for heterogeneous iris verification. Pattern Recognit. Lett. 82, 154–161 (2016) 18. Shervin, M., Abdolrashidiy, A., Wang, Y.: An Experimental study of deep convolutional features for iris recognition. In: 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), p. 1 (2016) 19. Nguyen Kien, S.S., Fookes, C., Ross, A.: Iris recognition with off-the-shelf CNN features: a deep learning perspective. IEEE Access 6, 18848–18855 (2018) 20. Al-Waisy, A.S., Qahwaji, R., Ipson, S., Al-Fahdawi, S., Nagem, T.A.: A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal. Appl. 21(3), 783–802 (2018) 21. Gu, H., Gao, Z., Wu, F.: Selection of optimal features for iris recognition. In: International Symposium on Neural Networks. Springer, Berlin, Heidelberg, pp. 81–86 (2005)

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22. Roy, K., Bhattacharya, P.: Optimal features subset selection and classification for iris recognition. EURASIP J. Image Video Process, 1–20 (2008) 23. Rathgeb, C., Uhl, A., Wild, P.: On combining selective best bits of iris codes. In: European Workshop on Biometrics and Identity Management. Springer, Berlin, Heidelberg, pp. 227–237 (2011) 24. Miki Yuma, H.F., Muramatsu, C., Hayashi, T., Zhou, X., Hara, T., Katsumata, A.: Classification of teeth in cone-beam CT using deep convolutional neural network. Comput. Biol. Med. 80, 24–29 (2017). 25. Rathgeb, C., Uhl A.: Adaptive fuzzy commitment scheme based on iris-code error analysis, pp. 41–44. Department of Computer Sciences University of Salzburg, A-5020 Salzburg, Austria (2010) 26. Li, C., Zhou, W., Yuan, S.: Iris recognition based on a novel variation of local binary pattern. Vis. Comput. Springer 31(10), 1419–1429 (2015) 27. Fei, H., Han, Y., Wang, H., Ji, J., Liu, Y., Ma, Z.: Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network. J. Electron. Imaging 26(2), 023005 (2017)

Enhancement of Mammogram Images Using CLAHE and Bilateral Filter Approaches M. Ravikumar, P. G. Rachana, B. J. Shivaprasad, and D. S. Guru

1 Introduction The world of the human race is now facing serious health issues as an impact of various factors such as unhealthy food habits, pollution, genetic disorder, and more. Some of these health issues are not much severe yet some others are life threatening. Cancer is one such disease which is deadliest if not detected and treated the earliest. Breast cancer is among different cancer types which are seen majorly in women. Breast cancer is the second leading cause for deaths among women around the world [1]. So, it is of main concern to detect it as early as possible, because early detection and diagnosis increase the rate of survival. In that fact, it matters that detection must be done properly at early stages. Mammography has been considered as a reliable method for early detection of breast cancer, which uses low-energy X-rays to examine the human breast for diagnosis and screening. Interpretation of mammograms requires sophisticated image processing methods that enhance visual interpretation and its efficacy depends on radiologists experience and knowledge. Radiologists should be able to detect the subtle signs of cancer which is very challenging as the mammogram images have low contrast. Hence, breast images must be M. Ravikumar · P. G. Rachana (B) · B. J. Shivaprasad Department of Computer Science, Kuvempu University, Jnanasahyadri Shimoga, Karnataka, India e-mail: [email protected] M. Ravikumar e-mail: [email protected] B. J. Shivaprasad e-mail: [email protected] D. S. Guru Department of Computer Science, University of Mysore, Manasagangothri, Mysore, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_29

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enhanced in order to improve the contrast and make abnormalities more visible and easier to identify. Enhancement can be done in spatial domain or frequency domain. Spatial domain enhancement techniques directly handle pixels, whereas frequency domain enhancement techniques operate on the transformed coefficient of image. There are various enhancement techniques in spatial domain such as image inversion (IN), thresholding, contrast stretching, log transformation (LT), power law transform (PLT), gray-level slicing (GLS), histogram equalization (HE), adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), conventional filters, and in frequency domain, there are different enhancements that can be used are image smoothing filters such as Butterworth low-pass filter, Gaussian low-pass filter and sharpening filters such as Butterworth high-pass filter, Gaussian high-pass filter, bilateral filter, and notch filters. Early breast cancer is subdivided into two major categories micro calcifications and circumscribed, where microcalcifications are small deposits of calcium and they appear as high-intensity bright spots in mammograms and circumscribed are tumors formed when an healthy DNA is damaged resulting in unchecked growth of mutated cells. The rest of the paper is organized as follows: related work is discussed in Sect. 2, Sect. 3 details the methodology and different enhancement techniques with their output images, Sect. 4 discusses the results, and Sect. 5 concludes the paper.

2 Literature Review Logarithmic transformation [2] is a nonlinear basic gray-level transformation function which maps a limited range of low gray values to a broader range of output levels and thus enhancing the contrast levels and brightness of the image. Histogram equalization [3–7] is a technique used to improve contrast in images by effectively spreading out the most frequent intensity values, i.e., stretching out the intensity range of an image. Contrast limited adaptive histogram equalization [4, 6, 7] used widely as it enhances the contrast of medical images. Here, the histogram is cut at some threshold and then equalization is applied, where the CLAHE applied on small data portions called tiles instead on entire image. Power law transformation also called gamma correction is an enhancement technique where different levels of enhancement can be obtained for different values of gamma. Median filtering [8] is a nonlinear digital filtering technique, which is applied mainly to remove noise from an image. It is beneficial in conditions where preservation of edges is important while removing noise. Bilateral filtering [9] is a nonlinear, edge preserving, and noise reducing smoothing filter for images. It replaces the intensity of every pixel with a weighted average of intensity values from nearby pixels. It can also be used in blocking artifacts. Bilateral filtering avoids the introduction of blur between objects while still removing noise in uniform areas. Butterworth filter [10] is a type of signal processing filter designed to have as flat frequency response as possible (no ripples) in the pass-band and zero roll off response in the stop-band.

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Gaussian filter [11] is considered as an isotropic filter with specific mathematical properties. Further, it is very common in nature and it is used within different applications involving the image processing. It has a unique property of no overshoot to a step function input while minimizing the rise and fall time. Gaussian filter is a linear filter. It is usually used to blur an image or to reduce the noise. It reduces the effect of image’s high frequency components, thus it is a low-pass filter. It is observed that variants of histogram equalization such as AHE and CLAHE are widely used in spatial domain image enhancement which increases the global contrast without loss of any information and yet enhances the contrast. In frequency domain, it is observed that Gaussian filter and variants of bilateral filters are used more as they remove noise effectively and preserve edges [12–32].

3 Proposed Methodology In this section, we discuss the proposed method for enhancement of breast images and the block diagram of the proposed method is given in Fig. 1. In image processing, enhancement plays a vital role. Its aim is to enhance the input image which will be further helpful in segmentation and classification. For the purpose of experimentation, mammogram images are obtained from MIAS database, and these images are X-ray images which are given as input for enhancement; the input images might not always be in the standard size so normalization is needed to make every sample the same which helps in detailed visualization. After resizing, enhancement techniques are applied on input image that are in spatial domain and in frequency domain. Enhancement techniques will remove any noise, and further enhances the image’s quality. Firstly, the resized image is given as input to spatial domain enhancement techniques such as log transform, histogram equalization, adaptive histogram equalization, and CLAHE. Among these, CLAHE gives a better enhancement based on

Fig. 1 Block diagram of the proposed method

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quantitative measures. It enhances the image’s contrast globally and without any loss in data. It also reduces the problem of noise amplification which was found in AHE. Then the output of CLAHE is given as input to the next stage as a pipe to frequency domain enhancement technique bilateral filtering, which is better among different frequency domain techniques such as Gaussian filtering, median filtering, and laplacian techniques. This technique not only smoothens the image but also preserves the edges, thus enhancing the image’s quality. The quantitative measures used are PSNR, entropy, SSIM, Michelson contrast, AMBE as parameters for image’s quality enhancement. In the next section, different spatial and frequency domain techniques are discussed and results are analyzed.

4 Result and Discussion The superiority of the proposed method is discussed in this section; here, spatial domain techniques and frequency domain techniques are discussed in detail with their results. Finally, the proposed method results are given. To measure the performance of various enhancement techniques, the image is quantitatively measured using PSNR, AMBE, entropy, Michelson contrast, and SSIM.

4.1 Enhancement Techniques for Spatial Domain Spatial domain enhancement techniques operate directly on the pixels of an image. There are different spatial domain techniques available such as log transform, histogram equalization variants such as AHE and CLAHE, piecewise transform. The mammogram image is given as input to the above-mentioned enhancement techniques. The output image’s quality is measured on quantitative parameters. Among these, CLAHE gives the better output. The results of gamma correction, log transform, and different spatial domain approaches are given in the Figs. 2 and 3, respectively. The results are tabulated and values are plotted on the different quantitative measures are given in Table 1a Fig. 4 for gamma correction, Table 1b Fig. 5 for log transform and Table 2a Fig. 6 for spatial domain approaches. In the same way, enhancement can be performed for frequency domain. That is described in the next section.

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(b)

Fig. 2 Different values of a gamma to enhance Mammogram image using Gamma correction, b c to enhance mammogram image using log transform

Fig. 3 Results obtained from various spatial domain approaches. a Original image, b inversed image, c log transformed image, d gamma = 0.4, e gamma = 1.6, f gamma = 2.2, g HE, h CLAHE

4.2 Enhancement Techniques for Frequency Domain Frequency domain enhancement techniques operates on transform coefficient of an image. There are different techniques available in frequency domain such as median filter, Gaussian filter, laplacian and bilateral filter Fig. 7. Among these, bilateral filtering gives the better output. The results are tabulated on the different quantitative measures are given in Table 2b and values are plotted in the graphs, which are given in the Fig. 8. We extract the best results obtained from spatial domain, i.e., CLAHE method and also from frequency domain, i.e., Bilateral Filter method. Finally, we have given CLAHE as a input to bilateral filter method. Then, the results are observed, which described in the next section.

4.3 Combination of CLAHE and Bilateral Filter In the proposed methodology, the output obtained from CLAHE is given as input to bilateral filtering technique, and the output is measured quantitatively and the results are given below.

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Table 1 Comparison of different quantitative values of a Gamma, b log transform (a) 0.2

3.63

0.89

11.83

40.5

0.77

0.4

3.83

0.52

16.5

23.46

0.82

0.6

3.92

0.29

21.58

12.76

0.89

0.8

3.98

0.12

28.95

5.33

0.96

1.0

4.02

0.0

Inf

0.0

1.0

1.2

3.66

0.11

30.14

4.55

0.96

1.4

3.4

0.2

25.01

8.08

0.92

1.6

3.22

0.27

22.22

11.02

0.88

1.8

3.08

0.33

20.37

13.51

0.85

2.0

2.98

0.39

19.03

15.65

0.83

(b) 0.2

2.51

0.85

12.27

30.89

07

0.4

3.09

0.69

14.02

25.09

0.8

0.6

3.42

0.54

16.21

19.29

0.89

0.8

3.59

0.39

19.13

13.49

0.95

1.0

3.73

0.23

23.51

7.73

0.98

1.2

3.95

0.09

31.85

1.88

0.99

1.4

4.03

0.1

30.87

3.93

0.99

1.6

4.11

0.24

23.07

9.73

0.98

1.8

4.0

0.39

19.1

15.12

0.96

2.0

3.72

0.49

17.1

18.95

0.92

Bold indicates the relatively best value among different values

(a)

(b)

Fig. 4 Different values of gamma a represent the results for entropy, MC and SSIM, b represent the results for AMBE and PSNR

Enhancement of Mammogram Images …

267

(a)

(b)

Fig. 5 Different values of c a represent the results for entropy, MC, and SSIM, b represents the results for PSNR and AMBE

Table 2 Comparison of different quantitative values of a different spatial domain methods, b different frequency domain methods (a) HE

3.96

1.0

18.96

0.85

18.04

Negative slicing

4.04

6.7

1.21

0.02

182.01

Slicing

4.04

1.0

12

1.0

0.0

Log transform (LT)

4.03

1.0

30.87

3.93

0.99

Gamma

3.66

1.0

30.14

4.55

0.96

CLAHE

4.25

0.99

25.49

0.57

6.25

Median blur

4.03

1.0

139.75

0.99

0.33

Gaussian blur

4.06

1.0

35.86

0.99

0.01

Laplacian

2.61

−19.0

10.78

0.61

36.45

Low-pass filter

17.93

1.0

−97.08

0.0

Bilateral filter

4.09

1.0

39.78

0.97

(b)

9,282,694.51 0.01

Bold indicates the relatively best value among different values

Comparative analysis of the proposed method with CLAHE and bilateral filter taken in given in Table 3. Above table defines the quantitative measures and their values for different enhancement techniques. The performance of various enhancement techniques are represented graphically in the Fig. 9.

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(a)

(b)

Fig. 6 Different values of spatial methods a represent the results for entropy, MC, and SSIM, b represents the results for AMBE and PSNR

Fig. 7 Results obtained from various frequency domain approaches. a Low-pass filter, b bilateral filtering, c Gaussian LPF, d Laplacian, e median

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(a)

(b)

Fig. 8 Different values of frequency methods a represent the results for entropy, MC, PSNR, and SSIM, b represents the results for PSNR and AMBE

Table 3 Comparative analysis Entropy

MC

PSNR

SSIM

AMBE

CLAHE

4.25

0.99

25.49

0.57

6.25

Bilateral filter

4.09

1.0

39.78

0.97

0.01

Proposed

4.4

1.0

42.19

0.59

5.75

Bold indicates the relatively best value among different values

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(a)

(b)

Fig. 9 Value of the proposed method a represents the results for entropy, MC, SSIM and AMBE, b represent the results for PSNR

References 1. Fear, E.C., Meaney, P.M., Stuchly, M.A.: Microwaves for breast cancer detection. IEEE Potentials 22, 12–18 (2003) 2. Maini, R., Aggarwal, H.: A comprehensive review of image enhancement technique. J. Comput. 2(3) (2010). ISSN 2151-9617 3. Saini, V., Gulati, T.: A comparative study on image enhancement using image fusion. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(10) (2012) 4. Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Eugene Johnston, R., Muller, K., Patricia Braeuning, M., Pizer, S.M.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digital Imaging 11(4), 193–200 (1998) 5. Grundland, M., Dodgson, N.A.: Automatic contrast enhancement by histogram warping. Computer Laboratory, University of Cambridge Cambridge, UK 6. Lu, L., Zhou, Y., Panetta, K., Agaian, S.: Comparative study of histogram equalization algorithms for image enhancement. In: InSPIEDefense, Security, and Sensing. International Society for Optics and Photonics (2010) 7. Sivaramakrishna, R., Obuchowski, N.A., Chilcote, W.A., Cardenosa, G., Powell, K.A.: Comparing the performance of mammographic enhancement algorithms: a preference study. Am. J. Roentgenol. 175(1), 45–51 (2000) 8. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process 38, 35–44 (2004) 9. Ko, S.-J.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circ. Syst. 0098–4094, 984–993 (1991). 10. Gairola, A.C., Shah, O.H.: Design and implementation of low pass butterworth filter. IJCRT 6(2) (2018). 2320-2882 11. Seddik, H., Braiek, E.B.: Efficient noise removing based optimized smart dynamic gaussian filter. Int. J. Comput. Appl. (0975–8887) 51(5) (2012) 12. Pisano, E.D., Cole, E.B., Hemminger, B.M., Yaffe, M.J., Aylward, S.R., Maidment, R., Johnston, E., et al.: Image processing algorithms for digital mammography: a pictorial essay 1. Radiographics 20(5), 1479–1491 (2000)

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13. Pratt, W.K.: Digital Image Processing. Prentice Hall (1989) 14. Lee, J.S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2, 165–168 15. Johnson, R.H., Nelson, A.C., Haralick, R.M., Goodsitt, M.M.: Optimal information retrieval from complex low frequency backgrounds in medical images. In: 11th Annual Conference on IEEE Engineering in Medicine and Biology Society, pp. 384, 385. IEEE (1989) 16. Raji, A., Thaibaoui, A., Petit, E., et al.: A gray-level transformation-based method for image enhancement. Pattern Recogn. Lett. 19(13), 1207–1212 (1998) 17. Morrow, W.M., Paranjape, R.B., Region based contrast enhancement of mammograms. IEEE Trans. Med. Imaging 11(3) (1992) 18. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, pp. 167–170, 478. Pearson Education (2007) 19. Xiao, F., Zhou, M., Geng, G.: Detail enhancement and noise reduction with color image detection based on wavelet multi-scale, pp. 1061–1064 (2011) 20. Jaya, V.L., Gopikakumari, R.: IEM: a new image enhancement metric for contrast and sharpness measurements. Int. J. Comput. Appl. 79(9) (2013) 21. Panetta, K., Samani, A., Agaian, S.: Choosing the optimal spatial domain mea- sure of enhancement for mammogram images. Int. J. Biomed. Imaging 2014(Article ID 937849), 8 p (2014) 22. Ye, Z., Mohamadian, H., Pang, S.-S., Iyengar, S.: Image contrast enhancement and quantitative measuring of information flow. In: Proceedings of the 6th International Conference on Information Security and Privacy (WSEAS’07), Tenerife, Spain, Dec 2007 23. Saleem, A., Beghdadi, A., Boashash, B.: Image fusion-based contrast enhancement. EURASIP J. 24. Ranota, H.K., Kaur, P.: Review and analysis of image enhancement techniques. Int. J. Inf. Comput. Technol. 4(6) 583–590 (2014). ISSN 0974-2239 (International Research Publications House) 25. Mahajan, S., Dogra, R.: A review on image enhancement techniques. Int. J. Eng. Innov. Technol. (IJEIT) 4(11) (2015) 26. Narnaware, S.K., Khedgaonkar, R.: A review on image enhancement using artificial neural network and fuzzy logic. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 6(1) (2015) 27. Bedi, S.S., Khandelwal, R.: Various image enhancement techniques- a critical review. Int. J. Adv. Res. Comput. Commun. Eng. 2(3) (2013) 28. Arun, R., Nair, M.S., Vrinthavani, R., Tatavarti. R.: An alpha rooting based hybrid technique for image enhancement. Online Publication in IAENG, 24th August 2011. 29. Chaofu, Z., Li-ni, M., Lu-na, J.: Mixed frequency domain and spatial of enhancement algorithm for infrared image. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012) 30. Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consumer Electron. 51(4), 1326–1334 (2005) 31. Abdullah-Al-Wadud, M., Kabir, Md.H., Ali Akber Dewan, M., Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consumer Electron. 53(2), 593–600 (2007). 32. Sundaram, M., Ramar, K., Arumugam, N., Prabin, G.: Histogram based contrast enhancement for mammogram images. In: 2011 International Conference on, Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp. 842–846. IEEE (2011) 33. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India

Supervised Cross-Database Transfer Learning-Based Facial Expression Recognition Arpita Gupta and Ramadoss Balakrishnan

1 Introduction The facial expression conveys messages between humans playing a major role in communication. Facial expression recognition (FER) is one of the most appropriate fields in computer vision which could be applied in cognitive psychology, human and computer interaction, computational neuroscience, and health care [1]. In the research of recent years, deep learning has achieved useful results in automation of FER. The main aim of FER is to train a model based on a facial expression dataset so that the trained classifier can predict the expressions precisely [2]. FER has six primary categories (sadness, happiness, fear, anger, surprise, and disgust) recognized by Ekman and Friesen [3] universal in humans and neutral categories. Most of the studies have considered features of the representation of facial expressions experimenting on the laboratory collected dataset. The datasets collected in laboratories are posed expressions, aiming at consistency not available in real life. There is some dataset collected in wild settings (SFEW [4], FER2013 [5, 6]). This variation in the consistency of the datasets could be overcome by using deep networks with pretraining. In such models, we use the pre-train method with a source dataset of the same or different domain and then training on the target dataset needed for classification. Many researchers are working and studying different methods in the field of FER. Most of the methods in FER either use the classical method of single training setting of the models. Moreover, the training and testing of the models are performed on the same [2]. One of the main issues with deep learning is the need to be trained on A. Gupta (B) · R. Balakrishnan Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India e-mail: [email protected] R. Balakrishnan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_30

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the labeled dataset, and collecting dataset is very costly [6]. There are studies using convolution neural network but not many of the deep residual network employing transfer learning [7]. This all leads to research in the models trained on crossdatabase using transfer learning solving the need for a large labeled dataset. In this paper, we have experimented on deep residual networks of 50 and 152 layers which are trained using double fine tuning on the models. The following paper is organized as follows:—Sect. 2 explains the studies related to transfer learning; Sect. 3 describes the proposed work of the paper. Section 4 explains the datasets used in the experimentation; Sect. 5 explains the results achieved, followed by Sect. 6 conclusion.

2 Related Work There are many studies proposed in the field of FER. There are many studies that have used the same training and testing dataset, while very few using the cross-database and double fine tuning studies in FER. These methods have shown performance with high accuracy not applicable in tough conditions of real-life scenarios. In this section, we have discussed some of the studies employing cross-database or transfer learning technique. There are many techniques for FER, and one of them is appearancebased features, using local- or filter-based features. Another is based on the distance between parts of the face to detect the expressions. Then there is hybrid in which both feature-based and distance-based techniques are combined for FER. There is not much existing work which has used transfer learning with multiple fine tuning and cross-database settings. One of the existing models has used the cross dataset in the applications of FER based on subspace transfer learning methos [8]. Another study has used a convolution and max-pooling layer for FER [9]. For feature, transfer Gabor features and distance features were used for FER application [10]. For unsupervised cross-dataset settings [2] has proposed the super-wide regression network to bridge the feature space. One more study used GAN for generating images for unsupervised domain adaption another subdomain in transfer learning for FER [1]. Deep learning has many methods and some of them used in FER are CNN [8, 11], Inception [12], AlexNet [13], VGG-CNN [14], and many more.

3 Proposed Work Our proposed work is an experimentation of deep residual network. We have pretrained our model on two different datasets. The deep residual network (ResNet) won first prize in the image classification task in ILSVRC 2015 and was proposed on ImageNet dataset. It consists of connections known as residual connections transferring the knowledge in the layers of the network, acting as skip connections propagating the gradient through the layers, as shown in Fig. 1.

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Fig. 1 Residual block

We have experimented on two ResNet of 50 and 152 layers. The networks are firstly pretrained on the larger annotated dataset (VGG and ImageNet) then the target dataset. The source dataset is the dataset on which the pretraining is done, and the target dataset is the dataset on which we want the task to perform in this case FER. The target labels are seven emotions (sadness, happiness, fear, anger, surprise, and disgust). In Fig. 2, the flow of the deep residual network is shown. Both the networks are trained in the settings shown in Fig. 2. Figure 2 depicts the network architecture of ResNet of 50 and 152 layers pretrained on ImageNet. We have used the cross-entropy loss and activation function. The model has a learning rate of 0.1 and is compiled with the SGD optimizer, and categorical cross-entropy loss is used. Some of the layers are frozen to fine-tune the network. The models employ batch normalization for making the network faster. The networks pretrained only on ImageNet dataset performed poorly, not giving any significant improvement, whereas model trained twice once has shown competitive results. Fine tuning could be done in two ways either by freezing the layers or by adjusting the parameters to fit in the observation. This study shows how the amount of data for training matter in deep learning. The networks are firstly trained on ImageNet and then fine-tuned for VGG dataset, then finally trained on the target dataset and fine-tuned. Both the networks ResNet 50 and 152 have shown significant results. The final output layer is designed to give seven basic emotions labels as output, and the networks are tested on 30 epochs.

4 Datasets In this study, we have used three datasets: two for supervised pretraining (ImageNet and VGG [15]) and the target dataset FER2013 [5, 16]. In this section, we have discussed the characteristics of the dataset that plays a massive role in the performance of the models.

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Fig. 2 Flow of the network

4.1 FER2013 Dataset Introduced in ICML-2013 FER2013 is a labelled dataset with six basic emotions and neutral. It contains 35,887 images which are wild in the collection. The dataset is divided into three categories: original dataset (27,709), public test data (3589), and final test data.

4.2 ImageNet and VGG Dataset ImageNet [17] dataset is one of the pretraining datasets for our model that contains 14 million images in the labelled form of 20,000 categories. VGG dataset is our other pretraining dataset which contains 2622 identities of images in labelled form and has

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proven to be enormous dataset for pretraining. These methods have shown that if the model is trained on the larger labelled dataset, it could perform better. We have performed double fine tuning and double pretraining on the network never done by residual networks model until now using only the face data collected in the wild. Fine tuning is the technique in which the pretrained model is made to perform another similar task. In this paper, the model classifies the images done in pretrained model, fine-tuned to perform emotion classification of images. This setting has proved to be of a great outcome.

5 Results and Discussions The study in this paper results achieved by experimenting on ResNet of 50 and 152 layers is shown in Table 1. We have used multiple pretraining and fine tuning of the network on cross-datasets. Our model has proved that if the network is trained with enough labelled datasets, the models can perform better. The reason we have used this is, it solves the problem of vanishing gradient using skip connections. We also tried the model with single training with ImageNet performing very poorly with the low accuracy of 29%, which shows that the effect of amount labelled training data. The ResNet-50 pretrained on ImageNet and VGG dataset has achieved the accuracy of 54.17%. The deeper model with 152 layers has achieved an accuracy of 55%. The networks have outperformed the existing models. The models are evaluated on 30 epochs as after that, and there was no significant change in the performance. The model has achieved better accuracy than the existing models. Figure 3 shows the performance evaluation of the model. The accuracy achieved is due to better learning as the model is pretrained twice for better emotion classification. The above graph shows that our models based on ResNet using double pretraining is superior to all the existing models on FER dataset when pretrained on VGG face datasets. Table 1 Performance evaluation details Model

Source dataset

Target dataset Accuracy

AlexNet [12]

ImageNet

FER-2013

VGG-CNN [12]

54.0 47.0

CNN [6]

51.8

Mollahosseini [11]

6 Datasets

34.0

CNN + MMD [6]

RAF

52.3

ImageNet and VGG

54.17

DETN [6] ResNet-50 + Double finetuning (Proposed) ResNet-152 + Double finetuning (Proposed)

52.37 55.0

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Fig. 3 Performance evaluation graph

6 Conclusion We have studied the effect of pretraining in the deep residual network, which has outperformed the existing frameworks. The main aim to utilize pretraining and to make the model learn and distinguish better by learning the features from larger labelled and better quality pretraining datasets. Reason for using deep residual networks is the advantage of solving the issue of vanishing gradients in because of skip connections leading to better knowledge transfer. The network was double fine-tuned for better classification of the network on a cross-database setting. We have experimented on two deep residual networks one of 50 layers and the deeper 152 layers. Our model has achieved an accuracy of 54.17% and 55% higher than the existing models showing the effect of multiple training and fine tuning.

References 1. Wang, X., Wang, X., Ni, Y.: Unsupervised domain adaptation for facial expression recognition using generative adversarial networks. Comput. Intell. Neurosci. (2018) 2. Liu, N., Zhang, B., Zong, Y., Liu, L., Chen, J. Zhao, G., Zhu, L.: Super wide regression network for unsupervised cross-database facial expression recognition. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1897–1901. IEEE (2018) 3. Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124 (1971) 4. Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Static facial expression analysis in tough conditions: data, evaluation protocol and benchmark. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2106–2112. IEEE (2011) 5. Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., et al.: Challenges in representation learning: a report on three machine learning contests. In International Conference on Neural Information Processing, pp. 117–124. Springer, Berlin, Heidelberg (2013)

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6. Li, S., Deng, W.: Deep emotion transfer network for cross-database facial expression recognition. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3092–3099. IEEE (2018) 7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) 8. Yan, H., Ang, M.H., Poo, A.N.: Cross-dataset facial expression recognition. In: 2011 IEEE International Conference on Robotics and Automation, pp. 5985–5990. IEEE (2011) 9. Liu, M., Li, S., Shan, S., Chen, X.: Au-aware deep networks for facial expression recognition. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013) 10. Xu, M., Cheng, W., Zhao, Q., Ma, L., Xu, F.: Facial expression recognition based on transfer learning from deep convolutional networks. In: 2015 11th International Conference on Natural Computation (ICNC), pp. 702–708. IEEE (2015) 11. Devries, T., Biswaranjan, K., Taylor, G.W.: Multi-task learning of facial landmarks and expression. In: 2014 Canadian Conference on Computer and Robot Vision, pp. 98–103. IEEE (2014) 12. Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. IEEE (2016) 13. Ng, H.-W., Nguyen, V.D., Vonikakis, V., Winkler, S.: Deep learning for emotion recognition on small datasets using transfer learning. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 443–449 (2015) 14. Ouellet, S.: Real-time emotion recognition for gaming using deep convolutional network features. arXiv:1408.3750 (2014) 15. Parkhi, O.M., et al.: Deep face recognition. In: British Machine Vision Association, pp. 1–12 (2015) 16. Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.H., Zhou, Y.: Challenges in representation learning: a report on three machine learning contests. In: International Conference on Neural Information Processing. Springer, Berlin, Heidelberg, pp. 117–124 (2013) 17. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248– 255 (2009)

Innovative Approach for Prediction of Cancer Disease by Improving Conventional Machine Learning Classifier Hrithik Sanyal, Priyanka Saxena, and Rajneesh Agrawal

1 Introduction Cancer has been a deadly disease which is curable when mostly it is diagnosed at the right time (at the early stage). Prediction of cancer is always been by the symptoms seen in the patients which are too many to process by manual systems which are not only a very tedious process but are time-consuming too. Growth in technology has made it fast as well as accurate. But, too many symptoms lead to big challenges for the researchers. ML has been proven to be boon for prediction of diseases particularly in cancer detection [1]. Since ML also has many different algorithms with different accuracy values, therefore researchers are continuously working to improve accuracy further. The data related with biomedical science is increasing day by day and requires them to be included in the prediction process, and hence, ML algorithms are being modified to provide high accuracy and efficiency with this increased set of data. Machine learning not only processes the data for prediction but also helps in preprocessing of data which may include noises and errors, i.e., it helps in cleaning the data before processing.

H. Sanyal (B) Department of Electronics & Telecommunications, Bharati Vidyapeeth College of Engineering, Pune, India e-mail: [email protected] P. Saxena Department of Computer Science, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India e-mail: [email protected] R. Agrawal Comp-Tel Consultancy, Mentor, Jabalpur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_31

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Among the various diseases, cancer particularly breast cancer is very deadly and reoccurs, causing the women to death which is as critical as lung cancer [2]. The approach of ML requires various classifiers such as decision tree, Naïve Bayes, support vector machine, logistic regression to classify the data in various ways before applying prediction. It is performed in two steps, i.e., training and testing. In this paper, a machine learning algorithm is being modified to enhance the accuracy and predict the breast cancer efficiently using Wisconsin dataset. Section 1 introduces the usage of machine learning in cancer diagnosis, Sect. 2 details about techniques of machine learning, Sect. 3 discusses the literature survey, Sect. 4 enlists with small details about the classifiers of machine learning. In Sect. 5, we have discussed on the Wisconsin dataset that will be used in the simulation, in Sect. 6, proposed methodology has been discussed with diagrammatic representation, and at the end, Sect. 7 will discuss the expected outcome of the proposed system as a conclusion.

2 Machine Learning Techniques Machine learning is considered a central part of artificial intelligence. It is a framework which takes in information, discovers designs, trains itself utilizing the information and yields a result. Right off the bat, machines can work a lot quicker than people. A biopsy, for the most part, takes Pathologist 10 days. A PC can do a prodigious number of biopsies surprisingly fast. Machines can accomplish something which people are not capable of. They can rehash themselves a humongous number of times without getting depleted. After every cycle, the machine rehashes the procedure to improve. People do it as well, we call it practice. While the practice may make great, no measure of training can put a human really near the computational speed of a PC. There is such a great amount of information out on the planet that people cannot, in any way, shape or form experience everything. That is the place machines help us. They can accomplish work quicker than us and make precise calculations and discover designs in the information. That is the reason they are called PCs. AI includes PCs finding how they can perform assignments without being unequivocally modified to do as such. For further developed errands, it tends to be tiring for a human to physically make the required calculations. Practically speaking, it can end up being increasingly compelling to enable the machine to build up its calculation; as opposed to having human software engineers indicate each required advance. In situations where immense quantities of potential answers exist, one methodology is to mark a portion of the right answers as legitimate. This would then be capable of utilizing by preparing information for the PC to improve the algorithm(s) it uses to decide the right answers. For instance, to prepare a framework for the undertaking of advanced character acknowledgement, the MNIST dataset has regularly been utilized (Fig. 1).

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Machine Learning

Supervised Learning

Regression

Unsupervised Learning

Classificaon

Clustering

Fig. 1 Machine learning techniques

ML is a subfield of artificial intelligence (AI). The machine learning understands the structure of data and puts that data into new structural models that are understandable and useful for people. Machine learning uses two types of techniques. These techniques are as follows:

2.1 Supervised Learning Supervised learning means a kind of learning which trains a model on known input and output data. This helps in predicting future outputs accurately. On taking and learning from known inputs and outputs, it builds and trains a model that would make predictions based on evidence in the ubiquity of uncertainty. Supervised learning is mostly used for the prediction when the output of the data is known. Supervised learning uses classification and regression techniques for building up a predictive model. Classification technique is used to predict discrete responses, e.g., whether a tumour is benign or malignant, whether an email is genuine or spam. This technique categorizes the input data into different categories. It is most useful when we can tag, categorize or separate data in classes or groups. Regression techniques are used to predict continuous responses.

2.2 Unsupervised Learning Unsupervised learning is a classification of ML which searches hidden patterns or structures in data. It helps to make inferences from datasets which are consisting of responses which are not tagged or labelled. Unsupervised learning mostly uses clustering technique.

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Clustering It is the most used unsupervised learning technique. It is used for finding hidden patterns or groupings in datasets and thus analyzing them.

3 Literature Review Breast cancer is considered to be the most deadly type of cancer amongst the rest of the cancers. Notwithstanding, being treatable and healable, if not diagnosed at an early stage, a humongous number of people does not survive since the diagnosis of the cancer is done at a very late stage when it becomes too late. An effective way to classify data in medical fields and also in other fields is by using ML data mining and classifiers, which helps to make important decisions by the methodology of diagnosis. The dataset which has been used is UCI’s Wisconsin dataset for breast cancer. The ultimate objective is to pigeonhole data from both the algorithm and show results in terms of preciseness. Our result concludes that decision tree classifier amongst all the other classifiers gives higher precision [1]. Cancer is a dangerous kind of disease, which is driven by variation in cells inside the body. Variation in cells is complemented with an exponential increase in malignant cell’s growth and control as well. Albeit dangerous, breast cancer is also a very frequent type of cancer. Among all the diseases, cancer has been undoubtedly the most, deadly disease. It occurs due to variation and mutation of infectious and malignant cells which spread quickly and infects surrounding cells as well. For increasing the survival rate of patients, suffering from breast cancer, early detection of the disease is very much required. Machine learning techniques help in the accurate and probable diagnosis of cancer in patients. It makes intelligent systems, which learn from the historical data and keep learning, from the recent predictions, to make the decisions more accurate and precise [3]. AI is considered man-made consciousness that enjoys an assortment of factual, probabilistic and improvement strategies that permits PCs to “learn” from earlier models and to distinguish hard-to-perceive designs from prodigious, loud or complex informational indexes. This capability is particularly proper to clinical applications, especially those that depend upon complex proteomic and genomic estimations. Hence, AI is a great part of the time used in threatening development examination and revelation. AI is likewise assisting with improving our essential comprehension of malignancy advancement and movement [2]. Chinese ladies are genuinely undermined by a bosom disease with high dreariness and mortality. The absence of potent anticipation facsimiles brings about trouble for specialists to set up a fitting treatment strategy that may draw out patient’s endurance time [4]. Information mining is a basic part in learning revelation process where keen specialists are consolidated for design extraction. During the time spent creating information mining applications, the most testing and fascinating undertaking is

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the ailment expectation. This paper will be useful for diagnosing precise ailment by clinical experts and examiners, depicting different information mining methods. Information mining applications in therapeutic administrations hold goliath potential and accommodation. Anyway, the effectiveness of information mining procedures on medicinal services space relies upon the accessibility of refined social insurance information. In our present examination, we talk about scarcely any classifier methods utilized in clinical information investigation. Additionally, not many sicknesses forecast examinations like bosom malignant growth expectation, coronary illness conclusion, thyroid expectation and diabetic are thought of. The outcome shows that decision tree calculation suits well for infection expectation as it creates better precision results [5].

4 Machine Learning Classifiers Bosom malignancy is the most unmistakable infection in the region of clinical determination which is expanding each year. A relative investigation of three broadly utilized AI methods is performed on Wisconsin breast cancer database (WBCD) to anticipate the bosom disease repeat: 1. 2. 3. 4. 5.

Multilayer perceptron (MLP), Decision tree (C4.5), Support vector machine (SVM), K-closest neighbour (K-NN). Naive Bayes.

Various classifiers are accessible in the business for the arrangement of tremendous information through AI strategies. A list of some well-known classifiers that are used in ML is as follows: 1. Bayes Network Classifier 2. Logistic Regression 3. Decision Tree a. b. c. d.

The Random Tree The C 4.5 tree (J48) The Decision Stump The Random Forest.

5 Dataset Description For the proposed work, the UCI’s Wisconsin dataset for Breast Cancer has been used as it is quite popular amongst various Machine Learning implementations. The

286 Table 1 Description of the Wisconsin datasets

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Attribute count Instance count Class count

Original data

11

699

2

Diagnosed data 32

569

2

Prognosis data

198

2

34

dataset was primarily used for recognizing and differentiating the malignant samples from the benevolent samples. Table 1 shows the different characteristic counting for different Wisconsin datasets [6].

6 Proposed Work This work proposes to create a decision tree-based cancer patient data processing environment which will not only be faster but will also provide high accuracy. The system will leverage the facility of a multi-threaded system in which two different decision trees shall be created having a different set of attributes processed in parallel. The results obtained from both the threads shall be combined to get the final results (Fig. 2). The proposed system will be executed in the following steps: 1. Identification and separation of attributes for making a decision tree 2. Generation of threads for implementation of multiple decision trees 3. Combining multiple decision trees for getting the final results (Fig. 3). This approach will have the following time complexities: 1. Separation Complexity O(z 1 ) = O(k/r ) ∗ r where k is the count of attributes, r is the count of “dt”. 2. Processing Complexity O(z 2 ) = O(h ∗ r2 ) where “h” is the scope of the training data, r is the count of “dt”. 3. Combination Complexity O(z 3 ) = r where r is the count of “dt”. 4. Overall complexity

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ATTRIBUTE SET

Set - 1

Set - 2

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Decision Tree - 2

Accuracy - 1

Accuracy - 2

Combine

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Fig. 2 System flow of the proposed work

O(h) = O(z 1 ) + O(z 2 ) + O(z 3 ) O(h) = O( p/r ) ∗ r + O(h ∗ r2 ) + O(r ) Comparison of the simple decision tree and the modified decision tree O(sdt) = O(h ∗ p2 ) [6] O(mdt) = O( p/r ) ∗ r + O(h ∗ r2 ) + O(r ) From the above two equations, it is clear that the O(mdt) < < O(sdt) when. r > 1 as the complexity of a simple decision tree will be two high if the number of attributes are too high.

7 Conclusion and Future Work This paper is proposing to provide a better decision tree algorithm which will not only have high performance but will also have high accuracy. Paper delivers a meticulous review of ML, its techniques and need for the industry and enhancement of artificial intelligence. Further, the studies of the earlier researches have been presented which

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Start

Input Dataset

Evaluate Aributes

Apply Division of Aributes

Start One Process for Each Set of Aributes

Dataset - 1

Dataset - 2

Apply Gini Index

Apply Gini Index

Calculate Accuracy

Calculate Accuracy

Combine Step

Display Final Accuracy

Stop

Fig. 3 Flowchart of the complete proposed system

clearly explains that the focus of the research is on having a better solution from the existing classifiers in different scenarios. A comparative complexity calculation of the simple decision tree algorithm & proposed modified decision tree algorithm shows the enhanced time complexity, and implementation will show how it will provide comparative accuracy. This work can be further enhanced by applying other mechanisms of separation of the attributes for building multiple decision trees. It can also be further enhanced and tested on real-time data for high performance and accuracies both.

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References 1. Sanyal, H., Agrawal, R.: Latest trends in machine learning & deep learning techniques and their applications. Int. Res. Anal. J. 14(1), 348–353 (2018) 2. Singh, S.N., Thakral, S.: Using data mining tools for breast cancer prediction and analysis. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, 2018, pp. 1–4. https://doi.org/10.1109/CCAA.2018.8777713 3. Su, J., Zhang, H.: A fast decision tree learning algorithm. In: Proceedings of the 21st national conference on Artificial intelligence, vol. 1 (AAAI’06), pp. 500–505. AAAI Press 4. Gupta, M., Gupta, B.: A comparative study of breast cancer diagnosis using supervised machine learning techniques. In: 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2018, pp. 997–1002. https://doi.org/10.1109/ICCMC. 2018.8487537 5. Fu, B., Liu, P., Lin, J., Deng, L., Hu, K., Zheng, H.: Predicting invasive disease-free survival for early stage breast cancer patients using follow-up clinical data. IEEE Trans. Biomed. Eng. 66(7), 2053–2064 (2019). https://doi.org/10.1109/TBME.2018.2882867 6. Angra, S., Ahuja, S.: Machine learning and its application. In: International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017) 7. Deepika, M., Kalaiselvi, K.: A empirical study on disease diagnosis using data mining techniques. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2018, pp. 615–620. https://doi.org/10.1109/ICI CCT.2018.8473185

Influence of AI on Detection of COVID-19 Pallavi Malik and A. Mukherjee

1 Introduction The aspect of digital clinical diagnosis raises concern in terms of certainty and completeness of medical knowledge. These aspects were duly addressed by researchers that culminated in better diagnostic features in cognizing the contagious diseases [1–6]. Due to the onset of COVID-19 pandemic, medical research worldwide has undergone rapid changes not only in terms of treatment protocols but also in terms of diagnosis. The introduction of computer-based diagnosis has been gradual due to social and technological reasons. However, researchers as early as 1980 introduced the concept of artificial intelligence in medical practice. Casimir [7] explored the idea of artificial intelligence methods for medical consultation. Some of the proposed knowledge-based systems were EMYCIN, EXPERT and AGE. Giger et al. [8] suggested pattern classification techniques to detect and characterize images of different patients. A low-power EEG data acquisition was also proposed by Verma et al. [9]. Due to large data available, it became imperative to adopt data analysis which would be effective in determination of ailments. Exploration in this context leads to hybridized data mining techniques to reduce the gaps arising from analysis of large data. Researchers introduced the concept of ensemble classifiers for diagnosis of different types of tuberculosis which subsequently leads to improved results. However, the main obstacle in prevention of wide usage of machine learning in medical diagnosis is lack of training data. The solution to this would be to collect a varied set of heterogenous data. Sorensan et al. and Marleen addressed the aspect of weak labels and suggested multiple instance classification to arrest the drawback [10–12].

P. Malik (B) · A. Mukherjee University of Engineerring and Management Jaipur, Jaipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_32

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The pharmaceutical and biotechnology organizations worldwide are working in tandem with the governments to check the outspread of the COVID-19 pandemic. This also includes the issues pertaining to the maintenance of the global supply chain management and possible invention of the vaccine. Research work carried out by Gundlapally et.al [13] classified that proteins, particularly viral membrane and those involved in the replication of the genetic material, are the best targets for vaccine and anti-viral drug. It was further explored that by disrupting this batch of proteins, the growth of virus may be curtailed largely. Keeping in view of the research work done so far on diagnosis of contagious diseases, an attempt has been made to re-visit the dimensions of clinical diagnosis with known model and training the machine with the available dataset as obtained from IEEE data port. The basic objective for carrying out this research is to clinically diagnose the patients for COVID-19 accurately and with minimal physical contact.

1.1 Model Used for Prediction The pandemic has opened up new frontiers of challenges in clinical diagnosis. It is true that machine learning can expedite the development of pharmaceutical drugs, but there are certain areas that need to be cautiously addressed. There are many hindrances pertaining to limitation of data available as extracting data from such a pandemic is quite challenging. Apart from this, the need to integrate the data into machine learning models and accessibility of the data is also an uphill task. Under these circumstances, certain areas of worth mentioning for the datasets available are as listed below [14–16]: (1) Most of the datasets available are based on the protein structure and their molecular interaction with chemical compounds. This is important to facilitate arrival of new pharmaceutical drugs. (2) Forecast the rate of rise of infection rates and subsequent spread of the disease so as to allow the healthcare system to be well prepared beforehand. (3) Diagnose medical images available of the population for the infection. (4) Mining of the data available on social media to gauge the public perception and the subsequent spread of disease. Of all the above components, the research work attempted in this paper falls under category (3) as mentioned above. The images obtained from the data available at IEEE data port have been taken into consideration. The algorithm used for the purpose has been depicted as hereunder: (a) X-ray image sets are obtained from the dataset available. (b) The images are processed based on their Hounsfield unit (HU) values. This defines the attenuation coefficient of a particular tissue with respect to attenuation coefficient of water.

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Fig. 1 Training of the model using TensorFlow in Python 3.6

(c) Image segmentation is done so as to sub-divide the image in various segments so as to aid in detection of the infection. Convolution neural network (CNN) is used for the purpose. (d) The images are then classified for each candidate region. Image classification process is performed by TensorFlow which is an open-source programming library in Python. (e) Finally, the infection probability is predicted using noisy or Bayesian function. The Bayesian function is used for quantifying the uncertainty of the model. For the purpose of training twenty-five images of pneumonia patient X-rays, twenty-five images of COVID-19-positive X-rays and twenty -five X-ray images of healthy patients are considered to train the model. The code used for training is as displayed below in Fig. 1. The output results can be observed from the figure with 100% confidence level for the images with respect to patients infected with COVID-19. A sample of one such image of patients infected with the COVID-19 positive is shown in Fig. 2. It clearly shows the white patches in the pulmonary segment of the image, thus indicating severe pulmonary congestion. The sample size chosen is small compared to the dataset which is over 500 just to train the model with the set of data. The epoch chosen for this was 200 for a batch size of 25 (Fig. 3). Thus, radiologists can inspect the set of images from the X-ray of different patients suspected with pulmonary infection and allow the code to detect the possibility of COVID-19 infection.

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Fig. 2 X-ray image of a sample for COVID-19 infected case

2 Conclusions From the study done so far, it can be inferred that artificial intelligence may be used for detection of contagious diseases with utmost accuracy. The dataset available from IEEE dataset was quite helpful and extensive in this regard. This method of using AI to detect the possibility of COVID-19 infection has reduced the possible human error in diagnosing the viral infection. In a country like India, community testing on a massive scale is relatively difficult given the time span required for the test for such contagious disease. Such measures may be adopted, wherein the scanned images of pulmonary portion may be used for detection of possible spread of the virus using AI. This shall not only reduce the dangers of medical practitioners getting infected while treating the patient but also shall help in error-free detection of the disease. However, the sample size needs to be sufficient enough to reduce the possibility of error. Further, it can also be understood that the different models are available and it may be a possible study to explore the advantages and disadvantages of different models in the process of prediction of spread and detection of the ailment.

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Fig. 3 Output in console with the evaluation time

References 1. Elstein, A.S., Shulman, L.S., Sprafka, S.A.: Medical problem solving: An analysis of clinical reasoning. Harvard University. Press, Cambridge, MA (1978) 2. Feinstein, A.R.: Clinical judgment. Williams & Wilkens, Baltimore, MD (1967) 3. Komaroff, A. L.: The variability and inaccuracy of medical data. Proceedings IEEE, vol. 67, no. 9, pp. 1196–1207 (1979) 4. Newell, A., Simon, H.A.: Human problem solving. Prentice-Hall, Engelwood Cliffs, NJ (1972) 5. Nii, H. P., Aiello, N.: AGE (attempt to generalize): A knowledge-based program for building knowledge based programs. In: Proceedings 6th International Joint Conference Artificial Intelligence, Tokyo, pp. 645–655 (1979) 6. Nilsson, N.: Principles of Artificial Intelligence. Tioga, Palo Alto, CA (1980) 7. Casimir, K.: Artificial intelligence methods and systems for medical consultation. IEEE Trans. Pattern Anal. Mach. Intell. 2(5), 464–476 (1980) 8. Giger, M. L.: Computer-aided diagnosis of breast lesions in medical images. In: Computing in Science and Engineering, vol. 2, no. 5, pp. 39–45, Sept–Oct 2000 9. Verma, N., Shoeb, A., Bohorquez, J., Dawson, J., Guttag, J., Chandrakasan, A.P.: A micropower EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J. Solid-State Circuits 45(4), 804–816 (2010) 10. Sorensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J.H., de Bruijne, M.: Texture-based analysis of COPD: A data-driven approach. IEEE Trans. Med. Imaging 31(1), 70–78 (2012) 11. Merleen de Bruijne: Machine learning approaches in medical image analysis : From detection to diagnosis, Elsevier Medical Image Analysis, vol. 33, pp. 94–97 (2016) 12. Shi, F., et. al.: Review of artificial intelligence techniques in imaging data acquisition segmentation and diagnosis for COVID 19. IEEE Rev. Biomed. Eng. 1–12 (2020) 13. Gundlapally, J., Kumar, A., Kashyap, A., Saxena, A.K., Sanyal, A.: In search of novel coronavirus 19 therapeutic targets. Helix 10(02), 01–08 (2020)

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14. Yuan, J., Liao, H., Luo, R., Luo, J.: Automatic radiology report generation based on multi-view image fusion and medical concept enrichment. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 721–729 (2019) 15. Wang, X., Peng, Y., Lu, L., Lu, Z., Summers, R. M.: TieNet: Text-image embedding network for common thorax disease classification and reporting in chest X-rays. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9049–9058 (2018) 16. Hao, J., Kim, Y., Mallavarapu, T., Oh, J.H., Kang, M.: Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data. BMC Med. Genomics 12, 1–13 (2019)

Study of Medicine Dispensing Machine and Health Monitoring Devices Aditi Sanjay Bhosale, Swapnil Sanjay Jadhav, Hemangi Sunil Ahire, Avinash Yuvraj Jaybhay, and K. Rajeswari

1 Introduction It is observed that people in rural areas face some different health issues as compared to people living in towns and cities. The chronic disease rate in individuals has increased in rural areas as compared to people in urban areas. In agriculture, there is a lot of use of chemical pesticides which are used on a large scale for farming but they are harmful for individuals which may lead to cancer and other severe diseases. In rural areas, people prefer home remedies instead of actually visiting a doctor for minor diseases. Home remedies might be good, but sometimes it is difficult to classify the cause of disease and home remedies might fail. So, providing medicine dispersing machines to dispense medicine which are prescribed by doctors where the medicines are dispensed by considering different symptoms. The medicine disperser machine is an embedded system which contains hardware and software for its working [1]. Hardware mainly contains IoT-based sensors or wearable devices which are used to measure the body temperature and body pulse [2]. In current years, a number of solutions are available for primary health care, but in rural areas, less facility is available so in emergency time people have to go to nearby A. S. Bhosale (B) · S. S. Jadhav · H. S. Ahire · A. Y. Jaybhay · K. Rajeswari Department of Computer Engineering, PCCOE, Pune, India e-mail: [email protected] S. S. Jadhav e-mail: [email protected] H. S. Ahire e-mail: [email protected] A. Y. Jaybhay e-mail: [email protected] K. Rajeswari e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_33

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cities for required medicine. So, this is not preferable every time. Sometimes, in difficult situations, it is not possible. Centre for Development of Advance Computing (CDAC) are actively working on products in which they can bring out the status of healthcare management and check how many PHC are available in rural areas and they also provide mobile based solutions in which they can enable web technologies through GSM module [3]. Utilizing IOT wearable gadgets estimating the internal heat level and others like heartbeat rate and so on through sensors and distantly observed by a specialist. Doctors monitor the physiological parameters of patients local as well as remote monitoring through sensors, and these input data will be uploaded to the server and sent to the computer or mobile for the referral of the doctor [2]. For the prediction of disease, there is a number of algorithms which will be provided by machine learning. Naïve Bayes, decision tree and J48 algorithms are used to predict multiple diseases at the same time using the pattern or same relations between them. The Algorithm must be check first whether they are providing the correct accuracy or not if it proves to be wrong then the outcome may affect the human life. So, using data mining and visualization techniques for extracting the data, and getting the Patient medical history and current disease. So, that data can be visualised from 2D/3D graph techniques [4]. Improved technology and its application in the healthcare sector will play an important role in urban as well as in rural areas. There are many software’s to provide the medical market. The software provides decision making in the field of medicine, education and training in the medical field. That software can provide how to take care of health, and online guidelines can be provided by doctors. Additionally analyzed illness online and gave medication. There are many software packages which provide home delivery services of medicines [5]. Today’s population can grow rapidly, so providing healthcare devices is too difficult. The patients can be monitored remotely and few cases which are in ICU and might be serious at that time Doctors will check the medical history of patient and give the exact report of patient to Doctor through email or some other media [6]. In rural areas, many people or families are illiterate so they do not know how to use apps; sometimes, this is also one of the difficult situations they can be facing during emergency times. And due to these circumstances, they visit big and expensive hospitals which are not at all cost effective for lower assets rural individuals. To control this situation of spreading the disease and reduce the growing rates of mortality due to a smaller number of facilities, special treatment needs to be given to health care in rural areas. There are many companies that will be working on that problem and improving the healthcare medicine delivery system or machine in rural areas. As per survey for disease prediction, many machine learning algorithms are used or comparatively big data are also used in the prediction of the disease. In that some structured and unstructured algorithms like CNN unstructured algorithms are used over big data [7]. As the hospital’s dataset contains lots of information, but the only need is to find appropriate data from that latent dataset. Datasets are used to analyse drug details which are bought by patients and that information aims to predict the disease they are probably suffering. The parameters included in that dataset are age, gender, name and prescribed medicine of each patient. For predicting

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the disease using data mining, different techniques and results illustrate into stacking method accuracy which is compared to other techniques [8].

2 Literature Review Due to unavailability of healthcare centres and sometimes healthcare personnel in remote areas, it becomes difficult for people to get treatment for diseases which they are facing. For this purpose, the following literature survey has been carried out by Desai et al. [1] that described a prototype which is a vending machine which dispenses medicines as per prescription given by doctor. This vending machine is controlled by microcontroller and provides an online portal which can be accessible for patients and doctors. Poonam Kumari et al. [2] give an IoT-based approach on monitoring and transmitting health parameters using sensors like temperature sensor, heart rate sensor, GSR monitor and ECG through wireless medium; this data is taken from patient and uploaded on a server and sent to doctor. Ramana Murthy [3] describes the increasing market of mobile devices in urban as well as in rural areas, and these mobile devices would be helpful in management of primary healthcare by considering mobile web technology which will make it transparent and easily accessible. Dar, K. H [9] As per survey, compared the Private Medical Practitioners and Primary Health Care, it defines that there is high treatment cost in Private Hospitals so people prefer Primary health care. Leoni Sharmila et al. [10] give description of few machine learning techniques in classifying liver dataset and specify that machine learning can be used for early detection, analysis and prediction of disease. Gurbetal et al. [11] use artificial neural networks and Bayesian networks for classification of diseases like diabetes and cardiovascular, Vitabile et al. [12] discuss data collection, fusion, models and technologies for medical data processing and analysis as well as big medical data analytics for remote health monitoring using concepts of IoT for sensing data such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds or blood pressure to monitor patients psychological and health conditions. Utekar and Umale [6] present an automated IoT-based healthcare system for remotely located patients which helps doctors by alerting via email if abnormal conditions are observed in patients by monitoring parameters using sensors like temperature and heart beat for real-time monitoring. Penna et al. [13] implement an automatic medicine dispensing machine which can be used in remote area; it stores medicines and dispenses it according to patients’ condition; and also provides testing of basic human parameters, blood pressure and temperature. Kimbahune and Pande [14] specify role of Information Communication Technology in Primary Health Centre which will help people in villages and tribes by studying the needs of logistic problem in rural or tribal population and considering existing Primary Health Centre. Kunjir et al. [4] give insight about classifying and predicting specific disease with healthcare data; naïve Bayes algorithm is used for prediction; it specifies that data mining system can be used to avoid wrong clinical decisions; data mining methods like naïve Bayes and J48 algorithm are compared for their accuracy and performance.

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Dehkordi and Sajedi [8] use techniques of data mining to find pattern in a dataset which was provided by a research centre in Tehran; it predicts which patient has referred to which physician to which disease patient are suffering; different data mining techniques were compared like k-nearest neighbour, decision tree, naïve Bayes and used stacking classifier which was considered to have better accuracy than that of single classifiers. Caban et al. [15] give a simple and robust classification technique that can be used to automatically identify prescription drugs by considering colour, shape and imprint on the pills to avoid medication error using modified shape distribution technique for the system. Lee and Ventola [5] describe advantages of using mobile and mobile application by healthcare Professionals for quick decisions with less error rate and accessibility in real time. Chen et al. [7] highlighted machine learning algorithms for better prediction of chronic disease and also proposed a new convolutional neural network-based multimodal disease risk prediction algorithm using data from hospital of Central China from 2013 to 2015 and gave accuracy of 94.8% with a convergence speed which is faster than that of the CNN-based unimodal disease risk prediction algorithm [16]. The paper gives information about big data predictive analytics for heart disease using machine learning techniques in which naive Bayes algorithm is used. SVM with sequential minimization optimization learning algorithm is considered good for medical disease diagnosis application in paper [17] in which India-centric dataset is used for heart disease diagnosis. Paper [18] have proposed a medicine dispensing machine in which microcontroller like Raspberry Pi and Arduino are used; here, Raspberry Pi is used to control a image processing module which authenticates the amount which is paid, and Arduino is used for controlling the dispense of medicine and payment module. Logical regression model is used in [19] with help of machine learning algorithms like decision tree, random forest and naïve Bayes; here symptoms from users are taken and disease is predicted accordingly. Paper [20] have compared various algorithms for prediction of heart disease; in conclusion, hyper-parameter optimization gives better accuracy than algorithms like KNN, SVM, naïve Bayes, RANDOM FOREST. In [21], it is mentioned that CNN-UDRP only uses structured data, but in CNN-MDRP, structured as well as\unstructured data can be used; the accuracy of disease prediction is more and fast with CNN-UDRP.

3 Methodology (1) Naive Bayes: In this paper [16], the heart disease prediction is done and for that naive Bayes algorithm is used; as it gives the highest accuracy and also it is based on some probabilistic logic where it is capable of working successfully with the health-related specification, naive Bayes algorithm uses the Bayes theorem where Bayes theorem is a form of mathematical probabilistic technique where calculating the probability of event is performed. The mathematical form of Bayes Theorem Fig. 1.

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Fig. 1 Flow diagram for database and operations

p(c/x) = p(x/c) p(c)/ p(x)

(1)

where p(c/x) is the posterior probability of class (c, target) given predictor (x, Ai ), where Ai = {Ai , Ai , …, Ai }. p(c) is the prior probability of class p(yes/no). p(x/c) is the likelihood which is probability of predictor given class p(x) is the prior probability of predictor. (2) SVM is the frontier which best segregates the two classes; SVM is used for classification and regression analysis as this algorithm falls under supervised learning. It performs the risk minimization of structure. The equation of hyper-plane is mentioned below Fig. 2. wT x + b = 0

(2)

where x = input vector, w = adjustable weight vector, and b = bias (Fig. 3). wT xi + b ≥ 0

(3)

where b ≥ 0, yi = +1 and b < 0, yi = −1. Paper [1] comprises a vending machine to dispense drugs as per a doctor’s prescription and online portal for generating e-prescription. Doctor’s portal is accessible to doctors from where they can upload prescription of medicine for patients, and the dispensing machine will dispense medicine from the vending machine as per mentioned in the prescription. In [8], stacking method is mainly used for prediction of diseases. It combines several machine learning algorithms into one model to improve the efficiency of prediction.

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Fig. 2 Block diagram for dispensing machine

Fig. 3 Comparison of the two algorithms with respect to accuracy, precision, recall, F 1 -measure

Stacking method uses two main approaches: 1. Data Collection The dataset contains all the information of patients. It collects data like prescriptions of medicine given by doctors. The stacking method considers different doctors for the accuracy purpose. Some of the doctors may give different medicines for the same disease, and hence, it considers all the approaches.

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The purpose was to predict which physician a particular patient has referred. Also, to predict which type of disease that patient is suffering. Using the dataset, it will predict the basic diseases like cold, fever, chill, poisoning. Hence, data is collected to determine the name of disease and the type of doctor 2. Modelling . The dataset contains a large number of attributes regarding the number of instances. But, it is difficult for prediction to consider all these attributes, and it may reduce the accuracy. Hence, to overcome the number of parameter, principle components analysis was used; it is a dimension reduction method which reduces the number of parameters. Penna et al. [13] used sensors to get health data (particularly for sprain, fever, BP, headache) and trigger specific compartments, with the help of Arduino and Stepper motor which dispenses medicines from that compartment. In [14], input from the patient is taken using sensors like temperature and heart beat sensor which are controlled by Arduino, It is a controlling unit, and it triggers stepper motor which dispenses medicine to the patients. The Internet of Things technology is used to measure the physical parameters of the body. As per the measured parameters, the disease is calculated major or minor. The outcome depends on the category of diseases and accordingly the medicine will be dispense out of the machine. So, this is a 24 h available machine for rural areas people as per their conditions. Also, patient history as per survey is stored in the cloud or data mining techniques so the doctor can easily access through the Internet or any other media. In [4], the machine learning disease prediction algorithm can give less accuracy when the data set provided is incomplete; so, some unique characteristics of disease are provided which may reduce the outbreak of this issue. The prediction can be done by several other algorithms, but use of CNN-MDRP is done as it gives the highest accuracy of about 94.8%, and the main aim is put over the prediction of diseases. The hospital dataset of the last 2 years was used for studying, and through these, they have more focus on the risk prediction.

4 Conclusion Conclusion of this paper describes the medical dispensing system which would help the patient in the rural area. As mostly in the rural area, there is unavailability of the primary healthcare, so these systems can be used to overcome such problems. These systems are easy to use and can be handled by each individual and gives the most accurate result. The direction of this paper is to focus on the description of primary health care in the rural area which will provide them easy access to facilities. The issues which have been mentioned above will create problems; so, to improve the medication in the rural area, the vending machine can be used which will accept the symptoms from the patient and then provide prescribed medicine to them. IoT-based health monitoring systems are used to get the real-time data of patients

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who might be in remote areas and accordingly provide them the proper medication. Some more features can be added to the system like blood pressure detection, weight checking system and some helpful guidelines which will be required to a patient in case of any inconvenience. As well, machine learning defines an important role in the classification and prediction of diseases. There are different machine learning prediction algorithms, and also IoT helps to analyse patients remotely. So, the overall system can be used to provide a proper medication to the patient.

References 1. Desai, P., Pattnaik, B., Aditya, T.S., Rajaraman, K., Dey, S., Aarthy, M.: All Time Medicine and Health Device. Vellore Institute of Technology Vellore, India 2. Neha1, Kumari2, P., Kang3, H.P.S.: Smart Health Monitoring System. UCIM/SAIF/CIL, Panjab University, Chandigarh, India 3. Ramana Murthy, M.V.: Mobile based Primary Health Care System for Rural India. In: Mobile Computing and Wireless Networks. CDAC, Electronics City, Bangalore, 560100, [email protected] 4. Kunjir, A., Sawant, H., Shaikh, N.F.: Data mining and visualization for prediction of multiple diseases in healthcare. Modern Education Society College of Engineering, Pune 5. Lee, C., Ventola, M.S.: Mobile devices and apps for healthcare professionals: uses and benefits 6. Utekar, R.G., Umale, J.S.: Automated IoT Based Healthcare System for Monitoring of Remotely Located Patients. Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune 411044 7. Chen, M., Hao, Y., Hwang, K., Fellow, IEEE, Wang, L., Wang*, L.: Disease prediction by machine learning over big data from healthcare communities 8. Dehkordi*, S.K., Sajedi*, H.: A prescription-based automatic medical diagnosis system using a stacking method. Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran 9. Dar, K.H., Junior Research Fellow: Utilization of the services of the primary health centres in India—An empirical study. Department of Economics, Central University Jammu, India. C. Ö˘gretmeno˘glu Fiçici, O. Ero˘gul 10. LeoniSharmila1, S., Dharuman2, C., Venkatesan3, P.: Disease classification using machine learning algorithms—A comparative study. Ramapuram Campus, SRM University, Chennai, 600089, India 11. Gurbetal2, L., Badnjevic, A.: 61,2,3 I Yeslab Ltd. Sarajevo2: Machine learning techniques for classification of diabetes and cardiovascular disease. International Burch University, Sarajevo 3, Technical faculty Bihac, University of Bihac, Berina Ali6 Genetics and Bioengineering International Burch University Sarajevo, Bosnia and Herzegovina 12. Vitabile1( B) , S., Marks2 , M., Stojanovic3 , D., Pllana4 , S., Molina5 , J.M.: Medical Data Processing and Analysis for Remote Health and Activities Monitoring 13. Penna, M., Gowda, D.V., Shivashankar, J.J.J.: Design and Implementation of Automatic Medicine Dispensing Machine. Department of ECE, Sri Venkateswara College of Engineering, Bengaluru 14. Kimbahune, S., Pande, A.: mHEALTH-PHC: a community informatic tool for primary healthcare in India. TCS Innovation Labs, Tata Consultancy Services Mumbai, India 15. Caban†‡, J.J., Rosebrock, A., Yoo‡†, J.S.: Automatic identification of prescription drugs using shape distribution models. National Intrepid Center of Excellence (NICoE), Naval Medical Center University of Maryland, UMBC ‡ National Institutes of Health, Bethesda, MD 16. Venkatesh, R., Balasubramanian, C., Kaliappan, M.: Development of big data predictive analytics model for disease prediction using machine learning technique

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17. Ghumbre1, S.U., Ghatol2, A.A.: Heart Disease Diagnosis Using Machine Learning Algorithm 1. Computer Engineering Department, College of Engineering Pune, Pune, Maharashtra, India 2 Dr. B.A.T.University, Lonere, Maharashtra 18. Tank, V., Assistant Professor, Warrier, S., Jakhiya, N.: Medicine Dispensing Machine Using Raspberry Pi and Arduino Controller. Department of Electronics and Communication Engineering, CHARUSAT, Anand, India 19. Sharma, S., Parmar, M.: Heart Diseases Prediction Using Deep Learning Neural Network Model 20. Shabaz Ali, N., Divya, G.: Prediction of Diseases in Smart Health Care System using Machine Learning 21. Shirsath, S.S., Prof. Patil, S.: Disease Prediction Using Machine Learning Over Big Data. Department of Computer Engineering, SITS, Lonavala, India

Building Image Classification Using CNN Prasenjit Saha, Utpal Kumar Nath, Jadumani Bhardawaj, Saurin Paul, and Gagarina Nath

1 Introduction Artificial intelligence (AI) is a well-organized alternative technique to classical modeling techniques. Until the 90s, only traditional machine learning approaches were used to classify image [1]. But accuracy and scope of the classification task were surrounded by several challenges such as hand-crafted feature extraction process. In the past years, the deep neural network (DNN), also we can call as deep learning , finds composite formation in large datasets using the backpropagation algorithm [2]. In deep learning, convolutional neural network has achieved very good result in task like computer vision, especially for image classification. Hubel and Wiesel discovered that animal visual cortex cells detect light in the small receptive field. Motivated from this work, in 1980, Kunihiko Fukushima introduced neocognitron which is a multi-layered neural network able to recognizing visual pattern hierarchically through learning. This network is observed as a theoretical innovation for CNN. To improve the performance, we can collect large datasets, learn more powerful models and use good techniques to stop overfitting [3]. Convolutional neural networks have a lot of success for image classification [4]. The theoretical inspiration for CNN is a multi-layered neural network capable of recognizing visual pattern hierarchically with the learning. CNN is most widely used in image classification because of its high accuracy in prediction, since it can predict without any pre-determined features, where other algorithms failed to achieve.

P. Saha (B) · U. K. Nath · J. Bhardawaj · S. Paul · G. Nath Assam Engineering College, Jalukbari, Guwahati, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_34

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2 Methodology 2.1 Definition and Working Principle A CNN is formation of single or multiple blocks of convolution and sub-sampling layers, after that one or more fully connected layers and an output layer [5]. The convolutional layers generate feature maps with linear convolutional filters, right next to activation functions.

2.2 Definition and Working Principle Convolutional layer is the prime part for a CNN. So, a characteristic learnt in one region can contest homogeneous design in another region. For a huge image, we take a tiny portion and move it in between all the points in the huge image (input). While processing through any part, we twist them into one spot (output). For respective tiny part of the image where it moves on top of the large image is called riddle (Kernel). The riddle is later designed based on the backpropagation technique.

2.3 Sub-Sampling or Pooling Layer Pooling is a type of process which examine an image. It extracts small area of the convolutional output as input and sub-samples it to produce a single output. Different types of pooling method are there such as max pooling, mean pooling and average pooling. Max pooling grab enormous pixel values of a region as shown in Fig. 3. Pooling minimizes the number of variable to be determined but makes the system unceasing to translations in shape, size and scale.

2.4 Fully Connected Layer Fully connected layer is mainly the last part of CNN. This layer takes input from all neurons in the previous layer and performs operation with individual neuron in the current layer to generate output.

2.5 Optimizer Here, adam optimizer is used. Adam a stochastic optimization requires gradients with the first order also a little bit of memory [6]. Adam method calculates individual

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Fig. 1 Block diagram of the CNN model

adaptive learning rates for various parameters from gradients which estimates the first and second moments of it. Dropout is basically used to reduce overfitting. The word dropout relates to dropping out units in a neural network [7] (Fig. 1).

3 Design Steps In our model, we have divided our dataset into two groups. One is train and other is test. The training part consists of 70% image, and test part contains 30% of our dataset. After that, we have done various data preprocessing like resize, greyscaling,

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etc. Then, we have built our CNN model which has several layers. In our model, when we input our images, it will automatically get the best features of the image, and by using it, it will classify our test image Input data: Here, we have used around 1000 images. All the images are collected from the nearby areas.

4 Results 4.1 Classification Classification is done on the three sets of data, where it can classify school building from the rest of the others with atleast 73% accuracy. Figures 2, 3 and 4 show the results obtained on the three sets of data for the classification of buildings.

Fig. 2 Classified dataset 1

Fig. 3 Classified dataset 2

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Fig. 4 Classified dataset 3

4.2 Accuracy Versus Training Step Graph is obtained to show the change in accuracy with the training step, both for the validation and for the prediction. It shows that with increase in training step, there is increase in accuracy both for validation and prediction step (Figs. 5 and 6).

4.3 Loss Versus Training Step Graph is obtained to show the change in loss with the training step, both for the validation and for the prediction. It shows that with increase in training step, there is a decrease in loss both for validation and prediction steps.

Fig. 5 Accuracy versus training step

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Fig. 6 Loss versus training step

Acknowledgment This work is a part of the project funded by ASTU, CRSAEC20, TEQIP-III

References 1. Dr. Singh, P.: Application of emerging artificial intelligence methods in structural engineering— A review. IRJET 05(11) (2018). e-ISSN 2395-0056 2. Sultana, F., Sufian, A., Dutta, P.: Advancements in image classification using convolutional neural network. In: 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) 3. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. http://papers.nips.cc/paper/ 4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf (2012) 4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016) 5. Lin, M., Chen, Q., Yan, S.: Network in network. In: 4th 2014. arXiv:1312.4400 [cs.NE] 6. Kingma, D.P., Ba, L.J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, 7–9, 2015, San Diego 7. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

Analysis of COVID-19 Pandemic and Lockdown Effects on the National Stock Exchange NIFTY Indices Ranjani Murali

1 Introduction The shock effects due to COVID-19 pandemic outbreak had been felt in many economies, and the effects are expected to persist. The resilience in various sectors of the economy can also be observed in equity segments in addition to various fiscal parameters. India being the fifth-largest economy by GDP and 60% of which is contributed by domestic consumption. In sector-wise GDP figures, the service sector contributes nearly 60%, industry at 23%, and agriculture at 15.4%. India has one of the largest workforces of around 520 million. The COVID-19 outbreak has resulted in complete lockdown on March 24, 2020, and consequent enforcement measures from the government when the number of cases was around 500, to prevent the spread, considering India’s mammoth population. The first lockdown of 21 days brought nearly all the sectors to a grinding halt. The continued outbreak necessitated further phases of lockdown 2 to 5 till June 30. Each of the phases had gradual relaxation of economic activities. The imposed lockdown resulted in several sequence of events such as returning of migrant laborers to their home states, economic stimulus package provision, travel restrictions, and use of work from home concept by many companies which further would impact the economy. Several assistance measures like Shramic train provision, food supply chain, free rations, direct account transfer, and relief packages to the economically poor strata of the country were also taken to minimize the effects. These effects of lockdown measures and the increase in cases are continuously impacting various sectors of the economy. Stock markets being traded on a daily basis would be one of the sensitive indicators to analyze the correlations and trends [1]. The BSE Sensex and NIFTY 50 are the two main stock indices used in Indian equity R. Murali (B) Department of Computer Science, University of Toronto, Toronto, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_35

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markets. NIFTY 50 covers around 17 sectors and represents the weighted average of 50 stocks. In the past, NIFTY index has shown sensitivity to major economic and social events like Demonetization [2], Brexit, Subprime Mortgage crisis in US, etc. [3]. This work leverages NIFTY index to analyze the effects of COVID-19 pandemic and the consequent lockdown events in various sectors of the Indian economy.

2 Literature Survey Stock market prediction and analysis has been widely reported in literature leveraging on both unique datasets and novel techniques. The work in [4] reviews relevant work in financial time series forecasting using deep learning and has grouped the work in categories of Convolutional neural networks, Deep Belief Networks, and Long ShortTerm Memory. The work helps in gaining insights to the efficiency and applicability of these architectures for the financial domain providing a comparative performance analysis. In [2], Demonetization event has been analyzed on the Indian stock markets using GARCH modeling on the NIFTY index for impact assessment and future policy decisions. The work in [5] proposes a novel deep learning architecture of combining Long Short-Term Memory (LSTM) and paragraph vector for financial time series forecasting which has the capacity to process both numerical and textual information that converts newspaper articles into distributed representations with extraction of temporal effects on opening prices. The work in [6] predicts the trend of the stock instead of the actual values by using adversarial training to make the neural network more robust and general to overcome the over fitting problem. The work in [7] proposes deep convolutional neural network architecture to model the long- and short-term influences of events on the S&P500 stock price movements and proves comparable to traditional baseline methods by effectively using the event based stimulus dynamics analysis. The work in [4] reviews relevant work in financial time series forecasting using deep learning and has grouped the work in categories of Convolutional neural networks, Deep Belief Networks, and Long Short-Term Memory. The work helps in gaining insights to the efficiency and applicability of these architectures for domain providing a comparative performance analysis. In [8], a three-stage method is used to obtain the most effective feature set with best risk return prediction capacity and is identified by filter and function-based clustering. The selected set is then evaluated by re-prediction of risk and return. In previous work [9], direct prediction of closing price is attempted using a combined architecture of a Generative Adversarial Network with a Multi-layer perceptron as the discriminator and the Long Short-Term Memory as the generator. The work in [10] leverages regression architecture with adversarial feature changes at test time for accurate prediction. The work studies adversarial linear regression with multiple learners and approximates the resulting parameters by an upper bound on the loss function with resulting unique equilibrium. In [11], impact of COVID-19 on consumption baskets are examined for inflation by analyzing the credit and debit data to calculate the Consumer Price Index of US. Regression

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models have also proven effective in stock market prediction [1] where ARIMA modeling was used for short-term forecasting during COVID-19. Event-based and dynamic feature changes have also been effectively handled by adversarial regression modeling in [12]. This reported research work attempts to analyze the NIFTY trends based on both discrete events like economic stimulus, lockdown, and independent variables like COVID-19 cases.

3 Data Analysis Methodology 3.1 Data The data from [3] including indices NIFTY 50 and sector-wise indices data have been taken from December 31, 2019, to June 8, 2020, for analyzing the effects of lockdown and the COVID-19 pandemic. The total number of COVID-19 positive cases and total deaths on each day were taken for representing the pandemic parameters [13]. The lockdown and its effects were represented by dataset created from news articles as attributes representing economic stimulus given, migrant laborers available, transport provision, and the stringency of lockdown measures.

3.2 Analysis Methodology The collected data was analyzed for the trends influenced by lockdown and COVID19 events through four modules. Data Preprocessing The first module performs data preprocessing by missing value replacement, feature extraction, and standardization. The missing values were replaced with last available temporal data points. Features of lockdown and derived attributes were obtained in this module. Lockdown phase parameter was represented via enumeration while economic stimulus and migrant laborer availability parameters were normalized [13]. Transport availability (T allowed ) parameter was obtained by using zone demarcation data and represented as a percentage compared to pre-lockdown conditions Tallowed = Rdist∗ Rallowed+ Odist∗ Oallowed+ Gdist∗ Gallowed During lockdown, the districts were divided by Red(Rdist ), Orange(Odist ), and Green(Gdist ) zones, each of which were allowed a certain percentage (Rallowed ,Oallowed ,Gallowed ) of public transport vehicles during different phases (Fig. 1).

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Data Preprocess

Trend Analysis

Correlaon Analysis

MulLinear Regression

Model Predicon

Fig. 1 Analysis methodology

Trend Analysis NIFTY indices were plotted from December 31, 2019, to observe the effects of COVID-19 pandemic and the consequent lockdown events. The sensitivity of NIFTY 50 index observed with respect to each lockdown announcement is illustrated in Table 1. Figure 2 gives the time trend of NIFTY indices where a steep fall is observed congruous to announcement of the Janata curfew event of lockdown. Correlation Analysis Pearson correlation values of the NIFTY stock indices with the lockdown events and the COVID-19 pandemic parameters are illustrated by Table 2. Sector-wise analysis has been carried out to understand the broad influence of COVID-19 pandemic during its incipient stages and lockdown. In case of public sector banks, pharmaceuticals, realty, service sectors, commodities sectors, and government securities started a suggestive trend well before the lockdown measures indicating the sensitivity for the pandemic elsewhere in the world. Certain sectors like government bonds and pharmaceuticals show positive correlation with the increase in number of COVID-19 positive cases indicating investor sentiment. Sectors like energy, consumption, oil and gas, and FMCG were least correlated with the COVID-19 parameters. Financial sectors and realty showed negative correlation and were affected by the increase in severity of the pandemic. Table 1 Variation of NIFTY 50 indices with lockdown announcements

Lockdown phases

Change in NIFTY 50 index

Janata curfew on 22 March 2020

−1135.2

Phase 1: 25 March 2020 to 14 April 2020

516.8

Phase 2: 15 April 2020 to 3 May 2020

273.95

Phase 3: 4 May 2020 to 17 May −566.4 2020 Phase 4: 18 May 2020 to 31 May 2020

187.45

Phase 5: 1 June 2020 to 30 June 152.95 2020

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Fig. 2 NIFTY indices before COVID-19 pandemic to June 8, 2020 [3]

Multi-Linear Regression The attributes of lockdown were used to derive a relation with individual NIFTY indices to fit a linear regression model. Yi,t = αi + βi,1 LDt + βi,2 ESt + βi,3 Tt + βi,4 MLt + βi,5 Ct + βi,6 Dt + βi,7 NCt + βi,8 NDt + ei,t . where Yi,t NIFTY indices. βi, n Coefficients. LD—Lockdown phase. ES—Economic stimulus. T—Transport availability. ML—Migrant Laborers Percentage availability. C—Number of Total Cases. D—Number of total COVID-19 deaths. ND—Number of new COVID-19 deaths. NC—Number of new COVID-19 cases.

(1)

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Table 2 Correlation of NIFTY indices with lockdown events Equity indices Total cases

Total death

Economic stimulus

Migrant laborers

NIFTY PSU Bank

– 0.48

– 0.50

– 0.52

0.75

0.78

– 0.78

NIFTY Commodities

– 0.25

– 0.26

– 0.30

0.55

0.59

– 0.57

NIFTY Composite G-sec

0.74

0.76

0.71

NIFTY Energy

– 0.13

– 0.14

– 0.20

0.38

0.42

– 0.41

NIFTY Financial Services

– 0.47

– 0.49

– 0.51

0.74

0.77

– 0.77

NIFTY FMCG

– 0.10

– 0.12

– 0.18

0.23

0.27

– 0.33

NIFTY 4–8 yr 0.74 G-sec

0.76

0.70

– 0.84

– 0.83

0.89

NIFTY 15 yr 0.67 and abv G-Sec

0.69

0.65

– 0.70

– 0.68

0.78

NIFTY Consumption

– 0.15

– 0.17

– 0.22

0.43

0.48

– 0.48

NIFTY Infra

– 0.12

– 0.14

– 0.18

0.77

0.48

– 0.45

NIFTY IT

– 0.18

– 0.20

– 0.23

0.53

0.58

– 0.52

NIFTY Media – 0.36

– 0.39

– 0.40

0.68

0.72

– 0.70

NIFTY Oil & Gas

– 0.04

– 0.05

– 0.10

0.24

0.28

– 0.28

NIFTY Pharma

0.69

0.70

0.60

NIFTY Realty – 0.48

– 0.50

– 0.51

0.76

0.79

– 0.79

NIFTY SERV SECTOR

– 0.40

– 0.43

– 0.45

0.70

0.73

– 0.72

NIFTY 50

– 0.31

– 0.33

– 0.37

0.60

0.64

– 0.63

– 0.82

– 0.79

Transport

−0.81

– 0.77

Lockdown

0.89

0.74

Table 4 illustrates R2 values, and Table 3 describes the multi-linear regression model coefficients for lockdown and COVID-19 parameters which have relatively higher correlation or R2 value when compared to the actual values. The R2 values of the models are in line with the relative rank obtained by the Pearson correlation values. NIFTY PSU, NIFTY Composite G-sec, NIFTY Financial Services, NIFTY 4–8 yr G-sec, NIFTY SERV SECTOR, NIFTY Realty, and NIFTY Media models have relatively higher correlation with the actual values and hence can be best estimated with the lockdown and COVID-19 parameters. NIFTY Oil &

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Table 3 Linear regression model parameters Independent variables

NIFTY PSU Bank

NIFTY Realty

NIFTY Composite G-sec

NIFTY Pharma

Lockdown

−4422.67

−49.96

−74.42

−398.50

0.87

Transport

39.28

0.61

−530.10

−16.70

0.18

Migrant laborers

−120.29

−1.89

−50.69

10.68

− 0.38

Economic stimulus

− 13.54

−0.08

−205.68

− 3.45

− 4.92

New death

4.42

− 0.01

− 825.01

0.57

0.03

Total death

14.35

0.12

224.45

2.54

0.37

New case

− 0.38

0.00

602.29

0.20

0.15

Total cases

− 0.30

0.00

0.65

− 0.70

Constant

37,440.8

420.02

− 0.22

0.43

Table 4 Correlation of NIFTY indices with lockdown events

Equity indices

NIFTY 4–8 yr G-sec

− 0.68 0.48 R2

NIFTY PSU Bank

0.78

NIFTY Commodities

0.61

NIFTY Composite G-sec

0.82

NIFTY Energy

0.47

NIFTY Financial Services

0.78

NIFTY FMCG

0.32

NIFTY 4–8 yr G-sec

0.83

NIFTY 15 yr and abv G-Sec

0.63

NIFTY Consumption

0.53

NIFTY Infra

0.56

NIFTY IT

0.60

NIFTY Media

0.72

NIFTY Metal

0.65

NIFTY Oil & Gas

0.27

NIFTY Pharma

0.71

NIFTY Realty

0.79

NIFTY SERV SECTOR

0.74

NIFTY 50

0.65

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Gas, NIFTY FMCG, and NIFTY Energy have low correlation indicating that they are not affected by the events of COVID-19 pandemic. Figure 3 illustrates the actual NIFTY trend and the linear regression models’ predicted values for various indices. The model is able to accommodate the shock effects in the initial phases and progressively fits closer to the actual trend for later phases 3 and 4 of the lockdown for all the indices.

Fig. 3 Predicted and actual NIFTY indices

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4 Conclusion COVID-19 pandemic and the consequent lockdowns have impacted the NIFTY indices. Short- and long-term government securities and NIFTY Pharma indicate higher positive correlation with increase in the pandemic severity. NIFTY SERV SECTOR, NIFTY Realty, and NIFTY PSU Bank show negative correlation. Investor sentiment is turning toward stable government securities and pharma sector due to volatility in other sectors. The discreet and independent variables as taken in the multi-linear regression models are successful in capturing the trends in the stock indices. Since the pandemic is still progressing, further extensive modeling of data would provide more clear insights and prediction.

References 1. Ahmar, A.S., Val, E.B.: Sutte ARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain: Science of the Total Environment, vol.729 (2020) 2. Patil, A., Narayan, P., Reddy, Y.V.: Analyzing the impact of demonetization on the Indian stock market: Sectoral evidence using GARCH Model. Australasian Accounting, Business and Finance J. 12, 104–116 (2018). https://doi.org/10.14453/aabfj.v12i2.7 3. https://www1.nseindia.com/products/content/equities/indices/historical_index_data.html (2020) 4. Sezer, O. B., et al.: Financial Time Series Forecasting with Deep learning: A Systematic Literature Review:2005–2019, preprint:arxiv:1911.13288v1 (2019) 5. Akita, R., et al.: Deep Learning for Stock Prediction using Numeral and Textual Information: ICIS (2016) 6. Feng, F., et al.: Enhancing Stock Movement Prediction with Adversarial Training: International Joint Conference on Artificial Intelligence, pp. 5843–5849 (2019) 7. Ding, X., et al.: Deep learning for Even-Driven stock prediction, International Joint Conference on Artificial Intelligence (2015) 8. Barak, S., Modarres, M.: Developing an approach to evaluate stocks by forecasting effective features with data mining methods: Expert Systems with Applications. Elsevier 42, 1325–1339 (2015) 9. Zhang, K., et al.: Stock Market Prediction Based on Generative Adversarial Network Heidelberg: Procedia Computer, Elsevier, vol. 147, pp. 400–406 (2019) 10. Liang, T., et al.: Adversarial Regression with Multiple Learners: International Conference on Machine Learning (2018) 11. Cavallo, A.: Inflation with COVID Consumption Baskets. https://ssrn.com/abstract=3622512 (2020) 12. Tong, L., et al.: Adversarial Regression with Multiple Learners: International Conference on Machine Learning (2018) 13. World Health Organization, Coronavirus disease (COVID-2019) situation reports. https://www. who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (2020)

COVID-19 Detection Using Computer Vision and Deep Convolution Neural Network V. Gokul Pillai and Lekshmi R. Chandran

1 Introduction The breakout of novel coronavirus disease (COVID-19) has spread widely across many populations around the globe, and it is spread from human to human contagious transmittable pneumonia generated by the SARS-COV-2 which has caused a pandemic situation all over the globe by now. Studies done by WHO states that 16– 21% of people with the virus have caused severe respiratory symptoms with a 4–5% mortality rate. The possibility of spreading the virus from infected to non-infected and immune population is about 3.77% [1]. Hence, it is highly necessary to detect the infected individuals early, and to undergo quarantine and treatment procedures to prevent a community spread. Polymerase chain reaction laboratory test consider as a confirmation test for COVID-19 [2], but in our research, we are showing the capability of CT image for predicting whether patients are affected with corona or not and also by taking the body temperature of the patients will support our prediction. [3–6]. The disease can be distinguished by the presence of lung ground-glass blurriness or opacities in the early stages, accompanied by “paving made of irregular pieces” and increasing consolidation. These findings led to the increase in CT scans in China, mainly in the Hubei province, eventually, becoming an efficient diagnostic tool. Artificial Intelligence (AI) is added in medical imaging deep learning system which has been developed for identifying and extracting the image features, including shape and spatial relational features of the image, so that it can give assistance to V. Gokul Pillai (B) · L. R. Chandran Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] L. R. Chandran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_36

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the doctor for taking a wise decision. The convolutional neural network was initially designed to perform deep learning task, and they use the concept of a “convolution,” a sliding window or “filter” that passes over the array of input, identifying essential features and analyzing them one at a time then reducing them down to their essential characteristics and repeating the process until we get the final product. Nowadays, CNN is used for enhancing low-light images from videos with very a smaller number of training data. There are numerous techniques to identify viral pathogens based on imaging patterns, which are correlated with their particular pathological process [7, 8]. COVID-19 is characterized by bilateral distribution of dappled profile and ground-glass blurriness, as shown in selected areas in Fig. 2. In this research work, our deep learning model was able to classify the CT images of the patient with COVID-19 positive or not with an accuracy of 90% using deep convolutional neural network. These observations elucidate the use of the deep learning method to identify and extract radiological graphical features from CT images for better and efficient COVID-19 diagnosis.

2 Background Chest radiographs are the most habitual examination articles in the radiology for identifying the problem in a patient [9], but now using the chest radiographs for predicting whether the individual is having COVID-19 or not. SVM, CNN, and other machine learning algorithms are used for classifying object in medical field and non-medical fields [10–12]. These classification methods along with computer vision can now solve pixel classification issues and improve appearance of objects in biomedical images [13–15]. Deep learning techniques had already solved many detection problems in identifying the various pathology types by using chest X-ray, but the best performance was achieved using CNN. Also using X-ray, it is able to identify broken bones in our body, and it can be achieved through deep learning [16, 17]. Mitosis detection in breast cancer performed by computer vision and the deep convolutional method reduces the error and improves results. A large number of RBG images were fed into the deep convolutional network for getting an efficiency of 71.8% to identify the spot correctly [10]. Convolutional neural networks along with computer vision are used to differentiate between benign and malignant skin lesions in skin cancer. Melanoma classification, seborrheic keratosis classification using CNN and pathogenesis of viral pneumonia using CT feature extraction was studied [18, 19]. Even Machine learning with the support of other tools can used for detection of various diseases [20, 21]. Chest imaging using computer tomography (CT) and X-ray are considered as potential screening tools due to their high sensitivity and expediency. COVID-19 prediction studies are going on with X-ray and CT images using classification methods such as SVM, transfer learning, and other deep learning techniques [22–28]. In the proposed method, the features are extracted using CNN, classified using K means, and evaluated using fourfold cross-validation on CT images of COVID-19.

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3 Methodology of COVID-19 Detection Using CNN The main purpose of our research is detection of COVID 19 and classification of them into normal, low-risk, and high-risk category. The proposed CNN based COVID-19 detection is shown as in Fig. 1. For our research, the architecture comprised of following processing steps and classified CT images is shown in Fig. 2. (a) CT image collection: A pool of CT images of chest having Corona infection and normal is collected [29]. (b) Feature extraction and training: Feature extraction is done by localizing the lung region in the CT image and used for CNN training. (c) CNN model prediction.

3.1 CNN Model and COVID-19 Detection The architecture of M-inception CNN model for COVID detection is shown in Fig. 1. In this, we are using CT image which is fed into the two-layer convolutional network, the convolutional layer would extracting the important features present in the input vector and analyzing them one at a time then reducing them down to their essential characteristics After the second max pooling function, we had updated the inception model by adding dropout of 0.5 in the last two dense classification layers. By adding our modification, it will prevent it from overfitting. The whole network carries two major functions, initially the pre-trained inception network is used to convert images into 1D features vector, the secondary part involves in the main role of prediction whether the patient is having COVID-19 or not using K means clustering. The entire part is fully connected network, and it will classify given test CT images with multiple classifiers.

3.2 Feature Extraction Feature extraction is done by localizing the lung region of interest in the chest CT images, so that we can reduce the computation complexity. Reference CT images showing the various cases are shown in Fig. 2. From the images, based on the features characterized by infection, the region of interest (ROI) is identified and extracted from CT images. An ROI is sized as 234*234 pixels. In this, 300 ROI from 503 CT images with COVID-19 positive patient which include both high- and low-risk level patients and 300 ROIs from 503 CT images with COVID-19 negative patients are selected. This helps for building a transfer learning neural network based on inception network.

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Fig. 1 M-Inception CNN model for COVID detection

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Fig. 2 CT images of CORONA positive and negative

3.3 Model Training The model is iterated for 400 epochs. A total of 500 ROIs had been used to train the model, and 100 ROI was extracted for validation. Training is carried out by updating the normal inception network and adjusting the updated network with our pre-trained weights. The original inception part was not trained during the training period. By adding our modification to the original model, we were able to reduce the error caused by overfitting. In our research, to identify prominent manifestations of the Coronavirus disease, we use unsupervised K means feature clustering. The K mean clustering was performed where the optimal number of clusters was found using the elbow method; we had clustered the predicted data into three clusters as shown in Fig. 4. By extracting the features from the training data and flattened it, it is classified into COVID-19 positive high risk and low risk or COVID-19 negative based on the input CT image. On analyzing the model, we got an accuracy of 93.4% in training data, and it shown in Fig. 3. The K means CT image clustering patterns into normal, low risk, and high risk are as shown in Fig. 4.

3.4 Prediction and Performance Evaluation After extracting the features from the training set by CNN, the last step is to classify the images based on the features which has been extracted. By activating the

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Fig. 3 Testing accuracy of CNN model

Fig. 4 K means clustering of Corona cases

classifiers, classification accuracy of our model was improved. In this research, the incoming data, i.e., the test data, is classified or decision is taken by comparing with previous CT images. By conducting the test on data which is total unseen, our model is capable of classifying the CT image with an accuracy of 90.03% as shown in Fig. 5.

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Fig. 5 Accuracy comparison in testing and prediction

Table 1 K fold validation of the model

Folds

Accuracy (%)

Folds-1

89.98

Folds-2

89.45

Folds-3

90.5

Folds-4

90.2

Average

90.03

The cross-validation is used to test the effectiveness of the CNN model for COVID19 prediction. In the K fold validation (k = 4) where the entire testing data is divided into four parts, each part is tested, and its accuracy for this study is given as in Table 1.

4 Conclusion COVID-19 created a pandemic situation of all around the globe taking millions of lives. So, to diagnose it, we have conducted a research to show the importance of computer vision and convolutional neural network in medical field for predicting whether the patients is having COVID-19 or not, and we got an accuracy of 90.4% using our CNN model in classifying the test CT images into positive and negative COVID-19. The effectiveness in prediction of the model can be made more accurate by massive training of different sort of related CT images of lung infections. This

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diagnosis along with temperature of the patient can be checked for increasing the accuracy of the prediction of COVID-19.

References 1. Yang, Y., Lu, Q., Liu, M., Wang, Y., Zhang, A., Jalali, N.: Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China 2. Bernheim, A.: Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection 3. Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J.: Clinical characteristics of 138 hospitalized patients with 2019 novel Coronavirus-Infected Pneumonia in Wuhan, China 4. Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F.: Han epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study, Lancet (2020) 5. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y.: Early transmission dynamics in Wuhan, China, of novel Coronavirus-Infected Pneumonia. In: The New England Journal of Medicine (2020) 6. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China in Lancet (2020) 7. Corman, V.M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D.K.: Detection of 2019 novel coronavirus by real-time RT-PCR. Euro surveillance. In: European communicable disease bulletin 2020, p. 25 (2020) 8. Chu, D.K.W., Pan, Y., Cheng, S.M.S., Hui, K.P.Y., Krishnan, P., Liu, Y.: Molecular diagnosis of a novel Coronavirus (2019-nCoV) causing an outbreak of Pneumonia. In: Clinical chemistry (2020) 9. Wang, S., Kang, B., Ma, J.: A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) 10. Shan, F., Gao, Y., Wang, J.: Lung infection quantification of COVID-19 in CT images with Deep Learning 11. Sethy, P.K., Behera, S.K.: Detection of coronavirus Disease (COVID-19) based on deep features. https://WwwPreprintsOrg/Manuscript/2020030300/V1 9 March 2020 12. Gomez, P., Semmler, M., Schutzenberger, A., Bohr, C., Dollinger, M.: Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network. Med. Biol. Eng. Comput. 57, 1451–1463 (2019) 13. Choe, J., Lee, S.M., Do, K.H., Lee, G., Lee, J.G., Lee, S.M.: Deep Learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 14. Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C.S., Liang, H., Baxter, S.L.: Identifying medical diagnoses and treatable diseases by image-based deep learning 15. Negassi, M., Suarez-Ibarrola, R., Hein, S., Miernik, A., Reiterer, A.: Application of artificial neural networks for automated analysis of cystoscopic images: A review of the current status and future prospects. World J. Urol. (2020) 16. Wang, P., Xiao, X., Glissen Brown, J.R., Berzin, T.M., Tu, M., Xiong, F.: Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat. Biomed. Eng. 17. Vezzetti, E., Moreno, R., Moos, S., Tanzi, L.: X-Ray bone fracture classification using Deep Learning: A baseline for designing a reliable approach 18. Bar, Y., Diamant, I, Wolf, L., Greenspan, H.: Deep learning with non- medical training used for chest pathology identification 19. Koo, H.J., Lim, S., Choe, J., Choi, S.H., Sung, H., Do, K.H.: Radiographic and CT features of viral Pneumonia. Radiographics

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20. Vignesh, D., Ramachandran, K. I., Adarsh, S.: Pedestrian detection using data fusion of leddar sensor and visual camera with the help of Machine Learning. Int. J. Pure Appl. Math. 118, 20 (2018) 21. Krishna Kumar, P., Ramesh Kumar, K., Ramachandran, K. I.: Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers. Int. J. Prognostics Health Manage. 9(8), 2153–2648 (2018) 22. Behnke, S.: Hierarchical neural networks for image interpretation. LNCS, vol. 2766 Springer, Heidelberg, p. 412 (2003) 23. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998). 412 24. Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, pp. 958–963 (2003) 412 25. Cires, D.C.¸ Alessandro, G., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks 26. Mahbod, A., Schaefer, G., Wang, C., Ecker, R., Ellinger, I.: Skin lesion classification using hybrid deep neural network 27. Lakshmi Priyadarsini, S., Suresh, M.: Factors influencing the epidemiological characteristics of pandemic COVID 19: A TISM approach. Int. J. Healthcare Manage. 1–10 (2020) 28. https://doi.org/10.1148/radiol.2020200274 29. Anand Kumar, A., Kumar, C. S., Negi, S.: Feature normalization for enhancing early detection of Cardiac Disorders. In: IEEE Annual India Conference. INDICON 2016, (2016) 30. Sreekumar, K. T. , George, K.K., Kumar, C., S., Ramachandran, K. I.: Performance enhancement of the machine-fault diagnosis system using feature mapping, normalisation and decision fusion. IET Sci. Meas. Technol. 13, 1287–1298 (2019)

Prediction of Stock Indices, Gold Index, and Real Estate Index Using Deep Neural Networks Sahil Jain, Pratyush Mandal, Birendra Singh, Pradnya V. Kulkarni, and Mateen Sayed

1 Introduction The investment from an individual perspective is an important aspect since just earning money is not sufficient, it requires hard work hence we need the money to work hard as well. This is the reason we let the money increase its value over time. Money lying idle in a bank account causes a lost opportunity. Therefore, the money should be invested such as to fetch good returns out of it. The options of investment can be broadly classified as Stocks, Gold and Real Estate. Stocks are company shares which allow us to participate in a company’s growth and earnings. Stocks are offered through the stock exchanges and can be bought by the individual. Gold is one of the precious metals and most popular investment among the commodities section. Gold market is subject to speculation and also very volatile. Real Estate is another category where an investor can directly acquire by directly buying commercial and residential properties. Alternatively, one can opt to purchase shares in REITs, i.e., Real Estate Investment Trust or Real Estate ETFs which are traded in the same way as company stocks. S. Jain (B) · P. Mandal · B. Singh · P. V. Kulkarni Maharashtra Institute of Technology, Pune, Maharashtra, India e-mail: [email protected] P. Mandal e-mail: [email protected] B. Singh e-mail: [email protected] P. V. Kulkarni e-mail: [email protected] M. Sayed Persistent Systems Limited, Pune, Maharashtra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_37

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2 Related Work For stock price prediction, Verma et al. [1] proposed an LSTM model which showed the importance of news data and how they impact the prices of stock indices. Selvin Sreelekshmy et al. [2] illustrated time-series forecasting on stock prices of three different NSE listed companies using three different architectures of LSTM, RNN, and sliding window CNN, with CNN producing the best results. Shah, Dev et al. [3] proposed an ARIMA and LSTM-based model using news data and a predefined dictionary to obtain a directional accuracy of 70%. According to the polarity score of news, a decision to buy, sell, or hold the stock is taken. Mankar, Tejas et al. [4] exhibited use of social media sentiments from Twitter to predict the prices of stock. Sentiment scores of tweets collected were fed to an SVM and Naïve Bayes classifiers separately. The limitation was that only the recent tweets were available, and hence, an exhaustive historical analysis was not possible. Suranart et al. [5] suggest a correlation between commodities and sentiment around them. The authors used the SentiWordNet library to quantify the emotions expressed in the text for on a weekly basis. The results observed after training the neural network show that publicly expressed sentiments impact the price movement. Liu, Dan et al. [6] developed and compared gold prices prediction using historical data with support vector regression and ANFIS models for a period of 8 years. Dubey et al. [7] studied and analyzed neural network, radial basis function, and support vector regression for the duration between June 2008 and April 2013. Keshawn Kunal et al. [8] observed multiple factors on the movement of gold prices and concluded that a combination of Dow Jones Industrial Average (DJIA) and Standard Poor’s 500 index reveals a favorable capability in the forecasting along fed into random forest model. Tabales et al. [9] built an ANN model which showed better results than other hedonic models in solving the common problems related non-linearity in extreme range of prices and effect of outliers. The above works have considered multitude of factors and models to predict indices, but there is no evidence of combining multiple factors into a single architecture. Our work combines the historical factors, news related factors, absolute prices, and movements of indices into a single architecture to achieve better performance.

3 Data Preprocessing and Feature Extraction The data used for the proposed model is collected from two sources.

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3.1 Numeric Historic Data Previous historic index values downloaded from yahoo API between current date and 2008. The required attributes of each day, i.e., opening, closing, and average are kept and other attributes discarded. The missing values are also removed from the dataset. To predict trend, movement of data for each day is calculated as avgcurrent −avgprevious . All the data values are normalized between scales of 0–1.

3.2 Textual News Data News headlines are collected using a custom web scraper. CountVectorizer (n-grams) and Doc2Vec models are used to represent the textual features in form of vectors.

3.3 Flow Diagrams for Data Preprocessing and Feature Extraction See Fig. 1.

4 Data Preprocessing and Feature Extraction Regression is the method of determining a relationship between a dependent variable and one or more independent variables such that the value of the independent variables can be determined by applying some operations to the dependent variables. It is possible to perform regression-based tasks using various machine learning techniques such as support vector machines, linear regression, decision trees, and

Fig. 1 Fetching dataset and feature extraction

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neural networks. During the training phase, the training data is fed to a machine learning algorithm and the algorithm thus “learns” the mapping between input and output variables based on the algorithm’s implementation. After training, the trained algorithm’s or the model’s performance needs to be evaluated using a completely independent set of data. This data is known as the testing data. The predictions for dependent variables made by the model using the independent variables of the training data are observed. These predictions are then compared with the actual or expected values of the independent variables in the testing data to evaluate the performance of the model. In our experiment, we train two different models, namely a support vector machine and a deep neural network, to predict future prices of stock indices.

4.1 Support Vector Machine Support vector machine works by fitting a hyper-plane between a set of points from the training data mapped into a multi-dimensional space. In support vector regression, a function is learned which maps input values to real numbers. The best hyper-plane is the one in which maximum points lie within the decision boundary. The testing is done on the data of recent one year, and the rest of the data is used for training. For each training or testing example, a time window of three days was used.

4.2 Deep Neural Network Deep neural network is a type of machine learning techniques where a model consists of an input layer, an output layer, and multiple hidden layers. Each layer is further composed of multiple nodes. An output of one layer is passed as an input to the next layers. The output of a certain layer is calculated using parameters such as weights and bias and also by applying activation functions. The output of the last layer is compared with an expected output to calculate error, which is used to rectify the values of parameters across the entire neural network. Thus, a neural network is capable if capturing various complex ideas such as speech, images and text. A convolutional neural network or a CNN uses a special operation called convolution, which is helpful for capturing spatial or temporal features of an input. CNN is generally used for image data, but could be applied to time-series data as well. A long short-term memory layer or an LSTM layer is used to capture attributes of sequential data, like human speech or weather data over the days. In our experiment, we create a neural network with CNN layers at the beginning, followed by LSTM layers and fully connected layers. The output is a single value which indicated the future price of the index. The training and testing sets are prepared in a similar fashion as the SVR.

Prediction of Stock Indices, Gold Index, and Real Estate Index … Table 1 Table of MAPE values by index and model

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Indices

SVM (%)

Neural network (%)

Stock index

20.69

0.9363

Gold index

12.21

1.4614

Real estate index

12.40

0.8597

5 Results We trained and tested our model separately for stock index, gold index, and housing price index. Also, each model was tested using both SVM and neural network model. The predictions generated by the model and actual prices were compared to evaluate the performance of our model. The metric used in our experiment is mean squared percentage error or MAPE. MAPE is calculated by taking the average of percentage deviation of predicted price from actual price for each index. MAPE can be mathematically denoted as  n  1  At - Ft  M= n t = 1  At 

(1)

where At and Ft are actual values and predicted or forecast values for example number t and n is the total number of samples. M denotes MAPE (Table 1).

5.1 Visualization of Prediction Results See Figs. 2, 3 and 4.

6 Conclusion This paper attempts to make contributions in the research and development of techniques in prediction and analysis of various indices representing different investment options such as stocks and gold aiding an individual in the decision making. For this purpose, have used machine learning models which try to find patterns among the previous historical prices and news sentiments and map it to the future price value. A major milestone achieved in our work is the integration of historical as well as news factor which has led to better identification of sudden changes in the future values. It can be deduced from the proposed implementation and dataset used that neural network models perform better than the SVM regression models due to the better mapping of non-linearity and hidden features in the textual news data in the layers of neural network architecture. The MAPE values corresponding to neural networks

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Fig. 2 Prediction results for stock index

Fig. 3 Prediction results for gold index

model is ~ 1% indicating a high amount of learning and hence better predictions. As part of future work, a model can be implemented which takes as input broad macroeconomic factors like inflation, GDP, and company related factors (for stocks) like profit and loss. One of the challenges observed is that of lagging of predicted prices in terms of actual price at certain instances due to high correlation between the previous day’s price and predicted price. Our model can be improved further upon this aspect to reduce the error in prediction and generate better results for the end user.

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Fig. 4 Prediction results for housing index

References 1. Verma, I., Lipika, D., Hardik, M.: Detecting, quantifying and accessing impact of news events on Indian stock indices. In: Proceedings of the International Conference on Web Intelligence, ACM (2017) 2. Selvin, S., et al.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE (2017) 3. Shah, D., Haruna, I., Farhana, Z.: Predicting the effects of news sentiments on the stock market. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE (2018) 4. Mankar, T., et al.: Stock market prediction based on social sentiments using machine learning. In: 2018 International Conference on Smart City and Emerging Technology (ICSCET). IEEE (2018) 5. Suranart, K., Kiattisin, S., Leelasantitham, A.:Analysis of Comparisons for Forecasting Gold Price using Neural Network, Radial Basis Function Network and Support Vector Regression. In: The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE). IEEE (2014) 6. Liu, D., Li, Z.: Gold price forecasting and related influence factors analysis based on random forest. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management. Springer, Singapore (2017) 7. Dubey, A.D.: Gold price prediction using support vector regression and ANFIS models. In: 2016 International Conference on Computer Communication and Informatics (ICCCI). IEEE (2016) 8. Keshwani, K., Agarwal, P., Kumar, D.: Prediction of market movement of Gold, Silver and Crude Oil using sentiment analysis. Advances in Computer and Computational Sciences. Springer, Singapore, pp. 101–109 (2018) 9. Tabales, J.M. N., Caridad, J.M., Carmona, F.J. R.: Artificial neural networks for predicting real estate price. Revista de Métodos Cuantitativos para la Economía y la Empresa 15 : 29–44 (2013)

Signal Strength-Based Routing Using Simple Ant Routing Algorithm Mani Bushan Dsouza and D. H. Manjaiah

1 Introduction Ant colony optimization (ACO) [1] uses foraging behavior of ants to determine the optimal path between the communicating nodes. In these algorithms, optimal solution to the routing problem is found by the cooperative actions of every node involved in communication. In such algorithms, control packets act as mobile agents during the route discovery and maintenance phases. Simple Ant Routing Algorithm (SARA) is an ACO protocol that detects congestion across the path and reduces overhead. Signal strength between the communicating nodes is depended on their distances. As the distance between two communicating nodes across a link increases, the strength of the signal reduces. By constantly monitoring signal strength, it is possible to predict, whether the nodes are moving toward or away from each other. If the signal strength is decreasing continuously, it indicates that the nodes are moving away from each other. As the nodes move away from each other, link between them breaks resulting in loss of packets as well as demanding extra overhead in repairing or establishing new path. We can set a threshold value for the signal strength between two nodes communicating across the link. When the measured signal strength goes below the threshold value, route repair process can be initiated so as to avoid data losses. In this work, SARA protocol is taken as a base and the signal strength is used as a deciding factor for changing pheromone concentration and route repair process. The algorithm is simulated using NS2, and it is found that the modified algorithm is able to provide better throughput and reduce delay during transmission.

M. B. Dsouza (B) · D. H. Manjaiah Mangalore University, Mangaluru, Karnataka, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_38

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2 Related Work Swarm intelligence (SI) is a technique of achieving optimization through the collective behavior of self-organized simple agents which locally interact with each other to collectively achieve a goal in a decentralized environment [2, 3]. Ant colony optimization (ACO) is one of the categories of SI that is used to find optimal route in ad hoc networks [4]. AntNet is one of the first routing algorithms based on ACO. In this algorithm, forward ants (FANT) are sent proactively toward random destinations and backward ants (BANT) are used to reply back to the source from the destination. During the traversal, BANT are used to update the routing table at intermediary nodes [5]. ARA [6] is another routing algorithm that has similar behavior as that of AntNet. It uses sequence numbers to avoid duplication of FANTs. It reduces the overhead by using data packets for route maintenance rather than periodic ants. Every node tries to repair the link failure and inform the neighbors about the link failure. If a node fails to recover from the link failure, a route error is sent back to the source and the source will initiate a fresh route discovery once again. In an improvement, PERA [7] uses three ant agents. The FANT and BANT have the similar behavior. And the third type of ants is called uniform FANTs, which are used to reinforce the discovered routes. They help in avoiding the congestions across the discovered routes. As an improvement, ANT-E [8] uses expanding ring search [9] to limit the local retransmission and achieves a better packet delivery ratio. SARA [10] follows a controlled neighbor broadcast of FANT. Here, only one of the neighboring nodes is allowed to broadcast the FANT further. Asymmetry in packet transmission occurs when the traffic flows more on one direction and less on other direction. This causes the pheromone concentration on one direction of the link to deplete faster than the other. SARA uses super FANT to balance the pheromone concentration across the link. In an ACO routing protocol, namely AntHocNet [11], six types of ants are used. These are proactive FANTs and BANTs, reactive FANTs and BANTs and repair FANTs and BANTs. Reactive ants are used to set up the routes and proactive ants are used to maintain the routes. To handle route failure, repair ants are used. Location of the nodes plays an important role in real-life applications, and it can be used to achieve further optimization. Determining coordinates of nodes that is consistent with neighboring nodes is really challenging [12]. There are many techniques used for estimating locations of nodes. These include acoustic methods [13], based on directional antenna or antenna array [14, 15], by using infrared [16] and by measuring received signal strength (RSS) [17]. POSition based ANT colony routing protocol (POSANT) [18] combines ACO with location information to reduce the time taken to establish the route and minimize the control packets. This algorithm assumes that every node knows the position of itself and its neighbors. Enhanced multi-path dynamic source routing (EMP-DSR) algorithm [19] is an enhancement of earlier algorithm MP-DSR [20]. It takes into consideration both link reliability and time consumed by the FANT, while selecting the path. In EMP-DSR [21], a local repair scheme is used wherein instead of changing whole path, adjacent nodes are used for re-route. Here, promiscuous mode is used for searching nearest adjacent

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node. However, this consumes more energy and overhead, thereby decreasing its efficiency. The algorithm suffers from high congestion, and it does properly handle the issue of broken link due to node failure. GSM [22] is another protocol that extends DSR. This protocol uses generalized salvaging and cache update of intermediate node during route request phase.

3 Proposed Solution An ad hoc network is an active wireless reconfigurable network in which every node acts as a router and cooperatively participates in routing activity. Arbitrary movements of the nodes often break the link between the nodes which leads to either route repair or fresh route discovery. As it is not possible to avoid link breaks due to random movement of the nodes, we can predict when the link is going to break and take alternate action before it really happens. By measuring the received signal strength (SS), we can predict the link breakage. Signal strength [23] of a node i situated at a distance x can be calculated as in (1), SSi x =

G r ∗ G t ∗ St (4π ∗ x/λ)2

(1)

Here, Gr and Gt are the gain of receiving and transmitting antenna. λ is the wavelength of electromagnetic wave used for transmission and St is the maximum transmitting power of transmitting antenna. Assuming antenna has circular coverage area of radius R, average distance between any two mobile nodes is given as 0.9054R [24]. The threshold value of signal strength [23] for a given link i can be calculated using the expression (2). T SSi =

G r ∗ G t ∗ St (4π ∗ 0.905R/λ)2

(2)

For a given node, its antenna gain Gr , cover range R and used wavelength λ are known. Every node exchanges HELLO packet to its neighboring node containing its transmitting antenna gain Gt and maximum transmitting power St . Using this, a node computes threshold TSSi for a given link. It can be observed that the threshold value does not depend on the position of the node and its value is fixed for a given neighboring node. By comparing the received signal strength in Eq. (1) with the threshold value in Eq. (2), we can compute Signal Strength Metric (SSM). For a given link that connects two nodes say i and j, if RSSix < TSSi, then pheromone concentration for that link is reduced by γ according to the following condition (3).  ph(i, j) =

ph(i, j) − γ , ph(i, j) > γ 0, ph(i, j) ≤ γ

(3)

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By reducing the pheromone level, the probability of selecting this link for communication can be reduced which can result in sustaining route for longer time. There are two cases during which route repair process gets activated. These situations are as follows. Whenever RSSix ≤ 0.5*TSSi , then the link is assumed to be broken and for an actively used link, route repair process is activated. However, if the link is not used for communication, then route entry for that link is removed from the routing table. The status of the link can also be evaluated based on the number of successful and unsuccessful transmission across the link. Whenever a successful transmission occurs across a link, packet transmission value is decreased by δ. Similarly for an unsuccessful transmission, the packet transmission value increased by λ. This can be formulated as (4)  NTxi =

NTxi − δ, NTxi + λ,

for successful transmission for unsuccessful transmission

(4)

When the value of NTxi exceeds maximum transmission attempts (MAX_Tx), for a given link, then the link is assumed to be problematic. In this situation, route repair process is activated. Deep search algorithm (DSA) [25] is used during route repair process. The DSA is a modified form of expanding ring search (ERS) [26] with the value of TTL set to 2. In DSA, Repair FANT (R_FANT) is broadcasted and a Route Repair Timer (RRT) is set. When R_FANT reaches a node with valid route to destination, it unicasts Repair BANT (R_BANT) to the sender. However, if the node is unable to repair the route within RRT, an error message is sent back to the source node.

4 Result Analysis Simulation of the algorithm was carried out using NS 2.34 simulator over a square area of 1000 × 1000 m2 for a time of 100 s. A pause time of 20 s was maintained with the number of nodes varied from 10, 30, 50, 70, 90 and 120. Values of the other parameters are as follows T0 = T1 = 100 ms, F = 5, τ = 1 s, δ = 1, α = 0.7, γ = 1, MAX_Tx = 5, frequency = 2.48 GHz Node initial energy 20 J, T x = 0.003 J and Rx = 0.001 J. Two protocols, namely SARA and Signal Strength-based Simple Ant Routing Algorithm (SS-SARA), were used, and the results are tabulated. It was observed that the modification indeed provides a better throughput with the increase in number of nodes. Due to the random motion of the nodes, they may move away from each other, resulting in link failure. As the nodes move far away from each other, their signal strength reduces, and when the strength is lower than the threshold value, route repair is initiated. In a sparse environment, where the number of nodes is less, route repair

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Fig. 1 Variation of normalized routing load with number of nodes

may not be effective and the source may be forced to initiate a fresh discovery once again. This causes extra control packets to float across the network. It is evident in Fig. 1, which shows higher control overhead with less number of nodes, as the number of nodes increases, the route repair may be effective which results in reduced overhead. As congestion across the node is not separately considered, there is no significant difference between the delays in both the protocols. However, it can be observed the SS-SARA takes slightly more time in delivering packets; this may be attributed to the fact that SS-SARA predicts link breakage and try to resolve the problem locally. Such an action could lead to additional packets being generated and may cause congestion that results in queuing of packets. This is evident from Fig. 2. As there are less chances of link failure in SS-SARA, it is able to deliver more packets. This is due to precaution that is taken in SS-SARA, which initiates route failure routine even before the actual route failure, as shown in Fig. 3. Lower delay and less packet loss at moderately dense network lead to the higher throughput, as evident by Fig. 4.

5 Conclusion Maintaining stable path for a prolonged time in MANET is challenging due to the rapid random movement of the nodes. As the nodes move away from each other, their signal strength decreases, and when it goes below a threshold level, we can

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Fig. 2 Variation of end-to-end delay with increasing number of nodes

Fig. 3 Variation of PDR with increasing number of nodes

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Fig. 4 Variation of throughput with increasing number of nodes

anticipate that the link will fail. Thus, before a link failure occurs, we can repair the route and select an alternate path. This way we can sustain the path for a longer time. In the proposed protocol, received signal strength is used as a measure for activating route repair. Simulation of the algorithm indicates that the modification does provide a better packet delivery and decently increase the throughput of the network. From this, we can conclude that received signal strength can be effectively used in providing better packet delivery and throughput in a moderately dense network but it does come with an increased delay and routing overhead.

References 1. Correia, F., Vazão, T.: Simple ant routing algorithm strategies for a (Multipurpose) MANET model. Ad Hoc Netw. 8, 810–823 (2010). https://doi.org/10.1016/j.adhoc.2010.03.003 2. Di Caro, G., Dorigo, M.: AntNet: Distributed Stigmergetic Control for Communications Networks. J Artif Intell Res 9, 317–365 (1998). https://doi.org/10.1613/jair.530 3. Chatterjee, S., Das, S.: Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network. Inf Sci (Ny) 295, 67–90 (2015). https://doi.org/10.1016/j. ins.2014.09.039 4. Bettstetter, C., Hartenstein, H., Pérez-Costa, X.: Stochastic Properties of the Random Waypoint Mobility Model. Wirel Networks 10, 555–567 (2004). https://doi.org/10.1023/b:wine.000003 6458.88990.e5 5. Günes M, Spaniol O, Kähmer M (2003) Ant-routing-algorithm (ARA) for mobile multi-hop ad-hoc networks-new features and results. Conf: the Med-Hoc Net 2003. 9–20

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6. Dorigo M, Stützle T (2003) The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. Handb. Metaheuristics 250–285 7. Kordon AK (2009) Swarm Intelligence: The Benefits of Swarms. Appl. Comput. Intell. 145– 174 8. Anderson, C.: Swarm Intelligence: From Natural to Artificial Systems. Eric Bonabeau, Marco Dorigo. Guy Theraulaz. Q Rev Biol 76, 268–269 (2001). https://doi.org/10.1086/393972 9. Di Caro G, Dorigo M Mobile agents for adaptive routing. Proc. Thirty-First Hawaii Int. Conf. Syst. Sci. 10. Baras J, Mehta H (2003) A probabilistic emergent routing algorithm for mobile ad hoc networks. WiOpt03 Model. Optim. Mob. 11. Sethi S, Udgata SK (2010) An Efficient Multicast Hybrid Routing Protocol for MANETs. Commun. Comput. Inf. Sci. 22–27 12. Pu, I.M., Shen, Y., (2009) Enhanced Blocking Expanding Ring Search in Mobile Ad Hoc Networks. : 3rd Int. Conf. New Technol. Mobil, Secur (2009) 13. Di Caro, G., Ducatelle, F., Gambardella, L.M.: AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun 16, 443–455 (2005). https://doi.org/10.1002/ett.1062 14. Navarro-Alvarez E, Siller M, O’Keefe K (2013) GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks. Int J Distrib Sens Networks 9:912029. https://doi. org/https://doi.org/10.1155/2013/912029 15. Kushwaha M, Molnar K, Sallai J, et al Sensor node localization using mobile acoustic beacons. IEEE Int. Conf. Mob. Adhoc Sens. Syst. Conf. 2005. 16. Niculescu, D., Nath, B.: Position and orientation in ad hoc networks. Ad Hoc Netw. 2, 133–151 (2004). https://doi.org/10.1016/s1570-8705(03)00051-9 17. Kułakowski, P., Vales-Alonso, J., Egea-López, E., et al.: Angle-of-arrival localization based on antenna arrays for wireless sensor networks. Comput Electr Eng 36, 1181–1186 (2010). https://doi.org/10.1016/j.compeleceng.2010.03.007 18. Want, R., Hopper, A., Falcão, V., Gibbons, J.: The active badge location system. ACM Trans Inf Syst 10, 91–102 (1992). https://doi.org/10.1145/128756.128759 19. Bahl P, Padmanabhan VN RADAR: an in-building RF-based user location and tracking system. Proc. IEEE INFOCOM 2000. Conf. Comput. Commun. Ninet. Annu. Jt. Conf. IEEE Comput. Commun. Soc. (Cat. No.00CH37064) 20. Kamali, S., Opatrny, J., (2007) POSANT: A Position Based Ant Colony Routing Algorithm for Mobile Ad-hoc Networks. : Third Int. Conf. Wirel. Mob, Commun (2007) 21. Asl, E.K., Damanafshan, M., Abbaspour, M., (2009) EMP-DSR: An Enhanced Multi-path Dynamic Source Routing Algorithm for MANETs Based on Ant Colony Optimization. , et al.: Third Asia Int. Conf. Model, Simul (2009) 22. Leung R, Liu J, Poon E, et al MP-DSR: a QoS-aware multi-path dynamic source routing protocol for wireless ad-hoc networks. Proc. LCN 2001. 26th Annu. IEEE Conf. Local Comput. Networks 23. Aissani M, Senouci MR, Demigna W, Mellouk A (2007) Optimizations and Performance Study of the Dynamic Source Routing Protocol. Int. Conf. Netw. Serv. (ICNS ’07) 24. Sung D-H, Youn J-S, Lee J-H, Kang C-H (2005) A Local Repair Scheme with Adaptive Promiscuous Mode in Mobile Ad Hoc Networks. Lect. Notes Comput. Sci. 351–361 25. Hassan J, Jia S Performance analysis of expanding ring search for multi-hop wireless networks. IEEE 60th Veh. Technol. Conf. 2004. VTC2004-Fall. 2004 26. Correia, F., Vazão, T., Lobo, V.J., (2009) Models for Pheromone Evaluation in Ant Systems for Mobile Ad-hoc Networks. : First Int. Conf. Emerg. Netw, Intell (2009)

Fake News Detection Using Convolutional Neural Networks and Random Forest—A Hybrid Approach Hitesh Narayan Soneji and Sughosh Sudhanvan

1 Introduction The rapid spread of unwanted, unsolicited news, rumors, etc., has increased exponentially over the years due to the boom in communication and technology making the Internet more easily accessible to the public. Furthermore, due to the lack of proper verification of the author of the news article, anyone on the web can write a fake article, and due to easy access by the public, the news spreads amazingly fast. The motive to spread fake news articles can be due to various factors such as political, monetary, and revenge. According to [1], in six weeks around the time of the 2016 presidential election, research suggests that as many as 25% of Americans visited a fake news website. Most US adults (62%) depend on news primarily sourced from social media. 66% of Facebook users, 59% of Twitter users, and 70% of Reddit users depend on the subsequent platforms for their news [2]. The increase and major awareness about this type of fake articles began during the 2016 United States presidential elections, which lead to many controversial news articles; the spread of such articles was purely for political gain. There have also been many documented incidents where fake news created riots, mob lynching, etc., around the world. Another reason for the increase of fake news articles besides the traditional news media is the huge presence of social media sites and people sharing those fake articles without verifying them. Social media sites like Facebook, Instagram, Twitter, and WhatsApp have given a huge boost to the spread of these fake articles. H. N. Soneji (B) · S. Sudhanvan MPSTME, NMIMS University, Mumbai, India e-mail: [email protected] S. Sudhanvan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_39

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A need to bifurcate the news articles into true or false has become very necessary; the spread of fake news articles has created a lot of problems, the general public cannot differentiate easily between the fake and true articles because of the various factors like the writing style and use of words that make the article feel real. The use of human fact-checking is also slow and not a cost-effective method. Hence, a lot of researches has been carried out to bifurcate the articles using automated methods and models using machine learning (ML), deep learning (DL), and natural language processing (NLP). In this paper, we have proposed a method using convolutional neural networks (CNN), random forest (RF), and PhishTank API [3] to differentiate between Phishing and Non-Phishing URLs. The model is divided into three phases. The first phase is URL detection and differentiating it into phishing and non-phishing using the PhishTank API. The article text body is searched for any URL and is then verified. The second phase consists of data pre-processing where the data is cleaned and shaped into the required format. In the last phase, CNN and RF classifiers are trained and used to classify the articles into fake and true. The remaining paper is organized as follows: Sect. 2 discusses related work in this field. In Sect. 3, we discuss the dataset and our proposed model—PhishTank API, data pre-processing, and the classifiers used for evaluation. Section 4 is the result analysis of the proposed model, and Sect. 5 includes the conclusion and future work.

2 Related Work Granik et al.[4] have used a dataset consisting of Facebook News Posts from Buzzfeed News, and the shares, comments, and reactions were also taken into consideration and then were classified as fake or true using Naive Bayes and Bag of Words method, which gave the authors an accuracy of 74%. Kaliyar [5] has worked in the same lines, and instead of using Naive Bayes as the classifier has used the CNN model and used term frequency–inverse document frequency(tf–idf) for pre-processing and achieved an accuracy of 91.3%. In [6, 7] different machine learning classifiers were compared using tf–idf for pre-processing. The models are compared, Katsaros et al. [6], and CNN gave a better accuracy compared to the other models, while Poddar et al. [7] used support vector machine (SVM) which performed better. Lin et al. [8] have, on a similar basis as the above papers compared different machine learning models on 134 different features, from which XGBoost gave the best accuracy of 86.6% compared to all other models. Han and Mehta [9] have compared machine learning and deep learning models on three features, normalized frequency, bi-gram tf–idf, and the union of the above two for training the model. While the machine learning models performed poorly, CNN and Recurrent Neural Network (RNN) gave accuracies above 80%. Kim and Jeong [10] tried to detect Korean fake news using fact database which is made by human’s direct judgment after collecting obvious facts. They have used Bidirectional Multi-Perspective Matching for Natural Language Sentence (BiMPM),

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as a deep learning model, but its accuracy decreases with an increase in the length of sentence. It got an accuracy of 88% for true and 47% for false. Al-Ash et al. [11] have detected fake news by combining frequency terms, inverse document frequency, and frequency reversed with tenfold cross-validation using a support vector machine algorithm classifier which gave accuracy of 96.74% across 2561 articles. Reddy et al.[12] have used a hybrid approach called a multinomial voting algorithm which gave an accuracy of 94%, whereas most of the machine learning algorithms have accuracies around 80%. Girgis et al. [13] used the LAIR dataset on RNN and long short-term memory (LSTM) deep learning models to classify articles as fake or not fake. RNN model gave the best accuracy. Kong et al. [14] have created n-gram vectors from the text and applied deep learning model on this. The best model gave an accuracy of 97%. Verma et al. [15] by using FastText model for word embeddings and LSTM along RNN for classification, which gave them a high accuracy of 94.3%. On evaluating the different models used in [16–18], Hlaing and Kham [16] mentioned decision tree (DT), RF as giving the best accuracies. Shabani and Sokhn [17] used logistic regression (LR), SVM, and neural network (NN) which gave high accuracies, while Harjule et al. [18] showed that RNN gave the highest accuracy. Refs. [19–21] have analyzed the different approaches taken for fake news classification among which Lahlou et al. [19] list linguistic and network approach as being the best suited, Mahid et al. [20] have proposed a new model for classification, and Manzoor et al. [21] discussed the different classifiers.

3 Dataset We have used a dataset that serves our needs to classify the news article. We are using our dataset from Kaggle.com [22] named Fake-News. The news articles are labeled with a 0 and 1 to identify whether the article is true or false. The dataset used was part of the Kaggle challenge. The dataset is given the CSV format. The news articles mentioned in the list contain news from different categories—political, business, etc. The contents of the dataset are provided in Table 1 (Table 2). A total of 20,800 news article entries are found in the dataset. We split the dataset into training and testing. As shown in Table 2 for CNN (title metadata), the split is Table 1 Description of the dataset columns Field

Description

Id

Unique ID for news article

Title

The title of the news article

Author

The author of the news article

Text

The text body of the article

Label

Used to mark the article as potentially unreliable, 1: Unreliable, 0: Reliable

352 Table 2 Accuracies for the different combinations of the models

H. N. Soneji and S. Sudhanvan Model used (metadata)

Accuracy (%)

CNN (title)

94

CNN (text)

96

RF (author)

96

CNN (title + text)

95

CNN (title) + RF (author)

95

CNN (text) + RF (author)

96

CNN (title + text) + RF (author)

95.33

30% and 70% for training and testing, respectively, for CNN (text metadata), the split is 50% each for training and testing, and for the RF (author), the split is 50% each for training and testing.

4 Proposed Model Figure 1 shows the flowchart of the proposed model: As we can see our proposed model is divided into four phases: 1. URL classification using PhishTank API, 2. Data pre-processing, 3. Training of classification models and combination, and 4. Classification.

4.1 URL Classification We have used an API called PhishTank which has a large database of URLs containing details of an URL whether it is phishing or not. Our motive here was to check for all the URLs if they are phishing or not. If they are phishing, then we can surely say that a news article is fake. For example, we consider ‘https://107.167.2.81/pc’ as the URL. When we pass this URL in the API call, then the database will be searched, and we got the result as it is a phishing URL. An API Call Example: https://checkurl.phishtank.com/checkurl/index.php?url=https://107.167.2.81/ pc&app_key=api_key.

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Fig. 1 Proposed model flowchart

4.2 Data Pre-processing Data pre-processing is an important step to convert the given data into the desired structure. Out of all the features in the dataset, only the required features were selected for the different classification models used. The title, text, author, and label were used in the different models, for instance, CNN for title classification used the title and the label, CNN for text classification used text and label, and RF used author and label. For the combined model, we used four features from the dataset title, author, text, and label. Figure 2 shows the steps followed for data pre-processing which are discussed below.

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Fig. 2 Steps followed for data pre-processing

Removing Punctuation: Removal of punctuation marks (full stop, comma, question mark, etc.) in the given data. Word Tokenization: With the help of word tokenization, we can convert our text into a list of words. A 2D list is made, in which each sub-list constitutes one news article entry. We used the Natural Language Kit (NLTK) module of Python for word tokenization. Stop Word Removal: Stop word is a commonly used word such as ‘a,’ ‘an,’ ‘the,’ ‘in,’ ‘for,’ and ‘of’ which need to be ignored for getting better accuracies and making the training and testing data consistent. We have used NLTK for stop word removal. Building vocabulary and finding maximum sentence length: A vocabulary is built for the training and testing data, with the help of the tokens created in the word tokenization step, and this vocabulary serves as a corpus for holding the words. Greater the vocabulary size better would be the results. The maximum sentence length helps us to assign the appropriate embedding values to the CNN model. Word Embedding: Converting the given words into word vectors with the help of the pre-trained word2doc model GoogleNews, the model contains vectors of size 300. Word embeddings are used to capture the semantic meaning of the words. The GoogleNews model has more than 3 billion such words which help in the word embedding process. Tokenizing and Pad Sequencing: Finally, we tokenize our text corpus into a vector of integers based on the embeddings, and then padding is done to have an equivalent size vector. Padding size is determined from the maximum sentence length found above.

4.3 Model Architecture The hybrid approach implemented in this model consists of the use of two different algorithms, CNN, and RF. It is a combination of the algorithms, where CNN is used when title and text are used for classification, whereas RF is used when the author name is used for classification. Using CNN for author metadata consumed unnecessary time and gave inferior results as expected; hence, we tried other algorithms and found out that RF performed the best compared to others. Using a hybrid approach— combination of CNN and RF—helped us improve the overall accuracy and reduced the overall space and time complexity. Both models are discussed below:

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Convolutional Neural Networks (CNN): In deep learning, CNN is a class of neural networks, which is most used for image classification. CNN consists of an input and an output layer and multiple hidden layers. The hidden layers consist of a series of convolutional layers that convolve with a dot product. The different layers in a CNN model are embedding layer, convolutional layer, MaxPooling layer, flatten layer, and fully connected layer. We have used the CNN model for two features text and title. The following architecture is used to build the model: Embedding Layer: At the input layer, for the title metadata, the tokens are embedded into a vector of size 50, and any text whose length in less than 50 is padded to make it 50. Sentence length is this case becomes 50, whereas in the case of text metadata, the vector size is kept at 1000. The outputs are then forwarded into the convolutional layer. Convolutional Layer: For the text metadata, five filters with five different sizes are used to extract the features. The activation function used is ReLu. Similarly, for the title metadata, we use three filters of three different sizes. MaxPooling Layer: The main work of the MaxPooling Layer is to reduce the size of the feature map. MaxPooling is used to retain the most important features. Flatten Layer: Flattening is used to transform the 2D matrix of features into a vector. Fully Connected Layer: The fully connected layer converts the passed matrix from the flattening layer into an output range between 0 and 1 using the sigmoid function. Random Forest: Random forest fits several decision tree classifiers, on the training data, and uses the average of all the decision trees for better predictive accuracy. We have used the random forest classifier model for the author name classification, it helps to distinguish between authors of fake and true news articles. We chose the random forest model because it gave better results compared to other models for author classification.

5 Result Analysis 5.1 Performance Parameters We have used different parameters for analyzing our results: Confusion matrix consists of true positive, true negative, false positive, and false negative. We have used precision and recall, F1 score, and accuracy. True positive (TP) indicates the outcome where the model predicts the true label as true.

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False positive (FP) indicates the outcome where the model predicts a true label as false. True negative (TN) indicates an outcome where the model predicts a false label as false. False negative (FN) indicates the outcome where the model predicts a false label as true. Precision gives the proportion of positive identifications that are correct. Precision =

TP TP + FP

Recall gives the proportion of actual positive that are identified correctly. Recall =

TP TP + FN

F1 score is a function of precision and recall and is used to seek a balance between precision and recall.  F1 = 2 ∗

Precision ∗ Recall Precision + Recall



Accuracy shows how close the value obtained is to its true value.

5.2 Results and Analysis We had three modules for detecting fake news using different metadata (author, text, and title). We have presented the confusion matrix for each model as below: From the above confusion matrices Figs. 3, 4, and 5, we can calculate TP, TN, FP, and FN and hence can calculate precision–recall and F1 score. For CNN model (text), precision was 0.96, recall was 0.97, and F1 score was 0.96. For the CNN Fig. 3 Confusion matrix for title

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Fig. 4 Confusion matrix for author

Fig. 5 Confusion matrix for text

model (title), precision was 0.94, the recall was 0.94, and the F1 score was 0.94, and for the RF model (author), precision was 0.96, the recall was 0.96, and the F1 score was 0.96. From the scores obtained, we can see high scores for F1 for all the three models, which indicates a balance between the precision and recall values. The accuracies for title, author, and text are 94%, 96%, and 96%, respectively. When we consider the combined accuracy for title and text, we get 95%, same way for title and author, we get 95%, and the rest two gives 96%. When we combine all three, we get an accuracy of 95.33%. So according to the accuracies, we can infer that when we detect fake news using the text and author, we get the best results. For detecting phishing URLs in the news text, we used PhishTank to verify, track, and share phishing URLs. If the URL is already in its database and is reported spam, then we can directly declare the news as fake as there is a phishing URL in it, which in turn increases the overall accuracy rate. On comparing, with other models from the literature survey, in [6, 13, 14], and [15], the different authors have applied deep learning models, i.e., CNN and RNN for detection of fake news. In all of these models, their accuracy is about 90% itself and the dataset used is either based on one category of news or dataset is very small to correctly detect the articles which was not used while training. Our model is an advanced model as compared to these, since our accuracy reaches about 95% and also we have incorporated a large dataset so that more articles can be included for training.

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6 Conclusion In today’s world, there is a rapid spread of fake news due to ease of access to social media sites, messaging, etc. That is the reason a person should verify the authenticity of the news article which is time-consuming and at times cannot be done. With our model, the user can easily check the authenticity by providing the title, author, and article text to our system. With the pre-trained machine learning and deep learning models, the output whether it is fake or not is obtained. This system is very efficient as we obtained a combined accuracy of around 96%, and hence, it is reliable. Although this work is giving high accuracies, we wish to continue working on the same. Our next challenges are: (i) to be able to train the models with even larger datasets by keeping the accuracy same or above par (ii) to try and increase the accuracy even more by taking some extra features like the URL of the news article.

References 1. Mike, W.: The (almost) complete history of ‘fake news’ retrieved from https://www.bbc.com/ news/blogs-trending-42724320 22 Jan 2018 2. Gottfried, J., Shearer, E.: News use across social media platforms 2016. Retrieved from https:// www.journalism.org/2016/05/26/news-use-across-social-mediaplatforms-2016/ 26 May 2016 3. Phishtank API Developer site. https://www.phishtank.com/api_info.php 4. Granik, M., Mesyura, V.: Fake news detection using naive Bayes classifier. 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kiev, pp. 900–903 (2017) 5. Kaliyar, R.K.: Fake News Detection Using A Deep Neural Network, 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 1–7 (2018) 6. Katsaros, d., Stavropoulos G., Papakostas, D.: Which machine learning paradigm for fake news detection? 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Thessaloniki, Greece, pp. 383–387 (2019) 7. Poddar, K., Amali, G.B.D., Umadevi, K.S.: Comparison of various machine learning models for accurate detection of fake news. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, pp. 1–5 (2019) 8. Lin, J., Tremblay-Taylor, G., Mou, G., You, D., Lee, K. Detecting fake news articles. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, pp. 3021–3025 (2019) 9. Han, W., Mehta, V.: Fake news detection in social networks using machine learning and deep learning: Performance evaluation. In: 2019 IEEE International Conference on Industrial Internet (ICII), Orlando, FL, USA, pp. 375–380 (2019) 10. Kim, K., Jeong, C.: Fake news detection system using article abstraction. In: 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), Chonburi, Thailand, pp. 209–212 (2019) 11. Al-Ash, H.S., Wibowo, W.C.: Fake news identification characteristics using named entity recognition and phrase detection. In: 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Kuta, pp. 12–17 (2018) 12. Reddy, P.B.P., Reddy, M.P.K., Reddy, G.V.M., Mehata, K.M.: Fake data analysis and detection using ensembled hybrid algorithm. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 890–897 (2019)

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13. Girgis, S., Amer, E., Gadallah, M.: Deep Learning Algorithms for Detecting Fake News in Online Text. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 93–97 (2018) 14. Kong, S.H., Tan, L.M., Gan, K.H., Samsudin, N.H.: Fake news detection using deep learning. In: 2020 IEEE 10th Symposium on Computer Applications and Industrial Electronics (ISCAIE), Malaysia, pp. 102–107 (2020) 15. Verma, A., Mittal, V., Dawn, S.: FIND: Fake information and news detections using deep learning. In: 2019 Twelfth International Conference on Contemporary Computing (IC3), Noida, India, pp. 1–7 (2019) 16. Hlaing, M.M.M., Kham, N.S.M.: Defining news authenticity on social media using machine learning approach. In: 2020 IEEE Conference on Computer Applications(ICCA), Yangon, Myanmar, pp. 1–6 (2020) 17. Shabani, S., Sokhn,M.: Hybrid machine-crowd approach for fake news detection. In: 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), Philadelphia, PA, pp. 299–306 (2018) 18. Harjule, P., Sharma, A., Chouhan, S., Joshi, S.: Reliability of news. in: 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), Jaipur, India, pp 165–170 (2020) 19. Lahlou, Y. El Fkihi, S., Faizi, R.: Automatic detection of fake news on online platforms: A survey. In: 2019 1st International Conference on Smart Systems and Data Science (ICSSD), Rabat, Morocco, pp. 1–4 (2019) 20. Mahid, Z. I., Manickam, S., Karuppayah, S.: Fake news on social media: Brief review on detection techniques. In: 2018 Fourth International Conference on Advances in Computing, Communication and Automation (ICACCA), Subang Jaya, Malaysia, pp. 1–5 (2018) 21. Manzoor, S.I., Singla J., Nikita: Fake news detection using machine learning approaches: A systematic Review. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, pp. 230–234 (2019) 22. Fake-news Dataset, retrieved from https://www.kaggle.com/c/fake-news/data?select=train.csv

An Enhanced Fuzzy TOPSIS in Soft Computing for the Best Selection of Health Insurance K. R. Sekar, M. Sarika, M. Mitchelle Flavia Jerome, V. Venkataraman, and C. Thaventhiran

1 Introduction In big data, the more complex form is dealing with decision making and various methods are suggested to solve different problem in making decisions. In those methods, the solution for solving problems related to uncertain qualitative information in decision making is fulfilled by fuzzy linguistic terms and linguistic decision making (LDM). In LDM, hesitant decision makers are represented by fuzzy linguistic values. Similar to the above method, another method is proposed which is the conventional fuzzy linguistic approach in which the linguistic variables are artificial language words, leading to certain constraints like linguistic terms numbers, complexity in computational, absence of accuracy, and information loss in the estimate process. Many similar methods are suggested to minimize these constraints, such as the linguistic model, 2-tuple model, linguistic virtual model and the “hesitant fuzzy linguistic term set". After concluding with the pros and cons of all the above methods proposed, we came to the conclusion that the decision making with hesitant fuzzy terms in linguistic sets are persistent in LDM and different hesitant fuzzy decision making are regarded to be the best model.Multi criteria Decision Makers are still limited in various application. This paper introduced one method called R-TOPSIS.. The approach proposed has also been validated using dispersion statistics and similarity to test the classic TOPSIS method [1]. In LDM methods, different criteria hesitant decision matrixes are expressed as linguistic assessments alternatives of linguistic assessments by all decision makers in community multicriteria decision making considered to be simple linguistic term, empty set, interval K. R. Sekar · M. Sarika · M. Mitchelle Flavia Jerome · C. Thaventhiran School of Computing, Sastra Deemed University, Thanjavur, India e-mail: [email protected] V. Venkataraman (B) School of Arts Science and Humanities, Sastra Deemed University, Thanjavur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_40

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in linguistic or hesitant fuzzy set, etc. In this paper proposed spherical fuzzy values are introduced with their score and accuracy under Intuitionistic sets and novel spherical fuzzy values evaluated by intervals. To test the approach developed, this method solves a problem of multiple selection criteria among 3D printers [2]. In this paper we presented the MCDM principle for decision-making and also the best idea to overcome all the bad results in decision-making. We have been commonly used in different fields for multi-criteria decision-analysis. One of the MCDA methods called TOPSIS has been analyzed and categorized into various application areas such as chain supply, environmental situation, conventional energy sources, business orientated, healthcare applications. There are different methods such as fuzzy values, Intuitionistic hesitant fuzzy values. Finally this is the most popular approach for group decision making [3]. This paper proposed a hybrid approach to mHealth applications, called AHP. AHP used criterion weight and subcriteria to be calculated, and TOPSIS approach was used to achieve the ranking of various applications. The approach proposed is used to pick the proper application of mHealth in this digital world [4]. Tools are required in the monitoring system to incorporate information, priorities and expectations from a community of Technical Experts. This paper proposes a weighted TOPSIS which is adjusted and unmodified. The weighted modified TOPSIS approach is a simple and effective technique for solving decision-making problems where ideal and non-ideal solutions are profiled by decision-makers [5]. Cloud Computing has developed into a way to deploy business processes and various applications. It also suggested two metrics, EC and EIF, for a better appreciation and study of trends, trend. A case study on generic parameters and parameters is proposed to rate effective values with the use of enhanced TOPSIS method [6]. Medical tourism is some part of health-care tourism. This paper suggested two decision making strategies with multi-criteria, DEMATEL and Fuzzy hesitant TOPSIS, to expose the interrelationships between the variables. Such findings would potentially allow the assignment of medical tourism investments in some developing countries [7]. It also implemented a method which is in particular the TOPSIS method, in which we defined and presented Fuzzy hesitant TOPSIS method for a multi-criteria group decision-making scenario, in which LINGUISTIC and INTUITIONSTIC decision-makers are given the optimal solutions. In the article fuzzy weightage has been calculated in two ways. One is every linguistic like low, medium, high and very high has got its significant intuitionistic values that all will be available with the scale of 1 to 10. The same has given as triplets. In our research work the above said has applied with the objective based weightage. So the accuracy has been increased through the work.

2 Relative Work This paper focuses on systematic human error investigation and Fuzzy TOPSIS to define primary human error features in Taiwan’s emergency departments. MCDM framework and hierarchical process approach have been introduced. This is used

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to calculate the important purpose of error feature to determine the accuracy of the criteria [8]. This paper proposes regression-TOPSIS, analyzing data from the disaster. This resource provides decision-makers and planners with open risk information to identify the most risk zone for flood risk management on a national and local scale [9]. This paper proposes an incorporated methodology based on SWOT approach, AHP, and F-TOPSIS are used to evaluate energy sustainable planning. It describes the critical factors and sub-factors for sustainable energy planning. The results validate the strength and scientific approach to develop and estimate energy assessing for renewable energy planning [10]. HFLTS and MAGDM are applied using the two methods, the first method deals with VIKOR and the second method deals with the TOPSIS method, resulting in a compromise between group benefit and individual regret and similarly with + ve and -ve solutions[11]. Vague Set TOPSIS is implemented for hotel selection in order to save travelers’ effort and time based on certain hotel options. The support decision algorithm is proposed to satisfy mathematical evidence, computer scientific stochastic model experiments and numerical case study using numerical values, resulting in the selection of the best hotel between several hotels [12]. Integration of GIS Geographical Information System—GIS, AHP and TOPSIS methods are applied in order to obtain structural and pair quantification, as well as in order to obtain the result for appropriate locations for industrial development for comparison between elements and for priority ranking purposes[13]. Fuzzy TOPSIS is carried out in sustainable acid rain control for the appropriate selection of affordable acid rain prevent options for society, under environmental, social economic and institutional considerations and positive and negative ideal solutions result in different energy sources in the highest ranked [14]. The modified and unmodified weightage of TOPSIS study are incorporated to prefer selection methods in surveillance. The modified weightage TOPSIS method resulted in high calculation of relative measures to the closeness of ideal-solution and the unmodified weightage TOPSIS analysis results in similar estimation of the relative measures to the closeness of ideal-solution.

3 Methods δpq =



 α pq β pq η pq  ˜ , , q ε B, cq = max c¯pq , q  ε B˜ cq cq cq

(1)

The above mentioned normalization technique is to maintain the property that consequences from normalized triangular fuzzy numbers p [0, 1]. By taking diverse potential in every condition, the weighted normalized hesitant fuzzy decision matrix produced by.   V  = v pq lxm , p  = 1, 2, . . . m and q  = 1, 2, . . . , n.

(2)

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This consequences in the weighted normalized hesitant fuzzy decision matrix, 

that the dynamic V pq , p  , q  are statistical normalized positive absolute fuzzy scores ˜ and their ranges p [0, 1]. Then, outline the fuzzy positive-ideal solution (FPIS, A*) # ˜ and fuzzy negative-ideal solution (FNIS, A ) as. ∗

dp =

n 

#

d1 (v pq , vq∗ ), d p =

q=1

n 

d1 (v pq , vq# ), p  = 1, 2, . . . , m

q=1

where d1 denotes the distance calculated between two hesitant fuzzy numbers. A closeness coefficient is described to determine the ranking order of all alternatives ∗ # are ranked once the d p and d p of each alternative A¯ p ( p  = 1, 2, …, m) had been calculated. The coefficient of relative closeness each chance formed by. ∗

CCi =

dp ∗ dp

+

# dp

, p  = 1, 2, . . . , m

(4)

˜ and more distant from FNIS ( A˜ # ) as An possible A˜ i is closer to the FPIS ( A*) CCi ways to deal with 1. The relative closeness coefficient makes a decision the positioning request all things are considered and select the high quality one from among a lots of attainable other options. Illustration of MAGDM problem: A MAGDM problem can be discussed as follows: Let  X  s = x spq l×m

⎛ s 1 c11 A ⎜ s 2 ⎜ c21 A ⎜ . ⎜. = ⎜ . ⎜. ⎜ . ⎝. s l cl1 A

decision matrix.  R  s = r spq l×m = , s ε T1 .

s c12 s c22 . . . s cl2

... ... . . . ...

⎞ s c1m s ⎟ c2m ⎟ ⎟ . ⎟ ⎟, s ε T1 , be decision matrix of sth . ⎟ ⎟ . ⎠ s clm



For weight vector W = (w1 ,w2 ,…,wn ) ε T1 of the attributes, it is feasible to get the weighted decision matrix as.  statistical  normalized Z  s = z spq l×m = w j r spq l×m =, s ε T1 .  The resultant multi criteria group decision matrix Z1 = z spq l×m by applying the calculation. t   Z1 = αs Z S = z pq l×m (5). 

s=1

T  where α  = α1 , α2 , ..., αt 1 is the weight of hesitant decision matrix where αt ≥ m t m    s 0 αs = 1, and z pq = αs z pq , yi = z pq , p  ∈ M the total of all the elements s=1

s=1

q=1

An Enhanced Fuzzy TOPSIS in Soft Computing for the Best Selection …

  s in the ith row of Z1 = z pq

l×m



365

and then results in the complete characteristic

values z p ( p  ε M) of the substitutes A¯ p ( p  ε M). To obtain this, the weight vector T  α  = α1 , α2 , ..., αt 1 of DMs provides an important role in Multi Attribute group decision making.

4 Results and Discussions In the research article work, the proposed applications, methodologies and outcomes literally gives the eye opener for the upcoming researchers. So far using the TOPSIS enterprises applications are 3D printers, health care, business resources, supply chain, surveillance system, cloud computing, Medical tourism and sustainable energy planning. The methodologies used to gauge the applications were R-TOPSIS, novel interval valued spherical fuzzy, Analytical Hierarchical Processing, Weight Normalization, DEMATEL, SWOT Analysis, Fuzzy Analytical Hierarchical Processing and PROMOTHEE. The outcomes of the methodologies are to determine accuracy of criteria, calculate to the closeness of ideal solution and finest ranking selection for hotels and best investment in medical tourism. The new method introduced here is more logical and efficient to solving various types of MCDM problems than the other approaches. Juptyer Python Interface has measured and used an Anaconda navigator in our research work an improved TOPSIS in the Health Insurance Program offers a greater accuracy of 92.26 percent It is necessary to make the final decision by applying MCDM algorithms to infer better results for the same problems in order to illustrate the applicability and potential of the method.

4.1 Illustration Work Linguistic values DM1

DM2

DM3

L

1,2,3

L

2,3,4

L

3,4,5

M

2,3,4

M

3,5,6

M

4,5,6

H

3,4,5

H

5,6,7

H

5,6,7

VH

4,5,6

VH

6,7,8

VH

6,7,8

LEGEND 1: C1—Individual plan, C2—Family plan, C3—Entry age, C4—Premium, C5—Claim, C6—Sum assured

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DM1 Health insurance

C1

C2

C3

C4

C5

C6

HI1

L

VH

H

VH

M

VH

HI2

H

M

M

L

L

M

HI3

VH

H

L

M

VH

H

HI4

M

L

VH

H

VH

L

Health insurance

C1

C2

C3

C4

C5

C6

HI1

H

VH

M

VH

L

VH

HI2

M

L

L

M

H

M

HI3

L

M

VH

H

VH

H

HI4

VH

H

VH

L

M

L

Health insurance

C1

C2

C3

C4

C5

C6

HI1

VH

L

VH

H

VH

M

HI2

M

H

M

M

L

L

HI3

H

VH

H

L

M

VH

HI4

L

M

L

VH

H

VH

DM2

DM3

5 Limitation of Hesitant TOPSIS In the hesitant TOPSIS method one of the main limitations of the out-ranking is based on two factors. One is about the attribute selection which is otherwise called as criterions. The attribute preference changes the due addition and deletion that will affect the ranking. Second one in the article that contains two objectives like beneficial and non beneficial attributes according to the application scenarios and another one objective applied is weightage which depends on the previous objective beneficial and non beneficial attributes. The above said phenomena will reversal or change the OUT-RANKING (Tables 1, 2, 3, 4, 5, 6 and 7).

6 Conclusion In the research work, identification of the good insurance company is the major and the hectic task for the customers. Selecting good insurance is very much be needed

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367

in our day to day practice because to get back the good service from them. We have taken the criteria like Individual Plan, Family Plan, Entry Age, Premium, Claim, Sum Assured and alternatives around four insurance companies. Multi-criterion with multi objectives were taken into account for the evaluating and ranking the insurance companies using the enhanced Fuzzy TOPSIS methodology. Three decision-makers with their linguistics and Intuitionistic values were incorporated for this study. The stable rankings were produced to identify the feasible insurance according to the need of the customer’s facilities. All the decision-makers’ Intuitionistic values were integrated as and named as an aggregation. In the aggregation table, we applied two objectives like beneficial, non beneficial and the second objective is weightage has be provided according to the beneficial order as a triple form. All the criterions and alternatives were thoroughly investigated using final positive ideal solutions which will increase the service and reduce the cost factor. On the other hand, final negative ideal solutions will increase the cost and reduce the services. Both the above methods were analyzed through the coefficient of closeness among the alternatives and welltaken criteria. In our contribution is to put the weightage as triple form according to the basis of the first objective beneficial and non-beneficial ordering. In the TOPSIS work the ranking insurance companies were made it optimum way and really the eye-opener for the upcoming researchers to do more work on the same basis for different applications.

Table 1 Intuitionistic values DM! Health insurance

c1

c2

c3

c4

c5

c6

HI 1

1

2

3

4

5

6

3

4

5

4

5

6

2

3

4

4

5

6

HI2

3

4

5

2

3

4

2

3

4

1

2

3

1

2

3

2

3

4

HI3

4

5

6

3

4

5

1

2

3

2

3

4

4

5

6

3

4

5

HI4

2

3

4

1

2

3

4

5

6

3

4

5

4

5

6

1

2

3

Table 2 Intuitionistic values DM2 Health insurance

c1

c2

c3

c4

c5

c6

HI 1

5

6

7

6

7

8

3

5

6

6

7

8

2

3

4

6

7

8

HI2

3

5

6

2

3

4

2

3

4

3

5

6

5

6

7

3

5

6

HI3

2

3

4

3

5

6

6

7

8

5

6

7

6

7

8

5

6

7

HI4

6

7

8

5

6

7

6

7

8

2

3

4

3

5

6

2

3

4

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K. R. Sekar et al.

Table 3 Intuitionistic values DM3 Health insurance

c1

c2

c3

c4

c5

c6

HI 1

6

7

8

3

4

5

6

7

8

5

6

7

6

7

8

4

5

6

HI2

4

5

6

5

6

7

4

5

6

4

5

6

3

4

5

3

4

5

HI3

5

6

7

6

7

8

5

6

7

3

4

5

4

5

6

6

7

8

HI4

3

4

5

4

5

6

3

4

5

6

7

8

5

6

7

6

7

8

Table 4 Aggregation phase DM3 Health c1 insurance

c2 8 3

c3

c4

c5

c6

HI 1

1

5

6.33 8 3

5.33 8 4

6

8 2

4.33 8 4

5.67 8

HI2

3

4.67 6 2

5

3.67 6 1

4

6 1

4

4

HI3

2

4.67 7 3

6.33 8 1

5

8 2

4.33 7 4

5.67 8 3

5.67 8

HI4

2

4.67 8 1

5.33 7 3

5.33 8 2

4.67 8 3

5.33 7 1

4

B

B

B

B

7 2

NB

NB

7 2

B—Beneficial, NB—Non beneficial Using (1)

Table 5 Final positive ideal solution (FPIS) Health insurance

C1

C2

HI1

32.7

0

HI2

55.2

40.6

HI3

23.2

HI4

9.67

C3 2.5185185 96.166667

C4

C5

C6

DI*

10.166667

51.6

0.02

97

0

117

116

425

0.02

1.1851852

1.7407407

0

8.17

34.3

56.8

2.9074074

6.462963

22.9

103

202

C5

C6

Di−

Using (3)

Table 6 Final negative ideal solution (FNIS) Health insurance

C1

C2

C3

C4

HI1

55.5

65.1

96.018519

0

22.9

156

395

HI2

32.7

2.4074074

10.166667

0

8.17

61.6

HI3

21.7

96.833333

4.0185185

117

116

422

HI4

62.2

96

1.8518519

51.6

54

267

Using (3)

8.17 66.3 1.19

6 8

An Enhanced Fuzzy TOPSIS in Soft Computing for the Best Selection …

369

Table 7 Coefficient of closeness Health insurance

DI +

DI−

Coefficient of closeness

Ranking

HI1

97

395

0.8029123

2

HI2

425

61.6

0.1266035

4

HI3

34.3

422

0.9248386

1

HI4

202

267

0.5693232

3

Using (4 & 5)

References 1. de Farias Aires, R.F., Ferreira, L.: A new approach to avoid rank reversal cases in the TOPSIS method. Comput. Indus. Eng. 132, 84–97 (2019) 2. Gündo˘gdu, F.K., Kahraman, C.: A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets. Eng. Appl. Artif. Intell. 85, 307–23 (2019) 3. Palczewski, K., Sałabun, W.: The fuzzy TOPSIS applications in the last decade. Procedia Comput. Sci. 159, 2294–303 (2019) 4. Rajak, M., Shaw, K.: Evaluation and selection of mobile health (mHealth) applications using AHP and fuzzy TOPSIS. Technol Soc 59, 101186 (2019) 5. El Allaki, F., Christensen, J., Vallières, A.: A modified TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) applied to choosing appropriate selection methods in ongoing surveillance for Avian Influenza in Canada. Preventive veterinary medicine. 165, 36–43 (2019) 6. Garg, D., Sidhu, J., Rani, S.: Improved TOPSIS: A multi-criteria decision making for research productivity in cloud security. Comput. Stand. Interf. 65, 61–78 (2019) 7. Nilashi, M., Samad, S., Manaf, A.A., Ahmadi, H., Rashid, T.A., Munshi, A., Almukadi, W., Ibrahim, O., Ahmed, O.H.: Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Comput. Indus. Eng. 137, 106005 (2019) 8. Hsieh, M.C., Wang, E.M., Lee, W.C., Li, L.W., Hsieh, C.Y., Tsai, W., Wang, C.P., Huang, J.L., Liu, T.C.: Application of HFACS, fuzzy TOPSIS, and AHP for identifying important human error factors in emergency departments in Taiwan. Int. J. Indus. Ergon. 67, 171–9 (2018) 9. Luu, C., von Meding, J., Mojtahedi, M.: Analyzing Vietnam’s national disaster loss database for flood risk assessment using multiple linear regression-TOPSIS. Int. J. Disaster Risk Reduct. 40, 101153 (2019) 10. Solangi, Y.A., Tan, Q., Mirjat, N.H., Ali, S.: Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. J. Clean. Prod. 236, 117655 (2019) 11. Wu, Z., Xu, J., Jiang, X., Zhong, L.: Two MAGDM models based on hesitant fuzzy linguistic term sets with possibility distributions: VIKOR and TOPSIS. Inf. Sci. 473, 101–20 (2019) 12. Kwok, P.K., Lau, H.Y.: Hotel selection using a modified TOPSIS-based decision support algorithm. Decision Support Systems 120, 95–105 (2019) 13. Ramya, S., Devadas, V:. Integration of GIS, AHP and TOPSIS in evaluating suitable locations for industrial development: A case of Tehri Garhwal district, Uttarakhand, India. J. Clean. Prod. 238, 117872 (2019) 14. Onu, P.U., Quan, X., Xu, L., Orji, J., Onu, E.: Evaluation of sustainable acid rain control options utilizing a fuzzy TOPSIS multi-criteria decision analysis model frame work. J. Clean. Prod. 141, 612–25 (2017)

Non-intrusive Load Monitoring with ANN-Based Active Power Disaggregation of Electrical Appliances R. Chandran Lekshmi, K. Ilango, G. Nair Manjula, V. Ashish, John Aleena, G. Abhijith, H. Kumar Anagha, and Raghavendra Akhil

1 Introduction The worldwide demand for energy is increasing rapidly and is predicted to double between 2020 and 2050. Hence, proper energy usage study should be carried out. In 1890s, C.W. Hart puts forward the purpose of NILM [1]. NILM is the process for analyzing the power of a house and deducing the consumption of energy of those particular appliances. The device has unique load signature that will allow the NILM system to analyze patterns and recognize which devices are running. In order to get useful input and energy-saving steps, real-time load monitoring method is more effective [2]. R. C. Lekshmi (B) · G. N. Manjula · V. Ashish · J. Aleena · G. Abhijith · H. K. Anagha · R. Akhil Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] V. Ashish e-mail: [email protected] J. Aleena e-mail: [email protected] G. Abhijith e-mail: [email protected] H. K. Anagha e-mail: [email protected] R. Akhil e-mail: [email protected] K. Ilango Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_41

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Non-intrusive load monitoring (NILM) methods have become a major replacement for energy disaggregation, as it helps in separating the individual consumption. The growth of NILM methods has been stimulated by using the platform of Internet of Things (IoT). NILM system analyzes voltage and current waveforms that classify load signatures that can be related to the design and state of operation of individual devices. Based on the analysis and processing of signals, it performs decomposition and identification of loads in order to determine power consumption of all loads [3]. Many systems using NILM techniques seek energy conservation, performing energy use surveys, both in residential and industrial. One of NILM’s major advantages is that it decides the actions of appliances from a centralized point without the need for individual sensors at individual loads. It is a pattern classification-based recognition algorithm and judges changes in load signature [3]. Choosing the load signature is a major task [4, 5]. The proposed paper helps to identify appliances with different active powers using NLM methods. The data of electrical loads and the classification methods used by a neural network are used to check the model accuracy for selecting items [6]. Thus, training of these appliances uses machine learning algorithms such as the artificial neural network to achieve greater accuracy of recognition with less error [7]. This paper consists of following sections. Section 2 involves the background of the NILM algorithms. Section 3 defines ANN-based active power disaggregation with results. Section 4 gives the conclusion.

2 Background Energy disaggregation and appliance identification algorithm using machine learning, deep learning and optimization are under progress [8, 9]. Machine learning algorithms of NILM include supervised and unsupervised algorithms [10]. Supervised learning algorithms have several limitations that include additional marking before training, as they are designed for limited implementations. Any NILM model uses either transient or steady-state features of electrical appliances. Each differs in feature extraction, sampling periods, number of samples, training time, processing time, etc. Decision tree is used for the identification of loads in NILM [3]. Super-state HMMs or factorial hidden Markov model (FHMM) is used for modeling a household and identifying different power states of appliances [11]. The Viterbi (sparse) algorithm and particle filter are used as algorithms for the disaggregation. However, these algorithms do not provide more accurate results for disaggregation, that is not able to run when the number of appliances grow [12]. On the basis of calculation trends and a set with pre-specified trends, a machine learning algorithm can identify appliances from aggregated datasets. This paper focuses on real-time disaggregation of active power using ANN by reducing the complexity of disaggregation algorithms.

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3 ANN Based Active Power Disaggregation Neural networks and classification algorithms are used to predict patterns of use, to classify and cluster electrical systems for the detection of real time machines [13, 14]. In this study, artificial neural network (ANN) is used to perform the disaggregation of active power of different appliances. ANN enables the cluster of a new dataset with an earlier trained dataset. The disaggregation process flow is shown as in Fig. 1. The ANN modeling for NILM has the following steps involved, 1. 2. 3. 4.

Data collection Feature extraction Training and validating Estimation or prediction.

3.1 Data Collection and Feature Extraction Data collection from smart meter is the initial step for monitoring the load as given in Fig. 1. The measured current and voltage using smart meter is used for the feature extraction. The data is collected with a sampling rate of 4 kHz. The next step is to obtain steady-state features, transient state features and nontraditional features. This model extracts active power and RMS value of current as steady-state features for NILM.

Fig. 1 Block diagram of NILM with ANN disaggregation

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3.2 Training and Validating After extracting the features, the next role is to identify the loads which are active at particular time. Figure 3 gives the process involved in the training using a flowchart. Training requires individual appliance feature data for artificial neural network so they can correctly identify and segregate the loads with respect to time. Figure 2 shows the steady-state features and the target as active power estimation of each appliance used for training. The proposed NILM considers four different appliances such as bulb, hairdryer, laptop charger and table fan. The design specifications of these appliances are specified in Table 1. The characteristic features are extracted for each load and given for training. Artificial neural network model consists of two input layers for each feature, ten hidden layers and four output layers which correspond to active power of individual loads. The network was trained with Bayesian regularization algorithm as it can produce most accurate outputs. After training for 1000 epochs, the neural network was found with a mean squared error (MSE) of 1.07995 for training and MSE of 1.5447 during the testing and validating of the model as shown in Figs. 2, 3, 4 and 5.

Fig. 2 Mean squared error during training and testing

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Fig. 3 Flowchart of training using ANN

Table 1 Appliances specifications used for modeling

Appliances

Voltage (V) Active power Reactive power (W) (VAR)

Bulb

230

60

0

Table fan

230

75

10

Hair dryer

230

1200

15

Laptop charger 230

40

0

3.3 Estimation or Prediction The trained ANN model can be used for the actual load estimation. The root mean square error (RMSE) is calculated for each real testing using the Eq. (1), where N is the number of sample, p is the predicted value and q is the actual value.

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Fig. 4 Flowchart of ANN testing

⎛ ⎞  N  ( p − q)2 ⎠ RMSE = ⎝ N i=1

(1)

The effectiveness of the model in estimating the active power is analyzed for different scenarios as shown below.

3.3.1

Scenario 1: All Loads are Present

In scenario 1, RMS current and active power consumption of bulb, hairdryer, laptop charger and table fan were taken and tested. The RMS current and active power obtained for scenario 1 are shown as in Fig. 6. Figure 7 shows actual and estimated ANN active power of different appliances. Table 2 shows RMSE values for different

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Fig. 5 ANN with feature inputs and outputs for NILM

Fig. 6 Total RMS current and total power consumption for scenario 1

scenarios. From Table 2, it can be inferred that the individual appliance power can be identified with an average root mean square error of 0.0783.

3.3.2

Scenario 2: Any 3 Loads are Present

In scenario 2, RMS current and active power consumption of hairdryer, laptop charger and table fan were taken and tested. The RMS current and active power obtained for

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Fig. 7 Actual verses predicted ANN estimation for scenario 1

Table 2 Root mean square error of ANN estimation of different loads at different scenarios Scenario

RMSE of Bulb

RMSE of Hairdryer

RMSE of laptop charger

RMSE of table fan

Average RMSE

1

0.0970

0.1644

0.211

0.0308

0.0783

2

–

0.2701

0.0058

0.0333

0.1031

3

–

0.1068

0.0195

–

0.0631

4

0.0124

–

–

–

0.0124

5

0.1650

0.1664

0.1845

0.1411

0.1642

scenario 3 are shown as in Fig. 8. Figure 9 shows actual and estimated ANN active power of different appliances. From Table 2, it can be inferred that the individual appliance power can be identified with an average root mean square error of 0.1031.

3.3.3

Scenario 3: Any 2 Loads are Present

In scenario 3, RMS current and active power consumption of bulb, hairdryer and laptop charger were taken and tested. The RMS current and active power obtained for scenario 3 are shown as in Fig. 10. Figure 11 shows actual and estimated ANN active power of different appliances. From Table 2, it can be inferred that the individual appliance power can be identified with an average root mean square error of 0.0631.

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Fig. 8 Total RMS current and total power consumption for scenario 2

Fig. 9 Actual versus predicted ANN estimation for scenario 2

3.3.4

Scenario 4: Any One Load is Present

In scenario 4, the RMS current and active power consumption of bulb are tested. The RMS current and active power for scenario 4 are shown as in Fig. 12. Figure 13 shows actual and estimated ANN active power of different appliances. From Table 2, it can be inferred that the individual appliance power can be identified with an average root mean square error of 0.0124.

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Fig. 10 Total RMS current and total power consumption for scenario 3

Fig. 11 Actual versus predicted ANN estimation for scenario 3

3.3.5

Scenario 5: Different Loads Operated on Different Time Zones

In this scenario, different loads are operated at different time and verified the effectiveness of the ANN model estimation. The RMS current and active power obtained for scenario 5 are shown as in Fig. 14. Figure 15 shows actual and estimated ANN active power of different appliances. From Table 2, it can be inferred that the individual appliance power can be identified with an average root mean square error percentage of 0.1642. From Table 2, it can be inferred that NILM with ANN-based power estimation has an overall RMSE of 0.08422 when tested for different load combinations.

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Fig. 12 Total RMS current and total power consumption for scenario 4

Fig. 13 Actual versus predicted ANN estimation for scenario 4

Fig. 14 Total RMS current and total power consumption for scenario 5

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Fig. 15 Actual versus predicted ANN estimation for scenario 5

4 Conclusion Energy management with affordable cost is a concern in today’s world. NILM with single-point measurement and appliance monitoring reduces the sensor cost and effective monitoring of the system. The ability to identify power usage based on signatures for electrical appliances will be essential for smart electronic control systems to automate their power consumption. In this paper, the effectiveness of the ANN for power disaggregation of appliances from single-point smart meter measurement was studied. From the analysis of different scenarios, it is obvious that ANN is able to recognize each system and also have better estimation with less error. In future, work can be extended focusing on identification and estimation of appliances with similar power characteristics.

References 1. Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992) 2. Hui, .L.Y., Logenthiran, T., Woo, W.L.: Non-intrusive appliance load monitoring and identification for smart home. In: 2016 IEEE 6th International Conference on Power Systems (ICPS), pp. 1–6. New Delhi (2016) 3. Sreevidhya, C., Kumar, M., Karuppasamy, I.: Design and implementation of non-intrusive load monitoring using machine learning algorithm for appliance monitoring. In: 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), pp. 1–6. Tamil Nadu, India (2019). https://doi.org/10.1109/INCOS45849.2019.895 1312 4. Yang, H., Chang, H., Lin, C.: Design a neural network for features selection in non-intrusive monitoring of industrial electrical loads. In: 2007 11th International Conference on Computer Supported Cooperative Work in Design, pp. 1022–1027. Melbourne, VIC (2007) 5. Hasan, T., Javed, F., Arshad, N.: An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring. In: 2014 IEEE PES General Meeting | Conference and Exposition, p. 1. National Harbor, MD (2014) 6. Popescu, F., Enache, F., Vizitiu, I., Ciotîrnae, P.: Recurrence plot analysis for characterization of appliance load signature. In: 2014 10th International Conference on Communications (COMM), pp. 1–4. Bucharest (2014)

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7. Jiao, D., Yu, H.Y., Wu, X.: A new construction method for load signature database of load identification. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–6. Beijing (2018) 8. Iksan, N., Sembiring, J., Haryanto, N., Supangkat, S.H.: Appliances identification method of non-intrusive load monitoring based on load signature of V-I trajectory. In: 2015 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 1–6. Bandung (2015) 9. Lin, Y., Tsai, M.: A novel feature extraction method for the development of nonintrusive load monitoring system based on BP-ANN. In: 2010 International Symposium on Computer, Communication, Control and Automation (3CA), pp. 215–218. Tainan (2010) 10. Rajasekaran, R.G., Manikandaraj, S., Kamaleshwar, R.: Implementation of machine learning algorithm for predicting user behavior and smart energy management. In: 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 24–30. Pune (2017) 11. Wang, L., Luo, X., Zhang, W.: Unsupervised energy disaggregation with factorial hidden Markov models based on generalized backfitting algorithm. In: 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013), pp. 1–4. Xi’an (2013) 12. Chang, H., Chen, K., Tsai, Y., Lee, W.: A new measurement method for power signatures of nonintrusive demand monitoring and load identification. IEEE Trans. Ind. Appl. 48(2), 764–771 (2012) 13. Janani, K., Himavathi, S.: Non-intrusive harmonic source identification using neural networks. In: 2013 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), pp. 59–64. Chennai (2013) 14. Srinivasan, D., Ng, W.S., Liew, A.C.: Neural-network-based signature recognition for harmonic source identification. IEEE Trans. Power Delivery 21(1), 398–405 (2006) 15. Abubakar, I., Khalid, S.N., Mustafa, M.W., Shareef, H., Mustapha, M.: An overview of nonintrusive load monitoring methodologies. In: 2015 IEEE Conference on Energy Conversion (CENCON), pp. 54–59. Johor Bahru (2015) 16. Dhananjaya, W.A.K., Rathnayake, R.M.M.R., Samarathunga, S.C.J., Senanayake, C.L., Wickramarachchi, N.: Appliance-level demand identification through signature analysis. In: 2015 Moratuwa Engineering Research Conference (MERCon), pp. 70–75. Moratuwa (2015) 17. Lin, Y., Tsai, M.: Non-intrusive load monitoring by novel neuro-fuzzy classification considering uncertainties. IEEE Trans. Smart Grid 5(5), 2376–2384 (2014) 18. Lin, Y.-H., Tsai, M.-S.: Application of neuro-fuzzy pattern recognition for non-intrusive appliance load monitoring in electricity energy conservation. In: 2012 IEEE International Conference on Fuzzy Systems, pp. 1–7. Brisbane, QLD (2012) 19. Alcalá, J., Ureña, J., Hernández, Á., Gualda, D.: Event-based energy disaggregation algorithm for activity monitoring from a single-point sensor. IEEE Trans. Instrum. Meas. 66(10), 2615– 2626 (2017) 20. Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE Trans. Power Delivery 6(4), 1380–1385 (1991) 21. Ridi, A., Gisler, C., Hennebert, J.: ACS-F2—a new database of appliance consumption signatures. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 145–150. Tunis (2014) 22. Chang, H., Lin, C., Lee, J.: Load identification in nonintrusive load monitoring using steadystate and turn-on transient energy algorithms. In: The 2010 14th International Conference on Computer Supported Cooperative Work in Design, pp. 27–32. Shanghai, China (2010) 23. Seema, P.N., Deepa, V., Manjula, G.N.: Implementation of consumer level intelligence in a smart micro-grid along with HEMS based price prediction scheme. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–5. Delhi (2016). https://doi.org/10.1109/ICPEICES.2016.7853143

Prediction of Dimension of a Building from Its Visual Data Using Machine Learning: A Case Study Prasenjit Saha, Utpal Kumar Nath, Jadumani Bhardawaj, and Saurin Paul

1 Introduction The circumstances where we need to measure something but without having a measurement tool, we only can suffice ourselves with the estimation provided by our eyes. There is a whole field in science, called photogrammetry, using which the size or dimension of a structure can be deduced by just the 2D image of it. Photogrammetry is the science and technology of obtaining reliable information about physical objects and the surroundings over the process of recording, surveying and illustrating photographic images and patterns of electromagnetic radiant imagery and other phenomena. In general photogrammetric approach can be applied collectively with the laser survey are being achieve for the complicated surface measurements. Various types of structural form can also be figured out applying this approach [1]. In this paper, we will inspect the condition of camera positions right after image capturing to achieve the best result [2]. In this paper, the size of a building is being predicted from its image by using machine learning using the concept of the photogrammetry. In machine learning, decision tree is being used.

2 Methodology 2.1 Definition and Working Principle Photogrammetry depends on certain type of a photographic report as the origin of data. Such a report represents a two-dimensional registration of, in general, a threeP. Saha (B) · U. K. Nath · J. Bhardawaj · S. Paul Assam Engineering College, Guwahati, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_42

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Fig. 1 Distance calculation using the concept of Photogrammetry

dimensional object. After absolute contraction, each photograph may be observed as a central prospect projection of the object [3]. The operation of photogrammetry is being used for substantial research from the continuous and rapid evolution of sensors to methodologies in different fields [4] (Fig. 1).

2.2 Data Acquisition It is interested in collecting reliable instruction about the equity of surfaces and objects. The remotely accepted instruction can be divided into three categories: Geometric information involves the spatial position and the shape of objects. Physical information refers to properties of electromagnetic radiation, temporal information is similar to the change of an item in time, mostly achieved by analyzing several images which were recorded at different times.

2.3 Photogrammetric Instruments The various photogrammetry instruments are as follows. A rectifier is kind of a copy machine for making plane rectifications. To accompanish orthophotos, an orthophoto projector is mandatory. A comparator is a instrument that is used to measure points on a diapositive (photo coordinates). A stereo plotte achieves the renewal central projection to orthogonal projection in an analog fashion. The analytical plotter gives the transformation computationally.

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2.4 Decision Tree Decision tree is one of the most extremely used inaugural assumption algorithms [5]. Decision tree algorithm is mainly used for analyzing data and forecasting. The algorithm is easy to understand and implement, and it is easy to evaluate the model through static testing [6]. Machine learning is one of the finest fields of computer science world which has given the innumerable and invaluable solutions to the mankind to solve its complex problems. Decision tree is one such modern solution to the decisionmaking problems by learning the data from the problem domain and building a model which can be used for prediction supported by the systematic analytics [7]. It is a function closed to discrete value and also can be treated to a Boolean function. It is an inductive learning algorithm based on the case, which can be commonly used to form a classification and prediction models. Focusing on a group of disorder, no rules’ cases, the classification rules can be analyzed to a set of rules according to decision tree. In various ways to solve classification problems, decision trees are commonly used as a method. It is a predictive modeling method for dividing the search space into a number of subsets through “divide and rule”. A tree must be required to build for the modeling of classification, clustering and prediction, which the classification process using this method. Once the tree is built, tuple in datasets is applied and get the classification result. It is a tree structure similar to the flowchart, using top-down recursive way. The internal nodes in this tree are compared by attribute value and judge the branches under this node according to different attribute value. Finally, the conclusion can be got from the leaf nodes. The entire process is repeated on the new node as a child of the root tree. Figure 1 shows the basic structure the decision tree. From the perspective of the whole tree, decision tree represents disjunction of attribute value restraint’s conjunction. Every way root to the leaves correspond to a set of attributes of conjunction, correspond to the tree itself (Fig. 2).

Fig. 2 Basic structure of a decision tree

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3 Prediction Model 3.1 Input Data A dataset is built by composing different images from the Guwahati City. The dataset consists of a building image its both front view and side view, along with the distance of the image for each of the images and the angle of view. The angle of view is calculated from the focal length of the camera.

3.2 Working of the Model In this model, the dataset is divided into two parts training and testing. The training part consists of 80%, and test part contains 30% of our dataset. The images are then encoded to their respective R, G and B pixel values by using Opencv and built a separate dataset to store the pixel values. The R, G and B pixel values are stored into a separate database along with its respective elements of the previous database. Now, decision tree regression model is first used to train the images from the dataset with at least 80% of the dataset, and after the training, the model is used to predict the image distance, angle of view, and the number of floors from the testing dataset (Fig. 3).

4 Results The images are encoded to the respective R, G and B pixel values. The pixel values for three sample images are found to be as follows, For the first image is: 110, 200, 112, for the second image 112, 90, 140 and for third image is 60, 100, 200. The below graph shows distance along with the X , Y , Z pixel values and along with their respective distances (Figs. 4 and 5).

Fig. 3 Block diagram of the prediction model

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Fig. 4 Pixel value of images for R, G, B in histogram

Fig. 5 Pixcel value of images for R, G, B in scattering

4.1 Output The predicted distance for the above buildings is found to be 10, 8 and 18 m. It has been found that the accuracy of the model is 98.27%. The below graph shows the accuracy versus training step, and it shows that with increase in the training step, the accuracy increases (Fig. 6).

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Fig. 6 Accuracy versus training step

5 Conclusion Thus, we can predict the image distance of a building from its visual data with machine learning with 98.27% accuracy. In the similar way, we can predict the angle of view of the image. Thus, using the concept of photogrammetry, we can find the length and breadth of a building. Hence, the total building area of the building is calculated from its image. Acknowledgment This work is a part of the project funded by ASTU, CRSAEC20, TEQIP-III.

References 1. Sužiedelyt˙e-Visockien˙e, J., Domantas, B.: Digital photogrammetry for building measurements and reverse-engineering. Geodesy Cartogr. (2009) 2. Aminia, A.S.: Optimization of close range photogrammetry network design applying fuzzy computation. Int. Arch. Photogram. Rem. Sens. Spat. Inf. Sci. XLII-4/W4, 31–36 (2017) 3. Mikhail, E.M.: An introduction to photogrammetry. In: Proceedings of SPIE 0045, Coherent Optics in Mapping. Rochester, United States (1974) 4. Mancini, F., Salvini, R.: Application of photogrammetry for environmental research. Int. J. Geo-Inform. (2019) 5. Zhong, Y.: The analysis of cases based on decision tree. In: 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (2016) 6. Tang, X., Liu, Z., Li, T., Wu, W., Wei, Z.: The application of decision tree in the prediction of winning team. In: 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), pp. 239–242. Changsha (2018). https://doi.org/10.1109/ICVRIS.2018.00065 7. Patil, S., Kulkarni, U.: Accuracy prediction for distributed decision tree using machine learning approach. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1365–1371. Tirunelveli, India (2019). https://doi.org/10.1109/ICOEI.2019. 8862580

Deep Learning Algorithms for Human Activity Recognition: A Comparative Analysis Aaditya Agrawal and Ravinder Ahuja

1 Introduction In the vast domain of computer science research, human activity recognition is emerging as a core concept to understand and develop computer vision and human– computer interaction. It forms the core of scientific endeavors like health care, surveillance, and human–computer interaction. But the new field is besotted with difficulties like sensor placement, sensor motion, and video camera installations in places to be monitored, tangled background, and we perform the diversity in the ways activities. To tackle the challenges mentioned above, a more efficient approach would be to analyze the information gathered from unit sensors that can perform inertial measurements worn by the user/tester or as a built-in function in the user’s smartphone track of his/her movements. For this purpose, a tri-axial accelerometer, a sensor is built-in smartphones to track the user’s movements. Human Activity Recognition: Human activity recognition (HAR) is the mechanism for “classifying sequences of accelerometer data recorded by the specialized harness or smartphones into known well-defined movements.” It is the process to understand human body gestures or movements by using sensors to ascertain that human activity or action is taking place. Our daily activities can be simplified and automated if any HAR system recognizes them (e.g., smart lights, maybe that recognizes hand gesture). Most importantly, HAR systems are of two types—unsupervised and supervised. A HAR system based on supervised method functions only after previous training is A. Agrawal (B) · R. Ahuja School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India e-mail: [email protected] R. Ahuja e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_43

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done with staunched datasets. A HAR system based on the unsupervised method can configure with a collection of regulations during augmentation. A short description of the various uses of HAR in diverse environments is provided below:

1.1 Surveillance System HAR surveillance systems were first instated in public places like airports and banks to avoid crimes and perilous activities occurring at public places. This idea of human activity prediction was introduced by Ryoo [1]. The results confirmed that the HAR surveillance system is able to recognize multiple in progress human interactions at the former stage. Lasecki et al. [2] proposed a system called named Legion: AR that would yield robust brings into force activity recognition by feeding already existing identification models with real-time activity recognition by incorporating inputs as gained from the multitude at any public places.

1.2 Healthcare In healthcare systems, HAR is instated in hospitals, residential areas, and rehabilitation centers for multiple reasons, such as supervising elderly people living in rehabilitation centres, disease prevention, and managing chronic diseases [3]. About rehabilitation centers, HAR system is particularly useful to track the activities of elderly people and fall detection, monitor physical exercises of children with motor disabilities and troubling motion conditions like autism spectrum disorders, monitor patients with dysfunction, post-stroke patients, patients with psychomotor slowing and abnormal conditions for cardiac patients [4, 5]. This monitoring ensures timely clinical intervention.

1.3 Human–Computer Interaction In this category, activity recognition is usually applied in gamercizing and gaming, such as Wii [6, 7], Kinect, Nintendo [8, 9], other full-body movement-based exercises, and gaming for adults and even for adults who neurological ailment [10]. HAR detects body gestures carried out by any person and accordingly directs the completion of stalwart tasks [11]. Senior citizens and some adults who are suffering from some category of neurological ailment can carry out uncomplicated gestures to engage with exergames and games without any discomfort. This feature also allows surgeons to have impalpable command over intraocular image monitoring by incorporating normalized free-hand maneuver.

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The paper is divided into the following sections: Sect. 2 contains various features used in sarcasm detection, Sect. 3 describes various datasets used, Sect. 4 contains various techniques used, and Sect. 5 contains performance reported by researchers followed by Sect. 6, which contains a conclusion and future scope.

2 Related Work The domain of HAR has proliferated with substantial progress in the last decade. Different research and surveys focusing on various approaches have been carried out to identify human actions and the latter’s significant effect in the real-world scenario. The different types of course of action can be categorized into four classes, as described below.

2.1 Based on Sensor The below paragraphs describe some of the literature focusing on activity recognition hinged on sensors (sensor-based). Chen et al. [12] have carried a thorough study in the sub-field of sensor-based work in human activity recognition. Their study classifies previous researches in two principal classifications: (i) sensor-based (based on the sensor) versus vision-based (based on vision) and (ii) knowledge-driven based versus data-driven based. This paper, however, concentrates on activity identification methods, which are data-centric. Alternately, one other study carried by Wang et al. [13] exemplified the various approaches of deep learning or deep neural network for HAR by incorporating sensors. Their study categorizes existing writings in activity identification on three factors, viz. stimulus modality, application area, and deep learning models. This paper outlines the research in activity recognition and focuses on the deep model, which is employed to analyze the information gathered from sensors.

2.2 Based on Wearable Device The following paragraphs demonstrate some of the literature that focuses on the elucidation of activity recognition based on wearable devices. Labarador and Lara [14] conducted their study on human activity recognition with wearable sensors. Their study expounds a comprehensive analysis of a variety of structural faults in a human activity recognition system. Some of them are sensor selection and traits, protocol and compilation of information, processing methods, consumption of energy, and recognition performance.

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Alternately, Cornacchia et al. [15] conducted an in-depth survey and accordingly divided the current exploration work into two comprehensive categories: global body movement activity, which involves displacement/motion of the entire body (e.g., jogging, walking, and running) and localized reciprocity activities, which include movements dealing with extremities (e.g., usage of an object). This particular literature also outlines the categorization on the basis of two factors—sensor type that is being utilized and position on the human body where the sensor is placed, such as sensor mounted on the user’s chest or wrist.

2.3 Based on Radio Frequency The following paragraphs describe some of the literature on the elucidation of activity recognition based on radio frequency. Scholz et al. [16] conducted a study in the domain of activity recognition based on equipment-free radio. Their study classifies previous research progress in this field into two primary categories- device/equipment free radio-based activity recognition (DFAR) and device/equipment-free radio-based localization (DFL). Alternately, the study conducted by Amendola et al. [17] summarizes the radio frequency identification (RFID) technology usage in health-related Internet of Things (IoT) applications. Their study discusses the multiple uses of RFID tags in cases such as the ambient passive sensors that comprise temperature sensors and volatile compound sensors and tags, which are body-centric such as implantable and wearable tags. Another study conducted by Wang and Zhou [18] reviews existing exploratory work in the domain of activity identification based on radio technology. Their study sums up the existing researches in four crucial classifications: (i) based on Wi-Fi, (ii) based on RFID, (iii) based on Zig-Bee, and (iv) additional radio-based such as microwave and FM radio. The researchers suggest a comparative analysis of all the methodologies mentioned above by incorporating the following characteristics, such as coverage, precision, types of activities, and the cost of deployment.

2.4 Based on Vision The below paragraph describes some research work that focuses on the elucidation of activity identification based on vision. Vrigkas et al. [19] study the existing body of exploratory analysis, which contains a vision-based approach for activity identification. They categorized their research into two major classes: multi-modal and uni-modal approaches. Alternately, the study conducted by Herath et al. [20] focused on the undertaking of crucial research in the domain of activity identification/recognition by deploying methods that are based on vision. Their study catalogs

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the previous works into two main groups: deep neural network-based solutions and representation-based solutions.

3 Deep Neural Network Deep neural network or deep learning is the subset/category of machine learning. A DNN comprises multiple levels of nonlinear operations with many hidden layers known as neural nets. The main focus of deep learning is to analyze feature hierarchies, where features at towering levels of the hierarchy are established with the help of features at lower levels. We will be employing two such networks to create deep learning models which are mentioned below.

3.1 Convolutional Neural Network (CNN) CNN, a type of feed-forward artificial neural network, is broadly used in image recognition and processing. It executes a set of descriptive and generative tasks using deep learning, often using computer vision that involves image and video recognition. Two major operations performed by CNN are pooling and convolution, which are applied along the temporal dimensions of sensor signals. Since HAR involves classifying time series data, we will be using a 1D CNN model since, in 1D CNN, the kernel slides along one direction.

3.2 Recurrent Neural Network (RNN) Unlike other types of traditional neural network where all the inputs/outputs behave independently of one another, in RNN, the outputs of the previous steps are provided as input to the present step. In this paper, we will be using the long short-term memory (LSTM) network, which is a category of RNN (a better version of RNN). The architecture of LSTM was prompted by the analysis of existing RNNs for error flow, which established that prolonged time lags were remote to pre-existing structure, the reason being that back-propagation of an error erupts or deteriorate exponentially. Focusing on the architecture of an LSTM, the layers or LSTM layers contain a union of incessantly coupled blocks, technically called the memory blocks or cells. These cells or memory blocks represent or share its concept (a comparative version) with the memory chips inside a digital computer. Every block consists of one or multiple incessantly threaded memory blocks/cells, three multiplicative units—the output, forget gates, and the input. They are responsible for providing uninterrupted analogs of reading, resetting, and writing operations for the cells.

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4 System Design Human activity recognition can be categorized into four broad aspects, as showcased in Fig. 1. These phases are (i) selecting the appropriate sensor and its deployment, (ii) collection of data using sensors (using wearable type in our case), (iii) preprocessing of the data like normalization and feature selection, and (iv) using deep learning algorithms to recognize activities.

4.1 Data Collection A tri-axial accelerometer in the smartphone was used to collect the data. The smartphones were carried in the pocket by 36 users while performing the six activities, which had a sampling rate of 20 Hz (20 values per second). The accelerometer’s data captures the acceleration of the users in the X-axis, Y-axis, and Z-axis (that’s why the term tri-axial), as shown in Fig. 2a. These axes represent the motion of the user in the horizontal, sideways, downward/upward and backward/forward directions. The distribution of dataset with respect to activities is shown in Fig. 2b. The dataset contains 1,098,207 rows and 6 columns.

4.2 Data Pre-processing Data pre-processing is the method employed to transform the available data as per the format that the machine can accept and fed to the algorithm. Our dataset is in a text file format. The data from the file is read, and then each of the accelerometer components (x, y, and z) is normalized. The accelerometer data is converted and transformed into a time-sliced representation and loaded in the data frame. We have

Sensor SelecƟon and Deployment

CollecƟng data from Sensor

Data pre-processing and Feature SelecƟon

Fig. 1 Process involved in human activity recognition

Developing a Classifier to Recognize acƟviƟes

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Fig. 2 (a) Direction of movement recorded by accelerometer. (b) Frequency of data corresponding to each activity

to add encoded value for own activities since the deep neural network cannot work with non-numerical labels.

4.3 Training and Test Set The neural network needs to learn from some of the users who have been through experiment, and after that, we need to check how well the neural network predicts the movements of the persons it has not seen before. We split the training and test sets in 80/20 parts.

4.4 Reshaping Data and Prepare for the ML Model The data stored in the data frame needs to be formatted to be fed into the neural network. Dimensions used would be (i) number of periods—The number of periods within one record, (ii) number of sensors—The value is three as we are using acceleration over the x, y, and z-axis, (iii) number of classes—This is the number of nodes for the output layer in neural network.

4.5 Building and Training the Model We will be designing two models, namely CNN and LSTM, with the same set of data and training and test sets. The CNN model will involve one convolution layer followed by a max-pooling layer and another convolution layer. This will result in a fully connected layer that would be associated with the Softmax layer. As given in

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Fig. 3 CNN architecture

Input Feature Time steps x Feature vectors

LSTM

LSTM

Fully Connected Layers

SoŌmax Layer

Fig. 4 Stacked LSTM architecture

Fig. 3, Softmax function/layer is a category of squashing function limiting the output in the range from 0 to 1. This allows the output to be read as a probability. Long short-term model consists of around two exhaustively interlinked layers and two layers of LSTM (LSTM layers), heaped on each other with about 60 hidden units each. Neural network like RNN is made up of cyclic links, enabling it to understand the temporal dynamics of historical data. A concealed layer in RNN consists of several nodes where every node has a motive for producing the present hidden state and a result or output by incorporating the present input and the prior hidden state [2]. Similar concept is involved in LSTM, but instead of nodes, there are memory cells. LSTM unit consists of a cell, an input, and output gate (Hidden State), and a forget gate. The three gates are responsible for regulating the flow of data into and out of the cell. These gates control when to overlook any prior hidden state and when it’s time to modify the rules with new information. We are using stacked LSTM as the addition of layers increases the level of abstraction of input observation (Fig. 4).

5 Results The next step is to train both the models with the training data that we prepared. The hyperparameter is used for the training: A batch size of 400 records will be used and will train the model for 50 epochs. Plotting the learning curve for both the models, as seen in Figs. 5 and 6, respectively, the CNN model faced issues during the testing phase. The test loss seems to rise after 20 epochs, while test accuracy maintains

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Fig. 5 Plot for learning curve for CNN

Fig. 6 Plot for learning curve for LSTM

consistency until 50 epochs. For the model to be more accurate, the test loss curve should have a downward curve. It is noteworthy that the model performs well during the training phase as the training loss has a downward curve. In the case of the LSTM model, from Fig. 6, we can make out that the model seems to learn pretty well as the test loss forms a downward curve with increasing epochs. Similarly, the test accuracy curve sees an increase at the start and then seems to acquire a consistent course along with the training accuracy. It is evident from both the learning curves that the LSTM model is more accurate compared to the CNN model. Figure 7 represents the prediction accuracy for the CNN model. Looking at the diagonal matrix, the accuracy of the CNN model turns out to be 87%. The model faced difficulty in identifying activities like upstairs and standing. The LSTM model, its confusion matrix in Fig. 8, tells us the model accurately predicts activity like walking and faces difficulty in identifying activities like standing and upstairs. Looking at the diagonal matrix, the LSTM model’s accuracy turns out

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Fig. 7 Confusion matrix for CNN

Fig. 8 Confusion matrix for LSTM

to be approximately 92%. Both the models had little bit problems identifying the same set of activities.

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6 Conclusion and Future Scope In this study, the LSTM model came out to be more accurate with an accuracy rate of 92% compared to the CNN model, which yielded 87% accuracy in identifying various everyday physical activities using a tri-axial accelerometer (wearable sensor). The data was obtained from various individuals in real-world conditions with the accelerometer device or smartphone carried by the subjects in their pockets. Our study’s goal was to compare how two deep neural network models like convolutional and long short-term memory neural networks would perform comparatively on the given set of data. With more vigorous tuning, it is estimated that CNN can perform even better, and so can the LSTM model. For future work, we plan to create hybrid models that combine the concept of two or more deep neural networks. We also plan to divide the dataset based on the ages of the participants/subjects to increase the abstraction level of our model and get new insights.

References 1. ARyoo, M.S.: Human activity prediction: early recognition of ongoing activities from streaming videos. In: 2011 International Conference on Computer Vision, pp. 1036–1043. IEEE, 6 Nov 2011 2. Lasecki, W.S., Song, Y.C., Kautz, H., Bigham, J.P.: Real-time crowd labeling for deployable activity recognition. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1203–212, 23 Feb 2013 3. Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily living activity recognition based on statistical feature quality group selection. Expert Syst. Appl. 39(9), 8013–8021 (2012) 4. Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2011) 5. Jalal, A., Uddin, M.Z., Kim, J.T., Kim, T.S.: Recognition of human home activities via depth silhouettes and  transformation for smart homes. Indoor Built Env. 21(1), 184–190 (2012) 6. Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with microsoft Kinect sensor: a review. IEEE Trans. Cybernet. 43(5), 1318–1334 (2013) 7. Zhang, Z.: Microsoft Kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012) 8. Huynh, D.T.: Human activity recognition with wearable sensors. Doctoral dissertation. Technische Universität 9. Lawrence, E., Sax, C., Navarro, K.F., Qiao, M.: Interactive games to improve quality of life for the elderly: Towards integration into a WSN monitoring system. In: 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine, pp. 106–112. IEEE, 10 Feb 2010 10. Lange, B., Chang, C.Y., Suma, E., Newman, B., Rizzo, A.S., Bolas, M.: Development and evaluation of low-cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1831–1834. IEEE, 30 Aug 2011 11. Yoshimitsu, K., Muragaki, Y., Maruyama, T., Yamato, M., Iseki, H.: Development and initial clinical testing of “OPECT”: an innovative device for fully intangible control of the intraoperative image-displaying monitor by the surgeon. Oper. Neurosurg. 10(1), 46–50 (2014) 12. Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybernet. Part C Appl. Rev. 42(6), 790–808 (2012) 13. Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. arXiv preprint arXiv:1707.03502 (2017)

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14. Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2012) 15. Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386–403 (2016) 16. Scholz, M., Sigg, S., Schmidtke, H.R., Beigl, M.: Challenges for device-free radio-based activity recognition. In: Workshop on Context Systems, Design, Evaluation and Optimisation, Dec 2011 17. Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., Marrocco, G.: RFID technology for IoT-based personal healthcare in smart spaces. IEEE Internet Things J. 1(2), 144–152 (2014) 18. Wang, S., Zhou, G.: A review on radio based activity recognition. Digit. Commun. Netw. 1(1), 20–29 (2015) 19. Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Vis. Comput. 1(60), 4–21 (2017) 20. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)

Comparison of Parameters of Sentimental Analysis Using Different Classifiers Akash Yadav and Ravinder Ahuja

1 Introduction Nowadays, we see the usage of social media is increasing exponentially, and by this, various sectors are targeting social media platform as their launch pad for example— usage of social media in influence the elections self-promotions, etc., so there is need to analyze the opinion as it draws responses to various responses available on social media. Twitter is one place where people view their views very strongly on different issues. For examining user thoughts, sentimental analysis has become a significant source for the purpose of solving hidden pattern in a large number of tweets with the help of machine learning algorithms. We prepared our work with ten algorithms to sort outperformance of classifiers. We used feature extraction and machine learning algorithm in two different entities. Our main contribution is to find out the best classification algorithm to be applied to get the maximum potential of sentimental analysis by comparing four significant factors of performance of each classification algorithm which is described as F1-score, precision, accuracy, and recall.

2 Related Work The sentiment140 dataset we used in our work was generated using the automated labeling method [1]. Baccianella et al. [1] they used automation to take the lead A. Yadav (B) · R. Ahuja School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India e-mail: [email protected] R. Ahuja e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_44

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of emoticons found in texts. They are a combination of symbols which show some type of feeling which depicts different polarities. Tweets were gathered and formed a dataset that shows all the details of texts with their polarities. A total of 1.6 million records were collected, and they performed machine learning algorithms, and performance was measured. Algorithms used in [1] are Naive Bayes, SVM, and maximum entropy. Various recent research has been done on sentimental analysis. We have seen these works based on subjectivity or sentiment level. We have seen in [2] that it uses both movie review and sentiment140 dataset and performed five machine learning algorithms on feature combinations, which shows the unique perspectives on sentimental analysis. Algorithms used in this were Naive Bayes, support vector machine, and maximum entropy.

3 Methodology Used This section describes the model to do our experiment for sentiment classification. The whole process of classification. Various steps are involved in the process of our system. We have taken a dataset and which is labeled that is why we have developed a supervised algorithm. In the first process, we have collected the data in the from one source and indexed it for the preprocessing. Next, we have applied various text preprocessing processing, text cleaning, etc., and in the next step, we have performed data visualization to understand the data on which we are working and performing various feature extraction and later use these extractions for performing at least ten classifications. Next, we analyze the performance of each classification will be compared as the final result so that you can select which rating has a higher rate of outcome with which feature extraction.

3.1 Data Description The sentiment140 dataset consists of six columns which include [3] sentiment, Id, date, query, user, text below the table best describes the format of the dataset (Table 1). Table 1 Format of the dataset Sentiment

Id

Date

Query

User

Text

0

111

4 Apr

No

@aa

ajejdkd

4

222

3 Mar

No

@1a

#aajjjd

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3.2 Performance Metrics We have discussed that we have four metrics to compare our results, and these measures will help in achieving the output, which is as follows.

3.2.1

Precision

Precision is a measure of closeness between two or more values it tells how accurate our model is of predicted truth.

3.2.2

Recall

It is termed as how many actual positive our model capture through labeling it as positive.

3.2.3

F1-Score

It is a measurement of the process’s accuracy. It uses two entity precision and recall for calculation. Harmonic mean of these entites helps in understanding more accurately in gaining results.

3.2.4

Accuracy

Accuracy simply in machine learning means division between the numbers of correct predictions by total numbers of the input number.

3.3 Data Preprocessing and Text Cleaning Data preprocessing contains various steps, which include tokenization, text cleanup, and encoding. Usually, raw tweet or texts contain mentions, escape negation words, special characters, different cases, URLs, user name, hash tag, punctuation, etc., which do not resemble any sentiment by removal of non-performing texts which do not originate any polarity, using [4] third-party Python package NLTK stop word corpus. After cleanup of textual data, the remaining texts are then stored in CSV for the further feature extraction process.

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3.4 Feature Extraction 3.4.1

Bag of Words

A bag of words is simply a representation of the occurrence of words within a document. It firstly creates a vocabulary of words exists in our dataset and starts counting the occurrence of vocabulary in our document.

3.4.2

N-Gram Features

We have experimented with unigram, bigram, and trigram as features and applied machine learning classifiers for sentimental analysis. We have used the pipeline to create feature vector and evaluation.

3.4.3

Term Frequency Inverse Document Frequency (TF-IDF)

Tf-IDF simply states that how an important a tokenized text in a document. Simply converts raw corpus.

3.5 Classification Models We have used ten classifiers to our feature vectors, so here these are used in our work.

3.5.1

Ridge Classifier

Ridge classifier uses ridge regression to find the estimate to minimize the bias and variance in linear classification. Its primary objective is to reduce the sum of the square of irregularities.

3.5.2

Multi-Layer Perceptron

Perceptron is a classifier that is used for the leaning model. It changes its values only if any error occurs; it does not have any learning rate and regularization parameter.

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3.5.3

407

Multinomial Naïve Bayes Classifiers

Multinomial Naïve Bayes classifiers are used to calculate the probabilistic result. It is based on the Bayesian theorem. This classifier calculates the conditional probability of specific words given a domain as the relative frequency of term q in the record belongs to the domain (d).

3.5.4

K-Nearest Neighbor

It helps in classifier by merely assigning them a label of class if its mean is closer to the observation.

3.5.5

Bernoulli Naive Bayes Classifier

Bernoulli Naive Bayes classifier is used to determine when the prediction is in Boolean form. It is much similar to multinomial NB.

3.5.6

AdaBoost Classifier (Adaptive Boosting)

It is a combination of many algorithms used to improve the performance of the model.

3.5.7

SGD Classifier

This classifier is used to determine the decrease in the loss of irregularity or errors to make the model become fit for classification.

3.5.8

Passive Aggressive Classifier

It is a simple classifier for large scaling learning which does not require any learning rate and have regularization parameter which is openly available in sklearn package.

3.5.9

LinearSVC

Linear SVC is part of the support vector machine, which helps in that explore a non-probabilistic binary algorithm by creating a hyperplane between two classes.

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Logistic Regression

Logistic regression simply learns to estimate the result with the help of its previous dataset; it is very efficient and does not require advanced computational requirements.

4 Experiment and Result Evaluation We have experimented with dataset firstly to clean our tweets, and then, we performed the data visualization and by the following results came out. During the examination, the dataset we try to find the exact numbers of positive and negative sentiment exists in the dataset (Table 2). With the help of word cloud, we were able to form a cloud of words which shows most frequent in our dataset and help us in finding out the words which suit with different polarity as below figures will explain the text of tweets related to positive and negative polarity (Fig. 1). By this, we can analyze how our data is defined in the dataset and how people’s opinions generally contain similar words that are used to define both the sentiments— positive and negative. Some words like, “today”, “one”, “still” can be termed as neutral. Words like “sad”, “bad”, “hate”, “suck”, “wish”, etc., make sense as negative words (Fig. 2). Table 2 Number of the dataset in both sentiments

Sentiment

Data in numbers

Negative

800,000

Positive

800,000

Fig. 1 Word cloud of negative words in the dataset

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Fig. 2 Word cloud of positive words in the dataset

Table 3 Words in the dataset and representing sentiments

Word

Count of the word in the dataset

“love” in negative tweets

21,548

“lol” in positive tweets

35,780

“lol” in negative tweets

22,754

In this word cloud of positive tweets, neutral words, like “today”, “tonight”, “still”, etc., are present. Also, words like “thank”, “haha”, “awesome”, “good”, etc., stand out as the positive words. With the help of word cloud, we found that words that generally symbolize positive polarity do not mean the same in the context of sentences. As we saw, “love” is used in negative tweets. “lol” is used in both contexts of sentiment; this study is shown in Table 3. During the process of data visualization, we used data to search the most frequent tokens and least frequent tokens of both polarities. In negative tokens, the most used token is words like “just”, “work”, “day”, “got,” which does not convey any negative emotions. On the other hand, words like sad, bad, miss, sorry, and hate clearly represent negative emotions. In positive tokens, most used tokens are excellent, love like, thanks, new, which clearly represent positive emotions, and other frequent words like just, got, today, day convey minimal emotions. For the further process of our experiment, we have to split the dataset, which breaks down our dataset into three parts, which are as follows. 1. 2. 3.

Train set: 98% of the dataset. Development set: 1% of the total dataset. Test set: 1% of the total dataset.

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We have used text blob as a baseline for our project. It is used as a point of reference. Text blob gave 61.41% accuracy on the validation set. The next step in our experiment is to apply feature extraction on these tokenized dataset records, which converts the text into vector forms. After performing feature extraction, we experimented with all the classifiers we have discussed in earlier segment firstly we have performed n-gram features and calculated validation accuracy of the bag of words and tf-IDF using n-gram. The outcome showed us which feature extraction shows the maximum accuracy at an exact number of features and following reading came out as results: 1. 2.

Bigram Tfidf at 90,000 features gives the highest validation accuracy at 82.45% trigram Count Vectorizer at 80,000 features gives to have the highest accuracy.

Now, we have seen which feature gives the highest accuracy; so now, we perform all the classifier on these vectors at their maximum features. After these performances, we store these values in two different tables. Term frequency and inverse document frequency bigram Classifier

Polarity

Accuracy (%) Precision Recall F1 score

Ridge classifier

Negative 82.29

0.83

0.81

Positive

0.81

0.84

0.82

Logistic regression

Negative 82.43

0.83

0.82

0.82

Positive

0.82

0.83

Multi-layer perceptron

Negative 76.39

0.82

0.82 0.76

Positive

0.76

0.76

0.76

Negative 79.86

0.80

0.81

0.80

Positive

0.80

0.79

0.80

Stochastic gradient descent

Negative 78.81

0.80

0.77

0.78

Positive

0.78

0.80

0.79

Linear SVC

Negative 82.26

0.82

0.83

0.82

Positive

0.83

0.81

0.82

Negative 80.15

0.80

0.81

0.80

Positive

0.81

0.79

0.80

K-nearest neighbor

Negative 72.55

0.72

0.74

0.73

Positive

0.73

0.71

0.72

Bernoulli Naïve Bayes

Negative 79.91

0.81

0.78

0.80

Positive

0.79

0.81

0.80

Negative 70.23

0.75

0.62

0.68

Positive

0.67

0.79

0.72

Negative 82.41

0.82

0.83

0.83

Positive

0.83

0.81

0.82

Passive aggressive classifier

Multinomial NB

Adaboost classifier L1-based linear SVC

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Count vectorizer trigram Classifier

Polarity

Accuracy (%)

Precision

Recall

F1 score

Ridge classifier

Negative

81.86

0.83

0.80

0.82

0.81

0.84

0.82

Logistic regression

Negative

82.38

0.83

0.81

0.82

0.82

0.83

0.83

0.77

0.73

0.75

0.74

0.78

0.76

75.93

0.73

0.82

0.77

0.79

0.70

0.74

81.39

0.83

0.79

0.81

0.80

0.84

0.82

0.84

0.80

0.82

0.81

0.84

0.82

79.73

0.79

0.81

0.80

0.80

0.79

0.80

71.87

0.75

0.66

0.70

0.69

0.78

0.73

0.81

0.77

0.79

0.78

0.82

0.80

70.23

0.74

0.62

0.68

0.67

0.78

0.72

82.14

0.84

0.80

0.82

0.81

0.84

0.82

Positive Positive Multi-layer perceptron

Negative

75.76

Positive Passive aggressive classifier

Negative

Stochastic gradient descent

Negative

Positive Positive Linear SVC

Negative

82.06

Positive Multinomial NB

Negative

K-neighbors classifier

Negative

Positive Positive Bernoulli NB

Negative

79.38

Positive Adaboost classifier

Negative

L1-based linear SVC

Negative

Positive Positive

5 Conclusion and Future Scope The above work addresses the task of examining the performance of classifiers in our system. After this work, we are able to determine which classifier gives a maximum result with a combination of feature extractor in the sentimental analysis of tweets or comments. By selecting appropriate methods for sentimental, we can achieve higher success, which helps in today’s business models. For the future, we will try the combination of feature extractions, which may help in achieving a high success rate than this paperwork provides.

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References 1. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, no. 2010, pp. 2200–2204, May 2010. 2. Iqbal, N., Chowdhury, A.M., Ahsan, T.: Enhancing the performance of sentiment analysis by using different feature combinations. In: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pp. 1–4. IEEE, Feb 2018 3. https://help.sentiment140.com/for-students 4. Bindal, N., Chatterjee, N.: A two-step method for sentiment analysis of tweets. In: 2016 International Conference on Information Technology (ICIT), pp. 218–224. IEEE, Dec 2016

System Model to Effectively Understand Programming Error Messages Using Similarity Matching and Natural Language Processing Veena Desai, Pratijnya Ajawan, and Balaji Betadur

1 Introduction The development and growth of technology bettered computers that are remarkably intelligent to read the mind of a human and display it on the screen. Human speech can be understood by humans, but the computer does not have a brain of its own, it must be trained and prepared in a manner to understand the human speech, recognize it, analyze it, process it, and respond appropriately. This is performed using programming techniques like natural language processing (NLP). In 1950, Alan Turing published an article titled “Computing Machinery and Intelligence” which proposed what is now called as Turing test as a criterion of intelligence that was used to decode the Russian messages, i.e., enigma [1]. Natural language processing is the process of understanding the speech and responding to it accordingly. It deals with two parts, natural language understanding and natural language generating. The speech is first segmented, and then it is divided into smaller parts called tokens. For each token, parts of speech are predicted. Simultaneously stemming and lemmatization that associate with doing things properly with the use of vocabulary and morphological analysis of words, commonly aiming to remove inflectional endings only and to return the base or dictionary form of a word. Stop words will be identified, and the followed data is presented to the computer for further processing. The algorithm recognizes the input accurately and renders a specific answer for even short queries. V. Desai (B) · P. Ajawan · B. Betadur KLS Gogte Institute of Technology, Belgaum, India e-mail: [email protected] P. Ajawan e-mail: [email protected] B. Betadur e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_45

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2 Methodology and Preprocessing There are preprocessing methods carried on the raw data and as well as the input query to analyze and respond. Natural language processing supports to perform these methods. The preprocessing methods are shown in Fig. 1. Once the input query and the data are processed, the algorithm uses cosine similarity matching to compare the input query and the processed data. The data with maximum similarity is returned as a result. Python provides various modules to carry out all these functions easily. Fig. 1 Sequence of events in the algorithm

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Fig. 2 Data after tokenization

2.1 Tokenization When raw data is fed to the algorithm, it includes unwanted noise which can cause defects, they need to be corrected. The algorithm performs tokenization as the first method which tokenizes both data and the input query and proceeds to further preprocess techniques. Data after tokenization is represented in Fig. 2. Every word is treated as a token, and it appears as shown in Fig. 2.

2.2 Stop Words Removal Stop words are helping verbs in the sentence, and they do not perform a much important role in natural language processing and similarity matching and also for the computer to understand the speech. Data before removal of stop words and after removal of stop words is represented in Figs. 3 and 4, respectively. The word cloud describes the frequency of words that occurred in the data. It is represented in Fig. 5. The size of the word in the word cloud is directly proportional to the frequency of the word in the data.

416 Fig. 3 Data before removal of stop words

Fig. 4 Data after removal of stop words

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Fig. 5 Word cloud of the tokens after tokenization

2.3 Count Vectorizer Count vectorizer provides a simplistic way to both tokenize a collection of text documents and build a vocabulary of known words, but also to encode new documents using that vocabulary. The below syntax is of count vectorizer along with all of its parameters. Method fit_transform applies to feature extraction objects such as count vectorizer and Tfidf transformer (Table 1). class sklearn.feature_extraction.text.CountVectorizer(input = ‘content’, encoding = ‘utf-8’, decode_error = ‘strict’, strip_accents = None, lowercase = True, preprocessor = None, tokenizer = None, stop_words = None, token_pattern = ‘(?u)\b\w\w + \b’, ngram_range = (1, 1), analyzer = ‘word’, max_df = 1.0, min_df = 1, max_features = None, vocabulary = None, binary = False, dtype = < class ‘numpy.int64’>). The “fit” element regards to the feature extractor. The “transform” element receives the data and returns transformed data. Count vectorizer counts the frequency of each word present in the data. In Table 1, [4] the first row shows the words that Table 1 Word frequency table

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Fig. 6 List of samples to illustrate IDF and tfidf transformer

are in the data and the second row says the frequency of occurrence of that word in the data. Figure 7 shows the computed IDF values by calling tfidf_transformer.fit (word_count_vector) on the word counts. The IDF value is inversely proportional to the frequency of the words in the sample data shown in Fig. 6, which also indicates IDF value is directly proportional to the uniqueness of the word [3]. Lower the IDF value of a word, the less unique it is to any particular document. In Fig. 8, only a few have values and the others do not have any value; this is because the first document is “SQL statements execute ()—multiple outputs” all the words in this document have a tf-idf score and everything else shows up as zeroes. Word “a” is missing from this list. This is due to the internal preprocessing of count vectorizer where it removes single characters. From Fig. 9, the frequency of the word in the document is inversely proportional to the tfidf value. From this, it can be said that uniqueness is inversely proportional to the tfidf value. With Tfidftransformer algorithm will systematically compute word counts using CountVectorizer and then compute the Inverse Document Frequency (IDF) values and only then compute the Tf-idf scores. With Tfidfvectorizer on the contrary, the algorithm will do all three steps at once. Under the hood, it computes the word counts, IDF values, and Tf-idf scores all using the same dataset [2].

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Fig. 7 IDF weights table

2.4 Similarity Matching Cosine similarity is a measure of similarity between two nonzero vectors of inner product space that measure the cosine of the angle between them. Similarity matching is the process of matching two data. i.e., comparison of two things and returns the index of similarity. In Eq. (1), Ai and Bi are components of vector A and B, respectively. The resulting similarity ranges from − 1 to 1, where −1 represents that two vectors are opposite to each other and 1 represents that two vectors are completely similar. Zero represents the orthogonality and other values show intermediate similarities or dissimilarities. Figure 9 illustrates the same with an example. Eqn 1: Cosine similarity matching n Ai Bi A·B =  i=1 similarity = cos(θ ) = n n AB A2 i=1

i

(1)

2 i=1 Bi

Figure 10 represents data where the user will compare and find the similarity match between different items. It shows you the data and the code where the user

420 Fig. 8 tfidf values table

Fig. 9 Cosine similarity match

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Fig. 10 Similarity match working

is defining three variables and assigning them some data. The result of the same is shown in Fig. 11. After comparing, the outcome will be in the form of a matrix where the words are compared with the three variables and they have two possible values they are 1 and 0. If the value is one, it indicates they are similar and if the value is 0 they are not similar, i.e., they are orthogonal. When the query is encountered, it is matched with all the data samples and returns a list of values that have maximum similarity values, among which the top n values will be selected and returned as the response. In Fig. 12, the first row has the maximum similarity which will be the most suitable answer.

Fig. 11 Output of code in Fig. 11

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Fig. 12 Listing of top 10 matched items in data

3 Algorithm The algorithm is the most important part of the project by which the entire process is analyzed. Figure 13 illustrates the flow of algorithm. The algorithm steps are given below: Fig. 13 Flow of algorithm

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1 2 3 4 5 6 7 8

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The user will provide the query as input, the algorithm will get the input and proceeds. The input given by the user is preprocessed by experiencing many steps like tokenization, stop-words removal, etc. Now the refined query is searched in the database for its match with the help of cosine similarity match. If the data is obtained with the maximum match, then the corresponding answer is returned to the user. If the given query did not match or matched with very less CSI value, then the data from the other sources is obtained for the query provided by the user. The obtained data is supplemented to the main database along with the query asked by the user. The obtained data is returned to the user and requests for the next input. Step 1 to step 7 are repeated until the user is finished with all his queries.

4 System Architecture The complete model is flexible and scalable that even if there is abrupt traffic, the system can handle it. There is local data that is first explored for matching the input query, and once the solution is found, the corresponding answer is returned as a response. This process is achieved by cosine similarity matching technique which returns the data with respect to the user’s input query which has maximum similarity value. In case if there is no match in the local data or there is data with less similarity value, then the answer corresponding to that data cannot be returned as that data is inappropriate. The algorithm needs to find the answer to that, which is done by web scraping. Here the algorithm takes the input given by the user preprocesses it and then searches the answer in the web using web scraper from the documents and many other resources from the web, then returns the top 10 results to the algorithm, which in turn returns it to the user. The algorithm not only returns the result to the user but also saves the copy of the new question asked by the user and its corresponding answer in the local data, if in a case the similar or related question is repeated, i.e., asked by the user then the answer can be given directly from the local data faster than the other method, i.e., web scraping method.

5 Scalability of the System For the system functionality, there are many search operations frequently over enormous agricultural documents or data from the database or local data so that the

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solution can be found. When these functions are going on every single day by thousands of user’s the data requirement grows bigger and bigger that the data in the database will not be enough and also it’s a limited data if in case the number of user’s increase and the type of queries reaching us come up to be very different from the database, then algorithm needs to find the alternative for the problem. The algorithm starts collecting data from other different resources to get results for the user’s queries. Professional documents can be obtained from different sources and the data retrieved can be added to a database which makes the database a huge pool of data and that must be a problem for a machine to process the data. This makes a delay in fetching the result and causes a delay in the process where the end-user must wait until the result loads. This can be solved by the web scraping methods which lets the user get the data from World Wide Web immediately after the query is asked. Web scraping is made easy by Python modules like beautiful soup, selenium, etc. The algorithm uses selenium for auto operating and downloading data automatically from the World Wide Web into the local database without any human input. The data get downloaded automatically every month and added to the database. The data for every month is also saved separately. Selenium creates auto clicks and downloads the new data file and moves the files to the required location.

6 Conclusion and Result This work shows the potential of data extraction besides how high availability and scalability can be achieved. For the same process, this prototype is designed that classifies and fetches the required solution and descriptions collecting everything in a queryable database with simple grammar to retrieve the collected knowledge anytime thereafter. Sometimes the data is obtained from various resources which make the system more scalable, accurate, and efficient. The problem is solvable in a single click without consuming much time. It can be advanced in such a manner that when the error has occurred, automatically the system will recognize the error and perform the complete algorithm providing a solution for the user or implements itself automatically if desired. There is no need for a complex general system when the user can tackle more efficiently the same issue from a domain-specific point of view.

References 1. Gómez-Pérez, P., Phan, T.N., Küeng, J.: Knowledge extraction from text sources using a distributed mapreduce cluster. In: 2016 27th International Workshop on Database and Expert Systems Applications (DEXA), pp. 29–33. IEEE (2016)

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2. Wei, Q., Zhong, C., Yu, J., Luo, C., Chen, L.: Human-machine dialogue system development based on semantic and location similarity of short text model. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 2029–2032. IEEE (2018) 3. Revathy, M., Madhavu, M.L.: Efficient author community generation on NLP based relevance feature detection. In: 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–5. IEEE (2017) 4. Google Images (google.com)

Enhanced Accuracy in Machine Learning Using Feature Set Bifurcation Hrithik Sanyal, Priyanka Saxena, and Rajneesh Agrawal

1 Introduction During the last few decades, medical research is being taking the advantage of highspeed data processing through technology [1]. Researchers applied different techniques, such as screening, to identify the stages of the diseases before they indicate symptoms. Additionally, experts have developed new approaches for the extrapolation of diagnosis and treatment in the early stages of the various diseases. With the development of new methods, the accurate prediction of disease and correct diagnosis has become one of the most challenging and exciting tasks for physicians. The large volume of data is being collected extremely fast in the field of biomedical and is a rich foundation of information in medical research. Machine learning encourages us to portion this huge data and extract information from this as per the past diagnosis and identify hard-to-see symptomatic knowledge from enormous and boisterous dataset according to World Health Organization (WHO) [1]. For example, breast cancer has some aspects like survivability and recurrence (return of cancer after treatment). These are a critical phenomenon in breast cancer behavior, inherently related to the death of the patient [2]. Prediction of diseases is possible by deciphering the information from the data which is found to be indicative of the H. Sanyal (B) Department of Electronics and Telecommunications, Bharati Vidyapeeth College of Engineering Pune, Pune, India e-mail: [email protected] P. Saxena Department of Computer Science, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India e-mail: [email protected] R. Agrawal Comp-Tel Consultancy, Mentor, Jabalpur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6_46

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disease. Application of ML in the medical domain is growing rapidly due to the effectiveness of its approach in prediction and classification, especially in medical diagnosis to predict diseases like breast cancer, now it is widely applied to biomedical research. The purpose of this work, in general, is to predict the disease from the historical data and in the research carried out, the comparative analysis of ML techniques is provided to increase the prediction rate using the example of breast cancer dataset and also is to provide better accuracy evaluation with higher speed. In this paper, Sect. 1 introduces the usage of ML in cancer diagnosis, Sect. 2 details about techniques of machine learning and their descriptions, Sect. 3 discusses the literature survey, Sect. 4 discusses the use of feature-set bifurcation and Wisconsin data set that will be used in the simulation. Section 5 details the proposed methodology with diagrammatic representation, and Sect. 6 results obtained are discussed and finally at the end in Sect. 7, we have the future work and conclusion as well.

2 Machine Learning Techniques 2.1 Machine Learning Techniques Machine learning is a framework which takes information, uses algorithmically developed designs, trains it by utilizing the input information and yields a result which is fed back to use it in future processing. ML is a subset of artificial intelligence (AI). The machine learning understands the structure of data and puts that data into new structural models that are understandable and useful for people. Machine learning uses different types of techniques. These techniques are: Supervised Learning: Supervised learning prepares a model on known information and yields information. This aide of classification and regression in foreseeing future yields precisely. On taking and learning from known inputs and outputs, it builds and trains a model that would forecast accurately from the known facts. Supervised learning is typically castoff for prediction when the output of the data is known. Unsupervised Learning: Unsupervised learning is a classification of ML which searches hidden patterns or structures in data. It helps to make inferences from datasets which are consisting of responses that are not tagged or labelled. Unsupervised learning mostly uses clustering technique. Clustering is furthermost used unsupervised learning technique. It is used for finding concealed outlines or federations in datasets and thus analyzing them.

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2.2 Machine Learning Classifiers Various classifiers have been designed by the researchers in the business. A list of some well-known classifiers that are used in ML is as follows: 1. 2. 3.

Logistic regression Naïve Bayes network classifier Decision tree.

Logistic Regression: Logistic regression is a classification algorithm in ML which makes use of one or more autonomous input variables to come up with an outcome/ output. It works on discrete output labels which can attain any of the two outputs such as yes/no, true/false, 1/0. Logistic regression is specifically implied for arrangement; it helps see how a lot of autonomous factors influence the result of the dependent variable. The main shortcoming of the logistic regression algorithm is that it possibly works when the anticipated variable is discrete. Naïve Bayes Network Classifier: It is based on Bayes’ algorithm assumes the presence of a specific feature in a class which is not related to any other feature’s presence. It is useful for an an enormous informational collection. Despite the fact its methodology is very complex, Naïve Bayes is known to perform better than the rest of the other classifiers calculations in ML. The Naïve Bayes classifier requires an extremely modest quantity of prepared informational index to give a correct estimation of the fundamental boundaries for getting the outcomes. When compared to other classifiers, they are extremely fast in nature. One big disadvantage is that their estimation is based on probability and hence not always very accurate. Decision Tree: The decision tree algorithm makes use of a tree structure using the “if-then” rules which are mutually exclusive in classification. The procedure of the decision tree starts on, by separating the information into little pieces/structures and thus connecting it with a tree, which will increase gradually. This procedure goes on and proceeds on the preparation of information collections until the endpoint is reached, i.e., the termination point. A decision tree is easy to comprehend, envision and imagine. It may work with very little datasets for predictions. One disadvantage of the decision tree is that it can make simple trees very complex, which may become waste later on. Similarly, it may be very precarious subsequently, ruining the entire structure of the decision tree.

3 Literature Review Breast cancer is considered to be the most deadly type of cancer among the rest of the cancers. Notwithstanding, being treatable and healable, if not diagnosed at an early stage, a humongous number of people do not survive since the diagnosis of the cancer is done at a very late stage when it becomes too late. An effective way to classify data

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in medical fields and also in other fields is by using ML data mining and classifiers, which helps to make important decisions by the methodology of diagnosis. The dataset which has been used is UCI’s Wisconsin dataset for breast cancer. The ultimate objective is to pigeonhole data from both the algorithm and show results in terms of preciseness. Our result concludes that decision tree classifier among all the other classifiers gives higher precision [1]. Cancer is a dangerous kind of disease, which is driven by variation in cells inside the body. Variation in cells is complemented with an exponential increase in malignant cell’s growth and control as well. Albeit dangerous, breast cancer is also a very frequent type of cancer. Among all the diseases, cancer has been undoubtedly the most deadly disease. It occurs due to variation and mutation of infectious and malignant cells which spread quickly and infect surrounding cells as well. For increasing the survival rate of patients, suffering from breast cancer, early detection of the disease is very much required. Machine learning techniques help in the accurate and probable diagnosis of cancer in patients. It makes intelligent systems, which learn from the historical data and keeps learning, from the recent predictions, to make the decisions more accurate and precise [3]. AI is considered man-made consciousness that enjoys an assortment of factual, probabilistic and improvement strategies that permits PCs to “learn” from earlier models and to distinguish hard-to-perceive designs from prodigious, loud or complex informational indexes. This capability is particularly proper to clinical applications, especially those that depend upon complex proteomic and genomic estimations. Hence, AI is a great part of the time used in threatening development examination and revelation. AI is likewise assisting with improving our essential comprehension of malignancy advancement and movement [2]. Chinese ladies are genuinely undermined by the bosom disease with high dreariness and mortality. The absence of potent anticipation facsimiles brings about trouble for specialists to set up a fitting treatment strategy that may draw out patient’s endurance time [4]. Information mining is a basic part in learning revelation process where keen specialists are consolidated for design extraction. During the time spent creating information mining applications, the most testing and fascinating undertaking is the ailment expectation. This paper will be useful for diagnosing precise ailment by clinical experts and examiners, depicting different information mining methods. Information mining applications in therapeutic administrations hold goliath potential and accommodation. Anyway, the effectiveness of information mining procedures on medicinal services space relies upon the accessibility of refined social insurance information. In our present examination, we talk about scarcely any classifier methods utilized in clinical information investigation. Additionally, not many sicknesses forecast examinations like bosom malignant growth expectation, coronary illness conclusion, thyroid expectation and diabetic are thought of. The outcome shows that decision tree calculation suits well for infection expectation as it creates better precision results [5].

Enhanced Accuracy in Machine Learning Using Feature … Table 1 Depiction of the breast cancer datasets

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Dataset

Attribute count Instance count Class count

Original data

11

699

2

Diagnosed data 32

569

2

Prognosis data

198

2

34

4 Feature Set Bifurcation In machine learning, decision tree classifier has been always a good choice for the authors due to its high prediction accuracy. But when the feature set is large means the attributes are too many then it reduces the performance and accuracy both. Therefore, this work proposes to form subsets from the main feature set which are used in implementing multiple decision trees. This can be done by simple bifurcation or maybe some algorithm is used for the same. In this work, simple bifurcation of feature set has been applied and implementation gives encouraging results. To recognize and differentiate the threatening samples from benevolent samples, UCI’s Wisconsin dataset for breast cancer is being used in different ML implementations. Table 1 shows the different characteristic counting for different Wisconsin datasets [6].

5 Proposed Work This work proposes to create a decision tree-based cancer patient data processing environment which will not only faster but will also provide high accuracy. The system will leverage the facility of the multi-threaded system in which two different decision trees shall be created having a different set of attributes processed in parallel. The outcomes acquired from both the threads shall be combined to get the final results. The proposed system will be executed in the following steps: 1. 2. 3.

Identification and separation of attributes for making a decision tree Generation of threads for implementation of multiple decision trees Combining multiple decision trees for getting the final results. This approach will have following time complexities:

1.

Separation Complexity O(z1) = O(k/r ) ∗ r

2.

where k is count of the attributes, and r is count of “dt”. Processing Complexity

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SET - 1

SET - 2

DECİSİON TREE - 1

DECİSİON TREE - 2

ACCURACY - 1

ACCURACY - 2

COMBİNE

FİNAL ACCURACY

Fig. 1 System flow of the proposed work

O(z2) = O(h ∗ r 2)

3.

where “h” is the scope of the training data, and r is count of “dt”. Combination Complexity O(z3) = r

4.

where r is the count of “dt”. Overall Complexity O(h) = O(z1) + O(z2) + O(z3) O(h) = O( p/r ) ∗ r + O(h ∗ r 2) + O(r ) Comparison of a simple decision tree and modified decision tree: O(sdt) = O(h ∗ p2)[6] O(mdt) = O( p/r ) ∗ r + O(h ∗ r 2) + O(r )

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Fig. 2 Flowchart of the complete proposed system

Start

Input Dataset Evaluate Attributes Apply Division of Attributes Start One Process for Each Set of Attributes

Label & Feature Set - 1

Label & Feature Set - 2

Apply Gini Index

Apply Gini Index

Calculate Accuracy

Calculate Accuracy

Combine Step Display Final Accuracy

Stop

From the above two equations, it is clear that the O(mdt)  O(sdt) when r > 1 as the complexity of simple decision tree will be two high if the number of attributes are too high.

6 Results Obtained Decision tree is one of the utmost used classifiers in machine learning which provides high accuracy in prediction of correct results. It is a supervised learning algorithm and works on a dataset which uses a LABEL (Result) and Feature Set (Inputs). It

434 Table 2 Accuracies obtained for the bifurcated feature sets and the composite feature set

H. Sanyal et al. S. No.

Applied set

Accuracy obtained

1

First bifurcated feature set

94.15

2

Second bifurcated feature set

86.55

3

Average

90.35

4

Composite feature set

90.06

learns from the provided dataset (training set) and evaluates results based on test inputs. Its basic strategy is to apply a condition on the input features and generate the binary results. The results are filtered with the application of each condition forming a binary decision tree. Since conditions are applied on each feature from the feature set, its accuracy is based on both relevant and non-relevant features. In this effort, a new feature set bifurcation technique is being applied. The feature set has been divided into two groups, and different decision trees have been formed to obtain the accuracies. These accuracies have been averaged to obtain the accurateness of the system. This proposed system has been implemented using Wisconsin dataset, and two sets of attributes have been formed to generate result accuracy depicted in the following table and graphs. Table 2 shows the accuracies obtained from the execution of the implemented system using Python. Interface: From the graph, it is shown that the accuracy obtained from the distributed feature sets is different due to the impact of the features. Accuracy of the first feature set is 94.15%, whereas the same from second feature set is 86.55%. The average accuracy is 90.35% in the available work bench.

Fig. 3 Accuracies obtained from the two feature set along with average accuracy

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Fig. 4 Comparison of accuracies obtained for the composite feature set and the distributed feature set average accuracy

Interface: The second graph shows the accuracies obtained from the composite feature set which is 90.06% and the average accuracy is 90.35%. It is found that the average accuracy is more than the composite accuracy to prove the hypothesis is correct. The reason for same is also explicit as the data available for decision making is smaller, filtered set remaining after application of the other features on the main dataset.

7 Conclusion and Future Work This paper is proposing to provide a better decision tree algorithm implementation which has not only has high performance but is also having high accuracy. Paper provides details of machine learning, its techniques and need for the industry and enhancement of artificial intelligence. Further, the studies of the earlier researches have been presented which clearly explains that the focus of the research is on having a better solution from the existing classifiers in different scenarios. It is found that the enhancement of accuracies and performance has been focused and for that no work applies bifurcation technique of feature set. A comparative complexity calculation of the simple decision tree algorithm and proposed modified decision tree algorithm shows the enhanced time complexity, and implementation shows the comparative accuracy between composite and average precisions, where average precision of the bifurcated distributed dataset is found to be better than the accuracy obtained from the composite feature set.

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This work can be further enhanced by applying other mechanisms of separation of the attributes for building multiple decision trees. It can also be further enhanced and tested on real-time data for high performance and accuracies both.

References 1. Sanyal, H., Agrawal, R.: Latest trends in machine learning & deep learning techniques and their applications. Int. Res. Anal. J. 14(1), 348–353 (2018) 2. Singh, S.N., Thakral, S.: Using data mining tools for breast cancer prediction and analysis. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1–4. Greater Noida, India (2018). https://doi.org/10.1109/CCAA.2018.8777713 3. Su, J., Zhang, H.: A fast decision tree learning algorithm. In: Proceedings of the 21st National Conference on Artificial ˙Intelligence (AAAI’06), vol. 1, pp. 500–505. AAAI Press (2006) 4. Gupta, M., Gupta, B.: A comparative study of breast cancer diagnosis using supervised machine learning techniques. In: 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), pp. 997–1002. Erode (2018). https://doi.org/10.1109/ICCMC. 2018.8487537 5. Fu, B., Liu, P., Lin, J., Deng, L., Hu, K., Zheng, H.: Predicting invasive disease-free survival for early stage breast cancer patients using follow-up clinical data. IEEE Trans. Biomed. Eng. 66(7), 2053–2064 (2019). https://doi.org/10.1109/TBME.2018.2882867 6. Angra, S., Ahuja, S.: Machine learning and its application. In: International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017)

Author Index

A Abhijith, G., 371 Adarsh, 79 Aggarwal, Archit, 211 Agrawal, Aaditya, 391 Agrawal, Rajneesh, 281, 427 Aher, Jayshree Ghorpade, 31 Ahire, Hemangi Sunil, 297 Ahmad, Anzar, 21 Ahuja, Ravinder, 391, 403 Ajawan, Pratijnya, 413 Akhil, Raghavendra, 371 Aleena, John, 371 Ambily, Gouri Babu, 109 Anagha, H. Kumar, 371 Anil, Alna, 109 Anusree, P. S., 71 Aravind, Akhil Chittathuparambil, 161 Ashish, V., 371

B Babulekshmanan, Parvathy Devi, 55 Balakrishnan, Ramadoss, 273 Balasubramanian, P., 71 Betadur, Balaji, 413 Bhardawaj, Jadumani, 307, 385 Bhosale, Aditi Sanjay, 297 Biswas, Rupanjana, 161 Bodda, Sandeep, 169

C Chandran, Lekshmi R., 323

D Desai, Veena, 413 Deshpande, Uttam U., 63 Diwakar, Shyam, 55, 109, 161, 169, 231 Doke, Vikramsinh, 239 Dsouza, Mani Bushan, 341

G Gokul Pillai, V., 323 Gola, Bhavya, 211 Gopika, Anil, 231 Gujarathi, Priyanka, 191 Gupta, Arpita, 273 Gupta, Gaurav, 79 Gupta, Richa, 249 Guru, D. S., 261

H Harisudhan, Athira Cheruvathery, 169 Hari, Vishnu, 109 Hirekodi, Ashwini R., 63

I Ilango, K., 371

J Jadhav, Swapnil Sanjay, 297 Jain, Ashik A., 79 Jain, Sahil, 333 Jamil, Kaif, 9 Jaybhay, Avinash Yuvraj, 297

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. K. Gunjan et al. (eds.), Cybernetics, Cognition and Machine Learning Applications, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-33-6691-6

437

438 Jithendra Gowd, K., 39 Johri, Prashant, 9

K Kailasan, Indulekha P., 231 Karande, Aarti, 1 Kawrkar, Sayali, 239 Kharche, Shubhangi, 139 Kolagani, Bhavita, 231 Kulkarni, Pradnya V., 333 Kumar, Dhanush, 169 Kumari, Neha, 131 Kumar, Rajeev, 131

L Lekshmi, R. Chandran, 371

M Magadum, Ashok P., 63 Malik, Pallavi, 291 Mandal, Pratyush, 333 Mane, Shree G., 117 Manjaiah, D. H., 341 Manjula, G. Nair, 371 Manoj, Gautham, 109 Mapari, Vinit, 1 Mehta, Harsh Nagesh, 31 Mitchelle Flavia Jerome, M., 361 Modak, Renuka, 239 Mohan, Krishna, 169 Mukherjee, A., 291 Murali, Ranjani, 313

N Nair, Anjali Suresh, 161 Nair, S. Varsha, 231 Narasimhulu, V., 39 Narkhede, Anurag, 1 Nath, Gagarina, 307 Nath, Utpal Kumar, 307, 385 Nizar, Nijin, 161 Nutakki, Chaitanya, 231

P Pandita, Sahil, 139 Pandurangi, Bhagyashri R., 63 Parmar, Praptiba, 203 Patel, Deep, 153 Patel, Foram, 153

Author Index Patel, Nihar, 153 Patel, Vibha, 153 Patil, Sandip Raosaheb, 191 Paul, Saurin, 307, 385 Pawar, Mayur M., 117 Prashanth, M. C., 221 Priya, Lekshmi Aji, 169 Priyadarshini, L., 185 Puthanveedu, Akshara Chelora, 169 R Rachana, P. G., 261 Radhamani, Rakhi, 109 Rai, Ajeet, 89 Raj, Aditya, 79 Rajeev, Anjali, 169 Rajeswari, K., 297 Ranbhare, Shubham V., 117 Rastogi, Deependra, 9 Raveendran, Praveen, 109 Ravikumar, M., 177, 221, 261 Roy, Suman, 95 S Sabarwal, Munish, 9 Saha, Prasenjit, 307, 385 Sahu, Ashish, 139 Sampath Kumar, S., 177 Sanghani, Disha, 203 Sankla, Tushar, 211 Sanyal, Hrithik, 281, 427 Saravana Kumar, S., 95 Sardar, Nikhil B., 117, 239 Sarika, M., 361 Sasidharakurup, Hemalatha, 55 Sathianarayanan, Sreehari, 55 Savitha, G., 79 Saxena, Priyanka, 281, 427 Sayed, Mateen, 333 Sehgal, Priti, 249 Sekar, K. R., 361 Senthilkumar, T., 47 Shah, Dhruvil, 153 Shekhar, Shashi, 21 Shivakumar, G., 177 Shivaprasad, B. J., 261 Shrinivasan, Lakshmi, 185 Singh, Aadityanand, 139 Singh, Birendra, 333 Soneji, Hitesh Narayan, 349 Sudhanvan, Sughosh, 349 Sujitha, Nima A., 231

Author Index

439

T Thaventhiran, C., 361

W Walimbe, Varun, 139

V Venkataraman, V., 361 Venu, Sukriti Nirayilatt, 161 Vijayan, Asha, 169

Y Yadav, Akash, 403