Advances in Computer, Communication and Computational Sciences: Proceedings of IC4S 2019 [1st ed.] 9789811544088, 9789811544095

This book discusses recent advances in computer and computational sciences from upcoming researchers and leading academi

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Advances in Computer, Communication and Computational Sciences: Proceedings of IC4S 2019 [1st ed.]
 9789811544088, 9789811544095

Table of contents :
Front Matter ....Pages i-xviii
Front Matter ....Pages 1-1
A Novel Crypto-Ransomware Family Classification Based on Horizontal Feature Simplification (Mohsen Kakavand, Lingges Arulsamy, Aida Mustapha, Mohammad Dabbagh)....Pages 3-14
Characteristic Analysis and Experimental Simulation of Diffuse Link Channel for Indoor Wireless Optical Communication (Peinan He, Mingyou He)....Pages 15-33
A Comparative Analysis of Malware Anomaly Detection (Priynka Sharma, Kaylash Chaudhary, Michael Wagner, M. G. M. Khan)....Pages 35-44
Future Identity Card Using Lattice-Based Cryptography and Steganography (Febrian Kurniawan, Gandeva Bayu Satrya)....Pages 45-56
Cryptanalysis on Attribute-Based Encryption from Ring-Learning with Error (R-LWE) (Tan Soo Fun, Azman Samsudin)....Pages 57-64
Enhanced Password-Based Authentication Mechanism in Cloud Computing with Extended Honey Encryption (XHE): A Case Study on Diabetes Dataset (Tan Soo Fun, Fatimah Ahmedy, Zhi Ming Foo, Suraya Alias, Rayner Alfred)....Pages 65-74
An Enhanced Wireless Presentation System for Large-Scale Content Distribution (Khong-Neng Choong, Vethanayagam Chrishanton, Shahnim Khalid Putri)....Pages 75-85
On Confidentiality, Integrity, Authenticity, and Freshness (CIAF) in WSN (Shafiqul Abidin, Vikas Rao Vadi, Ankur Rana)....Pages 87-97
Networking Analysis and Performance Comparison of Kubernetes CNI Plugins (Ritik Kumar, Munesh Chandra Trivedi)....Pages 99-109
Classifying Time-Bound Hierarchical Key Assignment Schemes (Vikas Rao Vadi, Naveen Kumar, Shafiqul Abidin)....Pages 111-119
A Survey on Cloud Workflow Collaborative Adaptive Scheduling (Delong Cui, Zhiping Peng, Qirui Li, Jieguang He, Lizi Zheng, Yiheng Yuan)....Pages 121-129
Lattice CP-ABE Scheme Supporting Reduced-OBDD Structure (Eric Affum, Xiasong Zhang, Xiaofen Wang)....Pages 131-142
Crypto-SAP Protocol for Sybil Attack Prevention in VANETs (Mohamed Khalil, Marianne A. Azer)....Pages 143-152
Managerial Computer Communication: Implementation of Applied Linguistics Approaches in Managing Electronic Communication (Marcel Pikhart, Blanka Klímová)....Pages 153-160
Advance Persistent Threat—A Systematic Review of Literature and Meta-Analysis of Threat Vectors (Safdar Hussain, Maaz Bin Ahmad, Shariq Siraj Uddin Ghouri)....Pages 161-178
Construction of a Teaching Support System Based on 5G Communication Technology (Hanhui Lin, Shaoqun Xie, Yongxia Luo)....Pages 179-186
Front Matter ....Pages 187-187
Investigating the Noise Barrier Impact on Aerodynamics Noise: Case Study at Jakarta MRT (Sugiono Sugiono, Siti Nurlaela, Andyka Kusuma, Achmad Wicaksono, Rio P. Lukodono)....Pages 189-197
3D Cylindrical Obstacle Avoidance Using the Minimum Distance Technique (Krishna Raghuwaiya, Jito Vanualailai, Jai Raj)....Pages 199-208
Path Planning of Multiple Mobile Robots in a Dynamic 3D Environment (Jai Raj, Krishna Raghuwaiya, Jito Vanualailai, Bibhya Sharma)....Pages 209-219
Autonomous Quadrotor Maneuvers in a 3D Complex Environment (Jito Vanualailai, Jai Raj, Krishna Raghuwaiya)....Pages 221-231
Tailoring Scrum Methodology for Game Development (Towsif Zahin Khan, Shairil Hossain Tusher, Mahady Hasan, M. Rokonuzzaman)....Pages 233-243
Designing and Developing a Game with Marketing Concepts (Towsif Zahin Khan, Shairil Hossain Tusher, Mahady Hasan, M. Rokonuzzaman)....Pages 245-252
Some Variants of Cellular Automata (Ray-Ming Chen)....Pages 253-263
An Exchange Center Based Digital Cash Payment Solution (Yong Xu, Jingwen Li)....Pages 265-274
Design and Implementation of Pianos Sharing System Based on PHP (Sheng Liu, Chu Yang, Xiaoming You)....Pages 275-285
A Stochastic Framework for Social Media Adoption or Abandonment: Higher Education (Mostafa Hamadi, Jamal El-Den, Cherry Narumon Sriratanaviriyakul, Sami Azam)....Pages 287-299
Low-Earth Orbital Internet of Things Satellite System on the Basis of Distributed Satellite Architecture (Mikhail Ilchenko, Teodor Narytnyk, Vladimir Prisyazhny, Segii Kapshtyk, Sergey Matvienko)....Pages 301-313
Automation of the Requisition Process in Material Supply Chain of Construction Firms (Adedeji Afolabi, Yewande Abraham, Rapheal Ojelabi, Oluwafikunmi Awosika)....Pages 315-323
Developing an Adaptable Web-Based Profile Record Management System for Construction Firms (Adedeji Afolabi, Yewande Abraham, Rapheal Ojelabi, Etuk Hephzibah)....Pages 325-333
Profile Control System for Improving Recommendation Services (Jaewon Park, B. Temuujin, Hyokyung Chang, Euiin Choi)....Pages 335-340
IoT-Based Smart Application System to Prevent Sexual Harassment in Public Transport (Md. Wahidul Hasan, Akil Hamid Chowdhury, Md Mehedi Hasan, Arup Ratan Datta, A. K. M. Mahbubur Rahman, M. Ashraful Amin)....Pages 341-351
A Decision Support System Based on WebGIS for Supporting Community Development (Wichai Puarungroj, Suchada Phromkhot, Narong Boonsirisumpun, Pathapong Pongpatrakant)....Pages 353-363
Structural Application of Medical Image Report Based on Bi-CNNs-LSTM-CRF (Aesha Abdullah Moallim, Li Ji Yun)....Pages 365-377
Integrating QR Code-Based Approach to University e-Class System for Managing Student Attendance (Suwaibah Abu Bakar, Shahril Nazim Mohamed Salleh, Azamuddin Rasidi, Rosmaini Tasmin, Nor Aziati Abd Hamid, Ramatu Muhammad Nda et al.)....Pages 379-387
Front Matter ....Pages 389-389
Decision-Making System in Tannery by Using Fuzzy Logic (Umaphorn Tan, Kanate Puntusavase)....Pages 391-398
A Study on Autoplay Model Using DNN in Turn-Based RPG (Myoungyoung Kim, Jaemin Kim, Deukgyu Lee, Jihyeong Son, Wonhyung Lee)....Pages 399-407
Simulation Optimization for Solving Multi-objective Stochastic Sustainable Liner Shipping (Saowanit Lekhavat, Habin Lee)....Pages 409-416
Fast Algorithm for Sequence Edit Distance Computation (Hou-Sheng Chen, Li-Ren Liu, Jian-Jiun Ding)....Pages 417-428
Predicting Student Final Score Using Deep Learning (Mohammad Alodat)....Pages 429-436
Stance Detection Using Transformer Architectures and Temporal Convolutional Networks (Kushal Jain, Fenil Doshi, Lakshmi Kurup)....Pages 437-447
Updated Frequency-Based Bat Algorithm (UFBBA) for Feature Selection and Vote Classifier in Predicting Heart Disease (Himanshu Sharma, Rohit Agarwal)....Pages 449-460
A New Enhanced Recurrent Extreme Learning Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection (Rohit Agarwal, Himanshu Sharma)....Pages 461-471
Deep Learning-Based Severe Dengue Prognosis Using Human Genome Data with Novel Feature Selection Method (Aasheesh Shukla, Vishal Goyal)....Pages 473-482
An Improved DCNN-Based Classification and Automatic Age Estimation from Multi-factorial MRI Data (Ashish Sharma, Anjani Rai)....Pages 483-495
The Application of Machine Learning Methods in Drug Consumption Prediction (Peng Han)....Pages 497-507
Set Representation of Itemset for Candidate Generation with Binary Search Technique (Carynthia Kharkongor, Bhabesh Nath)....Pages 509-520
Robust Moving Targets Detection Based on Multiple Features (Jing Jin, Jianwu Dang, Yangpin Wang, Dong Shen, Fengwen Zhai)....Pages 521-531
Digital Rock Image Enhancement via a Deep Learning Approach (Yunfeng Bai, Vladimir Berezovsky)....Pages 533-537
Enhancing PSO for Dealing with Large Data Dimensionality by Cooperative Coevolutionary with Dynamic Species-Structure Strategy (Kittipong Boonlong, Karoon Suksonghong)....Pages 539-549
A New Encoded Scheme GA for Solving Portfolio Optimization Problems in the Big Data Environment (Karoon Suksonghong, Kittipong Boonlong)....Pages 551-560
Multistage Search for Performance Enhancement of Ant Colony Optimization in Randomly Generated Road Profile Identification Using a Quarter Vehicle Vibration Responses (Kittikon Chantarattanakamol, Kittipong Boonlong)....Pages 561-570
Classification and Visualization of Poverty Status: Analyzing the Need for Poverty Assistance Using SVM (Maricel P. Naviamos, Jasmin D. Niguidula)....Pages 571-581
Comparative Analysis of Prediction Algorithms for Heart Diseases (Ishita Karun)....Pages 583-591
Sarcasm Detection Approaches Survey (Anirudh Kamath, Rahul Guhekar, Mihir Makwana, Sudhir N. Dhage)....Pages 593-609
Front Matter ....Pages 611-611
Interactive Animation and Affective Teaching and Learning in Programming Courses (Alvin Prasad, Kaylash Chaudhary)....Pages 613-623
IoT and Computer Vision-Based Electronic Voting System (Md. Nazmul Islam Shuzan, Mahmudur Rashid, Md. Aowrongajab Uaday, M. Monir Uddin)....Pages 625-638
Lexical Repository Development for Bugis, a Minority Language (Sharifah Raihan Syed Jaafar, Nor Hashimah Jalaluddin, Rosmiza Mohd Zainol, Rahilah Omar)....Pages 639-648
Toward EU-GDPR Compliant Blockchains with Intentional Forking (Wolf Posdorfer, Julian Kalinowski, Heiko Bornholdt)....Pages 649-658
Incorum: A Citizen-Centric Sensor Data Marketplace for Urban Participation (Heiko Bornholdt, Dirk Bade, Wolf Posdorfer)....Pages 659-669
Developing an Instrument for Cloud-Based E-Learning Adoption: Higher Education Institutions Perspective (Qasim AlAjmi, Ruzaini Abdullah Arshah, Adzhar Kamaludin, Mohammed A. Al-Sharafi)....Pages 671-681
Gamification Application in Different Business Software Systems—State of Art (Zornitsa Yordanova)....Pages 683-693
Data Exchange Between JADE and Simulink Model for Multi-agent Control Using NoSQL Database Redis (Yulia Berezovskaya, Vladimir Berezovsky, Margarita Undozerova)....Pages 695-705
Visualizing Academic Experts on a Subject Domain Map of Cartographic-Alike (Diana Purwitasari, Rezky Alamsyah, Dini Adni Navastara, Chastine Fatichah, Surya Sumpeno, Mauridhi Hery Purnomo)....Pages 707-719
An Empirical Analysis of Spatial Regression for Vegetation Monitoring (Hemlata Goyal, Sunita Singhal, Chilka Sharma, Mahaveer Punia)....Pages 721-729
Extracting Temporal-Based Spatial Features in Imbalanced Data for Predicting Dengue Virus Transmission (Arfinda Setiyoutami, Wiwik Anggraeni, Diana Purwitasari, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo)....Pages 731-742
The Application of Medical and Health Informatics Among the Malaysian Medical Tourism Hospital: A Preliminary Study (Hazila Timan, Nazri Kama, Rasimah Che Mohd Yusoff, Mazlan Ali)....Pages 743-754
Design of Learning Digital Tools Through a User Experience Design Methodology (Gloria Mendoza-Franco, Jesús Manuel Dorador-González, Patricia Díaz-Pérez, Rolando Zarco-Hernández)....Pages 755-764
Fake Identity in Political Crisis: Case Study in Indonesia (Kristina Setyowati, Apneta Vionuke Dihandiska, Rino A. Nugroho, Teguh Budi Santoso, Okki Chandra Ambarwati, Is Hadri Utomo)....Pages 765-769
Cloud Computing in the World and Czech Republic—A Comparative Study (Petra Poulová, Blanka Klímová, Martin àvarc)....Pages 771-778
Data Quality Improvement Strategy for the Certification of Telecommunication Tools and Equipment: Case Study at an Indonesia Government Institution (E. A. Puspitaningrum, R. F. Aji, Y. Ruldeviyani)....Pages 779-794
Evolution of Neural Text Generation: Comparative Analysis (Lakshmi Kurup, Meera Narvekar, Rahil Sarvaiya, Aditya Shah)....Pages 795-804
Research on the Status and Strategy of Developing Financial Technology in China Commercial Bank (Ze-peng Chen, Jie-hua Xie, Cheng-qing Li, Jie Xiao, Zi-yi Huang)....Pages 805-819
Understanding Issues Affecting the Dissemination of Weather Forecast in the Philippines: A Case Study on DOST PAGASA Mobile Application (Lory Jean L. Canillo, Bryan G. Dadiz)....Pages 821-830
Guideme: An Optimized Mobile Learning Model Based on Cloud Offloading Computation (Rasha Elstohy, Wael Karam, Nouran Radwan, Eman Monir)....Pages 831-842
Model Development in Predicting Seaweed Production Using Data Mining Techniques (Joseph G. Acebo, Larmie S. Feliscuzo, Cherry Lyn C. Sta. Romana)....Pages 843-850
A Survey on Crowd Counting Methods and Datasets (Wang Jingying)....Pages 851-863
Decentralized Marketplace Using Blockchain, Cryptocurrency, and Swarm Technology (Jorge Ramón Fonseca Cacho, Binay Dahal, Yoohwan Kim)....Pages 865-882
A Expansion Method for DriveMonitor Trace Function (Dong Liu)....Pages 883-892
Load Prediction Energy Efficient VM Consolidation Policy in Multimedia Cloud (K. P. N. Jayasena, G. K. Suren W. de Chickera)....Pages 893-903
An Attribute-Based Access Control Mechanism for Blockchain-Enabled Internet of Vehicles (Sheng Ding, Maode Ma)....Pages 905-915
Front Matter ....Pages 917-917
An Investigation on the Effectiveness of OpenCV and OpenFace Libraries for Facial Recognition Application (Pui Kwan Fong, Ven Yu Sien)....Pages 919-927
Virtual Reality as Support of Cognitive Behavioral Therapy of Adults with Post-Traumatic Stress Disorder (Ivan Kovar)....Pages 929-940
Facial Expression Recognition Using Wavelet Transform and Convolutional Neural Network (Dini Adni Navastara, Hendry Wiranto, Chastine Fatichah, Nanik Suciati)....Pages 941-952
Survey of Automated Waste Segregation Methods (Vaibhav Bagri, Lekha Sharma, Bhaktij Patil, Sudhir N. Dhage)....Pages 953-964
Classification of Human Blastocyst Quality Using Wavelets and Transfer Learning ( Irmawati, Basari, Dadang Gunawan)....Pages 965-974
Affinity-Preserving Integer Projected Fixed Point Under Spectral Technique for Graph Matching (Beibei Cui, Jean-Charles Créput)....Pages 975-985
A New Optimized GA-RBF Neural Network Algorithm for Oil Spill Detection in SAR Images (Vishal Goyal, Aasheesh Shukla)....Pages 987-999
Survey of Occluded and Unoccluded Face Recognition (Shiye Xu)....Pages 1001-1014
A Survey on Dynamic Sign Language Recognition (Ziqian Sun)....Pages 1015-1022
Extract and Merge: Merging Extracted Humans from Different Images (Minkesh Asati, Worranitta Kraisittipong, Taizo Miyachi)....Pages 1023-1033
A Survey of Image Enhancement and Object Detection Methods (Jinay Parekh, Poojan Turakhia, Hussain Bhinderwala, Sudhir N. Dhage)....Pages 1035-1047

Citation preview

Advances in Intelligent Systems and Computing 1158

Sanjiv K. Bhatia · Shailesh Tiwari · Su Ruidan · Munesh Chandra Trivedi · K. K. Mishra   Editors

Advances in Computer, Communication and Computational Sciences Proceedings of IC4S 2019

Advances in Intelligent Systems and Computing Volume 1158

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen , Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/11156

Sanjiv K. Bhatia Shailesh Tiwari Su Ruidan Munesh Chandra Trivedi K. K. Mishra •







Editors

Advances in Computer, Communication and Computational Sciences Proceedings of IC4S 2019

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Editors Sanjiv K. Bhatia Department of Mathematics and Computer Science University of Missouri–St. Louis Chesterfield, MO, USA Su Ruidan Shanghai Advanced Research Institute Pudong, China

Shailesh Tiwari Computer Science Engineering Department ABES Engineering College Ghaziabad, Uttar Pradesh, India Munesh Chandra Trivedi National Institute of Technology Agartala Agartala, Tripura, India

K. K. Mishra Computer Science Engineering Department Motilal Nehru National Institute of Technology Allahabad, Uttar Pradesh, India

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-15-4408-8 ISBN 978-981-15-4409-5 (eBook) https://doi.org/10.1007/978-981-15-4409-5 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved 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

The IC4S is a major multidisciplinary conference organized with the objective of bringing together researchers, developers and practitioners from academia and industry working in all areas of computer and computational sciences. It is organized specifically to help computer industry to derive the advances of next-generation computer and communication technology. Researchers invited to speak will present the latest developments and technical solutions. Technological developments all over the world are dependent upon globalization of various research activities. Exchange of information and innovative ideas is necessary to accelerate the development of technology. Keeping this ideology in preference, the International Conference on Computer, Communication and Computational Sciences (IC4S 2019) has been organized at Mandarin Hotel Bangkok, Bangkok, Thailand, during 11–12 October 2019. This is the third time the International Conference on Computer, Communication and Computational Sciences has been organized with a foreseen objective of enhancing the research activities at a large scale. Technical Program Committee and Advisory Board of IC4S include eminent academicians, researchers and practitioners from abroad as well as from all over the nation. In this book, selected manuscripts have been subdivided into various tracks named—Advanced Communications and Security, Intelligent Hardware and Software Design, Intelligent Computing Techniques, Web and Informatics and Intelligent Image Processing. A sincere effort has been made to make it an immense source of knowledge for all and includes 91 manuscripts. The selected manuscripts have gone through a rigorous review process and are revised by authors after incorporating the suggestions of the reviewers. IC4S 2018 received around 490 submissions from around 770 authors of 22 different countries such as USA, Iceland, China, Saudi Arabia, South Africa, Taiwan, Malaysia, Indonesia, Europe and many more. Each submission has been gone through the plagiarism check. On the basis of plagiarism report, each submission was rigorously reviewed by atleast two reviewers with an average of 2.4 per reviewer. Even some submissions have more than two reviews. On the basis

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Preface

of these reviews, 91 high-quality papers were selected for publication in this proceedings volume, with an acceptance rate of 18.57%. We are thankful to the keynote speakers—Prof. Shyi-Ming Chen, IEEE Fellow, IET Fellow, IFSA Fellow, Chair Professor in National Taiwan University of Science and Technology, Taiwan, and Prof. Maode Ma, IET Fellow, Nanyang Technological University, Singapore, to enlighten the participants with their knowledge and insights. We are also thankful to delegates and the authors for their participation and their interest in IC4S 2019 as a platform to share their ideas and innovation. We are also thankful to the Prof. Dr. Janusz Kacprzyk, Series Editor, AISC, Springer, for providing guidance and support. Also, we extend our heartfelt gratitude to the reviewers and Technical Program Committee Members for showing their concern and efforts in the review process. We are indeed thankful to everyone directly or indirectly associated with the conference organizing team leading it towards the success. Although utmost care has been taken in compilation and editing, however, a few errors may still occur. We request the participants to bear with such errors and lapses (if any). We wish you all the best.

Bangkok, Thailand

Editors Sanjiv K. Bhatia Shailesh Tiwari Munesh Chandra Trivedi K. K. Mishra

About This Book

With advent of technology, intelligent and soft computing techniques came into existence with a wide scope of implementation in engineering sciences. Nowadays, technology is changing with a speedy pace and innovative proposals that solve the engineering problems intelligently are gaining popularity and advantages over the conventional solutions to these problems. It is very important for research community to track the latest advancements in the field of computer sciences. Keeping this ideology in preference, this book includes the insights that reflect the Advances in Computer and Computational Sciences from upcoming researchers and leading academicians across the globe. It contains the high-quality peer-reviewed papers of ‘International Conference on Computer, Communication and Computational Sciences (IC4S-2019)’, held during 11–12 October 2019 at Mandarin Hotel Bangkok, Bangkok, Thailand. These papers are arranged in the form of chapters. The content of this book is divided into five broader tracks that cover variety of topics. These tracks are: Advanced Communications and Security, Intelligent Hardware and Software Design, Intelligent Computing Techniques, Web and Informatics and Intelligent Image Processing. This book helps the perspective readers’ from computer and communication industry and academia to derive the immediate surroundings developments in the field of communication and computer sciences and shape them into real-life applications.

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Contents

Advanced Communications and Security A Novel Crypto-Ransomware Family Classification Based on Horizontal Feature Simplification . . . . . . . . . . . . . . . . . . . . . . . . . . Mohsen Kakavand, Lingges Arulsamy, Aida Mustapha, and Mohammad Dabbagh Characteristic Analysis and Experimental Simulation of Diffuse Link Channel for Indoor Wireless Optical Communication . . . . . . . . . . . . . Peinan He and Mingyou He A Comparative Analysis of Malware Anomaly Detection . . . . . . . . . . . Priynka Sharma, Kaylash Chaudhary, Michael Wagner, and M. G. M. Khan

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Future Identity Card Using Lattice-Based Cryptography and Steganography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Febrian Kurniawan and Gandeva Bayu Satrya

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Cryptanalysis on Attribute-Based Encryption from Ring-Learning with Error (R-LWE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tan Soo Fun and Azman Samsudin

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Enhanced Password-Based Authentication Mechanism in Cloud Computing with Extended Honey Encryption (XHE): A Case Study on Diabetes Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tan Soo Fun, Fatimah Ahmedy, Zhi Ming Foo, Suraya Alias, and Rayner Alfred

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An Enhanced Wireless Presentation System for Large-Scale Content Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Khong-Neng Choong, Vethanayagam Chrishanton, and Shahnim Khalid Putri

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On Confidentiality, Integrity, Authenticity, and Freshness (CIAF) in WSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shafiqul Abidin, Vikas Rao Vadi, and Ankur Rana

87

Networking Analysis and Performance Comparison of Kubernetes CNI Plugins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ritik Kumar and Munesh Chandra Trivedi

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Classifying Time-Bound Hierarchical Key Assignment Schemes . . . . . Vikas Rao Vadi, Naveen Kumar, and Shafiqul Abidin

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A Survey on Cloud Workflow Collaborative Adaptive Scheduling . . . . Delong Cui, Zhiping Peng, Qirui Li, Jieguang He, Lizi Zheng, and Yiheng Yuan

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Lattice CP-ABE Scheme Supporting Reduced-OBDD Structure . . . . . Eric Affum, Xiasong Zhang, and Xiaofen Wang

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Crypto-SAP Protocol for Sybil Attack Prevention in VANETs . . . . . . Mohamed Khalil and Marianne A. Azer

143

Managerial Computer Communication: Implementation of Applied Linguistics Approaches in Managing Electronic Communication . . . . . Marcel Pikhart and Blanka Klímová

153

Advance Persistent Threat—A Systematic Review of Literature and Meta-Analysis of Threat Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . Safdar Hussain, Maaz Bin Ahmad, and Shariq Siraj Uddin Ghouri

161

Construction of a Teaching Support System Based on 5G Communication Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hanhui Lin, Shaoqun Xie, and Yongxia Luo

179

Intelligent Hardware and Software Design Investigating the Noise Barrier Impact on Aerodynamics Noise: Case Study at Jakarta MRT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sugiono Sugiono, Siti Nurlaela, Andyka Kusuma, Achmad Wicaksono, and Rio P. Lukodono

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3D Cylindrical Obstacle Avoidance Using the Minimum Distance Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Krishna Raghuwaiya, Jito Vanualailai, and Jai Raj

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Path Planning of Multiple Mobile Robots in a Dynamic 3D Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jai Raj, Krishna Raghuwaiya, Jito Vanualailai, and Bibhya Sharma

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Autonomous Quadrotor Maneuvers in a 3D Complex Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jito Vanualailai, Jai Raj, and Krishna Raghuwaiya

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Tailoring Scrum Methodology for Game Development . . . . . . . . . . . . Towsif Zahin Khan, Shairil Hossain Tusher, Mahady Hasan, and M. Rokonuzzaman

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Designing and Developing a Game with Marketing Concepts . . . . . . . Towsif Zahin Khan, Shairil Hossain Tusher, Mahady Hasan, and M. Rokonuzzaman

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Some Variants of Cellular Automata . . . . . . . . . . . . . . . . . . . . . . . . . . Ray-Ming Chen

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An Exchange Center Based Digital Cash Payment Solution . . . . . . . . . Yong Xu and Jingwen Li

265

Design and Implementation of Pianos Sharing System Based on PHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sheng Liu, Chu Yang, and Xiaoming You A Stochastic Framework for Social Media Adoption or Abandonment: Higher Education . . . . . . . . . . . . . . . . . . . . . . . . . . Mostafa Hamadi, Jamal El-Den, Cherry Narumon Sriratanaviriyakul, and Sami Azam Low-Earth Orbital Internet of Things Satellite System on the Basis of Distributed Satellite Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . Mikhail Ilchenko, Teodor Narytnyk, Vladimir Prisyazhny, Segii Kapshtyk, and Sergey Matvienko Automation of the Requisition Process in Material Supply Chain of Construction Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adedeji Afolabi, Yewande Abraham, Rapheal Ojelabi, and Oluwafikunmi Awosika Developing an Adaptable Web-Based Profile Record Management System for Construction Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adedeji Afolabi, Yewande Abraham, Rapheal Ojelabi, and Etuk Hephzibah Profile Control System for Improving Recommendation Services . . . . Jaewon Park, B. Temuujin, Hyokyung Chang, and Euiin Choi IoT-Based Smart Application System to Prevent Sexual Harassment in Public Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Md. Wahidul Hasan, Akil Hamid Chowdhury, Md Mehedi Hasan, Arup Ratan Datta, A. K. M. Mahbubur Rahman, and M. Ashraful Amin A Decision Support System Based on WebGIS for Supporting Community Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wichai Puarungroj, Suchada Phromkhot, Narong Boonsirisumpun, and Pathapong Pongpatrakant

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Contents

Structural Application of Medical Image Report Based on Bi-CNNs-LSTM-CRF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aesha Abdullah Moallim and Li Ji Yun Integrating QR Code-Based Approach to University e-Class System for Managing Student Attendance . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suwaibah Abu Bakar, Shahril Nazim Mohamed Salleh, Azamuddin Rasidi, Rosmaini Tasmin, Nor Aziati Abd Hamid, Ramatu Muhammad Nda, and Mohd Saufi Che Rusuli

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Intelligent Computing Techniques Decision-Making System in Tannery by Using Fuzzy Logic . . . . . . . . . Umaphorn Tan and Kanate Puntusavase

391

A Study on Autoplay Model Using DNN in Turn-Based RPG . . . . . . . Myoungyoung Kim, Jaemin Kim, Deukgyu Lee, Jihyeong Son, and Wonhyung Lee

399

Simulation Optimization for Solving Multi-objective Stochastic Sustainable Liner Shipping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saowanit Lekhavat and Habin Lee

409

Fast Algorithm for Sequence Edit Distance Computation . . . . . . . . . . Hou-Sheng Chen, Li-Ren Liu, and Jian-Jiun Ding

417

Predicting Student Final Score Using Deep Learning . . . . . . . . . . . . . . Mohammad Alodat

429

Stance Detection Using Transformer Architectures and Temporal Convolutional Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kushal Jain, Fenil Doshi, and Lakshmi Kurup

437

Updated Frequency-Based Bat Algorithm (UFBBA) for Feature Selection and Vote Classifier in Predicting Heart Disease . . . . . . . . . . Himanshu Sharma and Rohit Agarwal

449

A New Enhanced Recurrent Extreme Learning Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rohit Agarwal and Himanshu Sharma

461

Deep Learning-Based Severe Dengue Prognosis Using Human Genome Data with Novel Feature Selection Method . . . . . . . . . . . . . . . Aasheesh Shukla and Vishal Goyal

473

An Improved DCNN-Based Classification and Automatic Age Estimation from Multi-factorial MRI Data . . . . . . . . . . . . . . . . . . . . . . Ashish Sharma and Anjani Rai

483

Contents

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The Application of Machine Learning Methods in Drug Consumption Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Han

497

Set Representation of Itemset for Candidate Generation with Binary Search Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carynthia Kharkongor and Bhabesh Nath

509

Robust Moving Targets Detection Based on Multiple Features . . . . . . Jing Jin, Jianwu Dang, Yangpin Wang, Dong Shen, and Fengwen Zhai Digital Rock Image Enhancement via a Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunfeng Bai and Vladimir Berezovsky Enhancing PSO for Dealing with Large Data Dimensionality by Cooperative Coevolutionary with Dynamic Species-Structure Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kittipong Boonlong and Karoon Suksonghong A New Encoded Scheme GA for Solving Portfolio Optimization Problems in the Big Data Environment . . . . . . . . . . . . . . . . . . . . . . . . Karoon Suksonghong and Kittipong Boonlong Multistage Search for Performance Enhancement of Ant Colony Optimization in Randomly Generated Road Profile Identification Using a Quarter Vehicle Vibration Responses . . . . . . . . . . . . . . . . . . . Kittikon Chantarattanakamol and Kittipong Boonlong

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Classification and Visualization of Poverty Status: Analyzing the Need for Poverty Assistance Using SVM . . . . . . . . . . . . . . . . . . . . Maricel P. Naviamos and Jasmin D. Niguidula

571

Comparative Analysis of Prediction Algorithms for Heart Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ishita Karun

583

Sarcasm Detection Approaches Survey . . . . . . . . . . . . . . . . . . . . . . . . . Anirudh Kamath, Rahul Guhekar, Mihir Makwana, and Sudhir N. Dhage

593

Web and Informatics Interactive Animation and Affective Teaching and Learning in Programming Courses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alvin Prasad and Kaylash Chaudhary IoT and Computer Vision-Based Electronic Voting System . . . . . . . . . Md. Nazmul Islam Shuzan, Mahmudur Rashid, Md. Aowrongajab Uaday, and M. Monir Uddin

613 625

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Lexical Repository Development for Bugis, a Minority Language . . . . Sharifah Raihan Syed Jaafar, Nor Hashimah Jalaluddin, Rosmiza Mohd Zainol, and Rahilah Omar

639

Toward EU-GDPR Compliant Blockchains with Intentional Forking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wolf Posdorfer, Julian Kalinowski, and Heiko Bornholdt

649

Incorum: A Citizen-Centric Sensor Data Marketplace for Urban Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heiko Bornholdt, Dirk Bade, and Wolf Posdorfer

659

Developing an Instrument for Cloud-Based E-Learning Adoption: Higher Education Institutions Perspective . . . . . . . . . . . . . . . . . . . . . . Qasim AlAjmi, Ruzaini Abdullah Arshah, Adzhar Kamaludin, and Mohammed A. Al-Sharafi

671

Gamification Application in Different Business Software Systems—State of Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zornitsa Yordanova

683

Data Exchange Between JADE and Simulink Model for Multi-agent Control Using NoSQL Database Redis . . . . . . . . . . . . . . . . . . . . . . . . . Yulia Berezovskaya, Vladimir Berezovsky, and Margarita Undozerova

695

Visualizing Academic Experts on a Subject Domain Map of Cartographic-Alike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diana Purwitasari, Rezky Alamsyah, Dini Adni Navastara, Chastine Fatichah, Surya Sumpeno, and Mauridhi Hery Purnomo An Empirical Analysis of Spatial Regression for Vegetation Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hemlata Goyal, Sunita Singhal, Chilka Sharma, and Mahaveer Punia Extracting Temporal-Based Spatial Features in Imbalanced Data for Predicting Dengue Virus Transmission . . . . . . . . . . . . . . . . . . . . . . Arfinda Setiyoutami, Wiwik Anggraeni, Diana Purwitasari, Eko Mulyanto Yuniarno, and Mauridhi Hery Purnomo The Application of Medical and Health Informatics Among the Malaysian Medical Tourism Hospital: A Preliminary Study . . . . . Hazila Timan, Nazri Kama, Rasimah Che Mohd Yusoff, and Mazlan Ali Design of Learning Digital Tools Through a User Experience Design Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gloria Mendoza-Franco, Jesús Manuel Dorador-González, Patricia Díaz-Pérez, and Rolando Zarco-Hernández

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Contents

Fake Identity in Political Crisis: Case Study in Indonesia . . . . . . . . . . Kristina Setyowati, Apneta Vionuke Dihandiska, Rino A. Nugroho, Teguh Budi Santoso, Okki Chandra Ambarwati, and Is Hadri Utomo Cloud Computing in the World and Czech Republic—A Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Petra Poulová, Blanka Klímová, and Martin Švarc Data Quality Improvement Strategy for the Certification of Telecommunication Tools and Equipment: Case Study at an Indonesia Government Institution . . . . . . . . . . . . . . . . . . . . . . . . E. A. Puspitaningrum, R. F. Aji, and Y. Ruldeviyani Evolution of Neural Text Generation: Comparative Analysis . . . . . . . . Lakshmi Kurup, Meera Narvekar, Rahil Sarvaiya, and Aditya Shah Research on the Status and Strategy of Developing Financial Technology in China Commercial Bank . . . . . . . . . . . . . . . . . . . . . . . . Ze-peng Chen, Jie-hua Xie, Cheng-qing Li, Jie Xiao, and Zi-yi Huang Understanding Issues Affecting the Dissemination of Weather Forecast in the Philippines: A Case Study on DOST PAGASA Mobile Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lory Jean L. Canillo and Bryan G. Dadiz

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Guideme: An Optimized Mobile Learning Model Based on Cloud Offloading Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rasha Elstohy, Wael Karam, Nouran Radwan, and Eman Monir

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Model Development in Predicting Seaweed Production Using Data Mining Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joseph G. Acebo, Larmie S. Feliscuzo, and Cherry Lyn C. Sta. Romana

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A Survey on Crowd Counting Methods and Datasets . . . . . . . . . . . . . Wang Jingying Decentralized Marketplace Using Blockchain, Cryptocurrency, and Swarm Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jorge Ramón Fonseca Cacho, Binay Dahal, and Yoohwan Kim A Expansion Method for DriveMonitor Trace Function . . . . . . . . . . . Dong Liu

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Load Prediction Energy Efficient VM Consolidation Policy in Multimedia Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. P. N. Jayasena and G. K. Suren W. de Chickera

893

An Attribute-Based Access Control Mechanism for Blockchain-Enabled Internet of Vehicles . . . . . . . . . . . . . . . . . . . . Sheng Ding and Maode Ma

905

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Contents

Intelligent Image Processing An Investigation on the Effectiveness of OpenCV and OpenFace Libraries for Facial Recognition Application . . . . . . . . . . . . . . . . . . . . Pui Kwan Fong and Ven Yu Sien

919

Virtual Reality as Support of Cognitive Behavioral Therapy of Adults with Post-Traumatic Stress Disorder . . . . . . . . . . . . . . . . . . Ivan Kovar

929

Facial Expression Recognition Using Wavelet Transform and Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . Dini Adni Navastara, Hendry Wiranto, Chastine Fatichah, and Nanik Suciati Survey of Automated Waste Segregation Methods . . . . . . . . . . . . . . . . Vaibhav Bagri, Lekha Sharma, Bhaktij Patil, and Sudhir N. Dhage

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Classification of Human Blastocyst Quality Using Wavelets and Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irmawati, Basari, and Dadang Gunawan

965

Affinity-Preserving Integer Projected Fixed Point Under Spectral Technique for Graph Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beibei Cui and Jean-Charles Créput

975

A New Optimized GA-RBF Neural Network Algorithm for Oil Spill Detection in SAR Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vishal Goyal and Aasheesh Shukla

987

Survey of Occluded and Unoccluded Face Recognition . . . . . . . . . . . . 1001 Shiye Xu A Survey on Dynamic Sign Language Recognition . . . . . . . . . . . . . . . 1015 Ziqian Sun Extract and Merge: Merging Extracted Humans from Different Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023 Minkesh Asati, Worranitta Kraisittipong, and Taizo Miyachi A Survey of Image Enhancement and Object Detection Methods . . . . 1035 Jinay Parekh, Poojan Turakhia, Hussain Bhinderwala, and Sudhir N. Dhage

About the Editors

Sanjiv K. Bhatia works as a Professor of Computer Science at the University of Missouri, St. Louis, USA. His primary areas of research include image databases, digital image processing, and computer vision. In addition to publishing many articles in these areas, he has consulted extensively with industry for commercial and military applications of computer vision. He is an expert on system programming and has worked on real-time and embedded applications. He has taught a broad range of courses in computer science and has been the recipient of the Chancellor’s Award for Excellence in Teaching. He is also the Graduate Director for Computer Science in his department. He is a senior member of ACM. Shailesh Tiwari currently works as a Professor at the Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India. He is an alumnus of Motilal Nehru National Institute of Technology Allahabad, India. His primary areas of research are software testing, implementation of optimization algorithms, and machine learning techniques in software engineering. He has authored more than 50 publications in international journals and the proceedings of leading international conferences. He also serves as an editor for various Scopus, SCI, and E-SCI-indexed journals and has organized several international conferences under the banner of the IEEE and Springer. He is a senior member of the IEEE and a member of the IEEE Computer Society. Su Ruidan is currently an Assistant Professor at Shanghai Advanced Research Institute, Chinese Academy of Sciences. He has completed his Ph.D. from Northeastern University in 2014. His research areas include machine learning, computational intelligence, software engineering, data analytics, system optimization, multi-population genetic algorithm. Dr. Su has served as Editor-in-Chief of the journal “Journal of Computational Intelligence and Electronic Systems” during 2012–2016. He has published more than 20 papers in international journals.

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About the Editors

Dr. Munesh Chandra Trivedi is currently working as Associate Professor, Department of Computer Science & Engineering, National Institute of Technology, Agartala (Tripura). He worked as Dean Academics, HoD & Associate Professor (IT), Rajkiya Engineering College with additional responsibility of Associate Dean UG Programs, Dr. APJ Abdul Kalam Technical University, Lucknow (State Technical University). He was also the Director (In charge) at Rajkiya Engineering College, Azamgarh. He has a very rich experience of teaching the undergraduate and postgraduate classes in Government Institutions as well as prestigious Private institutions. He has 11 patents in his credit. He has published 12 text books and 107 research papers publications in different International Journals and in Proceedings of International Conferences of repute. He has also edited 21 books of the Springer Nature and also written 23 book chapters for Springer Nature. He has received numerous awards including Young Scientist Visiting Fellowship, Best Senior Faculty award, outstanding Scientist, Dronacharya Award, Author of Year and Vigyan Ratan Award from different national as well international forum. He has organized more than 32 international conferences technically sponsored by IEEE, ACM and Springer’s. He has also worked as Member of organizing committee in several IEEE international conferences in India and abroad. He was Executive Committee Member of IEEE UP Section, IEEE computer Society Chapter India Council and also IEEE Asia Pacific Region-10. He is an active member of IEEE Computer Society, International Association of Computer Science and Information Technology, Computer Society of India, International Association of Engineers, and life member of ISTE. K. K. Mishra is currently working as an Assistant Professor at the Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, India. He has also been a Visiting Faculty at the Department of Mathematics and Computer Science, University of Missouri, St. Louis, USA. His primary areas of research include evolutionary algorithms, optimization techniques and design, and analysis of algorithms. He has also authored more than 50 publications in international journals and the proceedings of leading international conferences. He currently serves as a program committee member of several conferences and an editor for various Scopus and SCI-indexed journals.

Advanced Communications and Security

A Novel Crypto-Ransomware Family Classification Based on Horizontal Feature Simplification Mohsen Kakavand, Lingges Arulsamy, Aida Mustapha, and Mohammad Dabbagh

Abstract Analytical research on a distinct form of malware otherwise known as crypto-ransomware was studied in this current research. Recent incidents around the globe indicate crypto-ransomware has been an increasing threat due to its nature of encrypting victims, targeted information and keeping the decryption key in the deep Web until a reasonable sum of ransom is paid, usually by cryptocurrency. In addition, current intrusion detection systems (IDSs) are not accurate enough to evade attacks with intelligently written crypto-ransomware features such as polymorphic, environment mapping, and partially encrypting files or saturating the system with low entropy file write operations in order to produce a lower encryption footprint, which can cause inability toward the intrusion detection system (IDS) to detect malicious crypto-ransomware activity. This research has explored diverse data preprocessing technique to depict crypto-ransomware as images. In effort to classify cryptoransomware images, this research will utilize the existing neural network methods to train a classifier to classify new crypto-ransomware files into their family classes. In a broader context, the concept for this research is to create a crypto-ransomware early detection approach. Nevertheless, the primary contribution is the proof of baselining horizontal feature simplification concept, whereby it provides an accurate real-time detection rate for crypto-ransomware with less system load toward the user device.

M. Kakavand (B) · L. Arulsamy · M. Dabbagh Department of Computing and Information Systems, Sunway University Sunway City, Petaling Jaya, Malaysia e-mail: [email protected] L. Arulsamy e-mail: [email protected] M. Dabbagh e-mail: [email protected] A. Mustapha Department of Dept of Software Engineering, Universiti Tun Hussein Onn Malaysia Johor, Johor, Malaysia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_1

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Keywords Sophisticated crypto-ransomware · Horizontal feature simplification · System load · Computational cost

1 Introduction The Internet of things (IoT) relates to the interconnected network of intelligent devices, sensors, embedded computers and so forth. IoT applications propagate most life infrastructure from health, food to sophisticated cities and urban handling. While IoT’s effectiveness and prevalence are increasing, security concerns for the sectors still remain a needed consideration [1]. Therefore, as cybersecurity threats continue to evolve, thus crypto-ransomware is also becoming the number one menace for both consumers and businesses using the Internet of things (IoT) devices worldwide. Moreover, crypto-ransomware has caused millions of dollars in loss for both information and money in paying the ransom. Current intrusion detection systems (IDSs) are not accurate enough to evade attacks with intelligently written crypto-ransomware such as polymorphic, environment mapping, partially encrypting files or saturating the system with low entropy file write operations [2]. Thus, this research study proposes a novel crypto-ransomware classification based on horizontal feature simplification (HFS) approach, in order to identify crypto-ransomware family attacks by monitoring the hexadecimal coding pattern of different structured and procedural programming languages to classify cryptoransomware family from non-malicious applications. Furthermore, this algorithm also helps to prevent sophisticated written crypto-ransomware from infecting the host. The primary contribution of this research is to proof the baselining horizontal feature simplification can provide a transfiguration of a data to an image without compromising the integrity of the features.

2 Related Work Crypto-ransomware development continues to dominate the threat landscape and influenced vital sectors (hospitals, banks, universities, government, law firms, mobile users) as well varied organizations equally worldwide [3, 4]. Securing from cryptoransomware is a vigorous analysis space. Furthermore, crypto-ransomware classification is one of many analyzed challenges and opportunities are known and associate current analysis topic [2, 5]. However, there are a few approaches to detect and remove the threat. One of the detection approaches includes using static-based analysis which means analyzing an application’s code prior to its execution to determine if it is capable of any malicious activities. On the other hand, the fundamental flaw of signature-based detection is its inability to detect unknown malware which has yet to be turned into a signature. A malicious executable is only detectable once

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it has first been reported as malicious and added to the malicious signature repository. Moreover, static-based detection is also ineffective against code obfuscation, high variant output and targeted attacks [2]. Furthermore, static-based detection is not an effective stand-alone approach to detect crypto-ransomware. Therefore, past researcher has reviewed on crypto-ransomware characteristics and developed few detection methods in order to overcome mitigation of crypto-ransomware [6]. Shaukat and Ribeiro proposed RansomWall [7], a cryptographic-ransomwarelayered defense scheme. It follows a mixture of static and dynamic analysis strategy to produce a new compact set of features characterizing the behavior of cryptoransomware. This can be accomplished when initial RansomWall layers tag a process for suspected crypto-ransomware behavior, and process-modified files are backed up for user data preservation until they have been categorized as crypto-ransomware or benign. On the other hand, behavioral-based detection methods are based on detecting mass file encryption where it could be effective however may come at a resource-intensive cost; this is because the file entropy needs to be calculated for every single write operation executed by an application [2]. In addition, these operations need to track file operations for each file separately over the life span of an observed process. Hence, such an approach may considerably deteriorate disk read and write performance and result in a high system load. Besides that, detecting crypto-ransomware by analyzing the file rename operations to identify ransom-like file names or extensions may work on simple crypto-ransomware, but will not work on more intelligently written crypto-ransomware such as CryptXXX which randomizes the file name or Spore which retains the original file name. Consequently, this will lead the model to produce a high false-positive rate. Azmoodeh et al. suggested a solution [8] that uses a strategy based on machine learning to identify crypto-ransomware attacks by tracking android device energy usage. In particular, it has been suggested that technique tracks the energy consumption patterns of distinct procedures to classify non-malicious apps for cryptoransomware. However, the use of energy consumption to detect crypto-ransomware can trigger a significant false negative indicating that a crypto-ransomware is not identified and marked as a non-malicious application [2]. Typically, this could occur because crypto-ransomware developers are aware of data transformation analysis techniques that have been known to use simple tricks to mask the presence of mass file encryption [2]. Nevertheless, the use of energy consumption to detect cryptoransomware can also set off a notable false positive, whereby benign application such as Web browsers uses high system resource which could lead the model to indicating benign application is identified and marked as a malicious application. Sgandurra et al. proposed EldeRan [9], a dynamically analyzing and classifying machine learning approach for crypto-ransomware. EldeRan observes a set of actions applied in their first phase of installation to check for crypto-ransomware characteristics. In addition, EldeRan operates without needing a complete family of cryptoransomware to be accessible in advance. EldeRan, however, has some limitations. The first limitation addresses the analysis and identification of crypto-ransomware samples that have been silent for some duration or are waiting for a trigger action done by the user. Hence, EldeRan does not properly extract their features; this is due

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Table 1 Similarity between past models Past models

Advantage

Disadvantage

Uses dynamic analysis

High detection rate classifies ransomware family

Resource-intensive Impractical commercial use Vulnerable to sophisticated attacks

to certain crypto-ransomware capable of environment mapping [2]. In other words, when crypto-ransomware is executed it will map the system environment before initiating its setup procedure. Typically, this is performed to determine whether it runs on a real user system or on a sandbox setting that might attempt to analyze it [2]. Another limitation of their approach is that in the current settings no other applications were running in the analyzed virtual machine (VM), except the ones coming packed with a fresh installation of Windows. This might not be a limitation by itself, but as in the previous cases, the crypto-ransomware might do some checks as to evade being analyzed. Recently, [10] conducted a research work toward ransomware threats, which leading to propose Deep Ransomware Threat Hunting and Intelligence System (DRTHIS), whereby it is capable of detecting earlier in invisible ransomware data from new ransomware families in a timely and precise way. However, DRTHIS is not capable for classifying some new threat such as TorrentLocker attack. In summary, the preliminary literature review shows that past studies are primarily focused on understanding distinct types of system design and layered architecture as stated in Table 1. In terms of the detection engine, it uses classifiers in order to increase the detection rate. Many system designs use a similar approach as shown in Table 2 in order to detect crypto-ransomware behavior. However, the false-positive and falsenegative rate towards intrusion samples differ due the unique research model design and layered architecture. Therefore, the detection rates for false positive and false negative toward intrusion samples differ. Therefore, the aim of this research is to create an approach baselining horizontal feature simplification to provide accurate real-time detection rate for crypto-ransomware with less system load toward the user device.

3 Objectives The objective of this research is to develop a novel crypto-ransomware family classification based on horizontal feature simplification with reduced system constraint approach. The term constraint is defined here as the process of identifying cryptoransomware without overloading the users’ machine. For example, encryption measures such as entropy change which requires the file entropy to be calculated for every single write operation executed by an application. Furthermore, these operations need to track file operations for each file separately over the life span of an observed process. Such an approach may considerably cause high system load

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Table 2 Past model summary References

Model architecture

Advantage

Disadvantage

Homayoun et al. (2019)

Deep Ransomware Threat Hunting and Intelligence System (DRTHIS) approach is used to distinguish ransomware from goodware

DRTHIS is capable of detecting previously unseen ransomware data from new ransomware families in a timely and accurate manner

DRTHIS is not capable for classifying some new threat such as TorrentLocker attack

Shaukat and Ribeiro (2018)

Each layer tags the process of the Portable Executable file for behavior such as read, write, rename and delete operation

Able to detect common crypto-ransomware with high detection rate

Resource-intensive, whereby the file entropy needs to be calculated for every single write operation. Moreover, this operation will also deteriorate the disk read and write performance. Furthermore, vulnerable toward intelligently written crypto-ransomware [2]

Azmoodeh et al. (2017)

Tracks the energy consumption pattern of distinct procedures to classify crypto-ransomware from non-malicious

Outperform other models such as k-nearest neighbors, neural network, support vector machine and random forest

Having significant false positive due to certain characteristics and weak against partially encryption files [2]

Sgandurra (2016)

Analyze a set of Able to classify actions applied in their common variant of first phase of ransomware family installation to check for crypto-ransomware, without needing a complete family of crypto-ransomware to be accessible in advance

The model does not properly extract their features as certain crypto-ransomware capable of environment mapping [2]

leading to deteriorating the disk read and write performance. In summary, the objective is to produce a classification algorithm with the practical approach for feature representation that is able to distinguish the crypto-ransomware family with a low computational cost.

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4 Methodology This section describes the approaches will be taken to achieve the proposed solution. Moreover, this section intends to describe and show the relationship between the various work activities that are performed during this research. Furthermore, the expected result from these activities will also be listed here.

4.1 Data Collection The crypto-ransomware and goodware dataset were obtained from [11] which consist of 100 working samples of 10 distinct classes of ransomware and 100 benign applications. The crypto-ransomware samples are gathered to represent the most common versions and variations presently found in the wild. Furthermore, each crypto-ransomware is grouped together into a well-established family name, as there are several discrepancies between the naming policies of anti-virus (AV) suppliers, and therefore it is not simple to obtain a common name for each ransomware family.

4.2 Android Application Package Conversion into Hexadecimal Codes This segment highlights the approaches used by the data preprocessing module to transform the raw information into a comprehensible format in order to support toward this research framework. Furthermore, three data preprocessing approaches will be utilized in this research. Generally, it is possible to consider all binary files as a series of ones and zeros. As shown in Fig. 1, the first method is to convert each android application package to binary. After that, the binary file will be converted to hexadecimal code. Moreover, during this process the data has retained the original integrity of the application. In line with our knowledge, the reason for using binary to hexadecimal conversion is to reduce the code complexity as shown in Fig. 2, which will be effective toward the next stage of the transfiguring image conversion process.

4.3 Hexadecimal Code to Dynamic Image Transfiguration In this process, the hexadecimal code content of the string is extracted into 6 characters which refers to 6 characters for every unit. Now knowing each unit as 6 characters, it is possible to take their unit as indicators of a two-dimensional color map that stores RGB values that match the particular unit. Furthermore, repeating this process for each unit allows me to get a sequence of RGB values (pixel values)

A Novel Crypto-Ransomware Family Classification Based …

9

Fig. 1 Hexadecimal code conversion framework

from the stage 1 preprocessed file. Next, we have transformed this series of pixel values into a two-dimensional matrix, which will be used in image transfiguration process resulting in an RGB picture representation. Besides, Fig. 3 shows the width of the image output is set to 510; however, the height image is set to be dynamic based on the hexadecimal to dynamic image transfiguration algorithm. The reason for setting the width of the image static and the height of the image dynamic is to create a standard baseline feature dimension. From this part of the analysis, we found out there is a frequent amount of the unique pattern appearing in the images corresponding to each crypto-ransomware family. Moreover, this statement can be proved in Fig. 4. Besides as we further dive into analyzing the crypto-ransomware family, we have discovered a complication whereby all the crypto-ransomware family image dimensions are not standardized. Furthermore, this complication will affect the convolution neural network model, whereby the model will assign inequal weight toward the stage 2 preprocessed images which will cause the loss function in the model to increase leading to bad predictions. In addition, general approaches to manipulate the images such as center crop, squashing or padding will not work toward this research dataset. This is because the images will be losing a significant number of important features and this will lead to bad classification. Therefore, in this research we have developed an algorithm which solves the problem faced by stage 2 preprocessed images. The algorithm will be explained in depth in the next stage of data preprocessing.

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Fig. 2 Android application package conversion into hexadecimal code output

Fig. 3 Hexadecimal code to dynamic image conversion flowchart

4.4 Horizontal Feature Simplification In this process, we have used the created algorithm known as “horizontal feature simplification (HFS)” to further preprocess the images produced by stage 2 data preprocessing. Moreover, the main condition for horizontal feature simplification is the width of the image should be fixed. The rule is applied because if the image does

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Fig. 4 Hexadecimal code to dynamic image transfiguration output for WannaLocker ransomware variant

not have a fixed number of features, it will cause the images to be not normalized leading bad prediction toward this research. If the condition meets the algorithm, then it will be executed. As shown in Fig. 5, the first process will be converting the stage 2 preprocessed image to two-dimensional plane array to extract each row pixel vector. Next, SimHash algorithm with a prime number hash bit is used to a

Fig. 5 Horizontal feature simplification flowchart

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Fig. 6 Horizontal feature simplification output for Koler ransomware variant output

create coordinate corresponding to row pixel vector, whereby the algorithm takes each row vector, passes through a segment, then acquires effective feature vectors and weighs each set of feature vectors (if a row pixel vector is given, then the feature vector is the pixels in the image and the weight is the number of times the pixel may appear). Furthermore, DJB2 algorithm with a prime number hash bit is used to produce 26-bit hash value which will be utilized to create an RGB color pixel. Besides, if there is a collision between two-row pixel vectors, then the RGB colors will be added together in order to maintain the integrity of the image. In summary, horizontal feature simplification will create a static image dimension which will be used in the convolution neural network model to create unbiased weight distribution in order to produce a better classification model. In this part, we will be analyzing HFS data output. From this part of the analysis, we found out there is still a frequent amount of unique pattern appearing in the images corresponding to each crypto-ransomware family even after the images been preprocessed from stage 2 to stage 3. Furthermore, this statement can be proved from Fig. 6. Besides, number pixel density per image is increased 5% due to using prime numbers for SimHash and DJB2 algorithm hash bits compared to non-prime numbers. Therefore, the number of characteristics in an image also increases, causing the classification model to produce a higher-quality prediction.

4.5 Crypto-Ransomware Classification Neural networks have been very effective in finding significance or acknowledging patterns from a collection of images. The model functions used in this research are sequential functions, which allowed me to build a linear stack of layers. Hence, each layer will be treated as an object that feeds data to the next one. The next layer is the convolution layer, whereby the parameters accept a “100 * 100 * 3” array of the pixel value. In addition, the feature map is passed through an activation layer called rectified linear unit (ReLU). In fact, ReLU is a nonlinear operation that replaces

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all the negative pixel values in the feature map with zero. Primarily, ReLU has the tendency to perform better compared to other operation functions. Nevertheless, the ReLU layer increases the nonlinear properties of our model, which means the proposed model will be able to learn more complex functions rather than just linear regression.

5 Results and Discussion In this research, we have used a convolutional neural network model with expression, frequency and modularity. Next, the dataset is divided into two directories which are training directories and validation directories using sklearn. After splitting all data into those directories, then a script is used to label which data belongs to the corresponding picture of the crypto-ransomware file and benign file by generating texture maps, training and validation, and feeding this information to the proposed model. Moreover, we begin training the dataset and printing the test precision result for every iteration. The convolutional neural network model tends to overfit during epochs 100; therefore, the ideal is epoch is in the range of 20 in order to reduce the loss function and increase the accuracy of the model. The accuracy is at 63%. Nevertheless, the overall accuracy is above average because the amount of live crypto-ransomware dataset is small due to the endangerment of crypto-ransomware itself. Furthermore, during the testing of behavioral phase of the model a few hyperparameters were fine-tuned to create a better classification model without overfitting it.

6 Conclusion Most of the methods of crypto-ransomware classification involve the malware to record its behavior or use techniques of disassembly to predict its behavior. Both malware execution and disassembly are time-consuming, which have elevated computing cost. This research can accomplish similar outcomes and important efficiency improvements with the assistance of image transfiguration. Behavioral or static-based assessment depends on the platform, so the distinct systems need distinct classifiers. While the image-based strategy described here is independent of the platform, it classifies data based on the degree of resemblance between the same types of malicious and benign hexadecimal pattern. Furthermore, the framework suggested in this research improves the prior work and paves the way for the application of the state-of-the-art methods in the data preprocessing assessment of detecting sophisticated crypto-ransomware with less computational cost. We achieved an accuracy of 63 percent in detecting crypto-ransomware families; however, horizontal feature simplification (HFS) algorithm has shown adequate performance in protection of the

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user device against sophisticated crypto-ransomware. Therefore, our future work is to improve the proposed model in the real-time test bed with different performance matrices.

References 1. S. Millar, Network Security Issues in The Internet of Things (IoT) (Queen’s University Belfast Research Portal, Belfast, 2016) 2. D. Nieuwenhuizen, A behavioural-based approach to ransomware detection. MWR Labs Whitepaper 2017 3. M. Kakavand, M. Dabbagh, A. Dehghantanha, Application of machine learning algorithms for android malware detection. in ACM International Conference on Computational Intelligence and Intelligent Systems (2018), pp. 32–36 4. I. Rijnetu, A closer look at ransomware attacks: Why they still Work. Heimdal security, 2018. [Online]. Available: https://heimdalsecurity.com/blog/why-ransomware-attacks-still-work/ 5. P. Luckett, J.T. Mcdonald, W.B. Glisson, R. Benton, J. Dawson, Identifying stealth malware using CPU power consumption and learning algorithms. J. Comput. Secur. 26, 589–613 (2018) 6. S. Maniath, P. Poornachandran, V.G. Sujadevi, Survey on prevention, mitigation and containment of ransomware attacks. (Springer Nature, Singapore, 2019) pp. 39–52 7. S.K. Shaukat, V.J. Ribeiro, Ransomwall : a layered defense system against cryptographic ransomware attacks using machine learning. 2018 10th International Conference on Communication Systems and Networks (2018) pp. 356–363 8. A. Azmoodeh, A. Dehghantanha, M. Conti, K.-K.R. Choo, Detecting crypto—ransomware in IoT networks based on energy consumption footprint. J. Ambient Intell. Humaniz. Comput. 9(4), 1141–1152 (2018) 9. D. Sgandurra, L. Muñoz-gonzález, R. Mohsen, E.C. Lupu, Automated dynamic analysis of ransomware : benefits, limitations and use for detection. ArXiv J. (2016) 10. S. Homayoun, A. Dehghantanha, M. Ahmadzadeh, S. Hashemi, R. Khayami, DRTHIS : Deep ransomware threat hunting and intelligence system at the fog layer. Futur. Gener. Comput. Syst. (2019) 11. A.H. Lashkari, A.F.A. Kadir, L. Taheri, A.A. Ghorbani, Toward developing a systematic approach to generate benchmark android malware datasets and classification. in 2018 International Carnahan Conference on Security Technology (ICCST), (2018), no. Cic, pp. 1–7

Characteristic Analysis and Experimental Simulation of Diffuse Link Channel for Indoor Wireless Optical Communication Peinan He and Mingyou He

Abstract Based on the existing solution of combining direct link and diffuse link to alternately transmit optical signals, the channel characteristics of diffuse link are deeply analyzed by using algorithm and simulation method, the influencing factors of average optical receiving power in four scenarios are discussed, and the experimental simulation is carried out. The purpose is to obtain the main parameters of diffuse link channel characteristics, to improve the reliability and practicability of a communication system and to provide a reference for the application of indoor wireless optical communication technology and the design and development of equipment and instruments. The simulation results show that the position of the emitter, the angle of the emitter (θ mig ) and the angle of the beam diffusion (θ max ) are the factors affecting the average receiving power of the light. In the design of indoor optical wireless communication system, the average receiving power can be improved and the expected performance of the communication system can be achieved by fully considering these factors. Keywords Wireless optical communication · Diffuse link channel · Channel characteristic analysis · Experimental simulation

1 Introduction Visible light communication (VLC) is a new wireless optical communication technology, which is one of the research hotspots at present. This technology has realized the huge expansion of the communication spectrum and combined the communication and lighting technologies. Compared with traditional wireless communication P. He College of Air Traffic Management, Civil Aviation Flight University of China, 618307 Guanghan, China e-mail: [email protected] M. He (B) Chengdu University of Technology, 610059 Chengdu, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_2

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technology, visible light communication technology has the advantages of high security, good confidentiality and abundant frequency resources. From the perspective of application, visible light communication technology is not only applicable to indoor wireless access, intelligent home and other lighting and communication fields, but also applicable to electromagnetic sensitive environments such as hospitals and aircraft cabins, train boxes and cars [1–5], as well as communication scenes with high confidentiality requirements. Aiming at the problem that optical signal transmission may be blocked by obstacles at any time during indoor point-to-point wireless optical communication, He proposes a solution that combines direct and diffuse links to transmit optical signals alternately [6, 7] and mainly embodies the advantages of this scheme in optical communication way flexible: Both direct link channels can be used for point-topoint transmission light signals, can also use the diffuse link channel way of diffuse light transmitted signals and also can use the combination of direct link and diffuse link to alternate transmission light signal, so as to realize continuous communication. Based on this scheme, the characteristics of diffuse link channel are analyzed and simulated. The purpose of this paper is to obtain the main parameters of the characteristics of diffuse link channel, to improve the reliability and practicability of the communication system and to provide reference for the application of indoor wireless optical communication technology and the design and development of equipment and instruments.

2 Diffuse Link Channel In order to simulate diffuse channel characteristics, a series of positions (x, y) in the effective communication area is plotted to simulate the range of light radiation. Assuming that the ceiling of the indoor room is Lambertian, then the signal power received by the receiver is Ps , and the following equation can be obtained: ¨ Ps = Ah 2



H (x, y)rect(x, y)dxdy h 2 + (x − x1 )2 + (y − y1 )2

2

(1)

Among them, x 1 and x 2 are the positions of the receiver, A is the area of the detector, h is the distance between the transmitter and the receiver, and H is the irradiance. For the radiation position (x, y) on the ceiling, the first-order radiation illumination is H (x, y) = 

ρ Ph 2 h2 + x 2 + y2

2

(2)

In the formula, P is the average transmitting power and ρ is the ceiling reflectance parameter. The distance from (x, y) to (x1 , y1 ) is

Characteristic Analysis and Experimental Simulation …

D=

 (x − x1 )2 + (y − y1 )2

17

(3)

Receiving power can be given as Aρ Ph 2 tan2 θa Ps−DIFF =  2 h2 + D2

(4)

For simplicity, suppose p = 1, x = y = x 1 = y1 , similar to Formula (4), the signalto-noise ratio (SNR) of diffuse (DIFF) link channel can be obtained, which can be expressed as SNRDIFF =

r A P 2 h 4 tan4 θa  4 2q I Bπ 3 h 2 + D 2 Hb sin2 θa

(5)

Here, we can explore the relationship between θ a and SNR of DIFF link channel and h and SNR by simulating the room. Suppose the transmitting power is 50 mW, r is 0.6 A/W, distance h is from 3 to 5 m, data receiving and receiving speed B is 10 Mbps, and angle A is 10–40°. The simulation results are shown in Figs. 1 and 2, respectively. As can be seen from Fig. 1, the SNR increases when θ a changes from 10 to 40°. This means that the receiving power of the receiver increases accordingly. As can be seen from Fig. 1, when the room height is 3, 4 and 5 m, the SNR decreases with the increase of the distance between the receiver and the ceiling radiation area.

Fig. 1 Simulation of distance D and SNR in diffuse link communication system. The abscissa D is the distance; the ordinate SNR is the signal-to-noise ratio; the angles θ a are 10°, 20°, 40°

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Fig. 2 Simulation of distance D and SNR in DIFF link communication system. The abscissa D is the distance; the ordinate SNR is the signal-to-noise ratio; the heights h are 3 m, 4 m, 5 m

In addition, as can be seen from Fig. 2, at points (4, 10−2 ), three lines intersect, and there is little difference between them. That is to say, the height of the room is not an important factor affecting the SNR of the DIFF link communication system.

3 Channel Characteristic Analysis In indoor scenes, diffuse light propagates in the area by reflection times of various objects, such as ceilings, walls, furniture and even people. In such a scenario, a transmitter faces the ceiling of an ordinary room. The first-order reflection of light will occur at a point on the ceiling and then on the surface of other objects. Because of the different lengths of the optical path, the time of the beam arriving at the receiver is also different. Therefore, the sum of the impulse response of the diffuse link can be regarded as the sum of all the light reflections. Diffuse link algorithms vary according to different environments or models, but basically, impulse response is usually modeled as the sum of two components, that is, first-order reflection and higher-order reflection. As shown in Fig. 3, the impulse response of the first-order reflection is obviously higher than that of other reflections, and the other peaks with attenuation are the impulse response of the higher-order reflection. The initial direction of light propagation depends on the light source location, the angle of emission (θ mig ) and the angle of beam diffusion (θ max ) [8, 9]. Reference [10, 11] describes a simple method for exploring channel characteristics by introducing

Characteristic Analysis and Experimental Simulation …

19

SUM

Impulse response of the first order reflection Impulse response of second and higher-order reflections

Impulse response of a furnished room(5m×3m×4m)

SUM-Impulse response of the sum of all-order reflections

Fig. 3 Step reflection pulse response of diffuse link (room size: 5 m × 3 m × 4 m)

some parameters, as they relate to the physical parameters of the channel, especially for primary reflection. However, in this method, the light source is directed directly to the ceiling, with no deflection. In order to further understand the characteristics of indoor diffuse channel and lay a solid theoretical foundation for exploring the new system, this paper introduces an improved method and analyzes the channel characteristics through scene-based simulation. In Fig. 4, the transmitter and receiver face the ceiling. By introducing the angle of θ 0 , which represents the normal direction of the emitter, and combining with the beam diffusion angle of θ max , it can be regarded as the maximum value of the half-angle horizontal light source, so as to determine the direction of the light source. In the equivalent uniform distribution, the normalized intensity of Lambert body light source and the angle at which the beam intensity is half of the total radiation intensity can be expressed as Я A

Ceiling

O'

O

B

C

Ceiling

θ0 θimg

hT

hR

θmax θ0 T

dTR

R

Opcal radiaon θimgθmax

Fig. 4 Optical paths in two scenarios. θ img —normal angle of light source; θ max —beam diffusion angle

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θhalf

  1 1 +1 = 2 · sin 2 m −1

(6)

in [10], by modifying equation (x), θ max can be expressed as θmax

⎛ ⎞ −1 (0.5) 1 1 − 0.4 × θ /cos half ⎠ = 2 · sin−1 ⎝ 2 m+1

(7)

As shown in Fig. 4, the normal angle of the light source (θ img ) is the angle between the light source and the receiver and transmitter. So, the angle θ img can be defined as θimg = tan−1



dT R hT + h R

 − θ0

(8)

where d TR is the distance between the transmitter and the receiver,  h T is the height T O  is the normal In addition, of transmitter and hR is the height of the receiver.     direction of the beams when the angle θ 0 is zero, T O is the normal direction of the beam, while |T O| is the normal of the light source. There are two cases as illustrated in Fig. 8: (a) When angle θ img < θ max , the point B, which is the cross point of the ceiling and the line of receiver’s image that connects the source, is within the area at where the light is illuminating; (b) when angle θ img > θ max , as the distance between transmitter and the receiver, d TR , is becoming larger, point B is out of the illuminating area. To describe the light propagation by geometrical way, length of different kinds of light paths can be calculated by L fast =

hT cos(θmax + θ0 )

+

 [dT R − h T · tan(θmax + θ0 )]2 + h 2R

(9)

Here, L fast represents the fast path of light, which in case (a) and case (b) is the line connecting points T, C and R. Then, the slow path L slow , in both of the cases, which is the connection of points T, A and R, is calculated by L slow =

hT cos(θmax + θ0 )

+



[dT R + h T · tan(θmax + θ0 )]2 + h 2R

(10)

As for the distance between the image of the receiver and transmitter, the length L img is L img =

 2  dT2 R + h 2R + h 2T

(11)

And the path where the strong part of light beam propagates is L strong =

  d02 + h 2T + (dT R − d0 )2 + h 2T

(12)

Characteristic Analysis and Experimental Simulation …

21

Moreover, in case (a), the shortest path is the one along with points T, B and R, while in case (b), the one is connecting points T, C and R. For both of the cases, if point O is approaching point B, thus the strongest path T-O-R is becoming the shortest path meanwhile. Additionally, considering the case of the position of the receiver varies, as shown in Fig. 5, paths of light are illustrated. In this figure, there are two positions for the receiver, keeping the source same; similarly, one can calculate the path length by L strong−R2 =

   2 d02 + h 2T + h 2T + dT R1 − d0 + h 2R1 R2

(13)

Since the distance between points O and A, and the distance between points T and A are |O A| = h T · tan(θmax − θ0 ) |T A| =

hT cos(θmax − θ0 )

Fig. 5 Light paths when the position of receiver changes

(14) (15)

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L slow−R2 =

hT cos(θmax − θ0 )

+



L img−R2 =

[h T · tan(θmax − θ0 ) + T R1 ]2 + d R2 1 R2 + h 2T 

h T + h R2

2

+ dT2 R2

(16)

Like the cases that just discussed previously, in this scenario, the shortest path will depend on the setting of light source angle and emitting angle. By simulating later in this chapter, the effect of positions of receiver for diffuse links will be discussed more. As one uses hLOS (t) to express the impulse response of line-of-sight channel, for the diffuse channel, the impulse response of the channel can be described as h DIFF (t) = h 1 (t − t1 ) + h n (t − tn )

(17)

where h1 (t) is the impulse response of the first-order reflection and hn (t) represents the impulse response of the higher-order reflections. T 1 and T n are, respectively, the starting times of the rise of the first-order reflection and the higher-order reflections. For modeling, gamma function is used to describe the impulse response of the channel in math; thus, the normalized channel response can be expressed as   −t λ−a a−1 ·t · exp h 1 (t) = (a) λ

(18)

where G(α) is the gamma function and the parameters λ, α which were introduced in reference [10] with variance σ 2 and t that given as below can express the response with delay T 1 as 1 |H1 ( f )|2 =  a 1 + (2π λ · f )2

(19)

where H 1 (f ) is the Fourier transform of h1 (t) and a, λ are σ 2 = α · λ2 t = α · λ

(20)

Therefore, the −3 dB bandwidth of the first-order reflection can be expressed as  f1 =

1

2a − 1 2π λ

(21)

Taking the spherical model to describe the higher-order reflection response, then   t n h n (t) = · exp − (22) τ τ

Characteristic Analysis and Experimental Simulation …

23

where hn =

Aeff ρ1 · ρ · sin2 (FOV) · Aroom 1 − ρ av τ = − InTρ 4·Vroom TaV = C·Aroom

(23)

Here, the ηn is the higher-order gain, FOV is the field of view, V room is the volume of a room, T aV is the average time of transmission, and the Aeff is the receiving area, ρ the mean of reflectivity of room objects. Therefore, taking Fourier transform, the higher-order reflection can be expressed as Hn ( f ) =

n

1 + j · 2π · τ · f

(24)

Then, the −3 dB bandwidth of higher-order reflections can be calculated by fn =

1 2π · τ

(25)

As Fig. 6 shows, corresponding to the equation, it is clear that as the reflectivity of room objects increases, the bandwidth is becoming smaller. Even though more light is counted in, the decay time is lengthening at meanwhile. Thus, the bandwidth decreases. And even more, the first-order reflection response is dominating the diffuse

Relative magnitued(dB)

First order reflection response

High order reflection response

Frequency(MHz)

Fig. 6 Frequency responses of first-order and higher-order reflections with reflectivity

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channel bandwidth; certainly, the parameters that are relative to the bandwidth reveal that the locations of the transmitter and receivers are also influencing the bandwidth.

4 Experimental Simulation and Discussion In order to understand the characteristics of optical channel in the indoor environment and its influence on the design of optical communication system, it is necessary to discuss the influence of physical parameters, field of view angle and half-angle power angle.

4.1 Effect of Physical Parameters 4.1.1

Room Size

Modeling a room, to simulate the performance of diffuse link in such environment, as shown in Fig. 7, by varying the size of it (enlarging from 6 m × 3 m × 6 m to 10 m × 3 m × 10 m) while keeping the coordinates of the transmitter and receiver staying [the transmitter is at Tx(3 m, 1 m, 2 m), and the receiver is at Rx (0.8 m, 1 m, 2 m)] in order to see how the change especially from the width and length of the room will affect the diffuse channel, the results of the impulse response of channels will show in terms of different orders of reflections and the sum.

(m) 1

0.8

3

Rx

Tx

(m)

0

Rx

2

Tx (m)

Fig. 7 Locations of the transceiver in a room a 2D view and b 3D view. a Position of transceiver in two-dimensional view and b position of transceiver in three-dimensional view (position of receiver Rx (0.8 m, 1 m, 2 m); position of transmitter Tx (3 m, 1 m, 2 m)

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Table 1 Experimental parameters T 1 —direct transmitter versus R1 —direct receiver

T 1 —direct transmitter versus R2 —DIFF receiver

Average received power

2.73e−05 mW

Average received power

2.38e−05 mW

Geometrical loss

−72.6 dB

Geometrical loss

−73.2 dB

DC channel gain

5.45E−08 dB

DC channel gain

4.74E−08 dB

T 2 —diffuse transmitter versus R1 —direct receiver

T 2 —diffuse transmitter versus R2 —DIFF receiver

Average received power

1.84e−05 mW

Average received power

1.62e−05 mW

Geometrical loss

−74.4 dB

Geometrical loss

−74.9 dB

DC channel gain

3.67E−08 dB

DC channel gain

−79.1 dB

Notes T 1 —direct transmitter; T 2 —diffuse transmitter; R1 —direct receiver; and R2 —diffuse receiver

Figure 7 illustrates 2D and 3D views of a room where the locations of the transmitter and receiver were mapped in such scenario. For the cases that simulated here, parameters are shown in Table 1. In Fig. 8, there is a comparison of the impulse response of the first-order reflection in the four cases. As seen in the results, the responses were started almost at the same time about 13 ns, this is because the place where the first-order reflection happened is on the ceiling for all four cases, with simulation parameters unchanged apart from the width and length of the room, the light path from transmitter to the ceiling is nearly the same, and thus the response time will keep the same. In addition, the blue line was added on the figure as the alignment stakes to measure the start time of the response impulses. Apparently, it is hard to see the difference in this figure, especially when comparing the results of the second-order reflection response that is shown in Fig. 9. By comparing the results of the impulse response of the second-order reflections, as shown in Fig. 9, it is much clear to see the delay of the commencement time of the second-order reflection response in all cases with the aid of the blue line. As the size of the room being enlarged, the time of response is decaying, while the duration of response time is also lengthened, since the slots between each peak are broadened, and the intensity of the response impulses is apparently becoming weaker and flatter. The results just well agree with the model that when the size of the room is getting bigger, in other words, as the light path is becoming longer, the light beams have to take longer time to reach the subsequent objects; in the case of room 9 m × 3 m × 9 m, the response decay time compared to the one in case of room 6 m × 3 m × 6 m is about 5 ns; the average magnitude of the number of the response peaks, which can reveal the objects that the light reached, like the side walls in the room, in the case of room 8 m × 3 m × 8 m is higher than the one in the case of room 6 m × 3 m × 6 m, and these peaks are spread in a long period of response time over 43 ns. Figure 10 shows the sum of impulse response of all cases. By calculating the average received power, it is apparent to see that the biggest figure (4.69 nW) had in

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Fig. 8 Comparison of impulse response of the first-order reflection by room size in: 6 m × 3 m × 6 m; 8 m × 3 m × 8 m; 9 m × 3 m × 9 m; and 10 m × 3 m × 10 m

room case 10 m × 3 m × 10 m. The trend shows that along the size getting bigger, the average received power is increased. It can be explained that the surface of the room is increased which creates more reflection area to light source, and the calculation includes all-order reflections, along the response time broadening, the more light that counted in calculation is added, and thus the received optical power is increased. However, corresponding to the model that is discussed previously with the aid of equations, even though the gain of the channel becomes bigger along with the

Characteristic Analysis and Experimental Simulation …

27

Fig. 9 Comparison of the impulse response of the second-order reflections by room size in: 6 m × 3 m × 6 m; 8 m × 3 m × 8 m; and 9 m × 3 m × 9 m

increase of room size, the decay time is becoming longer; therefore, the −3 dB bandwidth is actually decreased with this trend.

4.1.2

Empty Room and Furnished Room

Because of the nature of the light, and the effect from reflection surfaces, it can be assumed that the diffuse channel response might vary under different conditions, like furnished or not. For this, there simulated a scenario of a furnished room to compare the channel response of the one in an empty room. Figure 11 shows the layout in 2D and 3D views. Both of the rooms are in same size 5 m × 3 m × 6 m, and the transmitter Tx is at position (3.8 m, 1 m, 3 m), while the receiver Rx is at (0.8 m, 1 m, 3 m). In the furnished room, a rack, three tables and a chair which are at positions (0 m, 1 m, 0 m), (1.5 m, 0 m, 0 m), (0.5 m, 0 m, 2.5 m), (3.7 m, 0 m, 2.5 m), (2.2 m, 0 m, 0.5 m) are added. The comparison of results is shown in Fig. 12.

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Fig. 10 Impulse response of the sum of all-order reflections

The obvious change as seen is that the first-order response of both of the cases is slightly different at the value of the figure; however, the difference of the secondorder response is as large as the gain order decreases from 10−9 to 10−10 . This change can be seen more clearly in Fig. 13. The first-order response pulse that can be seen in the result of a furnished room came bit fast than the one in an empty room. This can be explained that with the tables, chair and rack’s add-in, some paths of the light are shortened which results that the light reaches the reflection surfaces faster so that the reflection response occurs earlier. Also, since the second-order reflections are mainly happened after the

Characteristic Analysis and Experimental Simulation …

29

(m) 1

0

3

0.8

3.8 (m)

Rx

Tx

(m) P os i ti on s: T x( 3 . 8 m , 1 m , 3 m ) R x( 0 . 8 m , 1 m , 3 m )

(m) 2

1.5

1 0

(a)

(b)

(c)

(d)

(m)

Chair 2.3 3

Rx

Tx

(m) P os i ti on s: T x( 3 . 8 m , 1 m , 3 m ) R x( 0 . 8 m , 1 m , 3 m ) R ack ( 0 m , 0 m , 0 m ) C hai r ( 2 . 2 m , 0 . 5 m )

T abl e 1 ( 1 . 5 m , 0 m ) T abl e 2 ( 0 . 5 m , 2 . 3 m ) T abl e 3 ( 3 m , 7 m , 2 . 3 m )

Fig. 11 Layout of transmitters and receivers in the room. a 2D view of an empty room; b 3D view of an empty room; c 2D view of a furnished room; and d 3D view of a furnished room

first-order reflections from the ceiling, thus it can be understandable that the rack might block some paths of the light so that the gain of the second-order reflection decreased. But the sum of the result shows that the first-order reflection response still dominates the all; in addition, it also reveals that even though the furniture at some location might result in light path blocked, they increase the reflection surfaces within the room on the other hand, which cause more reflections to calculate, so that the sum of the average gain was not varied largely in this scenario.

H0

P. He and M. He

H0

30

Time(ns)

H1

H1

Time(ns)

Time(ns)

H2

H2

Time(ns)

Time(ns)

SUM

SUM

Time(ns)

Time(ns)

Time(ns)

H0-First order reflection response; H1-First order reflection response; H2-Secod order reflection response; SUM-Impulse response of the sum of all-order reflections (a)Empty room

(b)Room with furniture

H2

Fig. 12 Order reflection impulse response of empty and furnished rooms. a Channel response of an empty room and b channel response of a furnished room

No furniture room

Room:5m×3m×6m

H2

Time(ns)

Furnished room

Room:5m×3m×6m

Time(ns) H2-Impulse comparison of the second order reflections Comparison of the impulse response of a room with and without furniture

Fig. 13 Impulse response of a room with or without furniture

Characteristic Analysis and Experimental Simulation …

31

Fig. 14 Average received power versus half-power angle versus FOV

4.2 Effect of Field of View (FOV) Angle and Half-Power (HP) Angle By calculating the average received power at the receiver, showing results in Fig. 14, one can find the relationship between the HP angle and the received power [12–14]. As shown in the figure, the trend of this relationship is that when the degree of the HP angle increases, the amount of average received power is increased. This is reasonable that the bigger the angle is, the more directions of light paths are generated. Thus, the receiver may have more opportunities to catch light from different directions. Also, along with the FOV increased, the average received power is increased too. It is easy to understand that because the receiving area is enlarged, and then more light arrived at the receiver can be collected. In addition, the average power that used through the radiation simulation was 1 W in this case. Moreover, it is apparent to see that there is a notch on the line waves at the point of 45°, where the value of average received power was sharply decreased. This point reveals that there is a threshold for HP angle that if the angle was too large that exceeds the threshold, some of the light would be misled to the less effective direction against destination so that the light beams had to undergo more reflections before the journey ended at the receiver, and possibly on their way, might suffer from blockage or even absorption which would cause received power to decrease. Therefore, taking the part of 45–85° section in Fig. 14 to confirm, the average received power is much weaker than one of their previous sections in which the largest figure (67.6 nW) can reach at 30° with FOV at 80° in this case.

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5 Conclusion To sum up, the following conclusions are drawn: 1. The calculation of receiver receiving power shows that the average optical receiving power is closely related to the FOV and the HP. When the HP angle increases, the average receiving power increases. In addition, with the increase of the FOV angle, the average receiving power also increases greatly. However, 45° is a threshold value. When the HP angle is greater than 45°, the average receiving power value decreases sharply. In fact, half-power angle of 30° is the best HP angle. 2. Channel characteristic analysis shows that in the indoor environment, different objects such as the ceiling, wall, furniture and people may cause diffuse light to produce diffraction. At the same time, due to the difference of the length of the light path, the time of the reflected beam arriving at the receiver is also different. Diffuse link algorithm can change this situation to be adapted to changing environments or modes. 3. The influence of physical parameters is mainly manifested in two aspects: The size of the room is the best, and the size of the room is 10 m × 3 m × 10 m; among them, the empty room is less affected by the diffraction light produced by diffuse light, while the furniture room is more affected by the diffraction light produced by diffuse light. Therefore, when designing indoor optical wireless communication system, as long as the above factors are properly handled, the expected performance of an optical communication system can be achieved. Acknowledgements This study was supported by the International Student Fund of the University of Warwick, UK, by Scientific Research Fund of the Civil Aviation Flight University of China (J2018-11) and by Sichuan Science and Technology Project (2019YJ0721). I would like to thank Professor Roger j. Green, an expert in optoelectronic communications at the University of Warwick and Professor Dominic O’Brien, an expert in optical communications at the University of Oxford, for highly appraised on the results. I would like to thank the School of Engineering of University of Warwick for providing detection equipment for my experimental testing.

References 1. M.E. Yousefi, S.M. Idrus, C.H. Lee, M. Arsat, A.S.M. Supa’At, N.M. Safri, Indoor Free Space Optical Communications for Aircraft Passenger Cabin (IEEE, 2001). 978-1-4577-00057/11,2011,:1-5 2. M.D. Higgins, M.S. Leeson, R.J. Green, An Analysis of Intra-Vehicle Optical Wireless Communications from a Passenger Perspective (ICTON, 2012), We.C4.4:1–4 3. M.D. Higgins, R.J. Green, M.S. Leeson, Optical wireless for intravehicle communications: a channel viability analysis. IEEE Trans. Veh. Technol. 61(1), 123–129 (2012)

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4. M.D. Higgins, R.J. Green, M.S. Leeson, Optical wireless for intravehicle communications: incorporating passenger presence scenarios. IEEE Tran. Veh. Technol. 62(8), 3510–3517 (2013) 5. D.C. O’Brien, G.E. Faulkner, S. Zikic, N.P. Schmitt, High data-rate optical wireless communications in passenger aircraft: measurements and simulation. IEEE, CSNDSP08. Proceedings (2008),978-1- 4244-1876-3/08:68-71 6. P. He, M. He, Orthogonal space- time coding and modulation technology for indoor wireless optical communication. Aero. Comput. Tech. 48(1), 127–134 (2018) 7. P. He, M. He, Dual-links configuration and simulation of indoor wireless optical communication system. Opt. Commun. Technol 43(7), 56–60 (2019) 8. A.G. Al-Ghamdi, J.M. Elmirghani, Analysis of diffuse optical wireless channels employing spot-diffusing techniques. Diversity receivers. And combining schemes. IEEE Trans. Commun. 52(10), 1622–1631 (2004) 9. A.K. Majumdar, Non-line-of-sight (NLOS) Ultraviolet and indoor free-space optical (FSO) communications. Adv. Free Space Optics (FSO) 186, 177–202 (2015) 10. H. Naoki, I. Takeshi, Channel modeling of non-directed wireless infrared indoor diffuse link. Electron. Commun. Japan. 90(6), 8–18 (2007) 11. F. Miranirkhani, M. Uysal, Channel modeling and characterization for visible light communications. IEEE J. Photon. 7(6), 1–16 (2015) 12. A.T. Hussein, J.M.H. Elmirghani, 10 Gbps mobile visible light communications systems employing angle diversity. Imaging receivers. And relay nodes. J. Optical Commun. Netw. 7(8), 718–735 (2015) 13. D.J.F. Barros, S.K. Wilson, J.M. Kahn, Comparison of orthogonal frequency division multiplexing and pulse-amplitude modulation in indoor optical wireless links. IEEE Trans. Commun. 60(1), 153–163 (2012) 14. M.T. Alresheedi, J.M.H. Elmirghani, 10 Gb/s Indoor optical wireless systems employing beam delay, power, and angle adaptation methods with imaging detection. J. Lightwave Technol. 30(12), 1843–1856 (2012)

A Comparative Analysis of Malware Anomaly Detection Priynka Sharma, Kaylash Chaudhary, Michael Wagner, and M. G. M. Khan

Abstract We propose a classification model with various machine learning algorithms to adequately recognise malware files and clean (not malware-affected) files with an objective to minimise the number of false positives. Malware anomaly detection systems are the system security component that monitors network and framework activities for malicious movements. It is becoming an essential component to keep data framework protected with high reliability. The objective of malware inconsistency recognition is to demonstrate common applications perceiving attacks through failure impacts. In this paper, we present machine learning strategies for malware location to distinguish normal and harmful activities on the system. This malware data analytics process carried out using the WEKA tool on the figshare dataset using the four most successful algorithms on the preprocessed dataset through cross-validation. Garrett’s Ranking Strategy has been used to rank various classifiers on their performance level. The results suggest that Instance-Based Learner (IBK) classification approach is the most successful. Keywords Anomaly · Malware · Data mining · Machine learning · Detection · Analysis

P. Sharma (B) · K. Chaudhary · M. Wagner · M. G. M. Khan School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Suva, Fiji e-mail: [email protected] K. Chaudhary e-mail: [email protected] M. Wagner e-mail: [email protected] M. G. M. Khan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_3

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1 Introduction Malware has continued to mature in volume and unpredictability in remarkable dangers to the security of computing machines and services [1]. This has motivated the increased use of machine learning to improve malware anomaly detection. The history of malware demonstrates to us that this malicious threat has been with us since the beginning of computing itself [2]. The concept of a computer virus goes back to 1949, when eminent computer scientist John von Neumann wrote a paper on how a computer program could reproduce itself [3]. During the 1950s, workers at Bell Labs offered life to von Neumann’s thought when they made a game called “Center Wars”. In the game, developers would release programming “life forms” that vied for control of the computer system. From these basic and benevolent beginnings, a gigantic and wicked industry was conceived [3]. Today, malware has tainted 33% of the world’s computers [4]. Cybersecurity Ventures report that the losses are due to cybercrime, including malware, and are foreseen to hit $6 trillion each year by 2021 [4]. Malware is software designed to infiltrate or harm a computer framework without the proprietor’s informed assent. Many strategies have been used to safeguard against different malware. Among these, malware anomaly detection (MAD) is the most encouraging strategy to shield from dynamic anomaly practices. MAD system groups information into different classifications known to be as typical and bizarre [5]. Different classification algorithms have been proposed to plan a powerful detection model [6, 7]. The exhibition of a classifier is a significant factor influencing the performance of MAD model. Thus, the choice of precise classifier improves the performance of malware detection framework. In this work, classification algorithms have been assessed using WEKA tool. Four different classifiers have been estimated through Accuracy, Receiver Operating Characteristics (ROC) esteem, Kappa, Training Time, False-Positive Rate (FPR) and Recall esteem. Positions have additionally been appointed to these algorithms by applying Garret’s positioning strategy [8]. The rest of the paper is dependent on the sections as follows. The next two subsections discuss anomaly detection and classification algorithms. Section 2 discusses related work, whereas Sect. 3 presents the chosen dataset and describes WEKA tool and different classification algorithms. Results and discussions are obtainable in Sect. 4. Finally, Sect. 5 leads with the conclusion of this research.

1.1 Anomaly Detection Anomaly detection is a type of innovation that uses human-made intellect to recognise unusual behaviour classified in the dataset. Datch frameworks characterise anomaly discovery as “a strategy used to distinguish unpredictable instances in a perplexing domain”. Ultimately, anomaly identification spots design such that a human reader

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Fig. 1 Anomaly detection on various problem-solving domains

cannot. Anomaly detection is bridging any issues among matrices and business procedures to give more proficiency [9]. Ever since the intensification in big data enterprises of all extents has been in a condition of vulnerability, anomaly detection is conquering prevention among measurements and business procedures to give more proficiency. There are two phases in anomaly-based detection. Phase 1 is training, and phase 2 is detecting [10, 11]. In the first phase, the machine learning attacks and then detects abnormal behaviour in the second phase. A key advantage of anomaly-based detection is its ability to detect zero-day attacks [12]. The limitations of anomalybased detection are high false alarm rate and difficulty in deciding features to be used for detection in the training phase [13]. Anomaly detection cracks these problems in numerous diverse ways, as depicted in Fig. 1. Anomaly detection stages can fall into the particulars of data identification where diminutive peculiarities cannot be seen by users observing datasets on a dashboard. Therefore, the best way to get continuous responsiveness to new data examples is to apply a machine learning technique.

1.2 Classification Algorithms Classification algorithms in data mining are overwhelmingly applied in anomaly detection system to order attacks from ordinary activities in the system. Classification algorithms take a supervised learning approach; that is, it does not require class marks for the forecast. There are essentially eight classifications of classifiers, and every classification contains diverse artificial intelligence algorithms. These classifications are:

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Bayes Classifier: Also known as belief networks has a place with the group of probabilistic graphical models (GMs) which are used to state in learning about uncertain areas. In the graph, nodes mean random factors and edges are probabilistic conditions. Bayes classifier depends on foreseeing the class based on the estimation of individuals from the highlights [14]. Function Classifier: Develops the idea of a neural network and relapse [I]. Eighteen classifiers fall under this classification. Radial Basis Function (RBF) Network and Sequential Minimal Optimization (SMO) are two classifiers which perform well with the dataset used in this paper. RBF classifiers can present any nonlinear function effectively, and it does not utilise crude information. The issue with RBF is the inclination to over-train the model [15]. Lazy Classifier: Requests to store total training information. While building the model, new examples are not incorporated into the training set by these classifiers. It is mostly utilised for classification on information streams [16]. Meta Classifier: Locates the ideal set of credits to prepare the base classifier. The parameters used in the base classifier will be used for predictions. There are twenty-six classifiers in this category [8]. Mi Classifier: There are twelve multi-instance classifiers. None fits the dataset used in this paper. This classifier is a variation of the directed learning procedure. These kinds of classifiers are initially made accessible through a different programming bundle [17]. Misc or Miscellaneous Classifier: Three classifiers fall under misc classifier. Two classifiers, Hyperpipes and Voting Feature Interval (VFI), are compatible with our dataset [8]. Rules Classifier: Association standards are used for the right expectations of class among all the behaviours, and it is linked with the level of accuracy. They may anticipate more than one end. Standards are fundamentally unrelated. These are learnt one at a time [17]. Trees: Famous classification procedures where a stream graph like tree structure is created in which every hub signifies a test on characteristics worth and each branch expresses the result of the test. Moreover, it is known as Decision Trees. Tree leaves characterise to the anticipated classes. Sixteen classifiers fall under this category [8].

2 Related Work Numerous analysts have proposed different strategies and algorithms for anomaly detection on data mining classification methods. Li et al. [19] present a rule-based technique which exploits the comprehended examples to recognise the malignant attacks [18]. Fu et al. [20] discuss the use of data mining in the anomaly detection framework. It is a significant course in Intrusion Detection System (IDS) research. The paper shows the improved affiliation anomaly detection dependent on frequent pattern growth and fuzzy c-means

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Table 1 An overview of publication that applied ML classifiers for malware detection Publication/year

ML algorithms

Sample size Optimal classifier

Evaluation of machine BN, MLP, J48, KNN, RF learning classifiers for mobile malware detection, 2016

3450

Using spatio-temporal IBK, J48, NB, Ripper, SMO information in API calls with ML algorithms for malware detection, 2009 DroidFusion: A novel multilevel classifier fusion approach for Android malware detection, 2018

516

RT, J48, Rep Tree, VP, RF, R. 3799 Comm., R. Sub., Adaboost, 15,036 Droid fusion 36,183

RF

SMO

DroidFusion

(FCM) network anomaly detection. Wenguang et al. proposed a smart anomaly detection framework dependent on web information mining, which is contrasted with other conventional anomaly detection frameworks [20]. However, for a total detection framework, there is still some work left such as improving information mining algorithms, best handling the connection between information mining module and different modules, improving the framework’s versatile limit, accomplishing the representation of test outcomes, and improving continuous proficiency and precision of the framework. Likewise, Panda and Patra [22] present the study of certain information mining systems, for instance, machine learning, feature selection, neural network, fuzzy logic, genetic algorithm, support vector machine, statistical methods and immunological-based strategies [21]. Table 1 presents an overview of papers that applied machine learning techniques for Android malware detection.

3 Dataset and Tool Description The Android malware dataset from figshare consists of 215 attributes, feature vectors detached from 15,036 applications (5560 malware applications from Drebin venture and 9476 amiable applications). Also, this dataset has been used to create multilevel classifier fusion approach for [1]. Table 2 shows that the dataset contains two classes, mainly malware and benign. There are 5560 instances of malware and 9476 instances of benign. Table 2 Dataset used for malware anomaly detection Dataset

Features

Samples

Class Malware

Benign

Drebin

215

15,036

5560

9476

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3.1 Waikato to Environment for Knowledge Analysis (WEKA) WEKA is a data analysis tool developed at the University of Waikato, New Zealand, in 1997 [22]. It consists of several machine learning algorithms that can be used to mine data and extract meaningful information. This tool is written in Java language and contains a graphical user interface to connect with information files. It contains 49 information pre-preparing tools, 76 classification algorithms, 15 trait evaluators and 10 quest algorithms for highlight choice. It has three graphical user interfaces: “The Explorer”, “The Experimenter” and “The Knowledge Flow”. WEKA bolsters information placed in Attribute Relation File Format (ARFF) document group. It has a lot of boards that can be utilised to perform explicit errands. WEKA gives the capacity to create and incorporate a new machine learning algorithm in it.

3.2 Cross-Validation The cross-validation is equivalent to a single holdout validation set to evaluate the model’s predictive performance on hidden data. Cross-validation does this more robustly, by iterating the trial multiple times, using all the various fragments of the training set as the validation sets. This gives an increasingly exact sign of how well the model sums up to inconspicuous data, thus avoiding overfitting (Fig. 2).

Fig. 2 Cross-validation (10 folds) method application

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Fig. 3 Confusion matrix application in machine learning

3.3 Performance Metrics Used The performance measurements used to assess classification strategies depicted via confusion matrix. It contains information regarding test dataset, which contains known values. The confusion matrix displays results of prediction as follows: 1. False Positive (FP): The model predicted a benign class as a malware attack. 2. False Negative (FN): It means wrong expectation or prediction. The prediction was benign, but it was a malware attack. 3. True Positive (TP): The model predicted a malware attack, and it was a malware attack. 4. True Negative (TN): The model predicted as benign, and it was benign. A confusion matrix, as shown in Fig. 3, is a method for condensing the presentation of a classification calculation. Classification accuracy alone can be misdirecting on the off chance that you have an inconsistent number of perceptions in each class or if there are multiple classes in your dataset. Computing a confusion matrix can give you a superior thought of what the classification model is predicting [17]. • • • • •

Accuracy = (TP + TN)/n True-Positive Rate (TPR) = (TP + TN)/n False-Positive Rate (FPR) = FP/(TN + FP) Recall = TP/(TP + FN) Precision = TP/(TP + FP).

4 Results and Discussions Android malware dataset was used to evaluate anomaly detection retrospectives. We have used WEKA to implement and evaluate anomaly detection. Feature ranking and file conversions in arff file format were additionally completed using WEKA tool. In every single investigation, we set K = 4, where K represents the number of base classifiers. In this paper, we used four base classifiers. Besides, we took N = 10 for the cross-validation and weight assignments separately. The four base classifiers are Instance-Based Learner (IBK), Logistic, Rotation Forest and Sequential Minimal

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Optimization (SMO). The classifier has been evaluated in WEKA environment using 215 attributes detached from 15,036 applications (5560 malware applications from Drebin venture and 9476 amiable applications). Garrett’s Ranking Technique has been used to rank different classifiers according to their performance. Figure 4 shows the predictive model evaluation using knowledge flow. The arff loader was used to load the dataset. The arff loader was associated with “ClassAssigner” (permits to pick which segment or column to be the class) component from the toolbar and was eventually set on the layout. The class value picker picks a class value to be considered as the “positive” class. Next was the “CrossValidationFoldMaker” component from the evaluation toolbar as described in Sect. 3.2. Upon completion, the outcomes were acquired by option showing results from the pop-up menu for the TextViewer part. Tables 3 and 5 illustrate the evaluation results of the classifiers. Table 3 shows the quantity of “statistically significant wins”; each algorithm has against all the other algorithms on the malware detection dataset used in this paper. A win implies an accuracy that is superior to the accuracy of another algorithm, and the difference was statistically significant. However, we can agree with the results table that IBK has a notable success when compared to RF, SMO and logistic.

Fig. 4 A predictive model evaluation using knowledge flow

Table 3 Results obtained with algorithm-based ranking (1 = Highest-Rank) Classifier ROC area

FPR

Accuracy Kappa

MAE

Recall Precision Training Rank Time (s)

IBK

0.994 0.013 98.76

0.9733 0.013

0.988

0.988

0.01

1

Rotation Forest

0.997 0.020 98.51

0.9679 0.0333 0.985

0.985

98.63

2

SMO

0.976 0.027 97.84

0.9535 0.0216 0.978

0.978

34.46

3

Logistic

0.995 0.027 97.81

0.953

0.978

22.44

4

0.0315 0.978

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Table 4 Predictive model evaluation using knowledge flow Dataset

Mean (Standard Deviation)

Drebin-215

IBK

Rotation Forest

SMO

Logistic

98.76 (0.27)

98.50 (0.32)*

97.81 (0.37)*

97.86 (0.39)*

*Significance of 0.05

Each algorithm was executed ten times. The mean and standard deviation of the accuracy are shown in Table 4. Therefore, the difference between the three accuracy scores is significant for RF, SMO and logistic and is less than by 0.05, indicating that these three techniques compared to IBK are statistically different. Henceforth, IBK leads the algorithm accuracy level in determining the malware anomaly detection on the Drebin dataset.

5 Conclusion and Future Work In this paper, we proposed a novel machine learning anomaly malware detection approach for Android malware data collection, which identifies malware attacks and achieves zero false-positive rate. We achieved an accuracy rate as high as 98.76%. Exclusively, if this system turns out to be a part of profoundly focused business entry, various deterministic exemption components must be included. As we contemplate, malware detection by means of machine learning will not substitute the standard detection strategies used by anti-virus merchants; however, it will come as an extension to them. Any business against infection item is liable to a certain speed and memory impediments. In this way, the most reliable algorithm among those introduced here is the IBK.

References 1. Y. Yerima, S. Sezer et al., Droidfusion: a novel multilevel classifier fusion approach for android malware detection. J. IEEE Trans. Cybern. 49, 453–466 (2018) 2. I. YouI, K. Yim, Malware obfuscation techniques: a brief survey. in Proceedings of the 5th International Conference on Broadband, Wireless Computing, Communication and Applications, Fukuoka, Japan, 4–6 November 3. J. Grcar, John von Neumann’s analysis of Gaussian elimination and the origins of modern numerical analysis. J. Soc. Ind. Appl. Mathe. 53, 607–682 (2011) 4. P. John, J. Mello, Report: malware poisons one-third of world’s computers. Retrieved June 6, 2019, from Tech News World. https://www.technewsworld.com/story/80707.html (2014) 5. G. Guofei, A. Porras et al., Method and Apparatus for Detecting Malware Infections (Patent Application Publication, United Sates, 2015), pp. 1–6 6. A. Shamili, C Bauckhage et al., Malware detection on mobile devices using distributed machine learning. in Proceedings of the 20th International Conference on Pattern Recognition (Istanbul, Turkey, 2010), pp. 4348–4351

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7. Y. Hamed, S. AbdulKader et al., Mobile malware detection: a survey. J. Comput. Sci. Inf. Sec. 17, 1–65 (2019) 8. B. India, S. Khurana, Comparison of classification techniques for intrusion detection dataset using WEKA. in Proceedings of the International Conference on Recent Advances and Innovations in Engineering, Jaipur, India, 9–11 May 9. M. Goldstein, S. Uchida, A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. J. PLOS ONE 11, 1–31 (2016) 10. L. Ruff, R. Vandermeulen et al., Deep semi-supervised anomaly detection. ArXiv 20, 1–22 (2019) 11. T. Schlegl, P. Seeböck et al., Unsupervised Anomaly Detection with (2017) 12. Generative adversarial networks to guide marker discovery. in Proceedings of the International Conference on Information Processing in Medical Imaging, Boone, United States, 25–30 June 13. A. Patch, J. Park, An overview of anomaly detection techniques: existing solutions and latest technological trends. Int. J. Comput. Telecommun. Netw. 51, 3448–3470 (2007) 14. V. Chandola, A. Banerjee, Anomaly detection: a survey. J. ACM Comput. Surv. 50, 1557–7341 (2009) 15. R. Bouckaert, Bayesian Network Classifiers in Weka. (Working paper series. University of Waikato, Department of Computer Science. No. 14/2004). Hamilton, New Zealand: University of Waikato: https://researchcommons.waikato.ac.nz/handle/10289/85 16. R. Mehata, S. Bath et al., An analysis of hybrid layered classification algorithms for object recognition. J. Comput. Eng. 20, 57–64 (2018) 17. S. Kalmegh, Effective classification of Indian news using lazy classifier IB1 and IBk from weka. J. Inf. Comput. Sci. 6, 160–168 (2019) 18. I. Pak, P. Teh, Machine learning classifiers: evaluation of the performance in online reviews. J. Sci. Technol. 45, 1–9 (2016) 19. L. Li, D. Yang et al., A novel rule-based intrusion detection system using data mining. in Proceeding of the International Conference on Computer Science and Information Technology. Chengdu, China, 9–11 July 20. D. Fu, S. Zhou et al., The Design and implementation of a distributed network intrusion detection system based on data mining. in Proceeding of the WRI World Congress on Software Engineering, Xiamen, China 19–21 2019 May 21. W. Chai, C. Tan et al., Research of intelligent intrusion detection system based on web data mining technology. in Proceedings of the International Conference on Business Intelligence and Financial Engineering. Wuhan, China, 17–18 October 22. M. Panda, M. Patra, Evaluating machine learning algorithms for detecting network intrusions. J. Recent Trends Eng. 1, 472–477 (2009)

Future Identity Card Using Lattice-Based Cryptography and Steganography Febrian Kurniawan and Gandeva Bayu Satrya

Abstract Unauthorized or illegal access to confidential data belonging to an individual or corporation is the biggest threat in information security. Many approaches have been proposed by other researchers to prevent credential data and identity theft, i.e., cryptography, steganography, digital watermarking, and hybrid system. In mid-90s, Shor’s algorithm was introduced to be used in quantum computing. This algorithm could break the well-known cryptography or steganography. Shor’s algorithm has been cleverly used in the quantum computing as a new breakthrough in computer science to parallelly solve problems (NP-hard). However, it can be a threat for security system or cryptosystem. This research proposed a new hybrid approach by using post-quantum cryptography and advanced steganography. N th degree truncated polynomial ring (NTRU) is one of the candidates of post-quantum cryptography that is claimed to be hard to break even with quantum computing. Least significant bit (LSB) is a spatial steganography technique done by replacing bit of the cover image with message bit. The result and comparison of the proposed approach with different existing cryptosystem proved that this approach is promising to be implemented in identity card, banking card, etc. Keywords Identity theft · Post-quantum cryptography · NTRU · Steganography · LSB · Identity card

Supported by PPM, SoC, and SAS, Telkom University, Indonesia. F. Kurniawan (B) School of Computing, Telkom University, Bandung, Republic of Indonesia e-mail: [email protected] G. B. Satrya School of Applied Science, Telkom University, Bandung, Republic of Indonesia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_4

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1 Introduction Toward incoming future communication, business in secured 5G networks flourishes on the confidentiality, integrity, and availability of personal or group business information. The task of ensuring business information safety is very simple in a closed environment but it can be complex in an open environment. It drives companies to outsource their system security and maintenance in order to reduce costs and streamlines. On the other hand, the outsourcing method allows at least two possible security breaches to the company’s system, i.e., a process perspective and a technology perspective. By outsourcing its data and system maintenance to other party, the company allows their business partner to access and process critical data such as government data, medical data, and intellectual capital. Not only that the company must get its business partners to commit to the formalized security measures and policies, but also must take steps to protect themselves in precautions that its business partners have a security breach. So as to prevent data breach, the company should ensure its data safety by implementing advanced information security for open environment. Based on Risk-Based Security’s data breach report issued on August 2019, there were more than 3,800 publicly disclosed breaches on the second quarter of 2019 exposing 3.2 billion of compromised records which were either being held for ransom or data stealing. A data breach occurs when confidential or private data and other sensitive information is accessed without authorization. There are two main risks in security system, i.e., threats and vulnerabilities. Threats can occur at every second in various ways, e.g., gaining access, denial of service, man-in-the-middle attack, etc. While vulnerabilities can be fixed by redesigning the security system so that adversary penetrating test cannot cause breakage. Those problems can be addressed by developing a new cryptography, steganography, digital watermarking or by mixing all those three systems called as a hybrid system. Many studies have proposed methods in information security system by combining cryptography and steganography. Bloisi and Iocchi described a method for integrating Vernam cryptography and discrete cosine transform (DCT) steganography through image processing [4]. Narayana and Prasad proposed methods for securing image by converting it into cipher text using S-DES algorithm and concealing this text in another image by using least significant bit (LSB) steganographic method [14]. Another approach was used by Joshi and Yadav where Vernam cipher algorithm was utilized to encrypt a text and then the encrypted message was embedded inside an image using the LSB with shifting (LSB-S) steganography [12]. Moreover, Abood proposed hybrid cryptography and steganography. The cryptography used RC4 stream cipher and the steganography used hash-LSB (HLSB) with RGB pixel shuffling [1]. Budianto et al. applied elliptic curve cryptography (ECC) to encrypt data in identity card and then used LSB to embed the chiper text into an image [6]. Considering quantum computing as an ultimate threat for a secured system or cyrptosystem, the researches in information security are thriving toward post-quantum cryptography. Provable security aspect of post-quantum cryptography is impossible to encrypt or decrypt as it requires a solution to NP-hard problems for attacks from

Future Identity Card Using Lattice-Based Cryptography …

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a quantum computer. Lattice-based cryptography is the most secured candidate of cryptosystem to resist attacks from quantum computing [5]. Recent developments in lattice-based cryptography are one-way function [2], collision-resistant hash function [9], and public-key cryptosystems [10, 11]. Furthermore, digital steganography is another approach that can be used in information security, e.g., spatial domain, transform domain, and spread spectrum. LSB steganography in spatial domain is manipulating and storing secret information into the least significant bits of a cover file. Based on previous researcher, hybrid system is one of the promising candidates to address the vulnerability in universal information secured system. The contributions of this research are as follows: (i) Presenting a new system architecture by using lattice-based cryptography and advanced steganography. (ii) Proposing NTRU cryptography for securing text to be embed into image. (iii) Using a new development of LSB steganography, e.g., spiral-LSB for embedding secret text into image. (iv) Comparing the proposed system (NTRU) with the existing cryptography, e.g., AES and RSA. As for the rest of this research, Sect. 2 reviews recent researches on advanced cryptography and steganography. Section 3 explains the proposed architecture including the encoding and decoding procedures. Then, the details of results and benchmarking with existing cryptography are provided in Sect. 4. Finally, Sect. 5 gives the conclusions of this research.

2 Literature Review 2.1 Hybrid Information Security A new method in information security was presented by Joshi and Yadav for image LSB steganography in gray images combined with Vernam cryptography [12]. First, the message was encrypted using Vernam cipher algorithm and then the encrypted message was embedded inside an image using the new image steganography. LSB with shifting steganography was proposed by performing circular left shift operation and XOR operation. The amount of pixels in an image was 256 * 256, i.e., 65,536 while the amount of hidden bit was 65,536. If all of LSB bits were being extracted by the intruder, they would not get the message. The experimental result showed that all PSNR values were below 70 dB. Mittal et al. combined and implemented RSA asymmetric key cryptography and least significant bit steganography technique [13]. The original message was encrypted by using RSA and the cipher text obtained as the output was taken as an input data for being embedded in the cover image, the subsequent stego-image had cipher text embedded in it. The analysis revealed that for preserving the implementa-

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tion of LSB technique and RSA data security algorithm, the most crucial thing was to ensure that the size of original image and stego image must be equivalent and it also applied for plaintext and cipher text used in RSA. Histogram, time complexity, and space complexity analysis were also provided as experiment results but the process to obtain the results was not described thoroughly. Rachmawanto et al. proposed steganographic mobile applications using LSB method by adding AES encryption to improve security [17]. First, entered the text and the key AES that will be hidden. Second, read the cover image and the LSB key, then performed the embedding process by using LSB algorithm. In the preprocessing data, it was tested using five different sizes of cover images. The result of PSNR and histogram was obtained with most of the averages to be under 70 dB. Abood introduced RC4 stream cipher for encryption and decryption processes based on image matrix. The study also proposed steganography by using hash-LSB (HLSB) with RGB pixel shuffling [1]. RC4 only requires byte-length manipulations so it is suitable for embedded systems. Despite the vulnerabilities in RC4, the combination of RC4 and RGB pixel shuffling makes it almost impossible to break. The image encryption and decryption processes used pixel shuffling. However, the PSNR and security quality values showed that this method still needed improvements. Alotaibi et al. designed security authentication systems on mobile devices by combining hash, cryptography, and steganography mechanisms [3]. The hash function provided a message authentication service to verify authentication and integrity, as found in MD5 and SHA-1. During signup or login, a cover image was chosen by the user as a digital signature. Afterward, the user would encrypt the password using AES algorithm with username as a key. Then, the result of AES algorithm would be hidden in cover image by using LSB algorithm. The study concluded three recommended techniques, e.g., AES with LSB, hash with LSB, and the combination of hash, AES, and LSB. According to the results, all PSNR values were less than 40 dB. To the best of authors’ knowledge, the preliminary stage in this research has been carried out by surveying eleven relevant literature in the last five years. Table 1 explained and compared the literature thoroughly in order to find a gap that can be used as the art of the state for this research in crypto-stegano system.

2.2 Implementation of NTRU This research implements NTRU cryptosystem introduced by Hoffstein, Pipher and Silverman as a new public-key cryptosystem for securing the message for general purposes [11]. The security of NTRU is obtained from the interaction of the polynomial mixing system with the independence of reduction modulo two relatively prime integers p and q. An NTRU cryptosystem derives on three integer parameters (N , p, q) and for set lattice-based ζ f , ζg , ζt , ζm of polynomials of degree N − 1 with integer coefficients [11]. Considering that p and q do not need to be prime, but this research assumes that gcd( p, q) = 1, and q will always be considerably larger than

Future Identity Card Using Lattice-Based Cryptography … Table 1 Recent studies in cryptography and steganography No. Relevant study Methodology Phase 1 Phase 2 1

2

3

4

5

6

7

8 9

10

11

Joshi and Yadav [12]

First encrypted using Vernam cipher algorithm

49

Remarks

LSB with shifting PSNR and (LSB-S) histrogram with different message sizes Reddy and Text is encrypted LSB with LL No evaluation of Kumar [18] using AES sub-band wavelet the stego image decomposed image Bukhari et al. [7] LSB Double random PSNR and steganography phase encoding entropy with (DRPE) noise type cryptography (Gaussian, salt & pepper and speckle) Mittal et al. [13] RSA for the LSB for the Time complexity, message images space complexity, histogram Patel and Meena Pseudo random Dynamic key PSNR [15] number (PRN) rotation which provide cryptography double layer security Phadte and Randomized LSB Encrypted using Histogram and Dhanaraj [16] steganography chaotic theory key sensitive analysis Rachmawanto et AES-128 bit for LSB for the PSNR and al. [17] the text images histrogram with different image sizes Chaucan et al. [8] Variable block LSB PSNR and size cryptography steganography entropy Abood [1] RC4 Hash-LSB PSNR, histogram, cryptography for steganography security quality, image elapsed time for secret image and cover images Saxena et al. [19] Proposed LSB PSNR and encryption entropy architecture using EI(secret image), k, and CI (cover image) Budianto et al. [6] ECC for data LSB for person PSNR information picture

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p. This research operates in the ring of R = Z[X ]/(X n − 1). An element F ∈ R will be written as a polynomial or a vector. To create an NTRU key, cryptosystem randomly designates two polynomials f, g ∈ ζg . The polynomial f must fulfill the additional requirement that it has inverses modulo q and modulo p. It will denote those inverses by Fq and F p as Eq. 1. Fq  f ≡ 1

mod q and Fq  f ≡ 1 mod q

(1)

Then the quantity is computed with Eq. 2. User’s public key is the polynomial μ and user’s private key is the polynomial f . μ ≡ Fq  g

2.2.1

mod q

(2)

Encrypting the Message

Presume that Alice wants to send a message to Bob. Alice starts selecting a message m from the set of plaintexts ζm . Next, Alice randomly chooses a polynomial t ∈ ζt and uses Bob’s public key h to compute as can be seen in Eq. 3. This ξ is the encrypted message. ξ ≡ pφ  μ + m

2.2.2

mod q

(3)

Decrypting the Message

In the event that Bob has received the message ξ from Alice, Bob wants to decrypt it using his private key f . To do this efficiently, Bob should precompute the polynomial F p . In order to decrypt ξ , Bob first has to compute as Eq. 4. Bob then chooses the coefficients of ψ in the interval from −q/2 to q/2. Now considering ψ as a polynomial with integer coefficient, Bob recovers the message by computing as Eq. 5. ψ ≡ f ξ Fp  ψ

mod q

mod p

(4)

(5)

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3 Proposed System 3.1 System Architecture The embedding system will be begun with message encryption, producing the cipher and preparing the cover image as the carrier as can be seen in Fig. 1. To manipulate the last bits of the designated pixels by using spiral-LSB, both the cipher and the cover image need to be converted to bits. The converted bits on the image are the values of the pixels R, G, B channel. After the bit conversion, next is calculating the cipher length, then the cipher bits and the image bits will be generated. To do encoding in spiral pattern, the first step is to generate the location list of the pi xels(x, y), the center will be the starting point. The cipher length will be encoded in the carrier on the first n-bytes (customize) as identifier of the amount of data embedded on the carrier so the decoder knows where to stop decoding. Then, every bits of the cipher is placed into the last bit of every R, G, B value of the pixel. When all last bit of the pixels is filled, the encoder will proceed to the previous bit slot and repeat the process for the rest of the cipher bits. The spiral-LSB encoding is expected to be an improvement of the conventional LSB.

3.2 Encoding Procedure Algorithm 1 is going to be conducted with a spiral pattern to prevent adversary or infiltrator from extracting the data embedded in the carrier. The extracting process will be more complex than the conventional LSB pattern where it uses the edge as starting point. It affects the security of the embedded data because the pattern will be far less predicted. Moreover, the spiral-LSB encoding will use the cipher length as the identifier on the amount of data supposed to be hidden in the carrier. In order to get the data, the decoder must know the length which encoded on the first n-bytes

Spiral-LSB

Message

EncrypƟon

Cipher

Bit Conversion

Cover Image Stegano Image

Fig. 1 Proposed crypto-stegano architecture

Cipher Length

Encode

Cipher Bits

Image Bits

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(this experiment used the first 2 bytes). Then the iteration will be started to decode the embedded data according on how many data are hidden inside. Algorithm 1 Secured Spiral Encoding 1: INITIALISE Message 2: Pixels ← LOAD image data 3: Cipher ← CALL cryptosystem 4: Length ← Length of cipher 5: Spiral ← GENERATE centered spiral Pixels location 6: PUT Length to first 2 bytes 7: for x in Length*8: do 8: PUT Cipher[x] to last bit of R/G/B channel spiral[1] of Pixels 9: NEXT channel 10: if Spiral[1] channels filled then 11: POP Spiral 12: end if 13: end for 14: SAVE encoded Pixel as an image

 determining the plain-text  determining the cover image  encryption with AES, RSA or NTRU

 stego image

3.3 Decoding Procedure The decoder needs to know the cipher length to get data inside the carrier. This security addition becomes one of the keys to extract the data. For example, if the encoder sets the cipher length identifier to 2 bytes then the decoder needs to get the first 2 bytes from the first pixel as starting point to get the length of the embedded data. Without this parameter, the decoder will not know where to start and where to stop the data extraction. The decoder also needs to acknowledge the spiral pattern to know the pixel location sequence of the hidden data. The generated spiral pixel location will be used to extract the data with the iteration from the extracted cipher length as described in Algorithm 2. Algorithm 2 Secured Spiral Decoding 1: Pixels ← LOAD stego image 2: Spiral ← GENERATE centered spiral pixels location 3: Length ← READ Length on first 2 bytes 4: Cipher → Empty space 5: for x=1 to Length*8: do 6: Cipher ← Cipher+ Last bit of R/G/B channel spiral[1] of Pixels 7: NEXT Channel 8: if Spiral[1] channels extracted then 9: POP Spiral 10: end if 11: end for 12: CALL cryptosystem 13: OUTPUT Message

 the output from Algorithm 1  to assign variable of ciphers

 decryption with AES, RSA or NTRU  extracting the plain-text

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4 Result and Analysis The results of NTRU implementation can be seen in Table 2 by comparing AES with RSA. The representative images for testing were the well-known image processing testing with the size of 512 × 512 in .png format, i.e., Lena, Baboon and Pepper. The parameters used in this research were encoding time, decoding time, histogram, and PSNR (w.r.t MSE), the highest parameters are highlighted. The test was carried out by using 32 bytes of plaintext and various key length for each cryptosystem. This research provided two NTRU recommendations with different keys. NTRU gave a fairly constant result even when the key length level was increased. As can be seen, there was quite a high increase in the key in RSA which was the time consumption of the encode and decode processes. In addition, by increasing the key length of NTRU also resulted in a fairly constant PSNR. To verify the quality of stegano images when embedded with encryption is shown in Fig. 2 from the histogram. The results of this research have successfully demonstrated the differences in histogram from the original image with AES and RSA. These results explained that NTRU can produce more stable stegano image quality as the difference between NTRU-439 and NTRU-743. Abiding the proceeding rules about number of pages, the detailed results for the histogram will not be shown instead the Lena image is used a representative of the results.

Table 2 Validating sample images with spiral-LSB on each cryptography system Image Parameter AES-128 RSA-3072 RSA-7680 NTRU-439 Lena

Baboon

Pepper

Encode time (ms) Decode time (ms) PSNR (dB) Encode time (ms) Decode time (ms) PSNR (dB) Encode time (ms) Decode time (ms) PSNR (dB) Length plain (bytes) Length cipher (bytes)

NTRU-743

260.625

445.047

714.479

408.889

453.681

182.577

439.030

685.992

386.814

434.599

82.971 283.994

72.237 459.688

68.215 726.358

73.989 389.848

72.557 425.883

284.549

441.952

682.593

393.097

436.533

82.726 356.599

72.281 501.394

68.214 770.478

73.882 431.288

72.565 476.118

273.602

439.756

681.508

294.788

329.983

83.108 32

72.153 32

68.252 32

73.953 32

72.543 32

64

768

1920

520

708

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(a) Histogram analysis of original Lena

(b) Histogram analysis of Lena with AES

(c) Histogram analysis of Lena with RSA

(d) Histogram analysis of Lena with NTRU

Fig. 2 Comparison between original, AES, RSA, NTRU in lena image

5 Conclusion This research proposed a new system architecture of crypto-stegano in order to improve information security. The overall experiments showed that combining NTRU cryptography and spiral-LSB steganography outperforms some aspects of conventional encryption, including time performance. This remarkable result indicated that NTRU lattice-based cryptography can be a candidate to be implemented as future identity card, banking card, etc. Indeed, some modifications are needed to gain optimal security for the implementation. Also need to be noted that the experiment may vary because of the matter of hardware performances and there are still some improvements needed on the hardware types.

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References 1. M.H. Abood, An efficient image cryptography using hash-lsb steganography with rc4 and pixel shuffling encryption algorithms, in 2017 Annual Conference on New Trends in Information Communications Technology Applications (NTICT) (March 2017), pp. 86–90. https://doi.org/ 10.1109/NTICT.2017.7976154 2. M. Ajtai, Generating hard instances of lattice problems (extended abstract), in Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, STOC ’96 (ACM, New York, NY, USA, 1996), pp. 99–108. https://doi.org/10.1145/237814.237838 3. M. Alotaibi, D. Al-hendi, B. Alroithy, M. AlGhamdi, A. Gutub, Secure mobile computing authentication utilizing hash, cryptography and steganography combination. J. Inf. Security Cybercrimes Res. (JISCR) 2(1) (2019). https://doi.org/10.26735/16587790.2019.001 4. D.D. Bloisi, L. Iocchi, Image based steganography and cryptography, in VISAPP, vol. 1 (Citeseer, 2007), pp. 127–134 5. Z. Brakerski, V. Vaikuntanathan, Lattice-based fhe as secure as pke, in Proceedings of the 5th Conference on Innovations in Theoretical Computer Science, ITCS ’14 (ACM, New York, NY, USA, 2014), pp. 1–12. https://doi.org/10.1145/2554797.2554799 6. C.D. Budianto, A. Wicaksana, S. Hansun, Elliptic curve cryptography and lsb steganography for securing identity data, in R. Lee (ed.), Applied Computing and Information Technology (Springer International Publishing, Cham, 2019), pp. 111–127. https://doi.org/10.1007/9783-030-25217-5_9 7. S. Bukhari, M.S. Arif, M.R. Anjum, S. Dilbar, Enhancing security of images by steganography and cryptography techniques, in 2016 Sixth International Conference on Innovative Computing Technology (INTECH) (Aug 2016), pp. 531–534. https://doi.org/10.1109/INTECH.2016. 7845050 8. S. Chauhan, K.J. Jyotsna, A. Doegar, Multiple layer text security using variable block size cryptography and image steganography, in 2017 3rd International Conference on Computational Intelligence Communication Technology (CICT) (Feb 2017), pp. 1–7. https://doi.org/10. 1109/CIACT.2017.7977303 9. O. Goldreich, S. Goldwasser, S. Halevi, Collision-free hashing from lattice problems. IACR Cryptol. ePrint Archive 9 (1996) 10. O. Goldreich, S. Goldwasser, S. Halevi, Public-key cryptosystems from lattice reduction problems, in B.S. Kaliski (ed.), Advances in Cryptology—CRYPTO ’97 (Springer, Berlin, Heidelberg, 1997), pp. 112–131. https://doi.org/10.1007/BFb0052231 11. J. Hoffstein, J. Pipher, J.H. Silverman, Ntru: a ring-based public key cryptosystem, in J.P. Buhler (ed.), Algorithmic Number Theory (Springer, Berlin, Heidelberg, 1998), pp. 267–288. https://doi.org/10.1007/BFb0054868 12. K. Joshi, R. Yadav, A new lsb-s image steganography method blend with cryptography for secret communication, in 2015 Third International Conference on Image Information Processing (ICIIP) (Dec 2015), pp. 86–90. https://doi.org/10.1109/ICIIP.2015.7414745 13. S. Mittal, S. Arora, R. Jain, Pdata security using rsa encryption combined with image steganography, in 2016 1st India International Conference on Information Processing (IICIP) (Aug 2016), pp. 1–5. https://doi.org/10.1109/IICIP.2016.7975347 14. S. Narayana, G. Prasad, Two new approaches for secured image steganography using cryptographic techniques and type conversions. Signal Image Process. Int. J. (SIPIJ) 1(2) (2010). https://doi.org/10.5121/sipij.2010.1206 15. N. Patel, S. Meena, Lsb based image steganography using dynamic key cryptography, in 2016 International Conference on Emerging Trends in Communication Technologies (ETCT) (Nov 2016), pp. 1–5. https://doi.org/10.1109/ETCT.2016.7882955 16. R.S. Phadte, R. Dhanaraj, Enhanced blend of image steganography and cryptography, in 2017 International Conference on Computing Methodologies and Communication (ICCMC) (July 2017), pp. 230–235. https://doi.org/10.1109/ICCMC.2017.8282682

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17. E.H. Rachmawanto, R.S. Amin, D.R.I.M. Setiadi, C.A. Sari, A performance analysis stegocrypt algorithm based on lsb-aes 128 bit in various image size, in 2017 International Seminar on Application for Technology of Information and Communication (iSemantic) (Oct 2017), pp. 16–21. https://doi.org/10.1109/ISEMANTIC.2017.8251836 18. M.I.S. Reddy, A.S. Kumar, Secured data transmission using wavelet based steganography and cryptography by using aes algorithm. Proc. Comput. Sci. 85, 62–69 (2016). https://doi.org/10. 1016/j.procs.2016.05.177; International Conference on Computational Modelling and Security (CMS 2016) 19. A.K. Saxena, S. Sinha, P. Shukla, Design and development of image security technique by using cryptography and steganography: a combine approach. Int. J. Image Graph. Signal Process. 10(4), 13 (2018). https://doi.org/10.5815/ijigsp.2018.04.02

Cryptanalysis on Attribute-Based Encryption from Ring-Learning with Error (R-LWE) Tan Soo Fun and Azman Samsudin

Abstract The encouraging outcomes on the hardness of the lattice-based problem in resisting the recent quantum attacks have aroused the recent works of attribute-based encryption (ABE) schemes. In October 2014, Zhu et al. presented an increasingly proficient ABE with the hardness of ring-learning with error (R-LWE), so-called as ABER-LW E scheme. The ABER-LW E scheme is assured under the selective-set model, and it can be further achieved the shortest vector problem (SVP) in the worst case of ideal lattices problem. However, there is a noteworthy defect in the structure of the ABER-LW E scheme. The key generation algorithm that served as the core component of the ABER-LW E is defenseless against collusion attack. The contribution of this paper includes: (i) discusses the collusion attacks in the hardness of R-LWE problem. Subsequently, this paper demonstrates how the illegitimate users collude with other users by pooling their insufficient attributes together to recover a user’s private key and recover an encrypted message; (ii) suggests several alternatives to prevent such attacks in the ring-LWE lattice problem. Keywords Attribute-based encryption · Lattice · Ring-learning with error (R-LWE) · Collusion attacks

1 Introduction Attribute-based encryption (ABE) dominated as the holy grail of modern cryptography recently, promising an efficient tool to defeat the long-standing performance bottleneck issues of public key infrastructure (PKI). The ABE is able to control a finegrained access on ciphertext by holding attributes set as a public key and associating it T. S. Fun (B) Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia e-mail: [email protected] A. Samsudin School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_5

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either with encrypted data (so-called as ciphertext attribute-based encryption scheme, CP-ABE) or user’s private key (so-called as key-policy attribute-based encryption scheme, KP-ABE). In general, the structure of ABE design can be ordered into two primary streams: bilinear pairings on elliptic curves structure and lattice structure. Majority of former researches aimed to design the ABE scheme based on the bilinear pairings on elliptic curves structure either KP-ABE [1–4] or CP-ABE [5–11]. Recent researchers had proposed ABE scheme from the quantum cryptology aspect, which is based on the lattice approach [12–14]. The lattice problems are generally being conjectured to be able to withstand quantum attacks and the provably secure from the worst-case hardness problem under a quantum reduction [15–17] form a solid foundation to design a secure ABE scheme. While the vast majority of the post-quantum ABE schemes are designed with the hardness of learning with error (LWE) problem [15–17], Zhu et al. [14] extended the pairing-based fuzzy identity-based encryption (FIBE) [1] into lattice cryptography with the aim of enhancing the algorithm efficiency, further named it as ABER-LW E scheme. Compared to the former lattice-based ABE schemes which were constructed based on lattice groups, which are as yet experiencing the quadratic overhead issue in the hardness of LWE problem, the ABER-LW E scheme is designed based on the ideal lattices, as originally proposed by Lyubashevsky et al. [18]. The ideal lattice is a special group of lattices which is a generalization of cyclic lattice that benefits performance efficiency as contrasted to lattices [19, 20]. The hardness of ABER-LW E scheme is originated with the assumptions of R-LWE problem, which had been proven to be capable to achieve the lattice worst-case scenario of shortest vector problem (SVP) [18, 21]. However, the ABER-LW E scheme inherited some design flaw from FIBE, in which FIBE has been proven to be IND-FID-CCA insecure [12]. This chapter demonstrates that the ABER-LW E scheme is inadequately against the collusion attack and demonstrates that there is a possibility that multiple unauthorized users can affiliate and aggregate their private keys to recover the original message. The rest of this paper is structured as follows. Section 2 defines lattice foundations and collusion attack. Section 3 reviews algorithms of ABER-LW E scheme and performs cryptanalysis on ABER-LW E scheme. Lastly, Sect. 4 suggests alternative for improving ABER-LW E scheme.

2 Preliminaries This section discusses the foundations of collusion attacks, which are needed to demonstrate a cryptanalysis attack on the ABER-LW E scheme are presented. The definition of lattice, ideal lattice, decision R-LWEd ,q,χ problem [16, 18] and the reduction of R-LWE problem [16] to achieve the worst case of SVP in ideal lattice over the ring over R can be referred to previous research in [22, 23].

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Definition 1 (Threshold (t,n) Attribute-Based Encryption, Threshold ABE [1, 19]): A threshold (t,n) ABE scheme is a fourfold of probabilistic polynomial time (PPT) algorithms that consists of Setup, KeyGen, Encrypt, Decrypt such that: Setup (1λ , U ). With security parameter λ, and universe attributes set U , the function generates the ABE’s master key, MK and public key, PK. KeyGen (MK, PK, W, t). Given the MK, the PK, a set of attributes W and a threshold value t, the function outputs a private key DW for W . Encrypt (PK, W  , t, M ). Given the PK, attributes set W  , the threshold value t and a message M , the function will produce the ciphertext C. Decrypt (PK, C, W  , t, DW ). Given the public key PK, the C for W  , the threshold value t, and private key DW for W , the function will produce the message M if |W ∩ W  | ≥ t. In a basic threshold (t, n) ABE scheme, a message is encoded with n attributes of W , such that a user can recover the original message correctly with his private key that consists of at least threshold t of common attributes W as compared to the attributes set that embedded in ciphertext W  . Similar to other ABE schemes, the user’s private key in the ABER-LW E scheme is generated with the Shamir’s (t, n) threshold secret sharing scheme [25], which further describes as follows. Definition 2 (Shamir’s Threshold (t,n) Secret Sharing Scheme [19, 24], Shamir’s Threshold (t, n) SSS): In the ABER-LW E , Shamir’s threshold (t, n) SSS is constructed based on Lagrange polynomials interpolation over the ring Rq . The Lagrange coeffi cient Li,S for i ∈ Zq and a set of S elements in Zq is formulated as Li,S = j∈S,j=i y−j i−j . Setup. Given P = {P1 , P2 , . . . , Pn } is participants set. Define the threshold value, t and select secret value, SK ∈ Rq to be shared. Next, the dealer selects a polyno j mial with degree t − 1 and constructs d (y) = SK + t−1 j=1 dj y , where dj ∈ Rq and ∗ dj = 0. The dealer further selects n distinct elements yi of Zq , calculates secret share, si = d (yi ) ∈ Rq and lastly sends a pair of (yi , si ) to each participant, Pi . Pooling of Shares.Any t participants pool their secret shares with the Lagrange interpola can  . Let Y = {yi }ti=1 , then the secret SK can tion formula: d (y) = ti=1 si tj=1,j=i y−j i−j t  t −yj be obtained as d (0) = i=1 si j=1,j=i yi −yj = i∈Y si Li,Y (0) ∈ Rq . Definition 3 (Selective-Set Model [14]): The security of ABE scheme is assured if each of the probabilistic polynomial time (PPT) adversary has at most a negligible advantage in the following selective-set game. Init. The adversary A defines attributes set W ∗ that he wishes to be challenged upon. Setup. The challenger executes the algorithm Setup, subsequently delivers the public key PK to A. Phase 1. A issues the adaptive queries q1 , q2 , . . . , qm of private keys Dγj , for the attributes γj of his choice, with the chosen γj fulfills the condition of |γj ∩ W ∗ | < t for all γj , where j ∈ {1, 2, . . . , m}. Challenge. A indicates its preparation to receive a challenge and proposes a message to encode. The challenger encodes the message with a set of challenged attributes, W ∗ . The challenger scramblers a random binary coin, r. If r = 1, the encrypted message is delivered to A, otherwise, the challenger generates and returns a random encrypted element of the ciphertext space. Phase 2. Rerun the Phase 1. Guess. A returns a guess of r  of r. The adversary A’s advantages,  is formulated as ad v(A) = |Pr[r  = r] − 21 |.

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Next, we further define collusion attack in their defined selective-set model as follows. Definition 4 (Collusion Attack [21]): Collusion attack of the ABER-LW E scheme is formulated in the game of challenger and an adversary A as follows. Init. The adversary A defines attributes set W ∗ that he wishes to be challenged upon. Setup. The challenger executes the algorithm Setup and delivers the public key PK to A. Phase 1. A issues the adaptive queries q1 , q2 , . . . , qm for private keys Dγj of his choosen attributes γj , with the chosen fulfills the condition of |γj ∩ W ∗ | < t for all γj , where j ∈ {1, 2, . . . , m}. Challenge. A outputs a secret key DW ∗ by combining each of Dγj . Given the public key PK, the ciphertext C for attributes set W  , the threshold value t, A decrypts a message M by choosing an arbitrary t-element subset of DW ∗ such that |W ∗ ∩ W  | ≥ t and outputs a valid message M . Then, the adversary A conduct a collusion attack successfully on the scheme.

3 Cryptanalysis of ABER-LW E Scheme 3.1 Review of ABER-LW E Scheme The ABER-LW E scheme extends the FIBE scheme into lattice cryptosystem, which structured with the hardness of R-LWE problem. In ABER-LW E scheme, both user’s private key and encrypted message are attached with attributes set, and decryption works correctly if the attached attribute set in both encrypted message and user’s private key is overlapping with leastwise threshold value, t. As noticed, the access structure of the ABER-LW E scheme is fairly straightforward as contrasted to others pairing-based ABE scheme whose are able to handle the non-monotonic and monotonic access structure includes OR and AND gate. The ABER-LW E scheme consists of four algorithms as follows: Setup (1n , U ). With security parameter, n = 2λ (λ ∈ Z+ ), and a universe set of attributes U of size u, choose an adequately large prime modulus q = 1 mod (2n) and a small non-negative integer p (typically p = 2 or p = 3). Set f (x) = xd + 1 ∈ Z[x] and Rq = Zq [x]/ < f (x) >. With χ ⊂ Rq be an error distribution, choose a uniformly random a ← Rq and SK ← Rq . Select a random error term, e ← χ and define PK0 = (a · SK + pe) ∈ Rq . Next, select a uniformly random −1 for each attribute i ∈ u, where SKi−1 is the inverse SKi ∈ R× q together with SKi × of SKi ∈ Rq . Choose a random error term, ei ← χ where each i ∈ U and compute PKi = (a · SKi−1 + pei ) ∈ Rq for each attribute i ∈ U . Lastly, outputs: Public key: PK = (PK0 , {PKi }ui=1 ) Master key: MK = (a, SK, {SKi }ui=1 , {SKi−1 }ui=1 ) KeyGen (MK, PK, W, t). Given the MK, PK, W ⊆ U with size of w and t ≤ u. j Choose a polynomial d (y) = SK + t=1 i=1 dj y of degree t − 1 such that d (0) = SK where dj ← Rq is a random element in Rq .

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Set Di = (d (i) · SKi ) ∈ Rq for all i ∈ W . Generates the private key, DW = {Di }i∈w for W . Encrypt (PK, W  , t, M ). Given the PK, t, W  ⊆ U with size w  , such that |w  | ≥ t and M ∈ {0, 1}n ⊂ Rq .generate a uniformly random r ∈ Rq , error terms e0 and ei for each i ∈ W  from χ ⊂ Rq . Outputs the ciphertext, C = (C0 , {Ci }i∈w ) where: C0 = (PK0 · r + pe0 + M ) ∈ Rq Ci = (PKi · r + pei ) ∈ Rq for all i ∈ W  Decrypt(PK, C, W  , t, DW ). Given the PK, C for W  , t, and DW for W . If |W  ∩ W | < t, output ⊥; otherwisegenerate an arbitrary t-element subset of W  ∩ W , and calculate M  = (C0 − ti=1 Ci · Di · Li,wD (0)) ∈ Rq where Li,wD (0) is Lagrange coefficient, then outputs the plaintext M = M  mod p.

3.2 Collusion Attack on ABER-LW E Scheme Collusion resistance serves as a security necessity linchpins in structuring the ABE design. Generally, collusion resistance denotes that multiple illegitimate users, whose associated attributes are inadequately to satisfies the control access policy, shape an alliance and aggregate their attributes together to form a valid private key and decrypt message correctly. Zhu et al. [25] claimed that the ABER-LW E scheme is assured under the hardness of R-LWE assumptions in selective-set model. However, we found that ABER-LW E scheme cannot resist the collusion attack under selective-set model. Firstly, the encryption of each attribute separately as Ci = (PKi · r + pei ) ∈ Rq and straightforwardly embedded each of these encrypted attributes as separate component into ciphertext, leads them vulnerable and exposed to others to collect the valuable information such as the total of attributes included and attribute list that are needed to recover the message. Secondly, the user’s secret key, DW = {Di }i∈w , is generated directly by combining each of their associated attributes vector as Di = (d (i) · SKi ) ∈ Rq for all i ∈ w. Such construction leads the ABER-LW E scheme to easily fall prey to simple collusion attacks. Without using randomization technique, any unauthorized users can re-generate the private key by aggregating the component of other user’s private keys. Subsequently, based on the information obtained from ciphertext, Ci and public key PK, select t-element subset of W  ∩ W randomly, subsequently a message M can be recovered correctly. It should be noted that similar to FIBE scheme, the ABER-LW E scheme did inherent a randomization technique from Shamir’s secret sharing scheme by incorporating independently the chosen secret shares into each d (i). However, this does not insure that their ABER-LW E scheme is collusion resistance due to the problem as mentioned above. In the following section, we show that there exist a polynomial time adversary A who can perform collusion attack against the ABER-LW E scheme. Init. The adversary A defines a set of attributes W ∗ ⊆ U with size w ∗ and |w ∗ | ≥ t that he wishes to be challenged upon.

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For a simple example, let U = {att1 , att2 , . . . , attu } be a universe attributes set with a size of u = 50 and threshold value, t = 4. A defines attributes set, W ∗ = {att1 , att2 , . . . , attw∗ } with a size of w ∗ = 10. Setup. A receives the public key, PK = (PK0 , {PKi }ui=1 ) from the challenger that runs Setup algorithm from the ABER-LW E scheme. Phase 1. A selects his choice of attributes γj where |γj ∩ W ∗ | < t. Then, A sends adaptive queries q1 , q2 , ..., qm in order to obtain the private key Dγj based on his choice of attribute γj for all j ∈ {1, 2, . . . , m}. For example, A sends adaptive queries based on his declaration of W ∗ = {att1 , att2 , . . . , attw∗ } with a size w ∗ = 10 during the Init phase, as follows: • For query q1 , A selects his choice of attributes γ1 = {att1 , att3 , att4 , att12 , att23 } and receives private key, Dγ1 = {Di }i∈γ1 = {Datt1 , Datt3 , Datt4 , Datt12 , Datt23 }. • For query q2 , A selects his choice of attributes γ2 = {att2 , att5 , att7 , att28 , att34 , att49 } and receives private key, Dγ2 = {Di }i∈γ2 = {Datt2 , Datt5 , Datt7 , Datt28 , Datt34 , Datt49 }. • For query q3 , A selects his choice of attributes γ3 = {att6 , att8 , att24 , att33 , att47 } and receives private key, Dγ3 = {Di }i∈γ3 = {Datt6 , Datt8 , Datt24 , Datt33 , Datt47 }. • For query q4 , A selects his choice of attributes γ4 = {att9 , att10 , att18 , att21 , att29 } and receives private key, Dγ4 = {Di }i∈γ4 = {Datt9 , Datt10 , Datt18 , Datt21 , Datt29 }. Challenge. A firstly pools his obtained private keys,Dγ1 , Dγ2 , . . . , Dγm together, to re-construct his secret key, Dw∗ . Given the PK, C for W  , t, A recover a M ∗  ∗ by choosing an arbitrary t t-element subset of DW such that |W ∩ W | ≥ t and  compute M = (C0 − i=1 Ci · Di · Li,w∗ D (0)) ∈ Rq where Li,wD (0) is Lagrange coefficient, then outputs a valid message, M = M  mod p. For example, A re-construct his private key, DW ∗ , by pooling his obtained private keys,Dγ1 , Dγ2 , Dγ3 and Dγ4 . Then, DW ∗ = Datt1 , Datt2 , . . . , Datt10 , Datt12 , Datt18 , Datt21 , Datt23 , Datt24 , Datt28 , Datt29 , Datt33 , Datt34 , Datt47 , Datt49 }. The encrypted message is attached with attributes set, W  = {att2 , att3 , att6 , att8 , att9 , att11 , att27 , att39 , att55 } with a threshold value, t = 4. Given a ciphertext, C = (C0 , {Ci }i∈w ) where: C0 = (PK0 · r + pe0 + M ) ∈ Rq ] Ci = (PKi · r + pei ) ∈ Rq for all i ∈ w  ] Since |W ∗ ∩ W  | ≥ t, A is able to recover a message M by choosing an arbitrary t-element subset of DW ∗ . For instance, t-element subset of DW ∗ can be {Datt2 , Datt3 , Datt6 , Datt8 }, {D att3 , Datt6 , Datt8 , Datt9 }, {Datt2 , Datt3 , Datt8 , Datt9 }, etc. Then, A calculate M  = (C0 − ti=1 Ci · Di · Li,w∗ D (0)) ∈ Rq where Li,wD (0) is Lagrange coefficient, and recover a valid message, M = M  mod p. Thus, the adversary A successfully conducts a collusion attack on the ABER-LW E scheme.

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4 Conclusion Generally, collusion attack on ABE scheme can be insured by applying secret key randomization technique [5, 11, 23]. In secret key randomization technique, each user’s private key component is attached with a random secret value that uniquely corresponds to that particular users, such that without the knowledge of this random secret value the user is unable to separately use their key components to take part in collusion attack. On the other hand, some researchers [11, 22, 25] proposed a more stringent method to achieve the collusion resistance properties of ABE scheme by hiding the access policies or credentials either into ciphertext for CP-ABE or user’s private key for KP-ABE [22]. In the ABER-LW E scheme, publishing a list of attributes leads to privacy issues and disclose the attributes set that needed to decrypt the encrypted data makes them vulnerable to attack. The KeyGen algorithm of ABER-LW E scheme [14] can be further improved by ensuring that different polynomials are independently selected to generate the private keys of attibutes att1 , att2 , . . . , attw∗ . As a result, the attackers cannot collude to retrieve the private key DW ∗ . In conclusion, this chapter analyzes ABER-LW E scheme [14] which extends the FIBE scheme into a lattice post-quantum cryptographic scheme. First, existing works on ABE research were briefly reviewed. Then, a collusion attack for the ABER-LW E scheme under selective-set model is presented. Subsequently, this chapter demonstrates that the ABER-LW E scheme is vulnerable against the collusion attacks due to the design flaw inherited from FIBE scheme. Subsequently, alternatives to prevent such attack are suggested.

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Enhanced Password-Based Authentication Mechanism in Cloud Computing with Extended Honey Encryption (XHE): A Case Study on Diabetes Dataset Tan Soo Fun, Fatimah Ahmedy, Zhi Ming Foo, Suraya Alias, and Rayner Alfred Abstract The recent advancement on cloud technologies promises a cost-effective, scalable and easier maintenance data solution for individuals, government agencies and corporations. However, existing cloud security solutions that exclusively depend on conventional password-based authentication mechanism cannot productively defence to ongoing password guessing and cracking attacks. Recent HashCat attack would brute be able to compel any hashed eight-characters-length secret key that comprises of any blend of 95 characters in less than 2.5 h. Several trivial approaches such as two-factor authentication, grid-based authentication and biometric authentication mechanisms have been enforced recently as an additional or optional countermeasure of defending password guessing and cracking attacks. These approaches, be that as it may, can be frustrated with an ongoing malware assault that capable of intercepting One-Time Password (OTP) sent to the mobile device. These stolen passwords often do not trigger any alerts and can be subsequently exploited to access other users’ cloud accounts (e.g. 61% of the users are utilizing the single secret key repeatedly to access different online records). To address these problems, this research aimed to implement an eXtended Honey Encryption (XHE) scheme for improving the assurance of conventional password-based authentication mechanism in cloud computing. At the point when the attacker tries to retrieve the patient’s diabetes information by speculating password, rather than dismissing their record access as a customary security defence mechanisms, the proposed XHE outputs an indistinct counterfeit patient’s record that closely resembles the legitimate patients’ diabetes information in light of each off base speculation on legitimate password. Along these lines, the implemented XHE scheme solidifies the multifaceted nature of password speculating and cracking assaults, as assailant cannot distinguish which of his speculated passwords is correct password. Then, a security message will be T. S. Fun (B) · Z. M. Foo · S. Alias · R. Alfred Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Malaysia e-mail: [email protected] F. Ahmedy Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_6

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produced and delivered to alert the network administrator and security responses team. Furthermore, the potential implementation of the proposed XHE scheme can be aimed at improving the password-based authentication system in other networks, including but not limited to Internet of Things (IoT) and mobile computing. Keywords Cloud security · Password-Based authentication · Honey encryption

1 Introduction Generally, the principle of cloud security mechanism has adopted existing security models, which includes confidentiality, integrity, availability (CIA) model and authentication, authorization and accounting (AAA) model. The CIA model concerns on ensuring the confidential and privacy of data with encryption algorithms (e.g. RSA, AES, etc.), data integrity with hashing algorithms (e.g. MD5, SHA-2, PBKDF2, etc.), as well as the data availability with network defenses approaches (e.g. firewalls, intrusion detection system, etc.), whereas, the AAA model focuses on the access control context, which involves identifying a user with a pre-defined credentials (e.g. username, passport number, etc.), authenticating and verifying the user’s claimed identity (e.g. password, fingerprints, retina scans, etc.) and authorizing the access to cloud resources (e.g. data, server, etc.). The CIA of cloud computing can recently be achieved using the Transport Layer Security (TLS) and Secure Socket Layer (SSL) mechanism, while password-based authentication mechanism has become the de facto standard for ensuring AAA model in cloud computing due to its practicality and ease to use. The password-based authentication mechanism is a composition of identification (username) and authentication (secret words), also well-known as password-only authentication or single-factor authentication. The strengthens of password-based authentication mechanism, however, are relies on the hardness of computationally secure, with the assumptions that the best-known approach of cracking the algorithms require an unreasonably extensive of computing power and processing time [1–3]. With the advanced of recent computing power, progressive distributed processing and parallelism algorithms, the ordinary composition of username and password as the methods of authenticating and controlling the access of cloud account poses extensive security risks of being challenged and susceptible to password-based attacks includes hashed password rainbow attack, brute-force password guessing and birthday attack [4–6]. Recent password cracking studies demonstrated that any hashed eight-characters-length secret words that comprise of a combination of 95 characters could be cracked in 330 min with the speed of 0.35 trillion-guess-per second [7] in 2012 and subsequently reduced to 150 min in 2019 [8]. To address this problem, recent trivial approaches are to use submissive approach (such as forcing a user to select a stronger password, educate or increase user awareness regarding password-based attacks), implement two-factor or multi-factor authentication, grid-based authentication or biometric authentication mechanism.

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The two-factor authentication mechanism approaches, however, vulnerable to withstand recent malware attacks, include Man-In-The-Middle (MITM), Man-In-TheBrowser (MITB) attacks, SSL stripping attack. These attacks are capable of intercepting Transaction Authorization Code (TAC), or One-Time Password (OTP) sent to mobile device [9, 10]. The grid-based authentication system is a challenge-responsebased system, in which the OTP is formulated using a predetermined pattern derived from a graphical grid on displayed screen, however, can be thwarted with social engineering attack. On the other hand, the biometric authentication mechanisms used to identify users using fingerprints, facial recognition, iris or voice recognition, always require integration or additional hardware. Biometrics is permanently linked to the user, if compromised, cannot be replaced or reset. If a biometric is eventually compromised in one application, it will compromise basically all applications where the same biometric is used. Besides, these cracked passwords present broad security hazards in these existing mechanisms since assaults utilizing the cracked passwords always do not trigger any security alarms. This project aimed to implement the eXtend Honey Encryption (XHE) scheme [1, 3] to increase the complexity of authentication and access control mechanisms based on password in the cloud computing platform. Considering the recent healthcare data breaches are increasing by tripled, affected 15.08 million patients records recently as compared to 5.58 million patient records in 2017 [11], the implementation uses the healthcare Pima Indians diabetes dataset as the patient account data, subsequently used to construct the message space and seed space of XHE scheme. When the vicious assault tries to access the patient’s cloud account unauthorizedly with his/her speculating password, rather than dismissing their access as recent online account practices, the implemented XHE algorithm produces a vague fake patient record that is firmly identified with the authentic patient record, in which the assault could not decide if the speculated password is working accurately or not. Consequently, it solidifying the multifaceted nature of password speculating and cracking attacks. Along these lines, a security alert will be activated to notify the security response team on a regular basis.

2 EXtended Honey Encryption (XHE) Algorithm in Securing Password and Diabetes Data in Cloud Computing The Honey Encryption (HE) scheme is a probabilistic method that output a conceivable looking yet incorrect original message when assailant attempting to unscramble the ciphertext with an off base speculated password, in this manner hardening the complexity of password speculating process. The idea of Honey Encryption scheme was firstly presented by Juels and Ristenpart [12, 13, 16] in 2014 to enhance the security of credit card applications with additional defence layer. Tyagi et al. [14] and Huang et al. [15] subsequently adopted the HE scheme in protecting the text

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messaging and genomic library, respectively. Then, HE scheme has been improved against the assaults of message recovery. Most recently, Tan et al. [1, 3, 18] improving the HE scheme in protecting the file storage in cloud computing, subsequently named it as eXtended Honey Encryption (XHE) scheme. In this research, we further extended the works of Tan et al. [1, 3, 18] in hardening the complexity of passwordbased authentication mechanisms in cloud computing. The construction of XHE scheme [3] with the implementation of Pima Indians diabetes dataset as presented in the following: Plaintext Space (M 1 , M 2−i , . . . , M 2−n ). Also called as message space. With the message, M 1 is a hashed 64-characters-length-password that comprises of 95 alphanumeric character combination. With the aggregate of 9664 probabilities, the distribution over M 1 is indicated as ψm1 ; subsequently, the M 1 distribution sampling is identified as M1 ← ψm1 M1 . Then, let M2− i , . . . , M2−n is the patient’s diabetes data that consist of numerical characters with maximum lengths of four characters (e.g. the number of pregnancies with maximum length of two characters, the measurement of diastolic blood pressure in maximum length of three characters, etc.), the distribution over Mi−1 , . . . , Mn , is denoted as ψmi−1 . . . , ψmn and the sampling over the distribution Mi−1 , . . . , Mn , is defined as Mi−1 , . . . , Mn ← ψmi−1 . . . , ψmn . Seed Space (S1 , S2−i , . . . , S2−n ). Seed space consists of prefix seed, S 1 , and suffix seed, S2−i , . . . , S2−n , over the n-bit binary strings. Each message in M 1 and M 2 are mapped to a seed in S 1 and S2−i , . . . , S2−n respectively such that Σs1−∈S1 p(S1 ) = 1 and Σs2−i∈S2−i p(S2−i ) = 1, . . . , Σs2−n∈S2−n p(S2−n ) = 1. Distribution-Transforming Encoder (DTE). The algorithm of DTE composed of DTE_sub1 and DTE_sub2 functions, defined as below: DTE_1(encode_sub1, decode_sub1). The encode_sub1 function inputs the password of a patient, M 1 ∈M 1 and outputs array seed value of prefixes, s1 from seed space, S 1 . The deterministic decode_sub1 function inputs a message s1 ∈ S 1 and outputs the corresponding plaintext space M 1 ∈ M 1 . DTE_2(encode_sub2, decode_sub2). The encode_sub2 algorithm inputs a patient diabetes data, M2−i ∈ M2−n , . . . , M2−i ∈ M2−n and outputs a series of suffix seed value, s2−i, . . . , s2−n from seed space, S2−i , . . . , S2−n . The deterministic function of decode_sub2 inputs seed message s2−i ∈ S2−i . . . , s2−n ∈ S2−n and outputs a message M2− i , . . . , M2−n by running the binary search on the inverse sampling table and linear search on the message space to locate the original password and diabetes data. Inverse Sampling DTE (IS-DTE). The algorithm of IS-DTE composed of ISDTE_sub1 and IS-DTE_sub2 functions, defined as below: IS-DTE_1(IS-encode_sub1, IS-decode_sub1). The function of IS-encode_sub1 executes the Cumulative Distribution Function (CDF), F m1 , with the distribution of pre-defined plaintext, ψ m1 and M1 = {M1−1 , M1−2 , . . . , M1−|M| } . Denote F m1 (M 1−0 )= 0, then outputs M 1−i such that Fm1 (Mi−1 ) ≤ S1 < Fm1 (M1−i ), where S 1 ← $ [0, 1). Eventually, encodes the M 1−i input plaintext by choosing an

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    arbitrary value the Fm1 M(1−i)−1 , Fm1 (M1−i ) set uniformly. Let the function 1 of IS-decode_sub1 is the inverse of CDF, specified as IS-decode_sub1 = F − m1 (S 1 ). IS-DTE_2(IS-encode_sub2, IS-decode_sub2). The function of IS-encode_sub2 executes the Cumulative Distribution Function (CDF), Fm2 = Fm2−i , . . . , Fm2−n and M2−i = {M2−−i−1 , M2−−i−2 , . . . , M2−−i−|M| }, . . . , M2−n = {M2−−n−1 , M2−−n−2 , . . . , M2−−n−|M| } . Define F m2 (M 2−0 )= 0, and produces M 2−i such that Fm2 (Mi−1 ) ≤ S2 < Fm2 (M2−i ), where S 2 ← $ [0,1) and M 2−i choosing an arbiS2−i , . . . , S2−n . Lastly,  encodes the input message   trary value from Fm2 M(2−i)−1 , Fm2 (M2−i ) set uniformly. The function of 1 IS-decode_sub2 is the inverse of CDF, defined as IS-decode_sub2 = F − m2 (S 2 ). DTE-then-Cipher (HE [DTE, SE]). Also called as DTE-then-Encrypt. The algorithm of HE [DTE, SE] is a pair of HEnc function and HDec function. The HE ciphers a plaintext by executing the DTE function, and then re-ciphers the output of DTE by using determined symmetric encryption function as follows. HEnc (SK, M 1 , M 2 ). Let the H be a determined hashing function and n is arbitrary number of bits, with the symmetric key SK, a password M 1 , and its extension M2 = M2−i, . . . , M2−n , choose the arbitrary values uniformly, s1 ← $ encode(M 1 ), s2 ← $ encode(M 2 ) and R ← $ {0,1}n , then generates the output cipher messages, C 1 = H(R, SK) ⊕ s1 and C 2 = H(R, SK) ⊕ s2 . HDec (SK, C 1 , C 2 , R). By providing the R, SK, C 1 and C2 = C2−i , . . . , C2−n , calculates s1 = C 1 ⊕ H(R, SK) and s2 = C 2 ⊕ H(R, SK). Then, recovers the patient’s diabetes data, M 1 = decode (s1 ) and its extension, M 2 = decode(s2 ) by looking up the inverse sampling tables.

3 Implementation and Performance Analysis Three dominant symmetric encryption algorithms have been selected to execute the algorithm of HEnc (K, M 1 , M 2 ) and HDec (K, R, C 1 , C 2 ), includes Advanced Encryption Standard (AES), Blowfish and Tiny Encryption Algorithm (TEA). AES algorithm is developed in response to the obsolescence of DES and the processing burden of the TripleDES algorithm. Recently, AES has become widely adopted in several recent cryptosystems due to its efficient processing power and RAM requirements. Whereas, both Blowfish and TEA algorithms use a 64-bit block size as compared to AES 128-bit block size. The used dataset is Pima Indians diabetes dataset [17], that consists of female patient’s diabetes data, includes quantities of times pregnant, measurement of blood pressure in mm Hg and thickness of skin in mm, the reading of plasma glucose concentration for 120 min in the test of oral glucose tolerance, BMI, diabetes pedigree function, age and outcome. The implementation measurements were recorded on Intel® Core™ i5-5200U CPU @ 2.2 GHz, 4 GB RAM, running 64 bits, and the values are the mean of 100 measurements of the respective algorithms. The web-based application that aimed to stimulate the legitimate patient login

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and attacker password guessing attacks is developed with PHP 7.2.0 in CodeIgniter Framework. The MySQL database is used to store the diabetes patients record in a structured format and deployed in a cloud computing platform as in Fig. 1. As shown in Fig. 2, the symmetric TEA algorithm enjoys a higher efficiency on generating the encryption key, encrypting the diabetes data and decrypting them as benchmarked to AES and Blowfish due to its implementation simplicity. AES is constructed based on the substitution-permutation network, therefore performed better than Blowfish that obstacle with its inefficient of Feistel Structure. However, after assessing the security aspect, the TEA algorithm is subjected to related-key differential attack. Therefore, this research decided to use the AES algorithm to further implement the HEnc (K, M 1 , M 2 ) and HDec (K, R, C 1 , C 2 ) in XHE scheme in cloud computing. The implementation of XHE algorithm in securing the diabetes patients data is demonstrated in Figs. 3, 4 and 5. When the legitimate patient enters the valid username and password, the XHE algorithm generates the legitimate user account. However, whenever there is an invalid password entered, the XHE algorithm outputs

Fig. 1 The implementation of XHE algorithm in securing diabetes patient data in cloud computing

CPU Time (ns)

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Comparison of Symmetric Key Algorithms

Key Generation

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Symmetric Algorithms AES

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Fig. 2 Comparison of symmetric algorithms

Fig. 3 The web-based login interface to access the diabetes data in cloud storage

the indistinguishable counterfeit patient’s diabetes data and triggers an email sends to alert the legitimate user and security incident response team.

4 Conclusion This paper presents the implementation of XHE algorithm in securing the diabetes patient data in cloud computing. Compared to recent works that focus on summative approach or multi-factor and multi-level authentication, this research can solve the root problem of password guessing and cracking attack by hardening the complexity of password guessing. With the generation of indistinguishable counterfeit diabetes data closely related to the data of the legitimate patient in response to the invalid password, the attacker cannot differentiate and decide if the conjectured password

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Fig. 4 The sample of XHE scheme generated legitimate patient’s data when the user enters the valid username and password

is working accurately or not, as well as the accessed diabetes data are belonging to that particular patient or not. The XHE scheme can be developed further in the future to hardening the password-based authentication and authorization system in other platforms including, but not limited to, the Internet of Things (IoT) and mobile computing.

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Fig. 5 The sample of XHE scheme generated bogus patient’s data when the attacker enters invalid username and password

Acknowledgements This work was supported by Universiti Malaysia Sabah grant [SLB0159/2017]. The authors also thank the anonymous reviewers of this manuscript for their careful reviews and valuable comment.

References 1. S.F. Tan, A. Samsudin, Enhanced security for public cloud storage with honey encryption. Adv. Sci. Lett. 23(5), 4232–4235 (2017). https://doi.org/10.1166/asl.2017.8324 2. M. Jiaqing, H. Zhongwang, C. Hang, S. Wei, An efficient and provably secure anonymous user authentication and key agreement for mobile cloud computing. in Wireless Communications and Mobile Computing (2019), pp. 1–12. https://doi.org/10.1155/2019/4520685 3. S.F. Tan A. Samsudin, Enhanced security of internet banking authentication with EXtended honey encryption (XHE) scheme. in Innovative Computing, Optimization and Its Applications ed by I. Zelinka, P. Vasant, V. Duy, T. Dao. Studies in Computational Intelligence vol 741 (Springer, Cham, 2018) pp. 201–216. https://doi.org/10.1007/978-3-319-66984-7_12 4. W. Xiaoyun, F. Dengguo L, Xuejia Y. Hongbo, Collisions for hash functions MD4, MD5, HAVAL-128 and RIPEMD. Cryptology ePrint Archive Report 2004/199, 16 Aug 2004, revised 17 Aug 2004. http://merlot.usc.edu/csacf06/papers/Wang05a.pdf

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5. M. Stevens, New collision attacks on SHA-1 based on optimal joint local-collision analysis. in Advances in Cryptology – EUROCRYPT 2013 ed by T. Johansson, P.Q. Nguyen. EUROCRYPT 2013. Lecture Notes in Computer Science vol 7881 (Springer, Berlin, Heidelberg, 2013), pp. 245–261. https://doi.org/10.1007/978-3-642-38348-9_15 6. A. Leekha, A. Shaikh, Implementation and comparison of the functions of building blocks in SHA-2 family used in secured cloud applications, J. Dis. Mathe. Sci. Cryptograp. 22(2) (2019). https://doi.org/10.1080/09720529.2019.1582865 7. D. Goodin, 25-GPU Cluster Cracks Every Standard Windows Password in < 6 Hours. (Arc Techica, 2012) 8. N. Hart, HashCat Can Now Crack An Eight-Character Windows NTLM Password Hash In Under 2.5 Hours (Information Security Buzz, 2019) 9. A. Mallik, M. Ahsan, M.Z. Shahadat, J.C. Tsou, Understanding Man-in-the-middle-attack through Survey of Literature. Indonesian J. Comput. Eng. Design 1, 44–56 (2019) 10. V. Haupert, S. Gabert, How to attack PSD2 internet banking. in Proceeding of 23rd International Conference on Financial Cryptography and Data Security (2019) 11. Protenus 2019 Breach Barometer, 15 M + Patient Records Breached in 2018 as Hacking Incidents Continue to Climb (Protenus Inc and DataBreaches.net, 2019) 12. A. Juels, T. Ristenpart, Honey encryption: encryption beyond the brute-force barrier. IEEE Sec.Priv. IEEE Press New York 12(4), 59–62 (2014) 13. A. Juels, T. Ristenpart, Honey encryption: security beyond the brute-force bound. in Advances in Cryptology—EUROCRYPT 2014 ed by P.Q. Nguyen, E. Oswald. EUROCRYPT 2014. Lecture Notes in Computer Science vol 8441 (Springer, Berlin, Heidelberg, 2014), pp. 293–310. https:// doi.org/10.1007/978-3-642-55220-5_17 14. N. Tyagi, J. Wang, K. Wen, D. Zuo, Honey Encryption Applications, Computer and Network Security Massachusetts Institute of Technology. Available via MIT (2015). http://www.mit.edu/ ~ntyagi/papers/honey-encryption-cc.pdf Retrieved 15 July 2017 15. Z. Huang, E. Ayday, J. Fellay, J.-P. Hubuax, A. Juels, GenoGuard: Protecting Genomic Data Against Brute-Force Attacks, IEEE Symposium on Security and Privacy (IEEE Press, California, 2015), pp. 447–462 16. J. Joseph, T. Ristenpart, Q. Tang, Honey Encryption Beyond Message Recovery Security (IACR Cryptology ePrint Archive, 2016), pp. 1–28 17. Pima Indians diabetes dataset, UCI Machine Learning Repository. Access Feb 2018 18. M. Edwin, S.F. Tan, A. Samsudin, Implementing the honey encryption for securing public cloud data storage. in First EAI International Conference on Computer Science and Engineering (2016)

An Enhanced Wireless Presentation System for Large-Scale Content Distribution Khong-Neng Choong, Vethanayagam Chrishanton, and Shahnim Khalid Putri

Abstract Wireless presentation system (WPS) is a video capturing and streaming device which enables users to wirelessly cast content from user devices such as PC and smartphone onto a larger display device with screen mirroring technology. This technology is applicable to any collaborative environment which includes meeting rooms, conference rooms and even classrooms. However, it is not designed to serve large number of users such as in auditorium and lecture hall, particularly for distributing the projected content to hundreds of audiences. In this paper, we describe an enhanced WPS (eWPS) which extends the functionality of a WPS with a content sharing and distribution capability. Performance results showed that serving a page of presentation content took an average of 1.74 ms with an audience size of 125. Keywords Wireless presentation system · Enhanced WPS · Presentation management system · Content sharing · Content distribution

1 Introduction Wireless presentation system (WPS) [1–5] is becoming a common technology in meeting room to enable seamless presentation wirelessly from any devices, ranging from laptop, tablet to smartphone. It offers flexibility and mobility to presenters to present from any computing devices, and also to move around in the meeting room while conducting the presentation without worrying about the limited VGA/HDMI cable length as in the conventional presentation setup. However, existing WPS K.-N. Choong (B) · V. Chrishanton · S. K. Putri Wireless Innovation Lab, Corporate Technology, MIMOS Berhad Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia e-mail: [email protected] V. Chrishanton e-mail: [email protected] S. K. Putri e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_7

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Fig. 1 An overview of wireless presentation system

focuses mainly on providing flexibility and functionality to the presenter, while ignoring potential needs of serving audience. A typical wireless presentation system is shown in Fig. 1. The wireless presentation box provides Wi-Fi connectivity which allows presenter device to connect and mirror its screen content to a display device such as overhead projector or TV. The WPS box can also be enabled to distribute the presentation content currently being projected to the audience with the use of webview clients. The webview client is basically web browser which connects and reads updated projected content from the WPS. The limitation of existing WPS is its inability to serve large number of audiences. WPS systems found in the market today typically state their capacity of concurrently connecting 32 or 64 client devices, but do not explicitly mentioned its maximum supported webview client size. This means it would be a challenge to employ existing WPS in an auditorium or lecture hall scenario for distributing content to an audience size of 150 and above. In this paper, we describe an improved version of WPS, with an emphasis on the mechanism used to distributing content to larger audience size along with performance measurement. Our enhanced WPS (eWPS) runs on top of a Wi-Fi infrastructure which consists of multiple access points and network switch connected over a wired gigabit Ethernet backhaul. It utilizes an external web server which receives periodical screenshots from the eWPS and subsequently makes them accessible by the audience web devices. As opposed to conducting simulation of large-scale Wi-Fibased system as in [6], we approach the challenges with prototype implementation over a testbed as in [7, 8] for practical performance studies.

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This paper is organized as follows. Related work is discussed in Sect. 2. Section 3 describes the experimental setups and the test configurations/flow. Section 4 explains the performance results. Summary and conclusion are found in Sect. 5.

2 Related Works A diversity of products and solutions can be found in the literature offering basic features of a WPS. Some of these products even offer content sharing/distribution. However, the number of supported client devices is below 64. Based on our latest review, none of these products is designed to support larger scale of audience size. Table 1 summarizes the features of some prominent WPS found in the market and their differences. Basic mirroring refers to the ability of a user device to capture and stream screen/desktop activity to the connected display. Basic webviewing means the system is capable of distributing screenshots captured at the presenter/user device. Advanced webviewing goes beyond basic webviewing, by further allowing users to navigate across all past/presented content. As an example, users who are late to the session are now able to flip back to previously presented slides/content and later flip forward to the current/latest screenshot content. Wireless projector [1] is the most common and widely used product in the market. Even though it is a self-contained device, it has many shortcomings. It is not a straightforward process for connecting laptops to the wireless projectors as each brand comes with its own configuration and execution steps. In addition, they support only basic mirroring and lacking of broadcasting presentation content to audience. Chromecast [2], Barco ClickShare [3] and DisplayCast [4] are standalone hardware appliance/systems which support only basic mirroring without any content sharing capability. The wePresent system [5] is the few in the market which provides basic webviewing capability. There is no product which supports advanced webviewing as proposed in this paper. Table 1 Features comparison of WPS products or solutions against the proposed eWPS

Basic mirroring

Basic webviewing

Advanced webviewing

Yes

No

No

Chromecast [2] Yes

No

No

Barco ClickShare [3]

Yes

No

No

DisplayCast [4]

Yes

No

No

Wireless projector [1]

wePresent [5]

Yes

Yes

No

eWPS

Yes

Yes

Yes

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The use of IP multicast on multimedia applications such as live presentation is increasingly popular in both corporate and university/campus networks. It is therefore a potential mechanism to be explored in the design of eWPS. However, multicast over Wi-Fi is restricted with various challenges such as low data rate, high packet loss and unfairness against competing unicast traffic [9]. Moreover, not all Wi-Fi access points support multicasting [9]. For the end client devices, customized application will be needed to subscribe and receive these multicast packets. Our design is to allow any user device which has a browser to work, without the need of any specific application installation. Hence, in this paper, we utilized unicast instead of multicast in our design and implementation of the current system.

3 Experimental Setup 3.1 Setup of Network Connectivity and Server The eWPS system is designed with scalability in mind to ensure continuous support for growing audience size. As shown in Fig. 2, the eWPS system is therefore divided into two parts, namely the connectivity infrastructure section and the presentation management section. The connectivity infrastructure section focuses mainly on managing the networking aspect, from providing Wi-Fi accessibility, load balancing across various Wi-Fi access points (AP) controlled by the access controller (AC), to

Fig. 2 Network architecture of eWPS consists of two parts, namely the connectivity infrastructure and the presentation management section

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Fig. 3 Layout of the auditorium

enabling Internet connectivity. There is no favor of any specific brand in our design, as long as the chosen Wi-Fi access points are matched to their designated access controller of the same brand/model. The presentation management section manages the content capturing from the source device, to store the content at the distribution server (HTTP server) for accessibility by the end client devices over the connectivity infrastructure described earlier. All the above devices, which include APs, AC and HTTP server, are connected via a 1000 Mbps Ethernet switch in a local area network (LAN) setup. Figure 3 shows the layout of the auditorium measured 54 × 40 where our experiment has been conducted. Commercially available network equipment from Huawei was used to set up the connectivity infrastructure, which includes one unit of access controller (AC) and six units of access points (APs) [4]. Two APs were located at the front of the auditorium, with the remaining four installed on the same row at the back of the auditorium. All six APs have been configured to share a common SSID named “Smart_Presentation” over channels 1, 6, 52, 56, 60, 64, 149 and 157 which are non-overlapped. Six channels operate on 5 GHz radio band, leaving only channels 1 and 6 sitting on 2.4 GHz. The reason to allocate 2.4 GHz serving band was to serve older phones which may not have 5 GHz radio support. The AC is used to perform load balancing/distribution, by directing different Wi-Fi stations (STAs), which are the end client devices, to associate to designated channels based on current radio condition.

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The distribution server runs on an Intel i5 micro-PC with 4 GB RAM and 500 GB SATA hard disk. We have chosen such form factors and configuration for its lightweight size to ease portability.

3.2 Test Event and Metrics A test run has been conducted on an actual event, which lasts about 4 h from 8:30 a.m. The event was conducted as a workshop which introduces the use of a new mobile app with general explanation followed by tutorial sessions. The flow of the workshop is as follows: 8:30 a.m.–10:45 a.m.: Welcoming speech 10:45 a.m.–12:15 p.m.: PowerPoint presentation, app demonstration. There were 15 and 52 pages of slides for the above session 1 and 2. Data from various monitoring sources were gathered for studying. Both the radio status and user devices were extracted from the AC at fixed intervals for the entire session. Radio status list provides detailed information on the wireless conditions and status of all connected APs, which include the link type, both uplink and downlink rate, number of sent/received packets, retransmission rate and packet loss ratio, etc. The glances [10] system tool was set up to run on the HTTP server to access/monitor its resource utilization which includes CPU and memory. The HTTP log of the distribution server was also used for studying the server performance, total number of accesses and server resource utilization.

4 Results and Analysis There were 125 client devices connected to the given Wi-Fi infrastructure during the event. The radio list recorded that 70 devices, which were 56% of the total, have been connected to more than one AP. This could be the effect of AC performing load balancing among the APs. It could also be caused by certain Wi-Fi stations which left the venue and later re-connect back to the SSID at a later time. Figure 4 shows the allocation of stations by radio band. By referring to the flow of the workshop in Sect. 3.2, the actual slide presentation was started around 10:45 a.m., which corresponds to the sudden spike in Fig. 4. It can be observed that post 10:45 a.m., there were roughly 20 + % stations associated with the 2.4 GHz frequency band leaving 70 + % connected to 5 GHz frequency band. It fit in quite nicely to our frequency band allocation as described in Sect. 3.1 where 2 out of 8 frequency bands (25%) are at 2.4 GHz. Figure 5 shows the allocation of stations by specific frequency channel. It can be observed that the distribution for 2.4 GHz is of an average difference of about five stations. Station distribution on 5 GHz has a much bigger variance with channel

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Fig. 4 Allocation of Wi-Fi stations by radio band

Fig. 5 Allocation of Wi-Fi stations by both 2.4 and 5 GHz frequency channels

149 utilized by more 25 stations, while channel 60 was utilized by fewer than five stations. This could be due to the seating location of the users, who could be closer to the AP configured to operate at channel 149. Figure 6 shows the average downlink rate of the stations by frequency band. The average downlink rate for both 2.4 and 5 GHz was 50 and 150 Mbps, respectively. Again this downlink rate fit nicely to the initial configuration, where 5 GHz was used to handle most of the traffic load. Figure 7 shows both the total number of slide accesses and downloads. An instant spike in the number of webview clients was observed around 10:45 a.m., which was the starting time scheduled for the second event. The sudden surge on slide accesses

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Fig. 6 Average downlink rate by frequency band

Fig. 7 Total number of content access for both direct access and downloads

corresponded to the sudden network load as seen in Figs. 4 and 5. The spike was mainly caused by the sudden connectivity requests from the audience immediately after the proposed system has been introduced in the session. A total of 9273 slides accesses were recorded for the entire session. Dividing this by the total session time of 1.5 h (session 2) showed us that about 1.71 HTTP requests/sec were made on average. There were no failed HTTP requests or responses throughout the session. A total of 563 downloads were recorded. Again, all these requests were successfully received and responded by our distribution HTTP server.

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Figure 8 shows a sudden increase and gradual decrease on the webview clients, with a maximum of 93 clients recorded. Given a total number of slide accesses at 9273, there was an average of 99 slide accesses per webview clients. Given that the total pages of slides for the entire session were 67, it shows that the eWPS has even captured and shared the animated details on certain slide pages, which made up of the remaining 32 slide accesses. Figure 9 shows the system resource utilizations of the distribution server as recorded with the glances tool. The first peak of utilization (at 20%) which occurred at the start of the session should be ignored because it was mainly caused by the loading of the glances software itself. The second peak, which was around 12.5%, would be a correct representation of the actual CPU utilization of serving HTTP

Fig. 8 Total number of Webclients connected for content access

Fig. 9 System resource utilization of the distribution server

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requests. This corresponded to the time of 10:45 a.m. where there was a surge on the number of webview clients. As for the memory utilization, it was maintained below 5% throughout the session. The chart in general shows that both the CPU and memory were very much idle throughout the session. Based on such utilization, we foresee the system is capable of serving six times more the workload as experienced in this current setup. The mean access time (delay) for webview client was 1.74 ms for a maximum of 125 stations. This means the setup is capable of serving 1000 stations with an estimated average access time of 13.92 ms. Such good performance was due to a few reasons. First, the backbone of the setup is on a 1000 Mbps Ethernet. Second is the use of six Wi-Fi APs which are overly sufficient for the client size. Further, the workload and traffic of these six APs were centrally managed by AC. Lastly, the size of the content (i.e., size of screenshot images range from 300 to 500 KB) has been optimized for distribution over network.

5 Conclusion In this paper, an enhanced wireless presentation system which focuses on largescale content distribution has been presented. This system has made use of Wi-Fi APs as the access infrastructure for end user devices/stations to request and receive presentation screenshots captured from the live presentation device. Results from our experiments showed that the system was capable of supporting workload of an actual event with good performance. Moving forward, we planned to investigate the optimum number of APs which are needed to deliver content within an acceptable range of delay intervals. Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interests. Ethical Approval This chapter does not contain any studies with human participants or animals performed by any of the authors. Informed Consent “Informed consent was obtained from all individual participants included in the study.”

References 1. Wireless Projector, https://www.epson.com.my/Projectors/Epson-EB-1785W-WirelessWXGA-3LCD-Projector/p/V11H793052 2. Chromecast, https://store.google.com/product/chromecast_2015?srp=/product/chromecast 3. Barco Clickshare, https://www.barco.com/en/clickshare

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4. S. Chandra, L.A. Rowe, Displaycast: a high performance screen sharing system for intranets. in 20th ACM international conference on Multimedia (2012) 5. wePresent, https://www.barco.com/en/page/wepresent 6. M. Nekovee, R.S. Saksena, Simulations of large-scale WiFi-based wireless networks: Interdisciplinary challenges and applications. Fut. Gener. Comput. Syst. 26(3), 514–520 (2010) 7. Y. Yiakoumis, M. Bansal, A. Covington, J.V. Reijendam, S. Katti, N. Mckeown, BeHop: A testbed for dense Wifi networks. ACM Sigmobile Mobile Comput. Commun. Rev. 18(3), 71–80 (2014) 8. C. Adjih, E. Baccelli, E. Fleury, G. Harter, N. Mitton, T. Noel, R. Pissard-Gibollet, F. SaintMarcel, G. Schreiner, J. Vandaele, T. Watteyne, FIT IoT-LAB: A large scale open experimental IoT testbed. in IEEE 2nd World Forum on Internet of Things (WF-IoT) (Milan, 2015) 9. P. Pace, G. Aloi, WEVCast: Wireless eavesdropping video casting architecture to overcome standard multicast transmission in Wi-Fi networks. J. Telecommun. Syst. 52(4), 2287–2297 (2013) 10. Glances—An eye on your system, https://github.com/nicolargo/glances

On Confidentiality, Integrity, Authenticity, and Freshness (CIAF) in WSN Shafiqul Abidin, Vikas Rao Vadi, and Ankur Rana

Abstract A Wireless sensor network (WSN) comprises several sensor nodes such as magnetic, thermal, and infrared, and the radar is set up in a particular geographical area. The primary aim of sensor network is to transmit reliable, secure data from one node to another node, node to base station and vice versa and from base station to all nodes in a network and to conserve the energy of sensor nodes. On the other hand, there are several restrictions such as large energy consumption, limited storage/memory and processing ability, higher latency, and insufficient resources. The related security issues in wireless sensor network are authenticity, confidentiality, robustness, integrity, and data freshness. The sensor nodes are susceptible to several attacks such as DOS, Sybil, flood, black hole, selective forwarding which results in the leakage of sensitive and valuable information. It is therefore necessary to provide security against these critical attacks in the network. Wireless sensor network were earlier used for military applications with the objective of monitoring friendly and opposing forces, battlefield surveillance, detection of attacks, but today Wireless Networking have a huge number of applications-environmental, healthcare, home, industrial, commercial and are still counting. This paper is an extensive review of the security requirements, attacks that are to be avoided and resolved for achieving a secure network connection. This paper also emphasizes various limitations and defense strategies to prevent threats and attacks. The issues of applications of wireless sensor network for smooth and reliable transmissions are also discussed. The sensor networks are popular for mission-critical-tasks and security is immensely required for such hostile environment employed networks. S. Abidin (B) HMR Institute of Technology & Management, (GGSIPU), New Delhi, Delhi, India e-mail: [email protected] V. R. Vadi Bosco Technical Training Society, New Delhi, India e-mail: [email protected] A. Rana Quantum University, Roorkee, Uttrakhand, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_8

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Keywords Cryptography · Data confidentiality · Sybil · Data authentication · Black hole attack · Attacks on WSN

1 Introduction During the past years, wireless sensor network has found a huge number of applications in various fields that can have a significant impact on the society. Sensor network may consist of many different types of sensor nodes such as magnetic, thermal, infrared, and radar which are able to monitor a wide number of environmental conditions, possessing either unique or different features, designated with some predetermined functions and functionalities. A sensor network consists of a large number of tiny sensor nodes and their number in a network may range from few, to hundreds, to thousands even more and possibly a few powerful nodes called base stations. These nodes are capable of communicating with each other by means of single or multiple channels. Multiple channel-based communications are preferred over single channel due to reliable communication, less collisions or interferences, greater bandwidth, and improved network throughput. Base stations are responsible for aggregating the data, monitoring, and network management [1]. Base stations act as a gateway between the sensor nodes and the base stations. The WSN also includes task manager, network manager, security manager communication hardware and compatible software as its related components. The principle working mechanism of WSN is that the sensor nodes senses, accumulates data from varied locations in the environment, co-operates among themselves, processes the information and then transmits the data to the main location that is base station. Some important parameters need to be considered while designing a network: end-to-end latency [2] (should decrease), energy consumption (should be less), packet delivery ratio (should be high), and throughput (should increase). These factors play a significant role in determining the efficiency of the network. Routing in another fundamental aspect of sensor network, determining the path between source and destination, and leads to secure transmission of data packets and can also help optimize application availability, improves productivity and responsiveness. The need for secure transmission resulted in the development of different routing protocols and algorithms [3]. The routing protocols are categorized into four broad categories based on network structure, based on protocol operation, based on path establishment, and based on the initiator of communication. The wireless sensor networks are considered application-specific. There are huge applications of wireless sensor networks including sensor-based alarming/fire system, smart homes and farming/harvesting, structural health monitoring, habitat monitoring, civil engineering, surveillance and emergency response systems, environmental research, disaster management, robotics, traffic monitoring, transportation and logistics, military, tracking, industrial and consumer process, monitoring and control applications [4]. The wide range of applications of the sensor network

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requires each sensor to be highly diminished in terms of size, power consumption, and price. One of the primary concerns of WSN is that transmitted information should be safe and secure [5]. No one should be allowed to access network and information. It is possible that a third party after receiving data packets may create mess and nuisance with the original packets if there is no secure mechanism has been implemented or imposed to protect data between nodes. To implement this, it is mandatory to explore challenges in design, intelligent applications with minimum memory and shortage of computing power. Further, tough radio transmission bandwidth is also needed. In short, smart security mechanisms are required to protect, detect, and pull through severe attacks. Levels of implementing these mechanisms may differ according to the nature of attacks. The transmitted data need to be confidential, authentic, private, robust, and sustainable and should also maintain the forward and backward secrecy [6]. WSN security is somewhat different from local area networks and wide area networks because sensor nodes equipped with computer power, battery, wireless, ad hoc, Unattended. Therefore, due to these limitations as well as constraints in WSNs a large number of protocols rely on symmetric-key cryptography. Key management (use of global, pair wise, and symmetric keys), encryption, secure data aggregations are some of the commonly employed security solutions for wireless sensor networks. There are different techniques that are used to prolong the lifetime of a sensor network [7]. The security requirements for WSN are: confidentiality, integrity, availability, and freshness [20]. Additional requirements may include authentication, access control, privacy, authorization, non-repudiation, and survivability. The paper has been narrated in the following manner: Sect. 2 highlights constraints and limitations, whereas Sect. 3 emphasizes upon the concerned security issues, requirements and the need of providing security in wireless sensor networks, Sect. 4 discusses the attacks/threats on WSN and also the defensive measures required to counter those attacks, Sect. 5 finally provides the conclusion, which briefly summarizes the paper.

2 Constraints in Wireless Sensor Network In this section, limitations and constraints on WSN have been discussed that are differentiated from a large number of interconnected sensor nodes and the sensor field. WSN is non-resistant to attacks where an adversary may alter original data, inject corrupt data, capture data, floods a particular node with same message repeatedly and perform other such restricted activities, which often results in data loss, misrouting, delays, destroy the network resources and the nodes are at a risk of physically being damaged. Malicious node can be added to exhaust other sensor node capability affecting the whole environment. Large energy consumption, limited storage/memory and processing ability, higher latency, lesser reliability, scalability

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and fault-tolerance, risky operating environment, poor data aggregation [8], slow data processing speed and its optimization, computation and communication capabilities are some of the constraints in the network. Energy is a very important factor in a WSN. Energy determines the life of a node. It is the most important criteria which distinguish a normal node from an attacker node. Therefore, based on the different energy levels, attackers, cluster heads, and base station are selected. There is energy loss when a packet is being sent or received. The reasons for energy loss in wireless sensor network communication [9].

2.1 Control Packet Overhead Control packet exhausts more energy in comparison with ordinary packets while transmission, receiving, and listening, thereby it is useful to use less numbers of control packet for transmission, thus it reduces the overhead.

2.2 Collision Basically, collision permits two or more stations try to exchange and transmit the data concurrently. When two stations transmit data concurrently, collision may likely to occur and the packets are junked and retransmitted and thereby resulting in energy loss.

2.3 Idle Listening Idle listening refers to, when sensor listen for incoming packet even when no data is being sent. This results in energy loss of the node and depleting the lifetime of wireless sensor network.

2.4 Overhearing Overhearing: It is an indirect communication method where an agent receives packet which is not an addressee which results in unnecessary traffic which in turn results in energy loss. Maximum Lifetime (Max-life) routing to balance the rate of energy consumption in different sensor nodes according to varying characteristics, energy-efficient routing algorithm (EEMR) to improve the energy utilization by varying the activities of

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wireless communication modules of sensor network are some of the methodologies implemented for energy concerned issues [10].

3 Concerned Security Issues It is very important to know and understand the security issues in WSN, prior to the knowledge of attacking techniques and the ways to prevent different attacks, so that the counter-attack strategies may be developed more efficiently. Typical WSN consists of many nodes which have been assigned a particular energy level and malfunction of any node can cause damage to the whole working environment thereby decreasing the lifespan of that network. Some of the basic security issues in WSN are confidentiality, integrity, authenticity, which have been discussed below.

3.1 Data Authentication Authenticity is a process by which one verifies that someone is who they claim they are and not someone else. Authentication is to check the authenticity. In WSN, authentication is a process by which the node makes sure that weather the incoming data is from correct source or has been injected by any intruder or any unknown surrounding network [11]. Data authentication puts on ice the acceptor that the data packet has not been altered during transmission. Authentication also makes sure that the data packets have been transmitted from exact source. Node authenticity tells user about genuineness of individuality of sender node. If authentication is not present, intruder without any obstacle can inject wrong data in the environment. Generally, to remove this problem of data authenticity public-key cryptography is used. Public key or asymmetric cryptography assigns same key to all the nodes in a particular network, so whenever a data is to be transmitted the sender node will only transmit the data if the receiving node has that public key and thus it verifies the authenticity of the node.

3.2 Data Confidentiality Confidentiality means making something confidential. Sensor node transmits hypersensitive information. If an attacker node views data between the two nodes, the confidentiality between the two nodes is broken. So, it is very important to keep in mind that any intruder cannot have access to hypersensitive data by intercepting the transmission between the two particular nodes. The most basic approach to attain data confidentiality is to provide a particular key and this process of assigning a particular key is called encryption. The data is assigned a particular key, encrypted

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with that key then it is transmitted and again decrypted at the receiving node with the help of that key, so even if the attacker views the data it cannot process it as that node does not have that particular key [12].

3.3 Robustness and Sustainability If a node is attacked by an attacker node and the attacker node replaces the normal node, the working of the whole environment will be affected as now the attacker node will be having the particulars of a particular node in that environment. The sensor network should be robust against several intruder attacks, even if an intruder attack succeeds its encounter should be knocked down or decreased. System should be constructed so that it can tolerate and adapt to failure of a particular node. Each particular node should be designed to be as robust as possible.

3.4 Data Integrity Integrity should be attained to verify that any intruder or unknown node cannot alter or modify the transmitted data. Data integrity ensures that the data being transmitted has not been altered during the transmission time and reaches the acceptor node in its original form. Data authentication may also be related to data integrity as data authentication provides data integrity.

3.5 Data Freshness Data freshness signifies that no old data should be transmitted between the nodes which have already transmitted the same message, i.e., data should be fresh. Each node in WSN is assigned with a particular energy level. Energy is spent whenever a node sends, accepts, or processes a data. In a particular wireless sensor network, there is a possibility that an intruder or an attacker can catch hold of the transmitted data and retransmit the copy of old transmitted data again and again to the nodes in a particular environment thereby decreasing the residual energy level of the node and gradually the node will get destroyed due to insufficient energy. An attacker can send expired packets to the whole network environment, wasting the network resources and decreasing the lifetime of the network system. Data freshness is achieved by using nonce or a timestamp can be included with each data.

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4 Attacks on WSN The WSNs attract a number of severe attacks due to the unfriendly environment, insecure wireless communication channels, energy restrictions, and various other networking circumstances. The malicious node or the attacker node when injected into the network could spread among all the neighboring nodes, potentially destroying the network, disrupting the services and taking over the control of entire network. The attacks on in WSN are generally classified into two broad categories as passive attacks-against data confidentiality and active attacks-against data confidentiality and data integrity. Furthermore, some of the important attacks are discussed in the section below.

4.1 Denial-of-Service Attack This attack attempts to demolish or anesthetize all the network’s resources available to its destined users either by the consumption of scarce or limited resources, alteration of configuration information, or physical destruction of the network components. The two general forms of the attacks are: the ones which crash the services and the others which flood the services. The most important attacking technique is IP address spoofing with the purpose of masking or suppressing the identity of sender or mimicking the identity of another node. It results in unusually slow, interrupted network performance and the inability of the sensor node to perform its designated (proposed) functions [13].

4.2 Sybil Attack This includes large-scale, distributed, decentralized, and several other types of protocols in WSN are primarily susceptible to such attacks. Earlier known as pseudo spoofing, a particular sensor node unjustifiably claims multiple identities and resides at multiple places in the networking environment. The Sybil attack has three orthogonal extending dimensions: direct versus indirect communication, fabricated versus stolen identities and simultaneity. Such type of attacks necessitates a one-to-one correspondence between the sensor nodes and their individual identities. Sybil attack can be used to initiate the attack on several types of protocols in WSN such as distributed protocols, data aggregation, misbehavior detection, voting, fair resource allocation protocols, and the routing protocols [14, 15].

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Fig. 1 Black hole attack

4.3 Black hole Attack The attack occurs when a compromised node appears to be more attractive in comparison with the surrounding nodes with respect to the residual energy level and the routing algorithm used, resulting in a black hole and the assemblage is known as the black hole region. The accumulated traffic that is the incoming and outgoing traffic is diverted to this region and the data packets are unable to reach to their expected destination. It also provides an opportunity for analyzing, detecting the aberrant activities and thereby applying specially designed security analysis tools to the traffic [16] (Fig. 1).

4.4 Selective Forwarding Selective forwarding is also known as drop-data packet attack. Any mischievous node behaving as normal nodes receives and selectively analyzes different data packets. These nodes allow only few packets to pass from one node to another but on the other hand make excuses and deny to forward certain data packets, suppresses, or modifies them and eventually drops them thereby increasing the congestion and lowering the performance of network system. Such attacks happen either on the data flow or on the controlling packets and are often used in combination with the wormhole/sinkhole attacks [17].

4.5 Wormhole Attack One of the severe attacks in wireless sensor networks specifically against the locationbased wireless security system since these attacks do not require compromising of any sensor nodes and could be initiated even if the system maintains authenticity, confidentiality, integrity, non-repudiation of the data transferred. The attacker records the data packets (messages/bits) at a particular individual location in the network and

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Fig. 2 Wormhole attack

traverses them to another location in the network via an established link (wired or wireless). The data packets can therefore be retransmitted selectively. In a way, the attacker simply fakes a route and destabilizes and interrupts the routing within the network [18] (Fig. 2).

4.6 Hello Flood Attack This is one of the simplest and easiest denial-of-service (DOS) attacks. The attacker is not an authorized node, broadcasts HELLO messages to all its neighboring authorized nodes, or simply bombards a targeted node with forged requests, successful or failure connection messages, with great transmission power. A guaranteed response is expected from the receiving node which assumes the sender node to be within its normal radio frequency range. However, these attacker nodes (sender nodes) are far away from frequency range of the network. The number of packets per second increases, processing of individual nodes decreases and the system or the network gets flooded with tremendously large amount of traffic [19, 21] (Fig. 3; Table 1).

5 Conclusions The sensor networks are popular for mission-critical-tasks, and security is immensely required for such hostile environment employed networks. Wireless sensor network is an interesting, fast emerging field in the modern scenario and provides great exposure for experimentation in the area of research and development. Through this paper, we have highlighted the four basic security issues in WSN like confidentiality, integrity, robustness, and authenticity. Various constraints to the network specifically focusing on the energy constraint and some applications of sensor network have also been talked about. The major types of attacks such as denial of service, black hole, wormhole, hello flood attack, Sybil and selective forwarding, and their defense strategy have been discussed briefly.

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Fig. 3 Hello flood attack

Table 1 Attacks on layers and defensive measures Layers

Attacks

Defensive measures

Physical layer

Jamming, Tampering

Region mapping, spread-spectrum, priority messages, duty cycle, channel hopping Encryption, tamper proofing, hiding

Data Link layer Collision, Resource exhaustion Unfairness Network layer

Error-correcting codes, time diversity Limited rate transmission Small frames

Selective Forwarding Multi-path routing, monitoring Spoofed routing information, Upstream and downstream detection Sinkhole, wormhole, Sybil, hello flood Egress filtering, authentication, monitoring Redundancy checking Monitoring Authentication, probing, transmission time-based mechanism, geographical and temporal packet leashes, graphical, topological and connectivity-based approaches Trusted certification, key validation, position verification, resource testing, authentication, redundancy Verification of the bi-directionality link, signal strengthen detection, identity verification

Transport layer Flooding, De-synchronization

Client puzzles, rate limitation Authentication

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References 1. G. Lu, B. Krishnamachari, C.S. Raghavendra, An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks. in IEEE IPDPS (2004) 2. B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wireless Commun. 1(4), 660–670 (2002) 3. S. Abidin, Key agreement protocols and digital signature algorithm. Int. J. Curr. Adv. Res. 6(8), 5359–5362 (2017) 4. N. Burri, P. von Rickenbach, R. Wattenhofer, Dozer: ultra-low power data gathering in sensor networks. in ACM/IEEE IPSN (2007) 5. Z. Jiang, J. Ma, W. Lou, J. Wu, A straightforward path routing in wireless ad hoc sensor networks. in IEEE International Conference on Distributed Computing Systems Workshops (2009), pp. 103–108 6. S. Bashyal, G.K. Venayagamoorthy, Collaborative Routing Algorithm for Wireless Sensor Network Longevity (IEEE, 2007) 7. J. Burrell, T. Brooke, R. Beckwith, Vineyard computing: sensor networks in agricultural production. Pervas. Comput. IEEE 3(1), 38–45 (2004) 8. S. Kim, S. Pakzad, D. Culler, J. Demmel, G. Fenves, S. Glaser, M. Turon, Health Monitoring of Civil Infrastructures using Wireless Sensor Networks. in ACM/IEEE IPSN (2007) 9. M. Ceriotti, L. Mottola, G.P. Picco, A.L. Murphy, S. Guna, M. Corra, M. Pozzi, D. Zonta, P. Zanon, Monitoring heritage buildings with wireless sensor networks: the torre aquila deployment. in ACM/IEEE IPSN (2009) 10. K. Lorincz, D. Malan, T.R.F. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G. Mainland, S. Moulton, M. Welsh, Sensor networks for emergency response: challenges and opportunities. IEEE Pervas. Comput. Special Issue. Pervas. Comput. First Resp. (2004) 11. S. Abidin, A novel construction of secure RFID authentication protocol. Int. J. Sec. Comput. Sci. J. Malaysia 8(8), 33–36 (2014) 12. N.M. Durrani, N. Kafi, J. Shamsi, W. Haider, A.M. Abbsi, Secure Multi-hop Routing Protocols in Wireless Sensor Networks: Requirements, Challenges and Solutions (IEEE, 2013) 13. V. Bulbenkiene, S. Jakovlev, G. Mumgaudis, G. Priotkas, Energy loss model in Wireless Sensor Networks. in IEEE Digital Information Processing and communication (ICDIPC), 2012 Second International conference (2012), pp. 36–38 14. J.H. Chang, L. Tassiulas, Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans. Netw. 12(4), 609–619 (2004) 15. M. Zhang, S. Wang, C. Liu, H. Feng, An Novel Energy-Efficient Minimum Routing Algorithm (EEMR) in Wireless Sensor Networks (IEEE, 2008) 16. M. Saraogi, SecurityiIn Wireless Sensor Networks (University of Tennessee, Knoxville) 17. A. Jain, K. Kant, M.R. Tripathy, Security solutions for wireless sensor networks. in 2012, Second International Conference on Advanced Computing & Communication Technologies 18. C. Karlof, D. Wanger, Secure routing in wireless sensor network: attacks and countermeasures. in First IEEE International Workshop on Network Protocols and Applications (2013), pp. 113– 127 19. M. Ahuja, S. Abidin, Performance analysis of vehicular ad-hoc network. Int. J. Comput. Appl. USA 151(7) 28–30 (2016) 20. N. Kumar, A. Mathuria, Improved Write Access Control and Stronger Freshness Guarantee to Outsourced Data. (ICDCN, 2017) 21. W. Feng, J. Liu, Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Commun. Surv. Tutor. (2011)

Networking Analysis and Performance Comparison of Kubernetes CNI Plugins Ritik Kumar and Munesh Chandra Trivedi

Abstract Containerisation, in recent world, has proved to be a better aspect to deploy large-scale applications in comparison with virtualisation. Containers provide a small and compact environment, containing all libraries and dependencies, to run an application. It has also come to acknowledgement that application deployment on a multi-node cluster has proved to be more efficient in terms of cost, maintenance and fault tolerance in comparison with single-server application deployment. Kubernetes play a vital role in container orchestration, deployment, configuration, scalability and load balancing. Kubernetes networking enable container-based virtualisation. In this paper, we have discussed the Kubernetes networking in detail and have tried to give a in-depth view of the communication that takes place internally. We have tried to provide a detailed analysis of all the aspects of Kubernetes networking including pods, deployment, services, ingress, network plugins and multi-host communication. We have also tried to provide in detail comparison of various container network interface(CNI) plugins. We have also compared the results of benchmark tests conducted on various network plugins keeping performance under consideration (Ducastel, Benchmark results of Kubernetes network plugins (CNI) over 10 Gbit/s network [1]). Keywords Networking analysis · Performance comparison · Kubernetes · CNI · Docker · Plugins · Containers · Pod · Virtualisation · Cluster

R. Kumar (B) · M. C. Trivedi Department of Computer Science and Engineering, National Institute of Technology, Agartala, Agartala, India e-mail: [email protected] M. C. Trivedi e-mail: [email protected] URL: https://bit.ly/2Zz4pB2 © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_9

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1 Introduction With increasing traffic to applications online scalability, load balancing, fault tolerance, configuration, rolling updates and maintenance remain an area of concern. Traditional method of solving these issue makes virtualisation come into play. Creating virtual machines requires hardware resources, processing power and memory same as an operating system would require. Requiring huge amount of resources limits the number of virtual machines that can be created. Moreover, the overhead of running virtual machines remains an area of deep concern. Although virtual machines are still widely used in the Infrastructure as a Service (IaaS) space, we see Linux containers dominating the platform as a Service (PaaS) landscape [2]. Containerisation comes to the rescue considering such huge disadvantages of virtualisation. In a container-based virtualisation, we create small compact environment comprising of all the libraries and dependencies to run the application. Various research studies have shown that containers incur negligible overhead and have performance at par with native deployment [3] and take much less space [4]. In real-world scenario, a large-scale application deployment as a single service is not feasible rather it should is fragmented into small microservices and deployed in a multi-node cluster inside containers using container orchestration tools such as Kubernetes and docker swarm. Kubernetes is most portable, configurable and modular and is widely supported across different public clouds like Amazon AWS, IBM Cloud, Microsoft Azure, and Google Cloud [5]. Deployment on multi-node cluster not only reduces the overhead but also make the deployment and maintenance economically viable.For instance, large-scale applications such as eBay and pokemon go are deployed on Kubernetes. Containers can be launched in a few seconds of duration and can be shortlived. Google Search launches about 7,000 containers every second [6] and a survey of 8 million container usage shows that 27% of containers have life span less than 5 min and 11% less than 1 min [7]. Container networking becomes very important when containers are created and destroyed so often [8]. Systems like Kubernetes are designed to provide all the primitives necessary for microservice architectures, and for these the network is vital [9]. Using Kubernetes, we can create a strongly bind cluster with one master node and multiple slave nodes. In this chapter, we have discussed in detail about all the aspects of Kubernetes networking, various third-party plugins that kubernetes support, container to container communication, container to host communication and host-to-host communication. At the same time, we have compared the available CNI plugins on various parameters to provide an in-depth analysis Sect. 4.

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Fig. 1 Pod network

2 Components of Kubernetes Networking 2.1 Pod Network In Kubernetes networking, pod is the most fundamental unit similar to what atom is to matter or a cell is to human body. A pod is a small environment consisting of one or more containers, an infrastructure (Pause) container and volumes. The idea of pod has been included in Kubernetes to reduce the overhead when two containers need to communicate very frequently. The infrastructure container is created to hold the network and inter-process communication namespaces shared by all the containers in the pod [10]. Inside a pod all the containers communicate through a shared network stack. Figure 1 depicts veth0 as a shared network stack between two containers inside a pod. Both the containers are addressable with IP 172.19.0.2 from outside the cluster. As both the containers have the same IP address, they are differentiated by the ports they listen to. Apart from this, each pod has a unique IP using which is referenced by other pods in cluster and data packets are transmitted and received. The shared network stack veth0 is connected to the eth0 via the cbr0 bridge (a custom bridge network) for communication with other nodes in the cluster. An overall address space is assigned for bridges in each node and then each bridge is assigned an address within this space depending on the node. The concept of custom bridge network is adopted instead of choosing docker0 bridge network to avoid IP address conflict between bridges in different nodes or namespaces. Whenever a packet is transmitted, it goes via the router or external gateway which is connected to each of the nodes in cluster. Figure 2 shows two nodes connected via the router or external gateway. When a pod sends a data packet to a pod in different node in the network, it goes via the router. On receiving a data packet at the gateway, routing rules are applied. The rules specify how data packets are destined for each bridge and how they should be routed. This combination of virtual network interface,

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Fig. 2 Pod network

bridges and routing rules is known as overlay network. In Kubernetes, it is known as a pod network as they enable pods on different nodes to communicate with each other.

2.2 Service Networking The pod networking is robust but not durable as the pods are not long lasting. The pods IP address cannot be taken as an endpoint for sending packets in the cluster as the IP changes everytime a new pod is created. Kubernetes solves this problem by creating services. A service is a collection of pods. It acts as a reverse-proxy load balancer [11]. The service contains a list of pods to which it redirects the client requests. Kubernetes also provides additional tools to check for health of pods. Service also enables load balancing, i.e. the external traffic is equally distributed among all the pods. There are four types of services in Kubernetes ClusterIP, NodePort, LoadBalancer and ExternalName. Similar to the pod network, the service network is also virtual [12]. Each service in Kubernetes has an virtual IP address. Kubernetes also provides an internal cluster DNS which maps service IP address to service name. The service IP is reachable from anywhere inside the cluster. Each node in cluster has a kube-proxy running on it. The kube-proxy is a network proxy that passes traffic between the

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client and the servers. It redirects client requests to server. The kube-proxy supports three modes of proxy, i.e. userspace, IPTables and IPVS. Now, whenever a packet is sent from client to server it passes the bridge at the client node and then moves to the router. The kube-proxy running on the nodes determines the path in which packet is to be routed. The packet is then forwarded to its destination. The IPTables rules are applied and the packet is redirected to its destination. Thus, this makes up the service network. Service proxy system is durable and provides effective routing to packets in the network. The kube-proxy runs on the each node and is restarted whenever it fails. The service network ensures a durable and secures means of transmission of data packets within the cluster.

2.3 Ingress and Egress The pod network and service network are complete within itself for communication between the pods inside the cluster. However, the services cannot be discovered by the external traffic. In order to access the services using HTTP or HTTPS requests, ingress controller is used. The traffic routing is controlled by the rules defined by the ingress resource. Figure 3 depicts how external requests are directed to the services via ingress.

Fig. 3 Ingress

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Egress network helps pods communicate to outside world. Egress network helps to establish communication path between the pods in the network to other services outside the cluster on the top of network policies.

3 Kubernetes Architecture A Kubernetes cluster comprises of a master node and one or more worker nodes. The master node runs the kube-api server, kube-control manager, kube-scheduler and etcd [11]. The kube-api server acts as an interface between the user and kubernetes engine. It creates, reads, update and delete pods in the cluster. It also provides a shared frontend through which all the components interact. The control manager watches the state of the cluster and shift the state to the desired state by maintaining the desired number of pods in the cluster. The scheduler is responsible scheduling pods to be most appropriate nodes in the cluster. The etcd is key-value database which stores configuration data of the cluster. It also represents the state of the cluster. Each worker node has a kubelet running on it. Kubelet is a Kubernetes agent running on each node which is responsible for communication between the master and the worker nodes. The kubelet executes commands received from the master node on the worker nodes.

4 Container Network Interfaces (CNI) Kubernetes offers a lot of flexibility in terms of kind of networking we want to establish. Instead of providing its own networking, Kubernetes allow us to create a plugin-based generic approach for networking of containers. CNI consists of a specification and libraries for writing plugins to configure network interfaces in Linux containers, along with a number of supported plugins [13]. The CNI is responsible for IP address assignment in the cluster and providing routes for communication inside the cluster. CNI is capable of addressing containers IP addresses without resorting to network address translation. The various netwrok plugins built on top of CNI which can be used with Kubernetes are Flannel, Calico, Weavenet, Contiv, Cilium, Kube-router and Romana. The detailed comparison of the plugins is shown in below [14–20].

Networking Analysis and Performance Comparison of Kubernetes CNI Plugins Flannel

Calico

WeaveNet

Contiv

Cilium

Language IP Version Network Policy Encryption

Go IPV4 None None

Go IPV4 IPV6 Ingress Egress None

Go IPV4 Ingress Egress NaCl crypto Libraries

Go IPV4 IPV6 Ingress Egress None

Network model Layer 2 Encapsulation Layer 3 routing

Layer 2

Layer 3

Layer 2

VXLAN



VXLAN

Layer 2 Layer 3 VXLAN

Go IPV4 IPV6 Ingress Egress Encryption with IPSec tunnels Layer 2

IP tables

IP tables Kube-proxy

IPTables Kube-proxy

Layer 3 Encapsulation Layer 4 route distribution Load balancing Database



IPIP



Platforms

Kube Router Go IPV4 Ingress

105 Romana

None

Bash IPV4 Ingress Egress None

Layer 3

Layer 3

VXLAN Geneve





IP tables

BPF Kubeproxy

IP tables

Sleeve

VLAN



BGP



BGP



IP tables IPVS IP sets IPVS/ LVS DR mode, GRE/IPIP BGP

BGP, OSPF



Available

Available

Available

Available

Available

Available

K8s ETCD

Storing state in K8s API datastore Windows linux

Storage pods

K8s ETCD

K8s ETCD

K8s ETCD

K8s ETCD

Linux

Linux

Linux

Linux

Windows Linux

Linux

in



5 Table Showing Comparison of CNI Plugins Conclusions from the above table 1. All plugins use ingress and egress network policies except Flannel. 2. Only Weavenet and Cilium offer encryption, thus providing security while transmission. The encryption and decryption increase the transmission time for packets. 3. Weavenet and Contiv offers both Layer 2 as well as Layer 3 encapsulation. 4. Romana does not offer any encapsulation. 5. Flannel, WeaveNet and Cilium is not supported by any Layer 4 route. 6. All network plugins have inbuilt load balancer except flannel.

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6 Performance Comparison The main aim of all CNI plugins is to achieve container abstraction for container clustering tools. Availability of so many network plugins makes it difficult to choose one considering advantages and disadvantages. In this section, we have compared the CNI plugins, using benchmark results obtained on testing network plugins on Supermicro bare-metal servers connected through a supermicro 10 Gbit switch, keeping performance under consideration [1]. Testing Setup 1. The test was performed by Alexis Ducastel on three supermicro bare-metal servers connected through a supermicro 10 Gbit switch [1]. 2. The Kubernetes (v1.14) cluster was set up on Ubuntu 18.04 LTS using docker 18.09.2. 3. The test compares Flannel (v0.11.0), Calico (v3.6), Canal (Flannel for networking + Calico for firewall) (v3.6), Cilium (v1.4.2), Kube-Router (v0.2.5) and Weavenet (v2.5.1). 4. The tests were conducted on configuring server and switch with jumbo frames activated (by setting MTU to 9000). 5. For each parameter, the tests were conducted thrice and the average of the three tests were noted.

6.1 Testing Parameters 1. The performance was tested on the basis of bandwidth offered by the plugins to different networking protocols such as TCP, UDP, HTTP, FTP and SCP. 2. The plugins were also compared on basis of the resource consumption such as RAM and CPU.

6.2 Test Results 6.2.1

Bandwidth Test for Different Network Protocol

In this test, the different network plugins were tested on the basis of various network protocol such as TCP, UDP, HTTP, FTP and SCP. The bandwidth offered by the plugins to each networking protocol was recorded. The table below summarises the result of the tests. All the test results are recorded at custom MTU of 9000.

Networking Analysis and Performance Comparison of Kubernetes CNI Plugins Network Plugins Flannel Calico Canal WeaveNet Cilium Kube router Cilium encrypted WeaveNet encrypted

TCP 9814 9838 9812 9803 9678 9762 815 1320

UDP 9772 9830 9810 9681 9662 9892 402 417

HTTP 9010 9110 9211 7920 9131 9375 247 351

FTP 9295 8976 9069 8121 8771 9376 747 1196

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SCP 1653 1613 1612 1591 1634 1681 522 705

Note: The bandwidth is measured in Mbit/s (Higher the better). Conclusions from bandwidth tests 1. For transmission control protocol (TCP) calico and flannel stand out as best cnis. 2. For user datagram protocol (UDP) kube-router and calico offered higher bandwidth. 3. For hyper text transfer protocol (HTTP) kube-router and canal offered higher bandwidth. 4. For file transfer protocol (FTP) and secure copy protocol (SCP) kube-router and flannel offered higher bandwidth. 5. Among weavenet and cilium encrypted, weavenet offered higher bandwidth for all of the network protocols. Network plugins

Bare metal (No plugins only K8s)

Flannel

Calico

Canal

Cilium

WeaveNet

Kube router

Cilium encrypted

WeaveNet encrypted

RAM CPU usage

374 40

427 57

441 59

443 58

781 111

501 89

420 57

765 125

495 92

Note: The RAM (without cache) and CPU usage are measured in percentage (Lower the better). Conclusions from resource utilisation tests 1. Kube Router and Flannel show a very good performance in resource utilisation consuming minimum percentage among all network plugins in terms of CPU and RAM. 2. Cilium consumes a lot of memory and processing power in comparison with other network plugins. 3. For encrypted network, plugins weavenet offers better performance in comparison with cilium.

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7 Conclusion The pod, services, ingress and CNIs collectively make the Kubernetes networking durable and robust. The Kubernetes architecture (including the pod, services, kube-apiserver, kube-scheduler, kube-control manager and etcd) manages the entire communication that takes place inside the cluster. The option of having CNI enables us to choose from a variety of plugins as per the requirement. From the comparison of the various plugins, none stood out efficent in all the parameters. The following conclusions can be drawn from the comparison results: 1. Flannel is easy to set up, auto-detects MTU, offers (on average) a good bandwidth for transmitting packets under all the protocols and has very less CPU and RAM utilisation. However, flannel does not support network policies and does not provide a load balancer. 2. Calico and kube-router on average offers a good bandwidth for transmitting packets, supports network policies, provides a load balancer and have a low resource utilisation. At the same time, both the networks do not auto-detect MTU. 3. Among the encrypted networks weavenet stand out better than cilium in most of the aspects including bandwidth offered to networking protocols and resource utilisation (CPU and RAM).

References 1. A. Ducastel, Benchmark results of Kubernetes network plugins (CNI) over 10 Gbit/s network. Available at https://itnext.io/benchmark-results-of-kubernetes-network-plugins-cniover-10gbit-s-network-updated-april-2019-4a9886efe9c4. Accessed on Aug 2019 2. L.H. Ivan Melia, Linux containers: why they’re in your future and what has to happen first, White paper. Available at http://www.cisco.com/c/dam/en/us/solutions/collateral/datacenter-virtualization/openstack-at-cisco/linux-containers-white-paper-cisco-red-hat.pdf. Downloaded in Aug 2019 3. R.R.W. Felter, A. Ferreira, J. Rubio, An updated performance comparison of virtual machines and linux containers, in Published at the International Symposium on Performance Analysis of Systems and Software (ISPASS) (IEEE, Philadelphia, PA, 2015), pp. 171–172 4. A.R.R.R. Dua, D. Kakadia, Virtualization vs containerization to support paas, in Published in the proceedings of IEEE IC2E (2014) 5. W.F. Cong Xu, K. Rajamani, NBWGuard: realizing network QoS for Kubernetes, in Published in proceeding Middleware ’18 Proceedings of the 19th International Middleware Conference Industry 6. A. Vasudeva, Containers: the future of virtualization & SDDC. Approved by SNIA Tutorial Storage Networking Industry Association (2015). Available at https://goo.gl/Mb3yFq. Downloaded in Aug 2019 7. K. McGuire, The Truth about Docker Container Lifecycles. Available at https://goo.gl/Wcj894. Downloaded in Aug 2019 8. Official Docker Documentation. Available at https://docs.docker.com/network/. Accessed on Aug 2019 9. R.J. Victor Marmol, T. Hockin, Networking in containers and container clusters. Published in Proceedings of netdev 0.1 (Ottawa, Canada, 2015). Downloaded in Aug 2019

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10. A. Kanso, H. Huang, A. Gherbi, Can linux containers clustering solutions offer high availability. Published by semantics scholar (2016) 11. Official Kubernetes Documentation Available at https://kubernetes.io/docs/concepts/. Accessed on Aug 2019 12. M. Hausenblas, Container Networking from Docker to Kubernetes 13. Container Networking Interface. Available at https://github.com/containernetworking/cni. Accessed on Aug 2019 14. Flannel. Available at https://github.com/coreos/flannel. Accessed on Aug 2019 15. Calico. Available at https://docs.projectcalico.org/v3.8/introduction/. Accessed on Aug 2019 16. WeaveNet. Available at https://www.weave.works/docs/net/latest/overview/. Accessed on Aug 2019 17. Contiv. Available at https://contiv.io/documents/networking/. Accessed on Aug 2019 18. Cilium. Available at https://cilium.readthedocs.io/en/v1.2/intro/. Accessed on Aug 2019 19. Kube Router. Available at https://github.com/cloudnativelabs/kube-router. Accessed on Aug 2019 20. Romana. Available at https://github.com/romana/romana. Accessed on Aug 2019

Classifying Time-Bound Hierarchical Key Assignment Schemes Vikas Rao Vadi, Naveen Kumar, and Shafiqul Abidin

Abstract A time-bound hierarchical key assignment scheme (TBHKAS) ensures time-restricted access to the hierarchical data. There is a large number of such schemes are proposed in the literature. Crampton et al. studied the existing hierarchical key assignment schemes in detail and classify them into generic schemes on the basis of storage and computational requirements. Such generic classification helps the implementers and researchers working in this area in identifying a suitable scheme for their work. This work studies the TBHKAS and classifies them with the same spirit. As best of our knowledge, the proposed classification captures every existing TBHKAS in the literature. Furthermore, the proposed schemes are compared and analyzed in detail. Keywords Hierarchical access control · Key management · Time-bound

1 Introduction A hierarchical access control system defines an access control in the hierarchy for controlled information flow with the help of a key called access key. A hierarchy is composed of security classes with partially ordered relation between them. A security class consists of a number of users and resources sharing some common security primitives. Let there is m number of security classes in the hierarchy say C1 , C2 ,…, Cm , having a partially ordered relationship between them denoted by V. R. Vadi TIPS, New Delhi, India e-mail: [email protected] N. Kumar (B) IIIT Vadodara, Gandhinagar, India e-mail: [email protected] S. Abidin HMR Institute of Technology & Management, (GGSIPU), New Delhi, Delhi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_10

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binary relation ‘’. Ci  C j denotes that the class Ci is at lower security level than class C j and users of class C j are authorized to access resources of class Ci and its descendants. There are other applications which require time-bound access control in the hierarchy like,first: digital pay-TV system, where the service provider organizes the channels into several possible subscription packages for the users and a user is authorized to access his subscription package for a fixed amount of time, second: electronic journal subscription system, where the user can subscribe to any combination of available journals for a specified amount of time, third: electronic newspaper subscription system where a user can access news online for a specified period of time, etc. In a Time-Bound Hierarchical Key Assignment Scheme (TBHKAS), total system time T is divided into z distinct time slots named t0 , t1 , . . . , tz−1 . Each time slot tx , 0 ≤ x ≤ z is of same size, for example, an hour or one month. The system time T is long enough so that it will not constrained the system. In a TBHKAS, each user associated with a class can access the own class resources and the resources associated with the descendant class for an authorized interval of time such as ta to tb , t0 ≤ ta ≤ tb ≤ tz−1 . Tzeng [1] in 2002 proposed first TBHKAS, that was based on RSA. A central authority (CA) is assumed in the system. CA assigns a secret information I (i, ta , t2 ) securely to each user at the time of their registration and is authorize to access the resources and data of their descendant classes between its specified time interval ta to tb . Class Ci ’s data is encrypted with a key ki,t for time slot t. To decrypt the data of an authorize class C j and for a authorize time slot t, ta ≤ t ≤ tb , the user needs to compute its decryption key k j,t using its secret information I (i, ta , tb ) and public data. A user associated with class Ci is authorized to access class C j ’s resources using encryption key ki,t , between time interval ti to t j (0 ≤ ti ≤ t j ≤ z), C j  Ci . Once an encryption key expires, the user will not able to access any resource with the help of expired key. Bertino et al. [2] immediately adopted the Tzeng’s scheme and showed that the scheme is readily applicable to broadcasting XML documents securely on the Internet. However, Yi and Ye [3] found collusion attack on the Tzeng’s [1] scheme. The scheme shows that three users can conspire to access some class encryption keys which they should not authorize to access. An another scheme was proposed by Tzeng [4] based on anonymous authentication scheme which provides an authentication mechanism so that the user’s identity is unknown while retrieving data from a website or a large database system through Internet. The system uses the TBHKAS to control the authorization. Yeh [5] propose another RSA-based TBHKAS with similar key derivation cost as in Tzeng’s scheme [1]. They claim that the scheme is collusion secure. However, later in 2006 Ateniese et al. [6] identified two-party collusion attack on the scheme which was even spreaded over a later scheme proposed by Yeh [7]. An alternative scheme was proposed by Wang and Laih [8] over Akl-Taylor [9] scheme. It uses the idea of merging keys where multiple keys can be combined (known

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as aggregate key) corresponding to a class users. The computational requirement for generating and deriving a key from an aggregate key requires only one modular exponentiation. Later Yeh and Shyam [10] scheme has improved on the Wang Laih scheme and has theoretically shown polynomial improvement in both memory and performance requirements. Shyam [11] does the performance analysis of the two given schemes and show that the scheme in [10] does better as compared to the Wang Laih scheme [8]. Another RSA-based time-bound key assignment scheme was proposed by Huang and Chang [12]. It uses distinct key for each time period so that if a user is revoked from a class, all communication should be immediately stopped. The objective is to reduce any possible damage over an insecure channel. The algorithms for key generation and derivation are simple. Dynamic operations such as class addition and deletion are given. The scheme improves public storage cost as compare to the Tzeng’s [1] scheme. However, later two-party collusion attack is given by Tang and Mitchell [13] on the scheme. A new time-bound hierarchical key assignment scheme was proposed by Chien [14] based on low-cost tamper-proof devices, where even the owner cannot access the protected data on the tamper-resistant device. In contrast of using Tzeng [1] style, a tamper-proof device performing mainly low-cost one-way hash functions. Compare with Tzeng’s [1] scheme and without public-key cryptography, Chien’s scheme reduces computation cost and implementation cost. The scheme uses indirect key derivation with independent keys. Yi [15] in 2005, show that this scheme is insecure against collusion attack whereby three users conspire to access some secret class keys that they should not know according to Chien’s scheme, but in a manner not as sophisticated as in case of against Tzeng’s scheme. In 2006, Alfredo et al. [16] give few solutions to the issues found in Chien et al.’s [14] scheme. Bertino et al. [17] in 2008 propose another secure time-bound HKAS for secure broadcasting based on a tamper-resistant device. The security of this scheme relies on hardness of discrete logarithm problem on Elliptic-curve over the finite field, that require more computation cost as compare to Chien’s [14] scheme. Sun et al. [18] recently in 2009 showed that scheme [17] is more vulnerable than the Chien’s [14] scheme aside from increased computation power. Also, they suggest a solution to the Chien’s scheme avoiding inefficient Elliptic-Curve cryptography.

1.1 Motivation Researchers have done a lot of work on Time-Bound Hierarchical Access Control (TBHAC) after Tzeng’s [1] work, however majority of the schemes either have design issues, takes significant computation cost or are not practical to implement. Researchers are still trying to find practical and cost-effective solutions to real problems. To move forward toward the effective solution to the problem, we required a classify existing TBHKAS’s and their analysis that will help researchers working in this field, to move in the right direction. Crampton et al. [19] in 2006 classify

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the existing non-TBHKAS’s from the literature and give a high-level comparison between them. Later, Kumar et al. [20] extend their classification to include indirect key derivation schemes with dependent keys. In this paper, we are trying to classify TBHKAS’s from the literature and give a high-level comparison between them without reference to their implementation details.

1.2 Notations Used We follow the notations of Crampton et al. [1]. Notations used in this paper are as follows: • • • • • • •

λ is a time interval between time slots t1 and t2 , t1 , t2 ∈ z Sx,λ is the secret information corresponding to class x and time interval λ Pub is the set of values stored in the public storage of the system k x,t is the key of the class labeled x for a time slot t x · y1> k(y) using above function g and produce k(y). Scheme 4 is an indirect scheme. It requires single-value private storage and comparable public storage with respect to scheme 2 and scheme 3. Advantages of such

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schemes are low key generation cost and low key derivation cost in comparison with other schemes as these schemes are using low-cost hash functions in comparison with exponential functions. The disadvantage of these type of schemes is that these schemes require some secure mechanism with each user such that it can derive all authorize derivation keys locally without revealing their secret information to the user, for example, tamper-proof device. Some schemes of this type in the literature are [14, 16–18, 21–25], etc.

3 Discussion In this section, we compare all proposed time-bound generic schemes with respect to private storage required by a user, public storage required by the system and their user revocation cost. Table 1 gives a comparison between all proposed time-bound generic schemes. In Table 1, we also consider user revocation cost for comparison all proposed generic TBHKAS when a user is revoked in the hierarchy. In the table, |E| represents the number of edges in the hierarchy. From the table, we can see that scheme 1 require a significant amount of private storage with a significant update cost when a user is revoked from the hierarchy. In comparison with scheme 1, scheme 2 require singlevalue private storage with each user to store their secret information but with the cost of a huge public store(i.e. m · z). Update operations needed when a user is revoked in the hierarchy affects all the users in the hierarchy. With respect to scheme 2, scheme 3 requited comparable update cost in private storage with additional update cost in public store when a user is revoked from the hierarchy but public storage required is significantly less, that makes this type of schemes better than previous schemes. Scheme 4 is an indirect scheme that more suited the pay-TV type of applications. This scheme is comparable to scheme 3 with an advantage that it requires less update cost in private storage when a user is revoked.

Table 1 Comparison of time-bound generic HKAS’s Scheme Storage Direct/Indirect Update cost Private Public Private Scheme 1 Scheme 2 Scheme 3 Scheme 4

| ↓ x| · |λ| 1 1 1

All m·z m+z |E| + z

Direct Direct Direct Indirect

| ↑ (↓ x)| ALL | ↑ (↓ x)| | ↓ x|

Public NIL NIL | ↓ x| | ↓ x|

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4 Conclusions and Future Work We proposed an extension to the work by Crampton et al. [1]. They have proposed a generic classification to non-time-bound HKASs. We extend their classification to classify TBHKAS’s that will capture all the existing TBHKAS’s from the literature as best of my knowledge. We are also given a comparison between all proposed generic TBHKAS’s in Table 1. From the table, we can conclude that scheme 3 is better if direct TBHKAS’s are taken into consideration. Scheme 4 is efficient than other schemes but requires an extra mechanism with each user for secure computation. We can see from Table 1 that TBHKAS’s are still taking a significant amount of public storage and user revocation cost. Also, many of the time-bound schemes are not having their security proofs. Another important problem is that we are still do not have any good dynamic TBHKAS. So, in all, we have a lot of opportunities in this area for research.

References 1. W.G. Tzeng, A time-bound cryptographic key assignment scheme for access control in the hierarchy. IEEE Trans. Knowl. Data Eng. 14(1), 182–188 (2002) 2. E. Bertino, B. Carminati, E. Ferrari, A temporal key management scheme for secure broadcasting of XML documents, in Proceedings of the ACM Conference on Computer and Communications Security (2002), pp. 31–40 3. X. Yi, Y. Ye, Security of Tzeng’s time-bound key assignment scheme for access control in a hierarchy. IEEE Trans. Knowl. Data Eng. 15(4), 1054–1055 (2003) 4. W.G. Tzeng, A secure system for data access based on anonymous and time-dependent hierarchical keys, in Proceedings of the ACM Symposium on Information, Computer and Communications Security (2006), pp. 223–230 5. J. Yeh, An RSA-based time-bound hierarchical key assignment scheme for electronic article subscription, in Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management (Bremen, Germany, Oct 31–Nov 5, 2005) 6. G. Ateniese, A. De. Santis, A.L. Ferrara, B. Masucci, Provably secure time-bound hierarchical key assignment key assignment scheme, in Proceedings 13th ACM Conference on Computer and Communications Security (CCS ’06) (2006), pp. 288–297 7. J. Yeh, A secure time-bound hierarchical key assignment scheme based on RSA public key cryptosystem. Inf. Process. Letters 105, 117–120 (2008). February 8. S.-Y. Wang, C. Laih, Merging: an efficient solution for a time-bound hierarchical key assignment scheme. IEEE Trans. Depend. Sec. Comput. 3(1), 91–100 (2006) 9. S.G. Akl, P.D. Taylor, Cryptographic solution to a problem of access control in a hierarchy. ACM Trans. Comput. Syst. 1(3), 239–248 (1983) 10. J. Yeh, R. Shyam, An efficient time-bound hierarchical key assignment scheme with a new merge function. Submitted to IEEE Trans. Depend. Sec. Comput. (2009) 11. R. Shyam, an efficient time-bound hierarchical key assignment scheme with a new merge function: a performance study, MS project posted at ScholarWorks (2009). http://scholarworks. boisestate.edu/cs_gradproj/1 12. H.F. Huang, C.C. Chang, A new cryptographic key assignment scheme with time-constraint access control in a hierarchy. Comput. Stand. Interf. 26, 159–166 (2004) 13. Q. Tang, C.J. Mitchell, Comments on a cryptographic key assignment scheme. Comput. Stand. Interf. 27, 323–326 (2005)

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14. H.Y. Chien, Efficient time-bound hierarchical key assignment scheme. IEEE Trans. Knowl. Data Eng. 16(10), 1301–1304 (2004). Oct 15. X. Yi, Security of Chien’s efficient time-bound hierarchical key assignment scheme. IEEE Trans. Knowl. Data Eng. 17(9), 1298–1299 (2005). Sept 16. A.D. Santis, A.L. Ferrara, B. Masucci, Enforcing the security of a time-bound hierarchical key assignment scheme. Inf. Sci. 176(12), 1684–1694 (2006) 17. E. Bertino, N. Shang, S. Samuel, Wagstaff Jr., Efficient time-bound hierarchical key management scheme for secure broadcasting. IEEE Trans. Depend. Sec. Comput. Arch. 5(2), 65–70 (2008) 18. H.M. Sun, K.H. Wang, C.M. Chen, On the security of an efficient time-bound hierarchical key management scheme. IEEE Trans. Depend. Sec. Comput. 6(2), 159–160 (2009). April 19. J. Crampton, K. Martin, P. Wild, On key assignment for hierarchical access control, in Proceedings of 19th Computer Security Foundations Workshop (2006), pp. 98–111 20. N. Kumar, A. Mathuria, M.L. Das, On classifying indirect key assignment schemes for hierarchical access control, in Proceedings of 10th National Workshop on Cryptology (NWCR 2010) (2010), pp. 2–4 21. F. Kollmann, A flexible subscription model for broadcasted digital contents, cis, in 2007 International Conference on Computational Intelligence and Security (CIS 2007) (2007), pp. 589– 593 22. N. Kumar, A. Mathuria, M.L. Das, Simple and efficient time-bound hierarchical key assignment scheme. ICISS 191–198, 2013 (2013) 23. H.-Y. Chien, Y.-L. Chen, C.-F. Lo, Y.-M. Huang, A novel e-newspapers publication system using provably secure time-bound hierarchical key assignment scheme and XML security, in Book on Advances in Grid and Pervasive Computing, vol. 6104 (May 2010), pp. 407–417. ISBN 9783-642-13066-3 24. N. Kumar, S. Tiwari, Z. Zheng, K.K. Mishra, A.K. Sangaiah, An efficient and provably secure time-limited key management scheme for outsourced data. Concurr. Comput. Pract. Exp. 30(15) (2018) 25. Q. Xu, M. He, L. Harn, An improved time-bound hierarchical key assignment scheme, in Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference (2008), pp. 1489– 1494, ISBN: 978-0-7695-3473-2 26. W.T. Zhu, R.H. Deng, J. Zhou, F. Bao, Time-bound hierarchical key assignment: an overview. IEICE Trans. Inf. Syst. E93-D(5), 1044–1052 (2010)

A Survey on Cloud Workflow Collaborative Adaptive Scheduling Delong Cui, Zhiping Peng, Qirui Li, Jieguang He, Lizi Zheng, and Yiheng Yuan

Abstract Objectively speaking, cloud workflow requires task assignment and virtual resource provisioning to work together in a collaborative manner for adaptive scheduling, thus to balance the interests of both supply and demand in the cloud service under the service level agreements. In this study, we present a survey on the current cloud workflow collaborative adaptive scheduling from the perspectives of resource provisioning and job scheduling, together with the existing cloud computing research, and look into the key problems to be solved and the future research. Keywords Cloud workflow · Collaborative scheduling · Adaptive scheduling

1 Introduction The scheduling problem of workflow under cloud computing environment has attracted considerable attention of researchers recently [1], and important progress has been made in the implementation of time minimization, fairness maximization, throughput maximization and resource optimization and allocation. But from the perspective of cloud service supply and demand, service level agreements (SLAs) and resource utilization rate are, respectively, the two most fundamental interests that cloud users and cloud service providers care for [2–4]. In complex, transient, and heterogeneous cloud environment, in order to balance the interests of supply and demand sides under the premise of ensuring user service level agreement, it is necessary to conduct cooperative and adaptive scheduling on workflow tasks and virtualized resources. When the supply and demand sides of cloud service achieve agreement on the work amount to be performed and service level agreements, cloud service suppliers are more concerned with how to maximize resource utilization rate with a certain resource combination scheme, thereby minimizing operational costs, and cloud service users are more concerned about how to minimize the rental time D. Cui (B) · Z. Peng · Q. Li · J. He · L. Zheng · Y. Yuan College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, 525000 Maoming, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_11

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with a certain kind of task scheduling method, thus minimizing the use cost. Therefore, a compromised solution is to achieve balance between supply and demand sides, and to achieve co-scheduling between cloud workflow tasks and virtualized resources. However, due to the cyclical and transient nature of cloud workflow, cloud computing resources have a high degree of complexity and uncertainty, and that the workflow tasks and virtualized resources achieve online optimization scheduling through collaborative self-adaptive means is required objectively [5–7]. However, it is very difficult to perform co-adaptive scheduling of workflow tasks and virtualized resources under the rapidly changing cloud computing environment [8]. From the perspective of optimized allocation of task, the scheduling of various types of cloud workflow tasks on multiple processing units has proved to be an NP complete problem [9]. From the perspective of resource optimization supply, the virtual unit placement needs to consider energy consumption, that is, to reduce the number of physical machines activated and network devices applied, and virtualized unit placement can be abstracted as packing problem, which is an NP complete problem [10]. On the other hand, it is necessary to consider the transmission of data between virtual units, that is, to reduce the use of network bandwidth. In this case, virtual unit placement can be abstracted as quadratic assignment problem, which is also an NP complete problem [10]. Current studies on cloud workflow scheduling focus on the allocation of workflow tasks under fixed virtualized resources [11], or focus on the flexible resource supply under the change of workflow load, or focus on how to integrate the existing workflow management system into cloud platform [12, 13], but rarely on the adaptive scheduling of the collaboration between workflow task allocation and virtualized resource supply. The execution of cloud workflow in cloud computing environment mainly includes task allocation and resource supply. The dependency and restriction relation between workflow tasks are usually described by directed acyclic graph (DAG). However, whether it is task allocation or resource supply, based on the integrity degree of information about external environment and scheduling object, the research methods used can be generally divided into three categories: static scheduling method, dynamic scheduling method, and hybrid scheduling method, and the relationship among the three types of research methods is shown in Fig. 1.

2 Research on Task Allocation of Cloud Workflow and Analysis on Its Limitations 2.1 Static Scheduling Method Shojafar et al. [14] proposed a hybrid optimization task scheduling algorithm based on fuzzy theory and genetic algorithm, to optimize the load balancing among virtual machines under the constraint of finish time and cost. Abrishami et al. [15] designed a unidirectional IC-PCP based on IaaS cloud and a two-way IC-PCP task allocation

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DAG features, structure, task length, side length and other information are known. Static scheduling

Hybrid scheduling

Dynamic scheduling

All the resources have been instantiated, and the performance is fixed. Conduct scheduling before the task runs. Task execution time and communication time can be estimated. Static planning is performed based on the estimated information before the task runs, but dynamic allocation is only conducted at run time. Due to incomplete DAG and resource information, task execution time and communication time can only be obtained when the task is running. Conduct scheduling when the task is running. For each scheduling time, select the task to be allocated and deploy it to the selected resource.

Fig. 1 Diagram of cloud workflow scheduling research methods

algorithm with dead-time constraint, respectively, based on their research on grid workflow scheduling, and pointed out the time complexity of these two algorithms is only polynomial time. Fan et al., Tsinghua University, [16] designed a multi-objective and multi-task scheduling algorithm based on extended order optimization, which greatly reduced the time cost and also proved the suboptimality of the algorithm. He et al., Dalian University of Technology, [17] proposed a workflow cost optimization model with communication overhead, and a hierarchical scheduling algorithm based on the model. Liang et al., Wuhan University, [18] proposed a multi-objective optimization algorithm of task completion time and reliability simulating the combination of annealing algorithm and genetic algorithm. Peng et al., Taiyuan University of Technology, [19] for the security threats confronted with cloud workflow scheduling problems, applied the security of cloud model quantization tasks and virtual machine resource, measured the security satisfaction degree of virtual machine resource allocated to the task by users through security cloud similarity degree, established a cloud workflow scheduling model considering security, completion time, and usage cost, and then proposed a cloud workflow scheduling algorithm based on discrete particle swarm optimization.

2.2 Dynamic Scheduling Method Szabo et al. [20] designed a dynamic allocation algorithm of task based on evolutionary method for data-intensive scientific workflow application, while taking into account the constraints of network transmission and execution time. Chen et al. [21] designed a dynamic task reordering and scheduling algorithm with priority constraint for the fair allocation of multiple workflow tasks. Li Xiaoping and his team of Southeast University had done a lot of research on dynamic task scheduling, and recently published a cost optimization algorithm of cloud service workflow with the constraints of preparation time and deadline [22]. The algorithm establishes corresponding integer programming mathematical model and introduces the strategy of

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individual learning from the global optimal solution, to improve the global search and local optimization ability of the algorithm. Li et al., Anhui University, [23] designed a matrix partition model based on data dependency destructiveness, aiming at the deficiencies of traditional data layout method and combining with the characteristics of data layout in hybrid cloud, and proposed a workflow task layout method facing data center, and the workflow tasks with high dependency are generally put in the same data center, thereby reducing the transmission time of data set crossing data center. Peng et al. designed a scheduling algorithm of cloud users task based on DAG critical path [24], and its improved algorithm [25–31].

3 Research on Resource Supply of Cloud Workflow and Analysis on Its Limitations 3.1 Static Scheduling Method Rodriguez and Buyya [32] designed a scheduling general model of cost minimization workflow under the constraint of execution time and designed a cloud resource scheduling supply algorithm based on particle swarm optimization algorithm. Chen et al., Sun Yat-sen University, [33] designed a cloud workflow resource supply strategy based on two-stage genetic algorithm for the problem of violating dead-time constraint in particle swarm optimization algorithm, based on the general model. The algorithm first searches the execution time of optimization task under the constraint of deadline, and then the feasible solution obtained is used as the initial condition to search for the resource supply scheme with minimal rental cost. Wang et al., Northeastern University, [34] proposed a cloud workflow scheduling algorithm based on service quality constraint for the low efficiency of using workflow scheduling strategy to schedule instance-intensive workflows in cloud environment.

3.2 Dynamic Scheduling Method Byun et al. proposed a hybrid cloud-based resource supply algorithm BTS [35] and its improved algorithm under the constraint of the cycle variation of virtual unit price [36]. The core idea of the two algorithms is to conduct priority setting according to the scheduling delay of the workflow task, and the task with smaller delay is assigned higher priority and more resources. Peng et al. proposed a resource scheduling algorithm based on reinforcement learning [37] by abstracting the resource scheduling in cloud environment into sequential decision problems, and introduced two performance indexes, namely segmented service level agreement and unit time cost utilization, to redesign the return function. Aiming at the problem of virtualization placement, a multi-objective comprehensive evaluation model of virtual machine

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is designed [38], and a multi-objective particle swarm optimization algorithm is proposed to dynamically place the virtual machine.

4 Hybrid Scheduling Method Maheshwari et al. [39] designed a multi-site workflow scheduling algorithm based on resource execution power and network throughput prediction model for hybrid resource supply environment. Lee et al. [40] introduced a simple and efficient objective function to help users make decisions for task-package application in hybrid cloud environment. Li et al. [41] designed a fair scheduling algorithm of heterogeneous resource in hybrid cloud, and introduced fairness index of superior resource to achieve the mutual restraint of the resource allocation among users and applications. For the problems of low utilization rate of private cloud and high cost of public cloud in hybrid cloud scheduling, Zuo et al. proposed two scheduling strategies, deadline-first and cost-first strategies based on performance and cost objectives [42], and established task and resource models in hybrid cloud, which can adaptively select suitable scheduling resources according to the task requirements submitted by users. Tasks with higher deadline requirement can be scheduled to the public cloud first, tasks with high cost requirement can be scheduled to private cloud first, and both two strategies can meet the deadline and a certain cost constraint.

5 Research on Adaptive Cooperative Scheduling and Analysis on Its Limitations The University of Southern California has developed Pegasus Cloud Workflow Management System [43], which takes into account the scientific workflow tasks and adaptive scheduling of cloud resources. However, the system needs to be further improved in terms of local user queue detection and user requirements customization. Part of the work of EGEE, global grid infrastructure project of EU, involves applying multi-objective reinforcement learning algorithm to conduct the optimization scheduling of task allocation and resource elasticity supply, respectively, and proposes to achieve the collaborative and integrating scheduling of the two in its alternative project EGI [44]. Vasile et al. [45] designed a resource-aware hybrid scheduling algorithm based on virtual machine groups. The algorithm applies task allocation algorithm of two stages. In the first stage, the user tasks are assigned to the established virtual machine group. In the second stage, classical scheduling algorithm is used to conduct second scheduling according to the number of resources in each group. However, this algorithm ignores the dependencies between workflow tasks, thus leading to the frequent transmission of user data among virtual machine groups, and even network congestion. Jiao et al. [46] proposed a hybrid Petri net model of

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cloud workflow under cooperative design, applied stochastic Petri net idea to analyze each process flow and work efficiency of collaborative design, and proposed optimization from the aspects of business conflict and model decomposition. But this model is only suitable for the collaborative optimization scheduling of single cloud workflow, so that it is not close to the real application scenario; Table 1 shows a summary of the articles reviewed in this section. Table 1 Summary of the reviewed literature about reinforcement learning in cloud computing envirnomnet Metric

SLA

Workloads

Experimental platform

Technologies

References

Successful task execution, utilization rate, global utility value

Deadline

Synthetic. Poisson distribution

Custom testbed

ANN, on-demand information sharing, adaptive

[47]

User request, Response VMs, time time, cost

Synthetic. Poisson distribution

MATLAB

Multi-agent; parallel Q-learning

[48]

GA

[49]

CPU, memory

Throughput, Real. Wikipedia VMware, Response time trace RUBiS, WikiBench

CPU, Response time Real. ClarkNet memory, I/O trace

Xen, TPC-C, TPC-W, SpecWeb

CMAC, distributed learning mechanism

[50]

MIPS, CPU

Average SLA Violation Percentage, Energy Consumption

Synthetic Real. CoMon project

CloudSim

Multi-agent

[51]

CPU utilization rate

Response time, Energy Consumption

Synthetic. Poisson distribution

Custom testbed

Intelligent controller, adaptive

[52]

Utility accrual

Response time Synthetic. Poisson distribution

Custom testbed

Failure and recovery

[53]

System parameters

Throughput, Synthetic. Response time workloadmixes

Xen, TPC-C, TPC-W, SpecWeb

parameter grouping

[54]

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6 Conclusion and Prospect In summary, at present related domestic and foreign research work mostly concentrates in cloud workflow task allocation or unilateral adaptive scheduling of virtual system resources supply, ignoring the inherent dependency relationship between the two, so it is difficult to achieve interest balance between supply and demand sides of cloud service on the premise of guaranteeing the SLA. The research of adaptive collaborative scheduling on the two is just beginning, the research results are deficient, and the depth of research and the application of research methods are also lacking. But it can be predicted that conducting adaptive scheduling on the two with collaborative method will inevitably become one of the core problems to be solved urgently in the big data processing technology typically represented by cloud workflow application. Acknowledgements The work presented in this paper was supported by National Natural Science Foundation of China (No. 61672174, 61772145). National Natural Science Foundation of China under Grants (No. 61803108). Maoming Engineering Research Center for Automation in PetroChemical Industry (No. 517013), and Guangdong University Student Science and Technology Innovation Cultivation Special Fund (no. pdjh2019b0326, 733409). Zhiping Peng is corresponding author.

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Lattice CP-ABE Scheme Supporting Reduced-OBDD Structure Eric Affum, Xiasong Zhang, and Xiaofen Wang

Abstract Ciphertext attribute-based encryption (CP-ABE) schemes from lattices are considered as a flexible way of ensuring access control, which allow publishers to encrypt data for many users without worrying about quantum attacks and exponential computational inefficiencies. However, most of the proposed schemes are inefficient, inflexible, and cannot support flexible expression of access policy. To achieve an efficient and flexible access policy expression, we construct CP-ABE scheme from lattice which supports reduced ordered binary decision diagram (reduced-OBDD) structure. This approach is entirely different but can achieve an efficient and flexible access policy. Encryption and decryption are based on walking on the links between the nodes instead of using the nodes. By adopting the strategy of using ReducedOBDD and walking on its links, we can obtain an optimized access structure for our policy which do not only support AND, OR, and Threshold operations but also support negative and positive attributes. We finally prove our scheme to be secured under decision R-Learning with error problem in a selective set model. Keywords Lattice · Access policy · CP-ABE · Reduce-OBDD

1 Introduction In a complex situation such as content-centric network (CCN) environ where data owners do not have control over their own published data, access control cannot be effectively enforced with the traditional one-to-one access control approach [1]. E. Affum · X. Zhang (B) · X. Wang School of Computer Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China e-mail: [email protected] E. Affum e-mail: [email protected] X. Wang e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_12

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The effective way to achieve a required secured data sharing is to provide a more scalable and flexible access control for this pervasive and distributed CCN environment [2]. Fortunately, attribute-based encryption (ABE) cryptosystem has been proposed as a fine-grained access control mechanism for the future CCN. This work will focus on ciphertext-policy ABE (CP-ABE) scheme for its special properties and advantages over other cryptography schemes such as symmetric, asymmetric, and KP-ABE. CP-ABE scheme has impressive properties of maintaining and describing access privileges of users in a more intuitive and scalable way. By using this scheme and without prior knowledge of the receivers of information, data could be shared according to the encrypted policy. There are two approaches of designing the algorithms of ABE schemes, including bilinear map over elliptical curve and lattice-based approach. Almost all of the existing schemes such as [3, 4] are based on bilinear map with a high computational complexity and also cannot address the problem of quantum attacks. To address the problems of quantum attacks, researcher [5] first introduced the idea of lattice into cryptography, and there has been recent progress in the construction of ABE schemes based from lattice. LSSS CP-ABE scheme access policy with lightweight ideal grid based on R-LWE problem was proposed by Tan et al. [6], which is capable to resist collision attack. Yan et al. [7] used LSSS access structure to propose the ideal multi-agency CP-ABE scheme. Wang et al. [8] achieved an effective encryption scheme based on R-LWE with high encryption and decryption run time and an integrity support features based chosen ciphertext security. Authors of [9] proposed CP-ABE scheme-based lattices. Their scheme is flexible and supports (k, n) threshold access policies on Boolean operation. Based on full-rank differences function, authors [10] proposed a large universe CP-ABE scheme to attain improvement in expressing of attributes and unbounded attribute space. An efficient revocable ABE scheme was constructed by [11], their revocation of attributes and granting is based on binary tree approach. A single random vector parameter was selected for nodes corresponding to attributes. In 2018, authors of [12] proposed attribute-based encryption scheme supporting tree-access structure on ideal lattices. They used an expressive and flexible access policy by Shamir threshold secret sharing technology, including “and,” “or,” and “threshold” operations. In order to construct more efficient lattice-based ABE to resolve inefficiency issues in lattice ABE cryptosystem, the accessed structures and some components such as the trapdoor design and the matrix dimension which play a significant role in the construction lattice-based ABE scheme need to optimized. The main contribution of our work is to propose a flexible lattice CP-ABE scheme based on Reduced-OBDD based on R-LWE. This scheme has a compacted and optimized access structure with less nodes and links. Encryption and decryption are based on walking on the links instead of nodes. This means that it has a less encryption and decryption computational time over rings. The proposed scheme supports threshold operation, Boolean operations, and multiple subscribers with positive and negative attributes. The remaining of the paper is organized as follows: The preliminaries are discussed in Sect. 2. We demonstrate our access structure and our scheme in Sect. 3.

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The security analysis of the scheme is in Sect. 4. The performance analysis of the simulated result is evaluated in Sect. 5. Finally, this paper is concluded in Sect. 6.

2 Preliminaries 2.1 Lattice Let us consider an integer and a real number as Z and R, respectively, and chosen a real number x from R. We denote x the largest integer less than or equal to x, x which is x − 1 while Round(x) = x + 21 . Let k denotes a positive integer and k represent {1, . . . , k}. Let bold capital letter A and x represent matrices and column vectors, respectively, and x T represent a transposed vector. Let ai represent the i-th column vector of A. The horizontal and vertical concatenation of A/x is presented as [A|Ax] and [A; Ax], respectively. Represent the maximal Gram--Schmidt length of the matrix A as A. Assuming n is the security value, let poly(n) represent a function of f (n) = O(n c ) for some constant, while O(·) represent the factor of log. Let negl(n) represent negligible, if f (n) = O(n c ) for any fixed constant. It is then concluded that an event occurs with probability if it occurs with the probability of at least 1 − negl(n). Given X and Y as two distributions, the distance between them is  considered as a function the domain D = 1/2 d∈D |(d) − Y (d)|. Statistically, we can then conclude that, the two distributions are closed if the distance between them is negl(n). Definition 1 (Lattice) A lattice is a set of points in m-dimensional space with a periodic structure, that satisfy the following properties: Given n linearly independent vectors b1 ,  . . . , bn ∈ Rm , the lattice  genern  ated by them is defined as: L(b1 , . . . , bn ) = xi bi |xi ∈ Z, 1 ≤ i ≤ n , where b1 , . . . , bn is basis of the lattice.

i=1

2.2 Gaussian Sampling for Rings To compute a solution to a hard problem, trapdoor is used. In this paper, we rely on the lattice trapdoors introduced and implemented in [13]. Let m be a parameter for the generation of trapdoor and define A ∈ Rq1×m as a distributed uniformly generated random row vector of ring element. Giving β  Rq it is computationally hard, finding a short vector polynomials. ω ∈ Rq1×m satisfying Aω = β. There must be spherically distributed in the solution with Gaussian with s as a distributed parameter which is ω ← D L ,s . Where a secret share and a pseudo-random trapdoor TA and A, respectively, and benefit from the hardness assumption of ring learning with error.

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As shown in Algorithm 1, m = [2n logq ] is the length of modulus q in base n of some integers. The parameters σ, q and n are selected based on the secrete parameter λ. Compute public key A by sampling secrete trapdoor TA . For an efficient construction, power of two bases is used. Using Gaussian distribution with a parameter of σ , we obtain a trapdoor of two short vectors, TA = (ρ, u). For the preimage sampling algorithm, we will implement and utilize the algorithm for G − lattice proposed by  ¯ as primitive vector proposed by [15], we [14]. Denoting g T = b0 , b1 , . . . , bm−1 generated an efficient preimage sampling, G −lattice and define its efficiency in terms of lattice A and TA as a trapdoor. For the Gaussian preimage sampling in Algorithm 2, to ensure a spherical distribution of Gaussian and for that matter preventive measures against information leakage, a perturb vector p is used for the solution y. In summary, Ay = β, while y ← D L q (A),σs , p ∈ R m , z ∈ R m¯ and m = m¯ + 2, where σs is called spectral norm, which is a Gaussian distribution parameter y. Given C as constant √ for √ 2 nk + 2n + 4.7 parameter, the spectral norm satisfies σs > C · Q · Algorithm 1 Generation of trapdoor

Algorithm 2 Gaussian preimage sampling

2.3 Decision Ring-Learning with Error Problem Given n as security parameter, let d and q be integers depending on n. Where f (x) = (x n + 1) and Rq = R/q R, let R = Z [x]/( f ). Given a distribution χ over Rq depending on n, the decision learning with error problem instance consists of access to an unspecified challenge oracle o, either a noisy pseudo-random sampler Os , for random secrete key S ← Rq , or a truly random sampler O$ . The decision RLWE problem is to distinguish the sampling between Os and O$ , i.e., Os : Given a uniform distribution constant invariant value across invocation as S ∈ Zqn , a new sample xi ∈ Zq from χ and a uniform sample u ∈ Zqn . Output a sample of form as

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(u i, vi = u i .u iT S + xi ) ∈ Zqn × Zq . O$ : An exact uniform output sample (u, v) drawn from Zqn × Zq . The aim of the decision ring-LWE problems is to allow a repeated querries to be sent to the challenge oracle O. The attacker’s algorithm decides the os = 1] − Pr[Attackero$ = decision ring-learning with error problem if |Pr [Attacker

n 1]| is non-negligible random value for s ∈ Zq . x defining integer x ∈ Z, noise distribution X LWE problem is hard as as SIVP and GapSVP under reduction

3 Our Construction Let the access policy of a Boolean function be f (u 0 , u 1 , . . . u n−1 ), where (0 ≤ i ≤ n − 1) and n is the whole number of attributes, denote a sequential predefined access policy number as u(0 ≤ i ≤ n − 1). The function f (u 0 , u 1 u n−1 ) is converted between fundamental logical operations such as AND, OR, and NOT. An operation is considered as threshold gate T (t, n) if and only if t attributes of a subset n can complete this operation successfully. To be able to decrypt a message in some security system, a user must be able to complete some specific threshold operations. To construct a Boolean function of a given T (t, n ∈ N ), where N is the attribute set. Extract all the subset of N with t attributes and separately compute the number of subsets C(n, t) = Com1, Com2 . . . ComC(n,t) by using permutation and combination. This is followed by the construction of a separate set level conjugate for each subset C(n, t) = Con1, Con2 . . . Con (n,t) . Finally, obtain Boolean function C(n,t) of f (t, n) = ∨i=1 Coni by a disjunctive operation on C(n, t).

3.1 Construction of Access Structure Based on Reduced-OBDD To construct reduced-OBDD for Boolean function f (x1 x2 . . . xn ) in Fig. 1a, Algorithms 3 and 4 based on the expansion theorem of Shannon are used. To obtain a specific a unique ordered-BDD, the variable ordering must be specified to give a specific diagram. Let N = {0, 1, 2, . . . n} be node numbers with a condition that the low terminal node is 0 and the high terminal nodes is 1. However, the terminal nodes have specific meaning and their attributes may not be considered. The variable ordering related to N is = (x0 0, then there exist an algorithm that can distinguish Z q , n, ψα − LWE problem with advantage of ε. The problem of LWE is provided as sample oracle O which can be really random O$ or noisy pseudo-random for some secret key S ∈ Z np .   Initialize: Adversary A, send the access structure τ = Ndi∈I to the id∈I D challenger’s simulator B. Instance: B make a request to the oracle  and the oracle respond by sending new pairs of (1 , υ1 ) ∈ Z qn × Z q to obtain m s1 si + 1, where i ∈ {1, 2, . . . , s} Target: A makes announcement of the the set of attributes that it is intended to challenge Setup: The public parameters are generated by B. Let us denoteυ as 1 .  For Ai, j ∈ A∗s , generate Ai, j =

1  i

m 2 , where ,  , . . . ,  i i p=1 s p + j p=1 s p + j  si 0 i = (1, . . . , s) and j = (1, . . . , si ) to obtain Ai = j=1 Ai, j . / A∗s by running trapdoor B generate matrix for each ai, j where attributes and ai, j ∈ ∗ m×m and compute Ai, ∗j = Ai Ri,∗ j . Using algorithm generate random matrix Ri, j ∈ Z q p=1 s p + j

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¯ Ai, j as an input, B generate random matrix Ri,∗ j ∈ Z qm×m and a trapdoor T Ai∗ j ∈ Z qm×u     for Lq⊥ Ai, ∗j . Finally, B generate Ai = Ai0 Ri, j and set PP = (Ai )i∈As , α to the A. ∗ Phase 1: A sends private key request for a set of attributes A G = ∗ as1 , a2∗ , · · · , a ∗j , where a1∗ ∈ / τ ∗ , ∀i. B compute s share of α and any user can get a share of ai . For any legitimate path, / there must exist an attributes a j ∈ A I satisfying cases such as a j ∈ As* ∧ a−j = a j ∈

As* ∧a−j = a j . Generally, A attributes should satisfy the condition; a j ∈ / A∗s ∧a−j = a j

and each attributes are assigned as follows: a j ∈ / As* ∧ a j = a j , y −

aj

= m · ya j ; for

ai = a j . B runs the key generation algorithm to generate Ai j = Ai Ri−1 j and a trapdoor   1 ∗ m+w ¯ for L q Ai j and invoke Gaussian algorithm to output di, j ∈ matrix T A ∈ Z q   ¯ Z qm+v = Z qm Set the private key of a ∗j as S K u = di, j ai, j∈As to the A Challenge: A agrees to accept the challenge and submit challenge message (m 0 , m 1 ) ∈ {0, 1} with the attribute set a ∗j and flips a coin to generate randomly m ∈ (0, 1). B generate ciphertext as C T ∗ = (C0 , {Ci }i, j|i∈τ ) to A

where C0* = α T s + θ + m q2 mod q and Ci,(i)j * = Ai, Tj · si + θi, j mod q It is clear that the encrypted message C T ∗ is a valid encryption of m under  the access policy of τ if O = O$ . The encrypted message is uniform in Z p , Z qm and     O = O$ υ, υi, j is uniform in Z p , Z qm . Phase 2: A continues by repeating Phase 1 Decision: A output a guess m for m. If m = m the challenger considers the samples O to be Os sample, else it guesses them as O$ samples. Assuming the adversary A can correctly guess m with probability of at least 1/2 + ε. Then, A canmake a decision of the decisionring-LWE problem with an advantage of      1 1 Prob. m = m|(w, u) ← Os + Prob. m = m|(w, u) ← O$ 2 2          1 1 1 1 ε 1 × +ε + × = + = 2 2 2 2 2 2

5 Performance Evaluation The implementation was conducted on Intel i7-8700 processor at 2.53 GHz and 8 GB memory running Windows 10 operating system of 64 bits. We simulated with PALISADE library version on C++ [16]. The analysis of the implementation results entails the comparison of execution time and storage capacity of ciphertext, key generation, encryption, and decryption operations. The capacity analysis entails public parameters PP size, secrete key SK size, masters key MK, and the ciphertext

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Table 3 Comparison in terms of access structures, operations, and supported attributes Scheme

Access structures

Operations AND

OR

Supporting attributes Threshold

[17]]

Tree

Yes

Yes

Yes

Positive

Ours

Reduced-OBDD

Yes

Yes

Yes

Negative and positive

size. The parameters are set based on [17]. As represented in Table 1, the storage size of our PP, MK, SK, and ciphertext is smaller than the scheme [17]. Table 2 summarizes the comparison analysis of the execution time of our proposed scheme and [17]. s = (4/8/12/16/20/) represents different number of attributes used. Our construction is faster than scheme [17] in so many aspects as shown in Table 2. Although, the key generation process was a little slow but the encryption and decryption processes are very fast. This is based on the efficiency of our optimized access structure and the technique of using the link instead of the nodes. Table 3 compares our scheme and scheme [17] in terms of access structure, supporting operations and supported attributes. Scheme [17] support tree access structure. However, we used reduced-OBDD which has been ordered and reduced with special properties of nonredundancy, uniqueness and canonicity. Our scheme does not only support AND, OR and Threshold but it can also support negative attributes. In scheme [17], key generation encryption and decryption operations are based on the number of nodes but ours is based on the number of paths which is less than the number of nodes. This makes our scheme more efficient than scheme [17]. On a whole, our scheme is practical with respect to execution time, storage size and secured against quantum attacks.

6 Conclusion To ensure an efficient and flexible CP-ABE scheme which is secured and resistant to quantum attacks, we proposed a lattice CP-ABE scheme supporting reduced-OBDD with more efficient and flexible access structure for the access policy expressions. This scheme supports multiple occurrences, Boolean operations, and attributes of positive and negative features in the access policy expression. Our scheme is secured under decision ring-learning with error problem in selective set model. In future, we will investigate into how to revoke attributes and also improve on the efficiency of the key generation, encryption, and decryption operations of our schemes. Acknowledgements This work is supported by the National Natural Science Foundation of China under Grants 61502086, the foundation from the State Key Laboratory of Integrated Services Networks, Xidian University (No. ISN18-09).

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References 1. H. Yin, Y. Jiang, C. Lin, C.Y. Luo, Y. Liu, Big data: Transforming the design philosophy of future Internet. IEEE Net. 28(4), 1419 (2014) 2. B. Anggorojati, P.N. Mahalle, N.R. Prasad, R. Prasad, Capability based access control delegation model on the federated IoT network. in Proceedings of IEEE International Symposium on Wireless Personal Multimedia Communications (2012), pp. 604–608 3. A. Grusho et al., Five SDN-oriented directions in information security. in IEEE Science and Technology Conference (2014), pp. 1–4 4. E. Affum, X. Zhang, X. Wang, J.B. Ansuura, Efficient CP-ABE scheme for IoT CCN based on ROBDD. in Proceedings International Conference Advances in Computer Communication and Computer Sciences, 924 (2019), pp 575–590 5. M. Ajtai, Generating hard instances of lattice problems (extend abstract). in Proceedings of STOC ACM (1996), pp 99–108 6. S.F. Tan, A. Samsudin, Lattice ciphertext-policy attribute-based encryption from RingLWE (IEEE Int. Sym. Technol. Manage. Emer. Technol., Langkawi, 2015), pp. 258–262 7. X. Yan, Y. Liu, Z. Li, Q. Huang, A privacy-preserving multi-authority attribute-based encryption scheme on ideal lattices in the cloud environment. Proc. Netinfo Sec. 8, 19–25 (2017) 8. T. Wang, G. Han, J. Yu, P. Zhang, X. Sun, Efficient chosen-ciphertext secure encryption from R-LWE. Wirel. Pers. Commun. 95, 1–16 (2017) 9. J. Zhang, Z. Zhang, A. Ge, Ciphertext policy attribute-based encryption from lattices. in Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security (2012), pp. 16–17 10. Y.T. Wang, Lattice ciphertext policy attribute-based encryption in the standard model’. Int. J. Netw. Sec. 16(6), 444–451 (2014) 11. S. Wang, X. Zhang, Y. Zhang, Efficient revocable and grantable attribute-based encryption from lattices with fine-grained access control. IET Inf. Secur. 12(2), 141–149 (2018) 12. J. Yu, C. Yang, Y. Tang, X. Yan, Attribute-based encryption scheme supporting tree-access structure on ideal lattices. in International Conference on Cloud Computing and Security (Springer, 2018), pp. 519–527 13. R.E. Bansarkhani, J.A. Buchmann, Improvement and efficient implementation of a lattice-based signature scheme selected areas in cryptography. SAC 8282, 48–67 (2013) 14. N. Genise, D. Micciancio, Faster Gaussian sampling for trapdoor lattices with arbitrary modulus (Cryptol. ePrint Arch., Report, 2017), p. 308 15. D. Micciancio, C. Peikert, Trapdoors for lattices: Simpler, tighter, faster, smaller. in EUROCRYPT (2012), pp. 700–718 16. The PALISADE Lattice Cryptography Libraryn https://git.njit.edu/palisade/ (PALISADE, 2018) 17. Y. Liu, L. Wang, L. Li, X. Yan, Secure and efficient multi-authority attribute-based encryption scheme from lattices. IEEE Access 7, 3665–3674 (2018)

Crypto-SAP Protocol for Sybil Attack Prevention in VANETs Mohamed Khalil and Marianne A. Azer

Abstract VANETs are considered as sub-category from MANETs. They provide the vehicles with the ability of communication among each other to guarantee safety and provide services for drivers. VANETs have many network vulnerabilities like: Working on wireless media makes it vulnerable to many kinds of attacks and nodes can join or leave the network dynamically making change in its topology which affects communication links stability. In VANETs, each car works as a node and router, so if a malicious attacker joins the network, the attacker could send false messages to disrupt the network operation and that is why VANETs are vulnerable to many types of attacks. Denial of service, spoofing, ID disclosure, and Sybil attacks can be launched against VANETs. In this paper, we present cryptographic protocol for Sybil Attacks Prevention (Crypto-SAP) which is a new protocol. Crypto-SAP uses symmetric cryptography to defend VANETs against Sybil nodes. Simulations were done to investigate how Crypto-SAP protocol affects the network performance. Keywords Ad hoc · Sybil attacks · Symmetric cryptography · VANETs

1 Introduction In vehicular ad hoc networks (VANETs), each vehicle acts as a host and a router at the same time. On-board unit (OBU) is an embedded device in the vehicle which makes it work as a node inside the network. Another entity called roadside unit (RSU) exists in VANETs for network management. VANETS are important for safety applications such as warning the drivers if people crossing the street, giving alarm while driving M. Khalil (B) · M. A. Azer (B) Nile University, Giza, Egypt e-mail: [email protected] M. A. Azer e-mail: [email protected] M. A. Azer National Telecommunication Institute, Cairo, Egypt © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_13

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so fast, giving information about the traffic signals and road signs and informing the driver if an accident happens or if there is a traffic jam to take another route. VANETs can support non-safety applications as well such as giving access to some web applications. This type of networks is characterized by having high mobility, frequent changing information, variable network density, better physical protection, and no power constraints. Vehicles can communicate with each other which is called vehicular to vehicular communication (V2V) and can communicate as well with RSUs and that is called (V2R) communication [1]. As VANETs are wireless networks, they are vulnerable to several types of attacks such as channel jamming which can result in denial of service (DoS). Another attack can be implemented against VANETs called timing attack in which a malicious vehicle can delay forwarding messages making them useless, as the receivers get the messages after the needed timing. Ad hoc networks, in general, suffer from many security challenges such as “authentication and identification” which means each node in the network must have single unique identity and authenticated using this identity within such networks. They also suffer from privacy and confidentiality challenges. These challenges can be solved by cryptography and that’s why we are going to introduce Crypto-SAP protocol to resolve these challenges and prevent Sybil attacks. Sybil attacks are very dangerous against VANETs. A malicious vehicle acts as many different vehicles with fake different identities and locations which can result in traffic congestion illusion and making the network works badly. The rest of this paper is organized as follows. Section 2 presents the work that has been done in the literature to mitigate Sybil attacks. In Sect. 3, we present our proposed scheme for Sybil attacks detection in VANETs as well as simulation results. Finally, future work and conclusions are presented in Sect. 5.

2 Literature Review In this section, we classify and present the different approaches that were used in the literature to prevent or detect Sybil attacks in VANETs.

2.1 Reputation Systems Reputation systems were proposed [2, 3] to maintain trust between nodes in VANETs based on having a scoring table at each node for the other nodes inside the network. However, the attackers are capable of changing reputation points by making use of the new created identities especially in the case of multi-nodes Sybil attacks.

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2.2 Time Stamp In [4, 5] detecting the Sybil attacks by using time-stamp series approach based on RSU support was presented. Two messages with same time-stamp series mean that they are Sybil messages sent by one vehicle. However, timing is not accurate within communication and small differences could be used by Sybil attacker to overcome this technique. Another disadvantage occurs when RSUs are implemented in intersections, a malicious vehicle could obtain multiple time stamps that have large time difference from other vehicles by stopping nearby the RSU.

2.3 Key Based In [6, 7] Sybil attacks detection using public key cryptography was proposed. The authors used public key infrastructure for VANETs (VPKI) to secure communication and addressed a mechanism for key distribution. One drawback is overloading the network and processing resources while revoking the certificates when the vehicle needs to join a new RSU network. Revoking the certificate of a malicious vehicle takes long time as well.

2.4 Received Signal Strength It is noticed by researchers that Sybil nodes have high signal strength, so by investigating received signal strength (RSS) value of vehicles, authors of [8, 9] found a threshold to use and detect Sybil attacks. However, transmission signal power can be tuned and make this technique limited.

2.5 Position Authors of [10, 11] presented a new technique to detect Sybil attacks through vehicles’ GPS positions. These positions are periodically broadcasted within the network. This method does not need any extra hardware. Therefore, it is a lightweight technique. However, by sending false GPS positioning information through manipulating network packets this security measure could be bypassed.

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2.6 MAC Address Another technique to detect Sybil nodes was proposed in [12] by using MAC addresses to verify nodes’ identities. This method protects the network against having multiple idetities for the same MAC address. However, MAC addresses are shared inside the network and can be collected by a sniffer, then it can be used as a spoofed MAC address for authentication and performing Sybil attacks.

3 Crypto-SAP Protocol We present Crypto-SAP scheme in this section. Basic requisites for the new protocol are presented in Sect. 3.1 and the new scheme algorithm is presented in Sect. 3.2. Section 3.3 provides security analysis of the protocol using BAN-Logic.

3.1 Prerequisites • • • •

There must be a law to install special OBUs in vehicles. A unique ID is applied for each OBU and it is known by the government. MAC address vs ID database is kept secret in a secure cloud database. The cloud database is wired connected to RSUs.

3.2 Proposed Scheme Algorithm The proposed protocol consists of three main stages. In the first stage of the proposed protocol, the system has to ensure the validity of the requester. In other words, the vehicle which wants to gain authentication to join the RSU network has to provide its MAC address and this MAC address has to be existing in the MAC database connected to the RSU. After verification, the RSU asks from neighbor RSUs to drop this “MAC address” from their authenticated list not to have repetition for the same vehicle on the road. The second stage is for ID verification. RSU sends to the requesting node an encrypted message using the unique ID as the encryption key. “ENCID (MSG)” the node receives the encrypted message and decrypts it automatically using its own ID and gets the message in a plain text. Then it runs its message authentication code on the message using the ID as the main key and outputs a tag “t = HMACID (MSG)” using SHA2, the one way hash function, then encrypts the tag using the ID and sends it to the RSU. The RSU decrypts the incoming message to get the tag and verify it by checking whether the tag is the corresponding output tag for that ID or not

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In the third and final stage, the Network Key (NK) is given to the requesting node if the ID has been verified. NK is a symmetric key generated by RSU for the vehicles to communicate with each other securely after successful authentication. Each RSU has a different NK and these NKs are periodically changed not to have an old key cached in a malicious vehicle and that can cause many problems. The RSU sends its network key encrypted by the ID of the vehicle. A vehicle could contact with other vehicles via encrypted messages using the given NK from the RSU as the encryption-decryption key and contact the RSU by encrypting the messages using its own ID. RSU sends the MAC address of the authenticated vehicle to other authenticated vehicles in the network to be trusted. The flow chart of the proposed system is presented in Fig. 1 while the messages exchanged between the OBU and RSU for the authentication are illustrated in Fig. 2 and Table 1. A session counter is applied for the RSU to count the messages exchanged between itself and the vehicle to protect the network against replay attacks [13]. If the network has a situation of a Sybil attack node, the malicious vehicle will try to send a fake MAC address to the RSU to gain at least one more identity. Then, it will be asked to decrypt a message using the ID related to that MAC address (if it exists already in the database) and it will not be able to do so as IDs cannot be shared by any means over the network. That makes it impossible for a malicious vehicle to perform Sybil attacks. We have done some simulations to validate the system using network simulator 2 (NS2). AODV routing protocol was selected in the simulations because route discovery is initiated only when a certian node needs to be in contact communicate

Fig. 1 Proposed scheme flowchart

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Fig. 2 Messages exchanged between RSUs and vehicles

Table 1 Notations used in the proposed scheme

Symbol

Meaning

||

Appending notation

MSG

Message created form RSU

SHA (MAC)

The one-way hashed function of the MAC address

EncID (MSG)

Encrypt MSG with the ID

DecID (MSG)

Decrypt MSG by the ID

RTAG

Received Tag from OBU

GTAG

Generated Tag at RSU

NK

Network key for the RSU network

with another node which results in less loading on the traffic of the network. Advanced encryption standard (AES) has been used in the encryption and decryption process while SHA2 has been used for hashing. Five scenarios were used to simulate Crypto-SAP against Sybil attackers in VANETs. The five scenarios had ten, twenty, thirty, forty, and fifty nodes. Each scenario was simulated in the presence of Sybil nodes representing ten, twenty, and thirty percent. Figure 3 illustrates the packet delivery ratio in all scenarios while Fig. 4 depicts the throughput within the simulation. It can be seen that the packet delivery ratio (PDR) decreases with the increase of the number of the attacking nodes, which meets the expectations. On the other hand,

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Fig. 3 Packet delivery ratio within the simulation

Fig. 4 Total throughput within the simulation

throughput seems to slightly decrease versus the increase of the number of attacking nodes. We have benchmarked our proposed scheme vs. the MAC Address verification method [12]. For malicious node detection, it can be seen from Fig. 5 that Crypto-SAP method outperforms the MAC verification method [12] in detecting Sybil attacks. The proposed Crypto-SAP protocol does not need any certification authorities (CAs). RSUs are going to need access control management to be able to query the information they need from the cloud database. It has been shown that Crypto-SAP protocol needs only few sending and receiving messages, which minimizes the delay and makes the mobility management much easier. The scheme never transmits clear text messages between RSU and a vehicle or among the vehicles themselves which solves one of the main security challenges in VANET, confidentiality. This system can

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Fig. 5 MAC-trust detection method versus Crypto-SAP

be used besides preventing Sybil attacks by the government to trace stolen vehicles and can help in forensics by analyzing the logs of RSU networks.

3.3 Security Analysis of Crypto-SAP using BAN Logic To prove the security of our protocol, we will perform an analysis with the wellknown BAN Logic algorithm. There are some basic rules of BAN Logic security module and they are: • If P (a network agent) believes that P and Q (another network agent) can communicate with the shared key K and P receives message X encrypted with the Key K ({X}K ), then P believes that Q sent X. This step is to verify the message origin. • If P believes that Q transmitted X and P believes that X has not previously been sent in any other messages, then P believes that Q acts as X is true message. This step is to make sure of the freshness of the message as X may be replayed by an attacker. • If P believes that Q has jurisdiction over X and P believes that Q acts as X is true message, then P believes X. This last step or rule is to verify the origin’s trustworthiness. So, in our algorithm, we have a basic assumption and it is that OBUi and RSU have a symmetric key (IDi) already shared by law as illustrated previously and our main

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goal is to make sure that this particular OBU has that particular ID corresponding to its MAC address. Step 1: While OBU tries to authenticate itself in the RSU network and get the Network Key (NK), it must first initialize the communication with the RSU with its MAC and there is a session counter to make sure of the freshness of the message (1). RSU will assume that it believes the particular OBU has jurisdiction over the MAC message. Step 2: Once the message received by the RSU, it sends an encrypted message (X) using IDi as a symmetric key to the OBU. OBU will assume that it believes RSU has jurisdiction over the encrypted message. Step 3: OBU can decrypt the message if and only if it has the corresponding ID to the MAC sent in step 1. Then it can generate a Tag containing the SHA of ID and X and encrypt that Tag with the ID. Finally, it sends the tag back to the RSU. Step 4: The RSU decrypts the Tag and generate the correct Tag then compare it with the received decrypted Tag. If both Tags are identical then the assumption made in step 1 is correct and RSU verifies the MAC origin (2). RSU also verifies the trustworthiness of the OBU (3). Step 5: OBUi receives NK from the RSU encrypted using IDi and OBU verifies the correction of the assumption made in step 2 (4). From (1), (2), (3) and (4) we can conclude that our algorithm is secure according to BAN Logic security module and our goal has been accomplished.

4 Conclusions The different methods to detect and prevent Sybil attacks in VANETs are classified and proposed in the literature. Cryptographic scheme for Sybil attack prevention (Crypto-SAP) protocol is presented as a new approach to protect VANETs form Sybil attacks. This protocol solves the confidentiality issue among the different nodes in the network. Simulations were done to check the performance and the ability of Crypto-SAP using NS2, and the results show that no Sybil attack succeeded against the proposed scheme, but the performance decreases while the increase of simultaneous Sybil attacks attempts. For future work, it is planned to use this approach in conjunction with other techniques to detect malicious behaviors of the nodes inside VANETs and isolate the malicious nodes out of the network.

References 1. A. Ullah et al., Advances in position based routing towards ITS enabled FoG-Oriented VANETA survey. in IEEE Transactions on Intelligent Transportation Systems (2019) 2. S. Buchegger, J. Le Boudec, A Robust reputation system for P2P and mobile ad hoc networks. in Proceedings WkshpEconomics of Peer-to-Peer Systems (2004)

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3. S. Marti, T.J. Giuli, K. Lai, M. Baker, Mitigating routing misbehavior in mobile ad hoc networks. in Mobile Computing and Networking (2000), pp. 255–265 4. S. Park, et al., Defense against sybil attack in vehicular ad hoc network based on roadside unit support.. in MILCOM 2009 (IEEE, 2009) 5. K. El Defrawy, T. Gene Tsudik, Prism: Privacy-friendly routing in suspicious manets (and vanets). in 2008 IEEE International Conference on Network Protocols (IEEE, 2008) 6. C. Zhang, On Achieving Secure Message Authentication for Vehicular Communications. Ph.D. Thesis, Waterloo, Ontario, Canada 2010 7. A.H. Salem et al., The case for dynamic key distribution for PKI-based VANETs. arXiv preprint arXiv:1605.04696 (2016) 8. G. Jyoti, et al., RSS-based Sybil attack detection in VANETs. in Proceedings of the International Conference Tencon2010 (IEEE, 2010) 9. A. Sohail et al., Lightweight sybil attack detection in manets. IEEE Syst. J. 7(2) (2013) 10. H. Yong, J. Tang, Y. Cheng, Cooperative sybil attack detection for position based applications in privacy preserved VANETs. in GLOBECOM 2011. (IEEE, 2011) 11. G. Yan, O. Stephan, C.W. Michele, Providing VANET security through active position detection. Comput. Commun. 31(12) (2008) 12. P. Anamika, M. Sharma, Detection and prevention of sybil attack in MANET using MAC address. Int. J. Comput. Appl. 122(21), 201 13. K. Jonathan, Y. Lindell, Introduction to Modern Cryptography (CRC Press, 2014)

Managerial Computer Communication: Implementation of Applied Linguistics Approaches in Managing Electronic Communication Marcel Pikhart and Blanka Klímová

Abstract Managerial communication in the past few decades has been much influenced by new trends in computer science bringing new means of communication. The use of computers for communication is ubiquitous and the trend toward computerization will be even stronger soon. However, the intercultural aspect of this communication must not be neglected as it poses a potential threat to company management and all the communication processes in the company and global business environment. Applied linguistics presents a potentially useful tool which could be used for IT developers of communication tools and apps so that they take into consideration critical aspects of human communication which must be implemented in these modern communication tools and methods. Applied linguistics and its practical implementation can prove very useful and can reinforce managerial communication when using modern technological tools. ICT departments, in the past few years, have been focusing more on technological aspects of communication when creating these tools; however, now it is clearly visible that there is an urgent need to have more parameters embedded in the communication tools and one of them is the interculturality aspect because electronic or computer communication is now in its essence global and intercultural. The conducted research into the intercultural communication awareness in ICT departments proves that the need for bringing this topic to priority in education ICT students is crucial to further competitiveness and lossless transfer of information. Keywords Communication · Managerial communication · Business communication · Electronic communication · Computer communication · Intercultural communication

M. Pikhart (B) · B. Klímová Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic e-mail: [email protected] B. Klímová e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_14

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1 Managerial Electronic Communication Managerial electronic communication is ubiquitous [1] all over the world and the trend of making communication more ICT based is evident [2]. Business communication and managerial competencies are considered to be culturally specific [3, 4] and it is a truism in today’s global world of interconnectedness [5, 6]. The interculturality means that what is acceptable for one culture will not be acceptable in another and one cultural context of communication may not overlap with another one, thus causing misunderstanding and conflicts in communication when electronics means of communication are used [7–9]. Business organizations and also ICT departments are based in a given culture, and therefore, necessarily represent and reflect the dominant paradigm of the society in which they are based. The western, European or the American model of communication is therefore not applicable to all global cultures and the ethnocentric communication paradigms are no more valid and available to be used when creating communication tools and apps [10, 11]. The obsolete paradigm of communication based on the western model cannot lead to success and cooperation, nor does it support competitiveness in the global business world [12]. Therefore, it is crucial for the employees of ICT departments to understand new communication paradigms and the paradigm shift in the past few years so that they are well equipped with modern tools which they will implement in their work [13, 14]. It will also improve managerial depth when using these communication tools which reflect modern trends of electronic communication [15– 17]. The utilization of managerial communication and its modern trends by ICT employees is crucial for further development of a healthy business environment and future competitiveness in the global world [18–21]. Management supported by ICT departments is reflected in a coordination of human effort when utilizing human and technological tools to achieve desired aims [12, 22, 23]. It is an interaction between human and non-human tools and resources and this interaction is always necessarily culturally rooted and reflects cultural traits of the users, but also of the designers or creators of communication tools [24]. The famous definition of culture by Hofstede as collective mental programming [25] leads us to view ICT as an area for connecting information and society by the electronic tools which are used for storing, processing, warehousing, mining and transferring information [26, 27].

2 Research Background The conducted research was based on literature review and past research done into the area of business electronic communication and the second half of the research was focused on the awareness and implementation of the intercultural aspects into

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international management in several multinational enterprises doing business in the Czech Republic. The qualitative research was carried out in two multinational corporations doing business in the Czech Republic and focused on potential issues which could influence company internal communication and potential problems which could arise between foreign management and the Czech employees. It was conducted in the first half of the year 2019 in a Taiwanese electronics company and a Belgium ICT company: the top management was questioned (five respondents, all of them foreigners), and middle management (19 respondents, all of them Czech) who communicates with the top management a few times a week as well. The data collection was done by interviews with guided and structures questions about the situation of internal communication using modern communication tools such as the intranet, presentation technology, instant messaging, emails, memos, reports, etc. Standard methods for the analysis of the answers were used.

2.1 Research Hypothesis The hypothesis was that both international management and the Czech as well are not aware of potential cultural issues which could influence the quality of internal electronic communication in the company.

2.2 Research Aim The aim of the research was to bring the attention of the management to the need for increasing the awareness of cultural aspects of doing business which is crucial in everyday managerial practice.

3 Research Results The results of the research are as follows: • Cultural differences which are transferred into communication are not considered by the top management to be very important. They also thought that they will influence communication and information loss only marginally. • Cultural differences which are transferred into communication are very important for the middle management and they see there is a potential for misunderstanding and loss of information during the communication process.

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• Both the top and the middle management are aware that that communication patterns have changed significantly, and they expect ICT employees to implement these changes into other communication tools they are responsible for. • Both the top and the middle management see the ICT department responsible for flawless processes and communication strategies because ICT is managing and maintaining communication from its technological aspect, and therefore, they must be ready to accommodate the needs of the management and global communication environment. Both researched companies, i.e., Taiwanese and Belgium, are well-established global companies so that communication paradigms used by them are more or less equalized and adapted to the global patterns of communication; however, the management still sees a lot of space for improvement and also a lot of potential problems which could cause significant misunderstanding. The biggest issue we observed was that the company based in the Asian culture is a high-context culture as defined by Hofstede; therefore, they would convey the meaning and ideas in radically different ways than Europeans. Explicitness and openness is not valued in these high-context cultures, and on the other hand, it is viewed as arrogance and even aggression. Moreover, European directness was viewed by Asian management as inappropriate and rude leading to taking offense and canceled communication, even when using electronic means of communication. On the other hand, the Czech management viewed this Asian high-context background as a lack of vision, leadership and was no motivation for the employees. Another issue view in these companies regarding electronic communication was the sense of collectivism in Asian management. They usually transferred the individual achievement to the whole team which was not vied positively by the Czech employees. When negotiation over the internet and instant messaging, the indirect answer from the Asian management was not interpreted as denial even if it had been meant so, but the Czech management understood it incorrectly as the space for further negotiation. The basic Asian way to show disagreement by using indirect communication means rather than confrontation was not understood, and therefore, the information transfer was blurred. It was Hofstede who as early as 1980s highlighted the importance for the Europeans to try to understand the means of communication of the Asian culture because it is the only way how to get through various communication pitfalls. This remains as a mutual challenge for both European and Asian to attempt to get over this obstacle. It is the ICT department who is now facing this challenge as the communication of both global (i.e., Asia and Europe) and electronic (i.e., the Internet, apps, instant messaging, intranet). These two parameters are an underlying principle which must be considered; otherwise, our communication can never succeed.

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3.1 Research Limitations The conducted research was conducted on a limited sample, further research is needed, however, we still claim that the results have a significance and help us understand current trends in company electronic communication patterns and problems. Further research could be focused on practical consequences of improving communication efficiency in intercultural environment alongside with creation of a certain manual which could help in achieving better communication skills in the intercultural environment for the employees of ICT departments.

4 Discussion The research proved the hypothesis that both the top management of the change of communication patterns and the middle management are aware of the urgent need. Generally, the top and the middle management are not very much aware of the potential problems in internal communication which is caused by the improper information transfer through electronic media due to cultural conditioning of our communication. The globalizing processes which are under way urge us to look for alliances and other forms of business cooperation throughout continents and cultures, and it is the optimized communication which is the basic building block of this multinational cooperation. Communication in various ways, such as verbal vs nonverbal, direct vs indirect, etc., is the means which influences information transfer and knowledge transfer but also regular everyday management in the global environment. It must not be forgotten that the main role of a subsidiary is to communicate the knowledge of the local market directly toward the parent company so that it can optimize its business performance. Therefore, the knowledge of local culture is a must.

4.1 Practical Implications • Implementation of business communication courses for the management of the companies which operate in the global market. • Implementation of business communication for the students of ICT so that they are well equipped with modern patterns of global communication and not only with their technical expertise. • Implementation of intercultural business communication in the global business environment for students of business so that the graduates are ready and well equipped to work in such a varied world of international business.

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• Further courses for ICT professionals so that they are able to implement new trends and improve communication channels within companies (internal communication) and among them (global business communication) as well.

5 Conclusion The communication mistakes in electronic communication caused by inappropriate interpretation of cultural values and symbols can deeply influence company profitability in a negative way and the paper tried to show how the development of communication competencies in the management of multicultural companies could be helpful in achieving better information transfer. The use of modern means of communication does not automatically create better information transfer environment since information quantity does not necessarily mean information quality. Technological means can enhance communication but only if the management acknowledges that the most important factor of communication is an individual person who exists in a certain cultural context and communication must be seen as a fragile part of any managerial activity. Acknowledgements This paper is part of the SPEV project at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic, and the authors would like to thank Ales Berger for his help when collecting data for this paper.

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Advance Persistent Threat—A Systematic Review of Literature and Meta-Analysis of Threat Vectors Safdar Hussain, Maaz Bin Ahmad, and Shariq Siraj Uddin Ghouri

Abstract Cyber adversaries have moved from conventional cyber threat to being advance, complex, targeted and well-coordinated attackers. These adversaries have come to use Advance Persistent Threat vectors to penetrate classified and large business organizations network by various evasive cyber techniques. This paper presents a systematic review of literature work carried out by different researchers on the topic and also explicates and compares the most significant contributions made by them in this area of APT. The paper addresses the shortfalls in the proposed techniques which will form the areas for further research. Keywords Advance Persistent Threat (APT) attack · Advance Persistent Adversary (APA) · Industrial control systems (ICS) · Review

1 Introduction The world is faced with a very dynamic and evolving threat landscape in the cyber space domain. Ever since computer networks have existed, cyber adversaries and cyber criminals as well as state sponsored cyber offenders have tried to exploit the computer network for notorious or personnel gains. They have by far succeeded in infiltrating not only the public but also the classified secure networks. Although cyber security organizations have provided state-of-the-art solutions to the existing cyber threats, hackers have succeeded in causing colossal damage to many multibillion S. Hussain (B) · M. B. Ahmad Graduate School of Science & Engineering, PAF Karachi Institute of Economics and Technology, Karachi, Pakistan e-mail: [email protected] M. B. Ahmad e-mail: [email protected] S. S. Uddin Ghouri Faculty of Electrical Engineering (Communication Systems), Pakistan Navy Engineering College—National University of Sciences and Technology (NUST), Karachi, Pakistan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_15

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dollar organizations. According to one estimate, cyber adversaries will cost business organization over $2 Trillion damage inform of data espionage and cyber frauds by 2019. Large business and military organizations have invested and are expected to invest millions of dollar in cyber security defence. According to another estimate, organizations plan to invest well over $1 Trillion on cyber security within the period 2017–2021. Despite investing considerably, incidents of cyber infiltration and security breaches continue an upward trend rather than descending. Cyber domain is an ever changing threat landscape that has evolved constantly and finds new dimensions to target enterprise organizations for either data espionage or causes permanent damage hardware system. Among many different threats that prevail today, Advance Persistent Threat (APT) has emerged as most damaging threat that has ever been encountered by security specialists [1]. Contrary to spontaneous untargeted cyberattacks by anonymous hackers, APT is well-coordinated high profile cyber-attack that targets large commercial organizations and government entities such as military and state institutions [2]. Initially, APT was considered an unrealistic threat and unwarranted hype, however latter on it proved to be a reality as more and more organization became the victim of APT attack [3]. This led security researcher to conclude enterprise business organizations, government and military institutions no longer immune to data breaches despite heavy investment in information security [4]. Statistically speaking, cyber security remains a prime concern throughout the world. More than 15% of the enterprise organizations have experienced targeted attack, out of which more than 53% ended up losing their classified sensitive data [5]. Organizations have seen more than 200% growths in initiation of system or data recovery process at the same day and after week of discovering a security breach in their organizations. When it comes to cost suffered by organizations targeted, it is estimated that approx $891 K average loss from single targeted attacks has been inflicted. Targeted attacks follow the same kill chain as outline in Fig. 1. This might lead to suggest that by automatically blocking the reconnaissance phase, a multistage cyber-attack such as APT can be thwarted; however, this is not the case with APT. APT attacks have reached unprecedented level of sophistication and nonlinearity in terms of their evolution and implementation. Therefore, a multiphase strategy such as continuous monitoring of communication, automated detection capabilities and monitoring of all types of threats is required to be implemented while thwarting APT type of attacks. The ascendance of APT has made many organization including government agencies more alert of the type of vulnerabilities that may exist within their network and information systems. Complexity and inimitability of the attack warrants it to go beyond the perimeters of traditional network defence. This approach has allowed organizations to protect it against attacker that has already penetrated in the organization network. APT has drawn considerable attention of security community due to ever increasing and changing threat scenario posed by it. This ever changing threat landscape leads to lack of clear and comprehensive understanding of its inner working of APT research quandary. Before proceeding further, it is imperative that we define APT, in order to get a clear understanding of the intensity of the cyber-attack. National Institute of Standard and Technology

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Fig. 1 Typical life cycle of APT attack

(NIST) defines APT [6]. As an adversary (state sponsored or otherwise) that has the capability at sophisticated levels at its disposal create opportunities to achieve its concurrent objectives by using multiple attack vectors such as cyber, physical and deception. It establishes a strong footing in the physical infrastructure of the target organization. Its sole purpose is to extricate valuable information or in some cases inflict damage to the physical infrastructure of resident hardware. This definition forms the basic foundation of understanding APT and distinguishes it from other cyber-attacks. The paper is organized in five sections. Section-1 details brief introduction of cyber-attack environment with broad introduction to APT and its lethality. Section 2 presents the most renowned APT detection and prevention frameworks including the inner working of targeted attack inform of iterative lifecycle process. Section 3 carries out detail critiques of the presented framework in order to highlight the research gaps in Sect. 4 in summarized form. Section 5 presents conclusion and future research directions.

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2 Advanced Persistent Threat (APT) Advance Persistent Adversary (APA) continues to develop unprecedented technological advancement in skills and orchestrating cyber-attacks with unparalleled dimensions. One of the threats the adversary has been able to formulate and bring into existence is called Advance Persistent Threat (APT) which is dissimilar and lethal than any other traditional cyber threat. The threat persistently pursues its objectives repeatedly over a longer and extended period of time and adapts to any efforts by the defender in detecting it. It also maintains communication level between the host and server in order to ex-filtrate its harvested data or inflict physical damage to hardware entity as and when required [7]. APT attacks are unique and complex in nature as the scope of attacks is very narrow in comparison to other common and unsophisticated attacks. APA designs a malware that aims to remain undetected over a longer duration. This aspect makes APT harder to detect and defend against [1, 6]. Generally, an APT attack is composed of six iterative phase as illustrated in Fig. 1 [5, 6] and defined as following:• Reconnaissance Phase: The first step that cyber adversaries carry out is the phase of reconnaissance during which gathering of intelligence is carried out on targeted organization. This involves both human and technological aspects to gather as much as information about the target organization network. This is used to identify weak areas to infiltrate within the organizations network. In this phase, intelligence reconnaissance involves both from technical as well gathering information from weak human links. • Incursion/Penetration Phase: In this phase of incursion, the APA attempts to penetrate the organization’s network using many diverse techniques. It employs social engineering tactics such as spear phishing technique, injecting malicious code using SQL injection for delivery of targeted malware. It also exploits zero day vulnerability in the software system to find opening into the network. • Maintaining Access Phase: Once inside the organization network, cyber adversaries maintain access to its payload by deploying a remote administration tool (RAT). The RAT communicates with command and control (C&C) server outside the organization. The communication between the host and external C&C server is normally encrypted HTTP communication. This allows it to easily bypass the firewall and defence systems of network by camouflaging itself in order to remain undetected. • Lateral Movement Phase: In this phase, the APT malware moves itself to other uninfected hosts over the network. The other host usually has higher privileged access which provides a better chance of containing classified information as well as better chance of data ex-filtration. • Data Ex-filtration Phase: The last phase involved in the APT cycle is data exfiltration. In this phase, the host uploads its harvested data to any external source or cloud. This process is either done in single burst or takes place slowly without the knowledge of the end user.

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Anonymous cyber-attacks are designed to target larger scale systems with an aim to disrupt normal operation of information systems [8]. In case of APT, the target theatre is quiet diverse. Its attack signatures are very unique than any other cyberattack which makes it highly target centric. APT become more challenging as it sometime involves combination of different attack vectors embedded with some unique strategies customized for the particular target organization. It involves network penetration and data ex-filtration tailored specifically for the target network. The horizon of the attacker in case of APT is fairly small and a well-coordinated targeted attack is aimed, mainly at government institutions and large-scale business organizations. Another characteristic of APT attack is that its attack vector is highly customized and sophisticated. It involves blend of tools and techniques which often are executed simultaneously to launch multiple attack vectors. It either exploits zero vulnerability or by attacking the target through malware, drive-by download or uses sink hole attacks to download APT malware [7]. Its communication is designed to conceal itself among other data packets which makes it harder to detect by normal IDS and anti-virus systems [6]. Another characteristic that defines APT is its objectivity of the attack which includes business rivalry to steal trade secrets, economic motivation or military intelligence gathering. Objectively of APT adversaries change overtime depending upon the target organization that is being targeted. Disruption of organization network or destruction of classified equipment in case of military organization and data pilferage are just few examples that define APT objectivity. APT group are highly staffed with ample technical and financial resources that may or may not operate with the support of state actors and machinery [9].

2.1 Target Organizations Cyber-attacks have moved from being generalized to well-coordinated and targeted and from being simple to being sophisticated. These are by nature extensive and more serious threats than ever faced [4]. With this change in threat landscape, cyber adversaries have now gone beyond the perimeters and moved to target rich environments involving clandestine operations, international espionage, cyber operations, etc. These long term, possibly state sponsored, targeted campaign mainly target following types of organization as summarized in following Table 1 [5].

2.2 Incursion Process of APT Attacker Cyber adversaries normally use traditional methods of incursion into targeted organization network. This often involves social engineering tactics using spear phishing emails targeted towards unsuspecting employee of organization to click on a link [5]. In addition, opening an attachment that appears to come from legitimate trusted

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Table 1 Type of organizations targeted By APT attacks Types of targeted organization

Attack target

Government, Military and Public Sector Organization

Confidential information pilferage Breach of security, disruption of services

Power Systems

Disruption of supply from power grid system as well as gas system

Financial and Corporate Sector

Pilferage of corporate and financial secrets

Health Sector and Medical System

Leakage of medical information and disruption of service

Manufacturing Sector

Leakage of corporate secret and disruption of operation

IT Industry

Disruption of IT services/internet

colleague of the same organization is also another commonly method used for incursion into the network [7]. Other method includes exploiting multiple zero day vulnerability focusing on highly targeted systems and network is also carried out to run multiple attack vectors simultaneously. This includes downloading of additional tools for the purpose of network exploration and assessing various vulnerabilities [10]. As discussed earlier, the objective of cyber criminals is to remain inside the organization network for infinite period of time undetected. This provides them with the opportunity to fully exploit the vulnerabilities of host network and harvest (as well as ex-filtrate) as much data from the organization it can, while remaining undetected under the radar [6]. This is achieved by specifically designing APT to avoid detection at all cost, which may include evasion techniques to make the attack more difficult to detect and determine [11].

2.3 Communication Mechanism Adopted by APT Attacker One of the most essential parts of APT malware is its communication with its command and control (C&C) server from where the persistent malware takes commands and harvested data is ex-filtrated [1]. The communication between the host and server is often low and slow and sometimes is camouflaged with normal network data traffic packets [9]. The communication is mostly HTTP based, which often acts like a normal network traffic. Other communication mechanisms such as peer to peer (P2P) and IRC are also used by cyber criminals who take advantages in terms of penetrability into the network and concealment of communication [11]. Use of HTTP protocol for communication is quiet high and amounts to more than 90% of the cases of APT infiltration [12]. In addition to HTTP-based communication, other existing protocols such as FTP, SMTP, IRC and traditional FTP-based email systems have been frequently leveraged to ex-filtrate or steal intellectual property, sensitive internal business and legal documents and other classified data from the penetrated

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organization [13]. Use of HTTP protocol for communication across network by the attacker mainly provides two advantages for their accessibility. Firstly, the communication protocol is most widely used by all organizations across the globe, and secondly, it generates huge amount of web traffic which allows them to hide their malevolent activity and bypass the organization firewall [2]. Various researchers [11, 14] have focused their detection strategy purely on detecting the communication between APT host and its command and control (C&C) server. This is considered to be the most essential part in APT detection as it is always essential to maintain the communication between compromised host and C&C server. Most of the communication that takes place uses supervised machine learning approach that train on the APT malware. Different malware samples have found different forms of communication methods between compromised host and its command server. In one instance, APT sample malware communicates with its C&C server by encoding the commands and response was sent in the form of cookies using base64 codes [15]. In another instance, APT malware logs the user commands by using key-logger, downloading and executing the code and ex-filtrating the stored data to a remote HTTP C&C server periodically [16]. In another and most common APT cases, spear phishing is used in tricking the user to download or initiate a web session [17]. Use of general web services such as blogs pages has also been used by cyber criminal for infiltration purpose [18].

3 Different Attack Vectors In addition to classified data pilferage from the target organization network, APT has another serious threat dimension that attacks the hardware system. The hardware system is normally an industrial control system or weapon systems in case of military organization. The sole purpose of this APT malware is to permanently make the system dysfunctional by causing hardware irreparable damage to its system controls.

3.1 APT—Industrial Threat Vector Advance cyber adversaries have not only attacked information system for data pilferage but also have found ways to infiltrate and take control of industrial control systems (ICS). This is possible because of tight coupling that exist between the cyber and physical components of industrial system such as power grid systems, nuclear power plants, etc. This aspect of cyber-attack has far reaching implications for human lives which are totally dependent on such systems for their normal daily activities. ICS systems such as Supervisory Control and Data Acquisitions (SCADA) systems mainly found in energy sector corporation have been the main target of cyber adversaries [19]. Despite segregation approaches applied by organizations to protect their network, cyber-attacks continue to persist. Contrary to the claims of isolated

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networks, complete network isolation remains a myth. Insider threats using microstorage devices or non-permanent modem often prove to be fatal and provide access to restricted networks [9]. This allows malware to spread into deep isolated networks that it becomes difficult if not impossible to assess the damage and depth of infiltration into the network.

3.2 APT—Military Threat Vector APA also aims to target military installations networks and weapon systems. This forms the core reason due to which APT is termed as the most dangerous in terms of lethality. It does not only target large business enterprises but their primary targets are military organizations and their weapon systems. Just to give an example of how terrifying this threat can become, recently some authorized malevolent hackers seized control of weapon system being acquired by the US military. The trial was conducted to assess the digital vulnerability of military assets of US [20]. Today, military assets such as radars, fighter jets, satellites, missiles, submarines, etc. and nuclear weapons delivery system of weapons manufacturing nations have become heavily dependent on computer systems. This has allowed cyber adversaries to exploit vulnerabilities present in the core shell of the system. The targeted attacks carried out by sophisticated adversaries have no linearity when it comes to defining the type of organization being attacked. Therefore, we state that APT can have catastrophic consequences if remain undetected and risk of weapons system being used in response to false alarm or slight miscalculation is very much real than a myth.

3.3 APT Datasets One of the most important aspects of any research is the ability to acquire useful dataset to carryout in-depth analysis. Availability of data relating to APT has been difficult to acquire as it is not publicly available and no organization is willing to share due to intellectual property laws and for the reason that this disclosure will publicly jeopardize the goodwill of the organization. Therefore, chances for obtaining specific dataset related to APT are quiet minimal. Symantec Corporation, being a large and renowned anti-malware solution provider, has the world’s largest repository of internet threats and vulnerability database [9]. It has over 240,000 sensors in over 200 countries of the world that monitors cyber related threats. The company also has gathered malicious code from over 133 million client servers as well as gateways in addition to deployment of honey-pot systems that collect cyberattacks across the world [9]. The organization also claims to gathers data of nonvisible threats that possibly include APT attack data. In addition to using the datasets of Symantec Corporation, development of honey-pot system and its deployment on live server is another method to gather APT datasets in addition to generating

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live attack scenarios during implementation. This method can also prove useful in gathering real-time datasets and eventually be used to train the model. One drawback with this approach is that it may yield just ordinary cyber-attacks and malwares and no data useful related to APT. Other available option to gather APT datasets is to use open source dataset available [21]. These datasets are highly anonymous datasets that encompasses numerous months and represents several successful authentication events as well as calibration datasets from users to computers.

4 Research Techniques and Gaps Cyber security researchers have proposed limited, yet significant research theories in countering APT which are claimed to be state of the art and comprehensive approaches. However, in-depth analysis suggests that the proposed framework has limited comprehensibility as well as inability to adapt to diverseness and ever changing cyber threat landscape. In this section, we have carried out comprehensiveness analysis of major approaches proposed by prominent researchers on APT detection and prevention frameworks. We have also summarized the strength and weakness of each approach, to give a comprehensive view of APT threat landscape.

4.1 APT Detection Framework Using Honey-Pot Systems The proposed framework is an implementation of a honey-pot system [4]. Honeypot systems are a computer application that simulates the behaviour of real system being used in organization network. Its sole purpose is to attract cyber-attacker in attacking an isolated and monitored system. The system mimics a real live system of organization which usually an information system. It studies the behaviour of the attacker that exploits the weakness and vulnerabilities found in the information system. The cyber-attacks on the system is recorded inform of logs, which afterwards are analyzed in order to gain comprehensive level of understanding into the types and sophistication of the attacks. In this approach to APT detection, researchers [4] suggested that a properly configured honey-pot system be connected to devised alarming system. This would set an alarm once an APT attack is detected. This would early warn the security experts in organization to take appropriate counter measures accordingly and thus protect the organizations vital assets. This approach offered by researcher may prove to be effective in detecting APT; however, it is a passive defence approach, rather than active one. The approach is simple and straight forward with less resource consumption. This approach is only limited to its system behaviour within its defined domain and cannot go beyond the prescribed domain area. The framework only focus on incoming network traffic and disregards any check on network traffic going outside the network. This leaves a grey area of vulnerability of

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network traffic going outside the organization network, unsupervised and undetected under the sensor.

4.2 Detection of APT Using Intrusion Kill Chains (IKC) In this approach, the author [22] proposes to detect multi stage cyber-attack. This approach uses the properties of Intrusion Kill Chain (IKC) to model the attack at an early stage. The model collects security event logs from various sensors such as host intrusion detection system (HIDS), firewalls and network intrusion detection systems (NIDS) for analysis and further processes it through Hadoop based log management module (HBLMM). Later on, the intelligent query system of this module correlates the event with IKC. Code and behavioural analysis is also carried out using the same module. The approaches also offer predicting IKC by analyzing collected sensors logs and maps each of the suspicious event identified to one of the seven stages of the attack model [23]. Analysis suggests that although this approach proves to be better and efficient, it involves in-depth analysis of network as well as host-related data flows and analysis of all system mined events. This process may prove to be time consuming and tedious task as the amount of data collected would be phenomenal. Moreover, this approach is solely passive one and mainly focuses on solitary analysis unit, i.e. system event data. Possibility of increase in false positives alarms cannot also be ruled out.

4.3 Countering Advanced Persistent Threats Through Security Intelligence and Big Data Analytics This approach [3] offers effective defence against APT and multidimensional approaches. Based upon big data analytic, the researcher intends to detect weak signals of APT intercommunication. It proposes a framework that works on two set of indicators. One being a compromised indicator, which prioritize the hosts based on suspicious network communication and second being the exposure indictor, that calculates the possibility of APT attack. The framework that the researcher proposes is called AUSPEX (named after an interpreter of omens in ancient Rome). It proposes to include human analyst who analyzes inter-system network communications to detect APT threat within internal hosts. The final outcome of the proposed framework is a list of internal hosts arranged according to the defined compromised and exposure indictors. At latter stage, the human analyst will analyze the internal host communication to detect APT signatures within the organization network. Critically analyzing this framework, AUSPEX is based on combining big data analytics with security intelligence of internal as well as external information. Although, this framework proposes a novel approach in detecting APT communication. However, big data

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analytics have been used in the past to detect security violations in varied sets of data such as detecting Stuxnet, Duqa, etc. malwares. In this framework, the researchers focus on assisting the security analyst in analyzing large sets of big data sets which are most likely to be compromised. This approach although offers the subset of hosts that are most likely to have been compromised. However, it is more human specialist centric, who analyzes the most likely APT infected host within the context of big data analytics. This approaches although novel may also generate more false alarms as it does not define any rule set for analyzing compromised big data and purely relies on the skills of human specialist. Secondly, the framework also falls short of prevention of strategy of APT; rather, it focuses on confining towards detection strategies of APT using big data analytics.

4.4 APT Countermeasures Using Collaborative Security Mechanisms In this approach, the researchers [24] have presented a framework for detecting APT malware which targets the system at an early stage of infiltration into the network. In this framework, open source version of Security Information and Event Management (SIEM) is used to detect Distributed Denial of Service (DDOS) attack. This is achieved by analyzing the system files and inter-process communication. The research revolves around the concepts of function hooking to detect zero day malware. It uses a tool called Ambush, which is an open source host-based intrusion prevention system (HIPS). The proposed system observes all type of function calls in operating system (OS) and detects its behaviour for any notable malevolent behaviour that might lead to detection of zero day malware. Critiquing this technique, we suggest that this approach to detect zero day attack using OS function hooking might prove useful to security analyst in detecting APT. Furthermore, the proposed theatrical framework is primarily used in detection of DOS attack on system services and may not work for APT-based malware, as the cyber criminals are highly skilled in obfuscating the intra function calls. Moreover, this framework may yield more false positive alarms as every function call is being monitored by the open source host based intrusion prevention system (HIPS). This method may also lead to updating the anomalies database with more false positive, thus rendering the database insignificant. The framework author suggests to comprehensively automating the zero day attack detection based upon the concept presented in their research paper. The researcher in this case does not provide any pre-infiltration phase (ability of the framework to monitor communication before penetration into the network takes place) detection in their framework. This area also needs to be included in the research in countering APT threat at the very initial stage in order make it a comprehensive approach. The major focus of this approach lies in detecting APT attack after its successful infiltration into the network thus falling short of comprehensibility of the approach.

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4.5 APT Detection Using a Context-Based Framework In this framework, the researchers propose a conceptual model that can inference the malware based on contextual data. It proposes [25] a conceptual framework that is based on the concept of attack tree which maps itself to form an attack pyramid. The attack pyramid forms a conceptual view of attacker goal that takes place inside is an organization network. An attack tree structure is based on the work of Amoroso [26] and Scheier [27] that have introduced the concept of correlating the attack with its lateral plain. The tree is formed by positioning the target of the attack as the root of the tree and various means of reaching the goal as the child node of the root. The attack tree depicts a visual view of vulnerable elements in hierarchal order as well as likely path of attack. This helps the security experts to obtain an overview picture of the security architecture of the attack. The second element of the framework is notated as an attack pyramid which is an extended modification of attack tree model. It positions the attacker goal as the root of the attack pyramid which corresponds to its lateral environment to locate the position of the attack. This detection framework inferences the attack based on context and correlation rules, confidence and risk level to reach a conclusion about the concentration of the threat posed. The detection rules are based on signatures, policy of the system and correlation among them. It is based on a mathematical correlation function that finds relationship between independent events and its corresponding attack pyramid plain. This framework is based on matching the signature and policy with various attack events, which is passive approach and may not lead to detecting APT type of attack.

4.6 APT Attack Detection Using Attack Intelligence The attack intelligence approach proposed by the researchers, [28] monitors and records all system events that occur in the system. Behaviour and pattern matching is carried out between all recorded events with all known attack behaviour and alarm is set whenever a match is found. The proposed approaches also make use of Deep Packet Inspection (DPI) in industrial control system using intelligence tools like Defender and Tofio [28]. Although, the approach is based on pattern matching between behaviour and event similar to formal method approach in language recognition. However, it does not offer state of the art solution to APT traffic detection as no real-time datasets are available to update the attack behaviour database. This may prove to be a big limitation as the approach is only useful as long as the attack behaviour database is up to date with latest rules base.

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4.7 Detection of Command and Control (C&C) Communication in Advanced Persistent Threat Researchers [2, 11] have also proposed another novel method of detection of APT malware. This approach primarily focuses on monitoring communication between comprised host and its command control (C&C) server and leaves other detection aspects. A post-malware infiltration approach, similar to Botnet communication, the communication between C&C server usually takes place inform of bulk HTTP web traffic. This approach is easier for an attacker to camouflage its traffic to avoid being detected by human expert as well as firewall. In this regard, various models have been proposed and tested for detection of APT traffic within web traffic with accuracy level of 99.5% of true positives as claimed. In one of the model presented [2], researchers use unsupervised machine learning approach to detect C&C channel in web traffic. APT follows a different set of communication pattern which is quiet dissimilar to regular web traffic. The approach reconstructs the dependencies between web request (analysis is done by plotting a web request graph) and filtering the nodes related to regular web browsing. Using this approach, an analyst can identify malware request without training a malware model. The first limitations with this framework are that it is a post-infiltration approach. In contrast to other approaches proposed by the researchers, we consider this as a shortfall. Once infiltrated inside the organization network, APT malware may cause some amount of damage to the organization inform of data pilferage until and unless detected at an early stage. Secondly, once the malware successfully infiltrates the network, it would be difficult to detect without comprehensively analyzing the entire communication that takes place from external source and inside network communication between two or more host. In addition, C&C traffic can adapt itself in a way that can mimic requests similar to web browsing traffic, thus hiding itself among bulk of HTTP packets. Another drawback with approach is that it may lead to increase in false positive alarm due to complexity of web request graphs. Therefore, this approach as claimed by the researcher to have high accuracy rate requires a supervised learning approach. An APT detection model can learn and accurately detect and correct APT communication with lesser false positives. Summarized form of APT defence techniques outlined by different researchers including its shortfall is stated as in Table 2. In addition to the limitations identified in the frameworks proposed by various researchers, one area that has been identified by the researcher to further carry forward research is the creation of relevant training and testing datasets for APT [19]. Researchers argue that cyber-attacks constantly change and adapt to defence mechanism placed by organizations in an unsupervised anomaly based detection methods. Therefore, datasets for learning and testing the model are either not available or expensive to create. Therefore, training the model would be difficult, which would result in poor detection rate and thus chances for higher false positives and lower true negatives may increase. Summarized defence mechanisms adopted by various researchers are illustrated in Table 3.

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Table 2 Summary of defence mechanism against APT and its shortfalls Sl. No.

Approaches

Strategy applied

Limitations

1

Honey-pot systems [4]

Deployment of windows-based low interaction honey-pot system as an alarm indicator

• Post-infiltration detection methodology • May only log normal cyber-attacks • No real-time detection and prevention

2

Detection of APT using Analysis of system Intrusion Kill Chains (IKC) event logs and [22] correlation with IKC

• Passive approach mainly focusing on post-infiltration detection methodology • No real-time prevention • Time consuming effort in detection • Chances of false positive high

3

Big data analytics [3]

Analysis of network flow structure for pattern matching

• Post-infiltration detection methodology • Involves interaction of human analysts to carryout analysis of priority threats, which may prove futile and generate higher false positives • No real-time prevention

4

Collaborative security mechanisms [24]

Monitoring the malware activities by implementing Open Source SIEM (OSSIM) system.

• Post-infiltration detection methodology • Monitor all abnormal processes of accessing system software DLL, which may prove to be tedious work for the deployed application and may thus prove to be less efficient • No real-time prevention

5

Context-based framework [25]

Matching the signature • A passive approach and policy with various post-infiltration detection attack events methodology • No real-time detection and prevention of APT attacks

6

APT attack detection using attack intelligence [28]

Deep packet inspection, pattern matching between behaviour and event

• Non-availability of APT datasets to update the database • Post-infiltration detection methodology (continued)

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Table 2 (continued) Sl. No.

Approaches

Strategy applied

Limitations

7

Analysis of Communication between C&C server and compromised host [2, 11]

Analysis of HTTP communication packet for discovering C&C server

• Post-infiltration detection methodology. C and C traffic can adapt itself to mimic benign • Web browsing traffic, which may go undetected • The framework may yield high false positives • No real-time prevention

8

Industrial solutions to APT [28]

Deep Package Inspection, Sandbox Analysis, DNS Analysis, Network Flow Analysis

• Cannot be assessed at this point in time as very little literature is available on the • Industrial solutions provided by renounced cyber security organizations

4.8 Industrial Solutions to APT Different security vendors such as Kaspersky, Symantec and others have also provided various solutions to against APT type of threats. Defence mechanisms such as Network Flow Analysis, Deep Packet Analysis as well as Sandbox Analysis and DNS-based intelligence have been presented by large security organizations [28]. These security mechanisms need to be tested for efficiency and cannot be commented, owing to limited literature available on their product solution. Secondly, these cannot be trusted to be used in the critical organizations of the country as the threat of covert channel presence is always there.

5 Conclusion and Future Research Work Advance Persistent Threat (APT) is a sophisticated and intelligent cyber threat authored by a highly skillful and resourceful adversary. It viewed as the most critical peril to private and public as well as military organizations. APT is quiet disparate than normal traditional cyber-attack as its targets are selective system organizations. The APT malware tends to hide itself for a very long time and bypass normal IDS. It has a rallying mechanism of maintaining communication to its C&C server outside the organization network and sending harvested organizational secrets outside the network. Various research frameworks relating to the topic have been analyzed and its shortfalls have been presented in the paper. Owing to the weakness in the analyzed detection frameworks, there is a need to propose a multilayered/multiphase comprehensive APT detection and prevention framework. We suggest that the framework to

✓ ✓

✓ ✓



Big data analytics [3] ✗





Collaborative security mechanism [24]

Context base framework [25]

APT attack detection ✗ using attack intelligence [28]

Analysis of ✗ communication between C&C server and compromised host [2, 11]

Industrial solution to – APT [28]

3

4

5

6

7

8







Detection of APT using Intrusion Kill Chain (IKC) [22]

2





Honey-pot systems [4]

1

Post-infiltration malware detection

Pre-infiltration malware detection

Framework approaches

Sl. No.

Table 3 APT defence mechanism summarized

















High probability of false positives

















Passive detection approach

















Real-time prevention

















Humanistic analysis

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have capability of defence-in-depth protection in a multilayer protection and detection system protecting the enterprise organization network across different layers. We suggest that the defence strategy should ensure that any APT attack must not bypass one or more of the defence layers. We also suggest that framework to offer a conceptual hybrid implementation strategy that uses AI-based technology such as multiagent system or neural networks. The AI technology offers the capability to design and implement state of the art self-learning framework that adapt to multidimensional evolving cyber threats. It also offers solutions for designing efficient defence system against APT or polymorphic malicious code. Once designed and implemented, the framework should prove to be an efficient in detecting APT threats without raising false alarms. Technology such as a multiagent paradigm delivers best performance of the system and ensures real-time mechanism in detecting and protecting against APT attacks exists. As far as the datasets related to the APT is concerned, we conclude that a good quality training and testing datasets are hard to come by and they are difficult if not impossible to generate. In this regard, we suggest that the best way to generate high quality datasets is to generate own datasets using customized hotpot systems uploaded on an isolated server. A honey-pot solution will generate much needed dataset as and when required for analysis and training the APT model. Additionally, own attack scenario can also prove helpful in deploying honey-pot systems to log a comprehensive training dataset. In order to verify the validity of this approach, same method can also be used to gather datasets which can be applied during the implementation phase. However on the other hand, we can also state that there is no guarantee the dataset collected can be classified as an APT dataset. Therefore, we also suggest that generating high quality datasets related to APT that can be used to train and test detection and prevention framework can be considered a much needed area for future research. Furthermore, there is also a need to carryout analysis and propose an APT defence framework for industrial control systems such as Supervisory Control and Data Acquisition (SCADA) system and also explore different means to efficiently create training and testing data sets to train and test APT prevention framework for industrial control systems. APT prevention framework for military control systems is also another avenue for future research work, provided access to military system is gained to carry out the research.

References 1. J.V. Chandra, N. Challa, S.K. Pasupuleti, Advanced persistent threat defense system using selfdestructive mechanism for cloud security. in Engineering and Technology (ICETECH), 2016 IEEE International Conference on IEEE (IEEE, 2016) 2. P. Lamprakis et al., Unsupervised detection of APT C&C channels using web request graphs. in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (Springer, 2017) 3. M. Marchetti et al., Countering Advanced Persistent Threats through security intelligence and big data analytics. in Cyber Conflict (CyCon), 2016 8th International Conference on IEEE. (IEEE, 2016)

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4. Z. Saud, M.H. Islam, Towards proactive detection of advanced persistent threat (APT) attacks using honeypots. in Proceedings of the 8th International Conference on Security of Information and Networks (ACM, 2015) 5. I. Jeun, Y. Lee D. Won, A practical study on advanced persistent threats. in Computer Applications for Security, Control and System Engineering (Springer, 2012), pp. 144–152 6. J. de Vries et al., Systems for detecting advanced persistent threats: A development roadmap using intelligent data analysis. in Cyber Security (CyberSecurity), 2012 International Conference on IEEE (IEEE, 2012) 7. P. Chen, L. Desmet, C. Huygens, A study on advanced persistent threats. in IFIP International Conference on Communications and Multimedia Security (Springer, 2014) 8. R. Gupta, R. Agarwal, S. Goyal, A Review of Cyber Security Techniques for Critical Infrastructure Protection 9. F. Skopik, T. Pahi, A Systematic Study and Comparison of Attack Scenarios and Involved Threat Actors, in Collaborative Cyber Threat Intelligence (Auerbach Publications, 2017) pp. 35–84 10. J. Vukalovi´c, D. Delija, Advanced persistent threats-detection and defense. in Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015 38th International Convention on IEEE (IEEE, 2015) 11. X. Wang et al., Detection of command and control in advanced persistent threat based on independent access. in Communications (ICC), 2016 IEEE International Conference on IEEE (IEEE, 2016) 12. D. Research, Malware Traffic Patterns (2018) 13. M. Ask et al., Advanced persistent threat (APT) beyond the hype. Project Report in IMT4582 Network Security at Gjøvik University College (Springer, 2013) 14. I. Friedberg et al., Combating advanced persistent threats: From network event correlation to incident detection. Comput. Sec. 48, 35–57 (2015) 15. C. Barbieri, J.-P. Darnis, C. Polito, Non-proliferation regime for cyber weapons. in A Tentative Study (2018) 16. S. McClure, Operation Cleaver. (Cylance Report, 2014 December) 17. R.G. Brody, E. Mulig, V. Kimball, Phishing, pharming and identity theft. Acad. Account. Finan. Stu. J. 11(3) (2007) 18. B. Stone-Gross et al., Your botnet is my botnet: analysis of a botnet takeover. in Proceedings of the 16th ACM conference on Computer and communications security (ACM, 2009) 19. C. Wueest, Targeted Attacks Against The Energy Sector (Symantec Security Response, Mountain View, CA, 2014) 20. G. Coleman, Hacker, Hoaxer, Whistleblower, Spy: The Many Faces of Anonymous (Verso books,2014) 21. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), pp. 504–507 (2006) 22. E.M. Hutchins, M.J. Cloppert, R.M. Amin, Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Leading Iss. Inf. Warfare Sec. Res. 1(1), 80 (2011) 23. P. Bhatt, E.T. Yano, P. Gustavsson, Towards a framework to detect multi-stage advanced persistent threats attacks. in Service Oriented System Engineering (SOSE), 2014 IEEE 8th International Symposium on IEEE. (IEEE, 2014) 24. N.A.S. Mirza et al., Anticipating Advanced Persistent Threat (APT) countermeasures using collaborative security mechanisms. in Biometrics and Security Technologies (ISBAST), 2014 International Symposium on IEEE (IEEE, 2014) 25. P. Giura, W. Wang, A context-based detection framework for advanced persistent threats. in IEEE (IEEE, 2012) 26. B. Schneier, Attack trees. Dr. Dobb’s J. 24(12), 21–29 (1999) 27. E.G. Amoroso, Fundamentals of Computer Security Technology. (PTR Prentice Hall New Jersy, 1994) 28. J.T. John, State of the art analysis of defense techniques against advanced persistent threats. in Future Internet (FI) and Innovative Internet Technologies and Mobile Communication (IITM) Focal Topic: Advanced Persistent Threats (2017)

Construction of a Teaching Support System Based on 5G Communication Technology Hanhui Lin, Shaoqun Xie, and Yongxia Luo

Abstract With the advent of the 5G era, new-type classroom teaching has welcomed a new development opportunity. It is urgent to design a teaching support system which utilizes high-speed network technology and new-type display technology, the existing 5G-supported educational research mainly concentrates on researches based on 5G educational application scenarios and those exploring the implementation path for intelligent education, but the teaching support system based on 5G communication technology has been less investigated. Based on an analysis of application of front-projected holographic display technology, panoramic video technology, haptic technology, VR technology and AR technology, these technologies were integrated to construct a support system applied to classroom teaching, its operating mode was introduced, and the expectation on this research was raised in the end. Keywords 5G · Teaching application · System

1 Research Background Under the policy guidance of “Internet +”, various industries have made fruitful achievements in the information construction in China. Importance has been attached to “Internet + education,” and especially with the advent of the 5G era, new-type classroom teaching has welcomed a new development opportunity. The past classroom teaching support systems only used text, picture, low-format video and so on H. Lin Center of Faculty Development and Educational Technology, Guangdong University of Finance and Economics, Guangzhou, China S. Xie (B) Center of Network and Formation, Guangdong University of Finance and Economics, Guangzhou, China e-mail: [email protected] H. Lin · Y. Luo College of Educational Information Technology, South China Normal University, Guangzhou, China © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_16

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to support classroom teaching. These manifestation modes were not vivid enough, and it is difficult for students to understand and master the learning contents within a short time, which was closely related to the past low-speed communication network. In April, 2018, China Mobile joined hands with ZTE to get through the first 5G phone across China, thus opening the curtain of 5G. In December 2018, the first 5G bus came into being. In April 2019, the first “5G + 8K” superhigh-definition video live broadcast gave its first show in Shanghai, etc. Especially on June 6, 2019, Ministry of Industry and Information Technology of the PRC formally issued 5G business license plates to China Telecom, China Mobile, China Unicom and China Broadcast Network, indicating that 5G has involved various aspects of our life. Under the support of 5G communication network, classroom teaching pattern has embraced a new development opportunity. In order to better support classroom teaching, promote cultivation of innovative talents and improve teaching quality, it is urgent to use highspeed network technology and new-type display technology to design and develop a teaching support system based on 5G communication technology.

2 Literature Review Recently, 5G-supported education has become a research hotspot, but the researches on 5G-supported education mainly focus on 5G educational application scenarios. For instance, Zhao et al. [1] exploratively proposed the connotations and scenarios of 5G application to education in 5G Application to Education: Connotation Explanation and Scenario Innovation (2019) and deemed that when teachers and students were used to new characteristics brought by 5G, heuristic mechanism should be used to reflect upon education and teaching, and the focus should be placed on new requirements, challenges and opportunities faced by students in the future study, work and life; by analyzing interactions among teachers, students, learning environment and learning resources on educational scenarios in the 5G era, Yuan et al. [2] expounded educational scenario element reform under the 5G background, and this reform was mainly embodied by intelligence of teachers’ teaching, autonomation of students’ learning, ever-increasing enrichment of learning environment and enhanced diversification of learning resources, etc., in Educational Scenario Element Reform and Countermeasures in the 5G era (2019); based on the development and application of 5G and AI technologies, Zhang et al. [3] analyzed the evolution of 5G + AI technology and all kinds of application scenarios from the perspective of technology fusion in Incoming Road and Access Road: A New Review on Teaching and Learning in 5G + AI Technology Field (2019). During the construction process of educational informatization 2.0, 5G + AI technology will be an important basis for the educational informatization ecosystem with technical traits of empowering, enabling and augmenting. Researches on 5G-supported technology also include researches exploring implementation paths for intelligent education. For instance, in 5G + AI: Construct A New Intelligent Educational Ecosystem in the “Intelligence +” Era (2019) [4], Lan G S

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et al. discussed opportunities and challenges brought by 5G-empowered intelligent technologies to the intelligent educational ecosystem, how to cope with challenges, and innovate and implement possible paths of intelligent education; in Connotation, Function and Implementation Path of Intelligent Adaptive Learning Platform under the AI + 5G Background—Intelligence-Based Concept Construction of A Seamless Learning Environment [5], Lu took full advantages of various intelligent technologies to construct a big data-based intelligent online learning and education platform under the promotion of AI, 5G and the development of big data technology. However, a void is left in systematic researches on classroom teaching supported by high-speed network technology and new-type display technology, especially design and development of 5G communication technology-based teaching support system using 5G-based front-projected holographic display technology, panoramic video technology, haptic technology, VR technology and AR technology are not involved. Therefore, studying how to construct a teaching support system based on high-speed communication technology will be great realistic significance.

3 Teaching Application of Emerging Technologies 3.1 Teaching Application of Front-Projected Holographic Display Technology Front-projected holographic display, a 3D technology, refers to recording and reproducing real 3D image of an object based on the interference principle [6]. After then, guided by science fiction films and commercial propaganda, the concept of holographic display has extended into commercial activities like stage performance, display and exhibition. However, the holography we understand regularly is not holographic display in strict sense but a kind of holographic-like display technology, which uses Pepper’s ghost, edge blanking and other methods to realize 3D effect. On the Third World Intelligence Congress on May 16, 2019, front-projected holographic display technology was presented. The front-projected holographic display technology, supported by 5G network transmission technology, is quite suitable for teaching application especially for schools running in a remote way or in two different places. It can realize online interaction between teachers and students in different places, thus transcending time and space, and it has been innovatively applied to the teaching field.

3.2 Teaching Application of Panoramic Video Technology Panorama, also called 3D panorama, is an emerging rich media technology, and its main differences from traditional streaming media like video, sound and picture

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are “operability and interaction.” Panorama is divided into virtual reality (VR) and 3D live-action, where the former uses software such as maya, the produced scenario representatives simulating the reality are virtual Forbidden City, Hebei virtual tourism, Mount Tai virtual tourism, etc.; the latter uses digital single lens reflex (DSLR) or street view vehicle to shoot real pictures, which are specially spliced and processed so that students can be placed in a picturesque scene, and the most beautiful side will be displayed out. Teachers can present teaching contents and information vividly to students, innovative teaching applications and enrich teaching means.

3.3 Teaching Application of Haptic Technology On April 1, 2014, Baidu Institute of Deep Learning (abbreviated as IDL) and Baidu Video jointly announced and developed a complicated touch sensing technology, which could detect different frequency ranges of sense of touch in cell phone screen commonly used so that this screen became a sensor, and this sensor could not only accurately recognize multi-point touch control behaviors of the user but also could detect object texture on the screen which could be perceived by the user by touching, e.g., different body parts of human could perceive liquid. Touch interaction has become a standard interaction mode of smartphones and tablet PCs. Therefore, such kind of interaction technology used, we can turn visual contents into reliable sense of touch, so it will be of enormous value for enriching the experience of students.

3.4 Teaching Application of VR/AR Technology VR immersion-type teaching is a teaching pattern featured by multi-person synchronization, real-time interaction and placement of participants in the virtual world [7]. It provides scenarios for teaching contents and immersion-type, practice-based and interactive virtual reality teaching and practical training environment for students [8]. Nowadays, with the advent of 5G, VR teaching has been deeply associated with 5G communication technology so that virtual reality technology has been more extensively and effectively applied to teaching. Under the 5G background, edge computing is utilized to upload videos to the cloud end. The model is constructed using highconfiguration clusters at the cloud end, followed by real-time rendering and real-time downloading of images to the head-mounted display, and the whole process does not take over 20 ms. For courses with strong operability like laboratory courses, VR head-mounted display can exert a greater effect. After wearing the head-mounted displays, which provide different angles of view, students can observe the teacher from front side, lateral side and even hands of the teacher to do the experiment, so as to realize immersion-type teaching.

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4 System Construction In order to overcome defects and deficiencies of the exiting systems, a teaching support system based on 5G communication technology was constructed. Featured by high-speed data interaction, diversified teaching modes and vivid display forms, this system could provide schools with modern classroom teaching tools. In order to solve the existing technical problems, the technical proposal adopted by this teaching support system is: teaching support system based on 5G communication technology, including high-speed wireless communication module, which is connected to front-projected holographic display module, panoramic video module and haptic technology module; high-speed wireless communication module is connected to VR module and AR module; high-speed wireless communication module is connected to high-speed processing memory module, which is then connected to students’ end and teachers’ end. The front-project holographic display module includes holographic display equipment which records and reproduces 3D image of the object according to principles of interference and diffraction, and it is very suitable for teaching application; panoramic video module includes high-definition shooting equipment, which consists of a group of high-definition image acquisition equipment, and they can realize 360° data acquisition; haptic technology module includes ultrasonic peripheral pad which can emit 40 kHz sound wave and create physical sense of touch by regulating ultrasonic wave so that the user can experience the sense of touch of different virtual objects like “edge” and “plane.” The VR module/AR module mainly includes head-mounted display which can totally place participants in a virtual world and provide them with immersiontype, practice-based and interactive virtual reality teaching and practical training environment. The high-speed wireless communication module is connected to a high-speed processing memory module, which is connected to teachers’ end and students’ end, where the former is used to manage the whole system and carry out teaching and the latter is used to classroom learning. This system is set with a high-speed wireless communication module, which is connected to front-projected holographic display module, panoramic video module, haptic technology module, VR module and AR module. The teacher controls and invokes equipment of modules like front-projected holographic display module, panoramic video module, haptic technology module, VR module and AR module through the teachers’ end to organize the classroom teaching. Under the guidance of the teacher, the students study at the students’ end. All teaching processes and teaching data are saved in the memorizer of the high-speed processing memory module.

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5 System Operation Mode The operation process of this system will be hereby listed combining Fig. 1. The system provides a teaching support system based on 5G communication technology, including high-speed wireless communication module 5, a 5G high-speed wireless communication module, and more specifically, 802.11ac communication module, which is commonly known as 5G module. As shown in Fig. 1, the high-speed wireless communication module 5 is connected to front-projected holographic display equipment 1, high-definition shooting equipment group 2 and ultrasonic peripheral pad 3, where front-projected holographic display equipment 1 is LED rotary 3D holographic video playing and animation display equipment, high-definition shooting equipment group 2 is 3D camera equipment group, and ultrasonic peripheral pad 3 can emit 40 kHz sound wave, create physical sense of touch by regulating ultrasonic wave and make it possible for users to experience sense of touch of different virtual objects like “edge” and “plane.”

2

1

3

front-projected holographic display equipment

high-definition shooting equipment group

ultrasonic peripheral pad

VR module

high-speed wireless communication module

AR module

4

6

5 students’ end

high-speed processing memory module

7 Fig. 1 The operation process of the system

8

teachers’ end

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As shown in Fig. 1, the high-speed wireless communication module 5 is connected to VR module 4 and AR module 6, where VR module 4 is implemented using a head-mounted display which can place the participants totally in a virtual world and provide students with immersion-type, practice-based and interactive virtual reality teaching and practical training environment, and AR module 6 is also implemented using a head-mounted display and embeds the virtual world in the real world for the sake of interactive teaching. As shown in Fig. 1, the high-speed wireless communication module 5 is connected to the high-speed processing memory module 8, which is connected to teachers’ end 9 and students’ end 7, where the former is used to manage the whole system and carry out teaching and the latter is used for classroom learning. As shown in Fig. 1, teachers’ end 9 consists of personal computer, intelligent terminal, etc. Students’ end 7 consists of smartphone, tablet PC, etc. The two are connected to each teaching support device via the high-speed wireless communication module 5, so as to realize the goal of controlling the utilization of modern teaching equipment; the high-speed wireless communication module is connected to high-speed processing memory module 8, which is used for data saving and processing.

6 Conclusion The advent of 5G has generated revolutionary influences on various walks of life, and educational industry is no exception. We have been aware that all kinds of cutting-edge technologies such as front-projected holographic display technology, panoramic video technology, haptic technology, VR technology and AR technology, which are supported by 5G communication technology, will be applied to the educational industry. Especially when applied to classroom teaching, it can improve students’ learning efficiency, improve teaching quality and promote cultivation of innovative talents. These new-type technologies were integrated in this paper to construct a teaching support system based on 5G communication technology. The operation mode of this system was discussed, expected to provide a design idea for our peers. The subsequent research work of this research group will apply this system to teaching practice and repeatedly optimize it as the 5G communication technology enters campuses. Acknowledgements This work was supported by the Education Project of Industry-University Cooperation (201801186008), the Guangdong Provincial Science and Technology Program (2017A040405051), the Higher Education Teaching Reform Project of Guangdong in 2017, the “Twelfth Five-Year” Plan Youth Program of National Education Information Technology Research (146242186), the Undergraduate Teaching Quality and Teaching Reform Project of Wuyi University (JX2018007), the Features Innovative Program in Colleges and Universities of Guangdong (2018GXJK177, 2017GXJK180).

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References 1. X. Zhao, L. Xu, Y. Li, 5G in education: connotation and scene innovation—new thinking on optimization of educational ecology based on emerging information technology. Theory Educ. Technol. (4), 5–9 (2019) 2. Y. Lei, Zhang Yanli, L. Gang, The change of elements of educational scene in 5G era and the strategies. J. Dist. Educ. 37(3), 27–37 (2019) 3. K. Zhang, Z. Xue, T. Chen, J. Wang, J. Zhang, Incoming road and approaches: New thoughts on teaching and learning from the perspective of 5G + AI. J. Dist. Educ. 37(3), 17–26 (2019) 4. G. Lan, Q. Guo, J. Wei, Y.X. Yu, J.Y. Chen, 5G + intelligent technology: Construct a new intelligent education ecosystem in the “intelligence+” era. J. Dist. Educ. 37(3), 3–16 (2019) 5. W. Lu, Connotation, function and implementation path of intelligent adaptive learning platform in the view of AI + 5G: Based on the construction of intelligent seamless learning environment. J. Dist. Educ. 37(3), 38–46 (2019) 6. Da Chu, J. Jia, J. Chen, Digital Holographic Display Encyclopedia of Modern Optics, 2(4) edn., 2018, pp. 113–129 7. T.-K. Huang, C.-H. Yang, Y.-H. Hsieh, J.-C. Wang, C.-C. Hung, Augmented reality (AR) and virtual reality (VR) applied in dentistry. The Kaohsiung J. Med. Sci. 34(4), 243–248 (2018) 8. A. Suh, J. Prophet, The state of immersive technology research: A literature analysis. Comput. Hum. Behav. 86, 77–90 (2018)

Intelligent Hardware and Software Design

Investigating the Noise Barrier Impact on Aerodynamics Noise: Case Study at Jakarta MRT Sugiono Sugiono, Siti Nurlaela, Andyka Kusuma, Achmad Wicaksono, and Rio P. Lukodono

Abstract The high noise exposure at MRT station due to the noises of the speeding trains can cause health problems for humans. This research aims to reduce the noise impact due to the speeding trains by modifying the design of the noise barrier on the outdoor MRT in Jakarta. The first step conducted in this research is a literature review on aerodynamics noise, CAD model, Computational Fluid Dynamics (CFD) and Computational Aeroacoustics (CAA), and human comfort. Furthermore, it was conducted a design of 3D noise barrier model and 3D train model in one of the outdoor MRT stations in Jakarta using the CAD software. The simulation using the CFD and CAA was implemented to acknowledge the distribution of airflow and sound occurred. The vorticity configuration resulted from the simulation was used as the foundation to modify the noise barrier. The addition of holes on the noise barrier for every 5 m is able to decrease the noise impact significantly. One of the results is that the existing aerodynamic noise of 1.2 dB up to 3.6 dB can be reduced to close to 0 dB with only minor noises around the holes. Scientifically, it can be stated that a way to lower the noise made by the train movement is by creating the right design of a noise barrier that can neutralize the source of the noise. Keywords Aerodynamics · CFD · Noise barrier · Aeroacoustics · MRT

S. Sugiono (B) · R. P. Lukodono Department of Industrial Engineering, Brawijaya University, Malang, Indonesia e-mail: [email protected] S. Nurlaela Department of Urban Area Planning, ITS, Surabaya, Indonesia A. Kusuma Department of Civil Engineering, Universitas Indonesia, Jakarta, Indonesia A. Wicaksono Department of Civil Engineering, Brawijaya University, Malang, Indonesia © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_17

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1 Research Background Noise is an unwanted disruptive voice or sound. The noise intensity is defined by one decibel or dB while frequency is defined by the Hz unit. The noise intensity that can be tolerated by humans during the working hour (which is about 8 h) is at a maximum of 85 dB. A noise with high intensity can damage human hearing, for instance, by lowering the hearing range up to deafness. Besides, it can cause health problems, such as increased blood pressure and heartbeat that are potentially lead workers to suffer from a heart attack and digestive problems. Meanwhile, lowintensity noise can cause stress, headache, sleep deprivation, concentration loss, and declining performance of workers. Modes of transportation, such as cars, ships, airplanes, and trains are included as sources of noise that do not only affect the passengers but also people in its surroundings. Multiple researchers have discussed noise related to mass rapid transit (MRT), including Pamanikabud and Paoprayoon [8] that explained the noise level measurement on elevated MRT stations. This research also argued that MRT noise contributes significantly to sleep deprivation on the residence around railways. Roman Golebiewski [2] in his research entitled “Influence of turbulence on train noise” argued that the propagation on outdoor noise is caused by several aspects including air absorption, wave-front divergence, ground effect, diffraction at obstacles, aerodynamics turbulence, and refraction. Zhang et al. [12] stated that the interior noise in high-speed trains causes discomfort to the conductor and, therefore, becomes an important part of a design. Latorre Iglesias et al. [4] explained the model used to predict aerodynamics noise using photos of high-speed trains. This model is able to reduce the number of simulations using CFD and CAA. Jik Lee and Griffin [3], Dai et al. [1], and Vittozzi et al. [11] emphasized that noise caused by MRT affects the residents of its surrounding environment. Land Transport and Authority explained that the presence of a noise barrier will decrease 10 dB of noise in the surrounding environment. In fact, the MRT in Jakarta is still very noisy, therefore, further investigation is necessary. Based on the background above, it is very necessary to conduct a study on the impact of aerodynamics noise of MRT trains in Jakarta, Indonesia, especially for the outdoor elevated stations. The design of the noise barrier will be simulated using the CFD and CAA simulation to acknowledge the existing condition. The overview of the condition of train stations is an inseparable part of studying aerodynamics and aeroacoustic on MRT motion.

2 Material and Research Methodology 2.1 Aerodynamics Noise Noise is a sound unwanted by humans or a sound that is unpleasant to humans who are exposed to it. Aerodynamics noise is sound caused by airflow due to turbulence

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or formulation of spiraling vortices. The direction and shape of airflow can also cause alternation in the level of noise from certain sources. In each working environment, there are many reasons to keep the sound at the appropriate level. The sound above this level is considered “noise”. Noises can disrupt labors’ attention, creating an unsafe working environment. An aeroacoustics investigation focuses on the sound source caused by turbulence or shifts in the aerodynamics surface. Lighthill [5] explained the basic aerodynamics noise theory based on the understanding of Navier–Stokes’ equations. Watson R., Downey O [9] in his book explained that the area or place wherein a sound is heard or measured is known as ‘sound field’. The sound field, based on the method and environmental effects, is classified into two categories: free field and diffuse field (Near-field and Far-field). The sound channeled from a contributing source is not combined into a unified sound, therefore, the sound radiation produces a variance in Sound Pressure Level (SPL) up to the measurement position that has been moved with a distance of one or double the longest sound dimension. If the level of acoustic sound pressures measured is close to the source, they usually show a quite big variance to the receiver’s position. In light of this, the position of the farfield receiver is the best choice, meanwhile, far from the source, acoustic pressure, and speed become merely related, such as in-plane wave. Fflowcs William Hawkings (FW-H) is a far-field acoustic model that implements the Lighthill–Curle equation to determine the sound signal propagation from the sound source to the receiver with the types of sound sources of the monopole, dipole, and quadruple. Figure 1 depicts the process of sound signal propagation to the receiver in the far-field x and t position of the time period of the sound signal. The primary unit of noise is the decibel (dB) as a logarithmic function. Generally, noise in aerodynamics is defined as Sound Pressure Level (SPL). The SPL formulation is written down in Eq. (1) below [9]: SPL(dB) = 20 log10 (P/Pref ) where Pref = sound power reference (=2 × 10−5 Pa).

Fig. 1 A far-field sound generation in turbulence current for M  1

(1)

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2.2 Research Methodology The objective of this research is to investigate the impact of the noise barrier on the residents in the station and also in the environment around the elevated MRT station, Jakarta. The design of the noise barrier and design of MRT station become the main aspect that must be implemented to reduce the existing noise. In this research, it was conducted an investigation on the noise barrier which is an elevated railroad fence on the elevated MRT station. In MRT Jakarta, there are seven elevated stations on a variance of heights between 10 m up to 30 m. Based on the initial observation, it was acquired that the sound sources are the trains rubbing against the railways, aerodynamic noise, and vehicles moving on the roads underneath the MRT stations. Figure 2 depicts the general overview of elevated MRT stations in Jakarta, and one shows the physical form of an elevated railroad fence made of concrete. Figure 3 depicts the steps of research implementation which includes observation and initial measurement, the 3D CAD design for trains and elevated train stations, the CFD and CAA simulation, and analysis/discussion. The CFD simulation was used to investigate the airflow, while the CAA was used to acknowledge the noise magnitude produced from the airflow. There are several instruments used to acquire the existing data, such as air velometer, camera, noise meter, and roll meter. The air velometer was to measure the wind velocity, temperature, and relative humidity. The noise meter was used to measure the sound pressure level (dB) on certain points. Other data collected were the dimension of trains (width = 2.9 m, height = 3.9 m, and length 20 m with a total of six rail cars), dimension of railroad noise barrier with a height of 1.5 m, thickness of 10 cm made of concrete, railroad width = 1.067 m, and a maximum outdoor speed = 80 km/hr. There were two types of simulations being compared, aerodynamics noise produced by one train and two trains passing.

3 Results and Discussion Overpass railways, or also known as a noise barrier on elevated railways or MRT, are used to protect the trains, as well as allocating the noise happening to the surrounding environment. However, on the other side, it will increase the noise at waiting rooms in MRT stations. Based on the result of field research observation in MRT Jakarta, the noise in elevated MRT stations came from the sound of friction between trains and railways, sound of vehicles around the stations, and additional sound from wind gust made by the speeding trains. Based on the measurement of the several existing elevated MRT stations, it is acquired that the noise existing in elevated MRT stations is considered high with an average = 82.66 dB, with maximum value during train arrivals of = 89.3 dB. Meanwhile, inside the train, the noise value experienced by the passengers when passing the overpass railways is 84.01 dB. In order to remove or reduce the noise impact, it is necessary to investigate the influence of the noise barrier or elevated railroad fence on noise distribution. Müller

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Fig. 2 An example of elevated MRT station in Jakarta and noise barrier design

and Obermeier [7], Manela [6] and Kelly et al. [13] explained the presence of a strong and clear relationship between the flows of vorticity fluid and sound generating. Figure 4 is a contour of air vorticity configuration which is formed by the passing trains on maximum speed on the elevated railways, which is 80 km/hr. The first picture elaborates on the shift of air pattern for a single train while the second is when two trains were passing. From the picture, it can be explained that there is an interesting moment in which the vorticity will be more in between the two rail cars, yet in the ends, the trains, respectively, neutralize each other so that the vorticity value becomes less (red dashed circle).

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Fig. 3 Flowchart of research steps for reducing noise in the elevated MRT station

Fig. 4 Contour of vorticity value on the noise barrier for a single train and double trains passing

The vorticity existing in the CFD simulation result can be used to elaborate the aeroacoustics value in a form of sound pressure level (dB) value. Figure 5 has 4 graphics that explain the noise difference due to the aerodynamics when one train passed (5a) and when two trains passed against each other (5b). From Fig. 5a, it can be explained that the noise will increase gradually from the front end (1.2 dB) to the back of the rail car (3.6 dB) for the surrounding noise between the noise barriers and tend to have no impact (0 dB noise) to the sides of trains with other railways. Figure 5b can explain that two trains passing against each other generally will reduce the noise

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Fig. 5 Sound pressure level (dB): a existing noise barrier—one train, b existing noise barrier—two trains passing, c modification noise barrier—single train, d modification noise barrier—two trains passing

that occurs due to the airflow, this can be further explained that the existing vorticity neutralizes each other due to the movement of train 1 and train 2. The size of noise value received by the passengers on the train and around the MRT stations must be reduced appropriately. The profile overview of the noise distribution created by the aerodynamics can be used as a foundation to reduce other noise sources, such as the one made by the friction between the wheels and railways. The noise release around the overpass railways can be implemented by modifying the shape of the existing noise barrier. Figure 5c and d show the impact of holes on noise barrier for a single train and double trains when passing against each other. From the two pictures, it can be explained that the modification is able to reduce the noise significantly (close to 0 dB) and the noise remains only in the noise barrier holes. Bendtsen et al. [10] and Ishizuka and Fujiwara [14] clearly explained the importance of material, shape, and size of the noise barrier to eliminate the occurring noise.

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4 Conclusion This paper has succeeded in simulating the aerodynamics condition for a train that moves alone or when passing against each other on elevated railways. The existence of noise barrier, like a canal, is proven to capitalizing the present noise sources so that it becomes more uncomfortable with noises in the elevated MRT stations = 89.3 dB when the train arrives and inside the train with a noise value of 84.01 dB. 89.3 dB. The experiment result of noise barrier modification by adding holes every 5 m on both the right and left sides of the train can reduce the noise significantly. For instance, the aerodynamic noise is able to be neutralized up to close to zero dB throughout the train, with a minor noise in the noise barrier holes. This research has become the foundation of future noise barrier design optimization. The future research on environmental impact due to noise barrier modification would be a distinctive challenge. Acknowledgements Thanks to the Ministry of National Education of the Republic of Indonesia for supporting this paper. The authors are also grateful to the Bioengineering research group and the Laboratory of Work Design and Ergonomics, Department of Industrial Engineering, the Brawijaya University, Malang Indonesia for their extraordinary courage.

References 1. W. Dai, X. Zheng, L. Luo, Z. Hao, Y. Qiu, Prediction of high-speed train full-spectrum interior noise using statistical vibration and acoustic energy flow. Appl. Acoust. (2019). https://doi.org/ 10.1016/j.apacoust.2018.10.010 2. R. Goł¸ebiewski, Influence of turbulence on train noise. Appl. Acoust. (2016). https://doi.org/ 10.1016/j.apacoust.2016.06.003 3. P. Jik Lee, M.J. Griffin, Combined effect of noise and vibration produced by high-speed trains on annoyance in buildings. J. Acoust. Soc. Am. (2013). https://doi.org/10.1121/1.4793271 4. E. Latorre Iglesias, D.J. Thompson, M.G. Smith, Component-based model to predict aerodynamic noise from high-speed train pantographs. J. Sound Vib. (2017). https://doi.org/10.1016/ j.jsv.2017.01.028 5. M.J. Lighthill, A new approach to thin aerofoil theory. Aeronaut. Q. (1951). https://doi.org/10. 1017/s0001925900000639 6. A. Manela, Sound generated by a vortex convected past an elastic sheet. J. Sound Vib. (2011). https://doi.org/10.1016/j.jsv.2010.08.023 7. E.A. Müller, F. Obermeier, Vortex sound. Fluid Dyn. Res. (1988). https://doi.org/10.1016/01695983(88)90042-1 8. P. Pamanikabud, S. Paoprayoon, Predicting mass rapid transit noise levels on an elevated station. J. Environ. Manage. (2003). https://doi.org/10.1016/S0301-4797(02)00219-0 9. R. Watson, O. Downey, The Little Red Book of Acoustics: A Practical Guide (Blue Tree Acoustics, 2008) 10. H. Bendtsen, E. Kohler, Q. Lu, B. Rymer, Acoustic aging of road pavements. in 39th International Congress on Noise Control Engineering 2010, INTER-NOISE 2010 (2010) 11. A. Vittozzi, G. Silvestri, L. Genca, M. Basili, Fluid dynamic interaction between train and noise barriers on High-Speed-Lines. Procedia Eng. (2017). https://doi.org/10.1016/j.proeng. 2017.09.035

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12. X. Zhang, R. Liu, Z. Cao, X. Wang, X. Li, Acoustic performance of a semi-closed noise barrier installed on a high-speed railway bridge: Measurement and analysis considering actual service conditions. Measur. J. Int. Measur. Confeder. (2019). https://doi.org/10.1016/j.measurement. 2019.02.030 13. M.E. Kelly, K. Duraisamy, R.E. Brown, Predicting blade vortex interaction, airloads and acoustics using the Vorticity Transport Model. in AHS Specialists Conference on Aerodynamics 2008 (2008) 14. T. Ishizuka, K. Fujiwara, Performance of noise barriers with various edge shapes and acoustical conditions. Appl. Acoust. 65(2), 125–141 (2004). https://doi.org/10.1016/j.apacoust.2003. 08.006

3D Cylindrical Obstacle Avoidance Using the Minimum Distance Technique Krishna Raghuwaiya, Jito Vanualailai, and Jai Raj

Abstract In this article, we address the motion planning and control problem of a mobile robot, herein considered as a navigating in a workspace with obstacle. We use Lyapunov’s second method to control the motion of the mobile robot. The minimum distance technique, incorporated for the avoidance of the cylindrical obstacle in a 3D space, used for the first time. Here, the minimum distance between the center of the point mass, representing a mobile robot, and the surface of the cylinder is calculated; thus, only the avoidance of a point on the surface of the cylinder is considered. We propose a set of artificial potential field functions that can be used for the avoidance of the cylindrical obstacle, and for the attraction to the assigned target. The effectiveness of the suggested robust, continuous, nonlinear control inputs is verified numerically via a computer simulation. Keywords Cylindrical obstacle · Lyapunov functions · Minimum distance technique · Stability

1 Introduction Recently, there has been numerous research on mobile robots and its applications [1, 2]. The findpath problem, which essentially is a geometric problem has gained huge support and attention over the years. It involves the identification of a continuous path that allows a mobile robot to reach its predefined final configuration from its initial configuration while ensuring collision and obstacle avoidance that may exist in the space [3, 4]. There are various motion planning and control (MPC) algorithms for collision-free navigation of single and multiple robotic systems, and researchers have attempted a number of different techniques, strategies and schemes [1, 5, 6]. Multi-agent systems research is more favored for its efficiency with respect to time, cost, harnessing preferred behaviors, achieve course not executable by an individual K. Raghuwaiya (B) · J. Vanualailai · J. Raj The University of the South Pacific, Suva, Fiji e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_18

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robot, to name a few [7]. There are three motion planning archetypes for robots working in a given domain with obstructions, namely (1) cell decomposition-based motion planning, (2) road maps, and (3) artificial potential field (APF) [1]. Here, we utilize the Lyapunov controllers, via the Lyapunov’s second method [1], which is an APF strategy for the control of point masses. Spearheaded by Khatib in 1986 [8], the impact-free way for a self-governing robot is dictated by setting up APFs with horrendous shafts around the obstruction and appealing posts around the objectives. The advantages of APF method include easier implementation, easier analytical representations of system singularities, limitations and inequalities, and the ease of modeling via the kinematic and dynamic equations of a robot. However, it is always a challenge in any APF method to construct total potentials without local minima. To tackle this, various analysts have effectively considered this issue by means of the utilization of special functions [9]. We introduce the avoidance of cylindrical obstacles via the minimum distance method used in ([6]) to construct parking bays and avoid line obstacles. It involves the computation of the minimum distance from the center of the mobile robot to a point on the surface of the cylinder and thus the avoidance of the resultant point on the cylinder surface. This strategy characteristically ensures that the repulsive potential field function is just for the nearest point on the outside of the cylindrical chamber to the center of the point mass at all times. Section 2 represents the kinematic model of the mobile robot; in Sect. 3, the APF functions are defined; subsequently, in Sect. 4, the Lyapunov function of the system is constructed and the robust nonlinear continuous control inputs for the mobile robot is extracted; in Sect. 5, the stability analysis of our system is performed; in Sect. 6, the motion control for the point mass representing the mobile robot is simulated and the results are presented to verify the effectiveness and robustness of the proposed controllers; and finally, the conclusion and future work are given in Sect. 7.

2 Modeling of the Point Mass Mobile Robot A simple kinematic model for the moving point mass is proposed in this section. A two-dimensional schematic representation of a point mass with and without obstacle avoidance is shown in Fig. 1. We begin with the following definition: Definition 1 A point mass mobile robot, Pi , is a sphere of radius rpi centered at (xi (t), yi (t), zi (t)) ∈ R3 for t ≥ 0. Specifically, the point mass represents the set   Pi = (Z1 , Z2 , Z3 ) ∈ R3 : (Z1 − xi )2 + (Z2 − yi )2 + (Z3 − zi )  rpi2 At every t ≥ 0, define the instantaneous velocities of Pi as (vi (t) , wi (t) , ui (t)) = (˙xi (t) , y˙ i (t) , z˙i (t)). With the initial conditions taken at t = t0 ≥ 0 for Pi , the differential system governing Pi :

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i

i

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Z1 Fig. 1 2D illustration of Pi in the Z1 − Z2 plane

x˙ i = vi (t) , y˙ i = wi (t) , z˙i = ui (t) , xi0 := xi (t0 ) , yi0 := yi (t0 ) , zi0 := zi (t0 ) ,

 (1)

for i = 1, . . . , n. The objective is to steer Pi to the target configuration in R3 .

3 Use of the APF Functions Using kinodynamic constraints, the collision-free trajectories of Pi are detailed out. We want to design the velocity controllers vi (t) , wi (t) and ui (t) for i = 1, . . . , n so that Pi explored securely in a 3D workspace toward its objective while avoiding cylindrical obstacles. To obtain a feasible collision-free path, we utilize the APF functions in the Lyapunov-based control scheme (LbCS) to design the new controllers.

3.1 Attractive Potential Field Functions Target Attraction In our MPC problem, we want Pi to navigate from some initial configuration and converge toward the center of its target. We characterize the fixed objective to be a sphere with focus (τi1 , τi2 , τi3 ) and span of rτi . The point mass mobile robot Pi needs to be attracted toward its predefined target, hence, we consider the accompanying Vi (x) = where i = 1, . . . , n.

 1 (xi − τi1 )2 + (yi − τi2 )2 + (zi − τi3 )2 , 2

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Auxiliary Function The seminal goal is to ensure that the point mass mobile robot stops at its assigned target. To ensure this, a function of the form G i (x) = Vi (x) ,

(3)

for i = 1, . . . , n is proposed to ensure the intermingling and convergence of Pi to its assigned objective. This function is then combined via multiplication with every one of the repulsive potential field functions.

3.2 Potential Field Functions for Obstacle Avoidance Workspace Boundary Limitations The motion of Pi is restricted within the confinement of a workspace which is a 3D framework of dimensions η1 × η2 × η3 . The walls of the robots workspace will be considered as fixed obstacles in the LbCS. Along these lines, for Pi to maintain a strategic distance from these, we propose the accompanying functions ⎫ W Si1 (x) = xi − rpi , ⎪ ⎪ ⎪ W Si2 (x) = η2 − (yi + rpi ) , ⎪ ⎪ ⎪ ⎬ W Si3 (x) = η1 − (xi + rpi ) , W Si4 (x) = yi − rpi , ⎪ ⎪ ⎪ ⎪ W Si5 (x) = zi − rpi , ⎪ ⎪ ⎭ W Si6 (x) = η3 − (zi + rpi ) ,

(4)

for i = 1, . . . , n. The workspace is a fixed, closed, and bounded region. Since η1 , η2 , η3 > 2 × rpi , the functions for the avoidance of the walls in the given workspace are positive. Cylindrical Obstacles The surface wall of the cylinder are fixed obstacles that needs to be avoided by Pi . We begin with the following definition: Definition 2 The kth surface wall is collapsed and buckled into a cylinder in the Z1 Z2 Z3 plane between the following coordinates, (ak , bk , ck1 ) and (ak , bk , ck2 ) and with radius rck . The parametric representation of the kth cylinder with height (ck2 − ck1 ) can be given as Cxk = ak ± rck cos χk , Cyk = bk ± rck sin χk and Czk = ck1 + λk (ck2 − ck1 ) where χk : R → (− π2 , π2 ) and λk : R2 → [0, 1]. To facilitate the avoidance of the surface wall of the cylinder, we adopt the MDT from [6], which computes the minimum distance between the center of Pi and the surface of the kth cylinder. The coordinates of this point can be expressed as Cxik = ak ± rck cos χik , Cyik = bk ± rck sin χik and Czik = ck1 + λik (ck2 − ck1 )

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yi − bk 1 and λik = (zi − ck1 ) , and the saturation xi − ak ⎧ (ck2 − ck1 ) ⎨ 0 , if λik < 0  π π . functions are given by λik = λik , if 0 ≤ λik ≤ 1 and χik = − , ⎩ 2 2 1 , if λik > 1 For the avoidance of the closest point on the surface of the kth cylinder by Pi , the following function is constructed

where χik = tan−1

COik (x) =



 1 (xi − Cxik )2 + (yi − Cyik )2 + (zi − Czik )2 − (rpi )2 , 2

(5)

where i = 1, . . . , n and k = 1, . . . , m.

4 Nonlinear Controllers Next, we design nonlinear control laws pertaining to system (1).

4.1 Lyapunov Function Using the tuning parameters, the Lyapunov function or the total energy function for system (1) is given below: L (x) :=

n 

 Vi (x) + G i (x)

i=1

 m  k=1

 ℘is ζik + COik (x) s=1 W Sis (x) 6

 .

(6)

4.2 Nonlinear Controllers Next, we look at the time derivative of various components of (6) along the solution of the kinematic system (1) and force it to be at least negative semi-definite. Using the convergence parameters, αi1 , αi2 , αi3 > 0, and upon suppressing x, the constituents of the controllers are of the form

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 m 6   ζik ℘is ℘i1 ℘i3 fi1 = 1 + + + Gi (xi − τi1 ) − G i 2 COik W S S Si3 )2 (W ) (W is i1 k=1 ⎞ ⎛ s=1 (yi − bk ) − Cx sin χ 1 ∓ r m (x ) ik k ik  ζik ⎜ i 2 ⎟ (xi − ak )2 + (yi − bk ) ⎟, ⎜ −G i ⎠ ⎝ (yi − bk ) COik ± (y − Cy ) r cos χ k=1 i ik k ik 2 2 (xi − ak ) + (yi − bk )   m 6   ζik ℘is ℘i2 ℘i4 fi2 = 1 + + − Gi (yi − τi2 ) + G i 2 COik W S S S )2 (W ) (W is i2 s=1 k=1 i4 ⎞ ⎛ (xi − ak ) ± r − Cx sin χ (x ) i ik k ik m ⎟ ⎜  (xi − ak )2 + (yi − bk )2 ζik ⎜ ⎟, −G i COik ⎝ ⎠ (xi − ak ) k=1 + (yi − Cyik ) 1 ∓ rk1 cos χik 2 2 (xi − ak ) + (yi − bk )   m 6   ζik ℘is ℘i5 ℘i6 fi3 = 1 + + + Gi , (zi − τi3 ) − G i 2 COik W S (W Si5 ) (W Si6 )2 is s=1 k=1 for i = 1, . . . , n. Consider the theorem that follows: Theorem 1 Consider a mobile robot, Pi whose movement is administered along a solution of process system (1). The key objective is to encourage the motion and control inside a restricted workspace with the ultimatum goal for convergence to its assigned target. The subtasks comprise of: convergence to predefined targets, avoidance of the fixed cylindrical obstacles, and the walls of the workspace. To intrinsically guaranty the stability in the sense of Lyapunov of system (1), we consider the accompanying continuous time-invariant velocity control inputs: 1 fi1 , αi1 1 wi = − fi2 , αi2 1 ui = − fi3 , αi3 vi = −

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

(7)

for i = 1, . . . , n.

5 Stability Analysis The stability analysis to system (1) is considered next. We begin with the following theorem: Theorem 2 Let (τi1 , τi2 , τi3 ) be the position of the target of Pi . Then, xe := (τi1 , τi2 , τi3 , 0, 0, 0) ∈ R6 is a stable critical point of (1).

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Proof For i = 1, . . . , n: 1. Over D(L(x)) = {x ∈ R6n : COik (x) > 0, k = 1, . . . , m; W Sis (x) > 0, s = 1, . . . , 6}, the function L(x) is continuous and positive; 2. L(xe ) = 0; 3. L(x) > 0 for all x ∈ D(L(x))/xe . Along a solution of system (1), the time derivative of (6) is L˙ (1) (x) =

n    fi1 x˙ i + fi2 y˙ i + fi3 z˙i . i=1

Using the ODEs for system (1) and the controllers given in (7), the accompanying negative semi-definite function is acquired L˙ (1) (x) = −

n    αi1 vi2 + αi2 wi2 + αi3 ui2 ≤ 0. i=1

Now, L˙ (1) (x) ≤ 0 ∀x ∈ D(L(x)) and L˙ (1) (xe ) = 0. Furthermore, L(x) ∈ C 1 (D(L(x))).  Hence, L(x) is a Lyapunov function for system (1).

6 Simulation Results To illustrate the effectiveness of the proposed continuous time-invariant nonlinear velocity control laws within the framework of the LbCS, this section will demonstrate a virtual scenario via a computer simulation. We consider a point mass in a 3D environment with a fixed cylindrical obstacle in its path. The robot starts from its initial configuration and navigates to reach its final configuration while avoiding the cylindrical obstacle. It is observed that the mobile robot, which could resemble a quadrotor aircraft, has a smooth trajectory as well as a smooth increase its altitude until it reaches its target configuration. Figure 2 shows the default 3D-view and Fig. 3 shows the top 3D view of the motion of the mobile robot. Table 1 gives every one of the estimations of the underlying conditions, requirements, and various parameters used in the reenactment.

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Fig. 2 Default 3D motion view of the point mass mobile robot at t = 0, 100, 200, 600 units

The behavior of the Lyapunov function L and its related time derivative L along the system trajectory is shown in Fig. 4. Essentially, it shows the intervals over which system (7) increments or diminishes its pace of vitality dissemination. The trajectory of the mobile robot is collision-free as indicated by the continuous evolution of the Lyapunov function.

7 Conclusion This paper provides the cylindrical obstacle avoidance method that incorporated into the LbCS. Controllers for the robotic system were derived and extracted using the LbCS, which successfully tackle the problem of MPC of point mass mobile robots. The MDT proposed in [6] has been modified for the avoidance of the cylindrical obstacle for the mobile robot. The robust controllers produced a smooth, feasible trajectory of the system with an amicable convergence to its equilibrium state. The effectiveness and robustness of the given control inputs were demonstrated in virtual scenario by the means of a computer recreation. To the author’s knowledge, this algorithm for the avoidance of cylindrical obstacles in a 3D space has not been proposed in literature. Future research will include the amalgamation of cylindrical

3D Cylindrical Obstacle Avoidance Using the Minimum Distance Technique

Fig. 3 Top 3D motion view of the point mass mobile robot at t = 0, 100, 200, 600 units Table 1 Initial and final states, constraints and parameters Description Value Initial state of the point mass mobile robot Workspace Rectangular position Radius of point mass Constraints Target center, radius Center of cylinder Height and radius of cylinder Control and convergence parameters Avoidance of cylindrical obstacles Avoidance of workspace Convergence

η1 = η2 = 200, η3 = 100 (x1 , y1 , z1 ) = (10, 90, 20) rp1 = 5 (τ11 , τ12 , τ13 ) = (180, 100, 850), rτ1 = 5 (a1 , b1 , c11 ) = (80, 100, 0), c12 = 90 and rc1 = 50 ζ11 = 1 ℘1s = 50 for s = 1, . . . , 6 α11 = α12 = α13 = 0.01

There is one point mass mobile robot, (n = 1) and 1 cylindrical obstacle, (k = 1)

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Fig. 4 Behavior of L(x) and its associated time derivative L˙ (1) (x) along a solution of system (7)

obstacles together with spherical-shaped obstacles in a dynamic environment with multiple mobile robots and finally the implementation of the quadrotor dynamic model into the MPC problem.

References 1. B.N. Sharma, J. Raj, J. Vanualailai, Navigation of carlike robots in an extended dynamic environment with swarm avoidance. Int. J. Robust Nonlinear Control 28(2), 678–698 (2018) 2. K. Raghuwaiya, B. Sharma, J. Vanualailai, Leader-follower based locally rigid formation control. J. Adv. Transport. 1–14, 2018 (2018) 3. J. Vanualailai, J. Ha, S. Nakagiri, A solution to the two-dimensional findpath problem. Dynamics Stab. Syst. 13, 373–401 (1998). Dec. 4. J. Raj, K. Raghuwaiya, J. Vanualailai, B. Sharma, Navigation of car-like robots in threedimensional space, in Proceedings of the 2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (2018), pp. 271–275 5. J. Raj, K. Raghuwaiya, S. Singh, B. Sharma, J. Vanualailai, Swarming intelligence of 1-trailer systems, in Advanced Computer and Communication Engineering Technology, ed. by H. A. Sulaiman, M. A. Othman, M.F.I. Othman, Y.A. Rahim, N.C. Pee (Springer International Publishing, Cham, 2016), pp. 251–264 6. B. Sharma, New Directions in the Applications of the Lyapunov-based Control Scheme to the Findpath Problem. PhD thesis, University of the South Pacific, Suva, Fiji Islands, July 2008 7. K. Raghuwaiya, S. Singh, Formation types of multiple steerable 1-trailer mobile robots via split/rejoin maneuvers. N. Z. J. Math. 43, 7–21 (2013) 8. O. Khatib, Real time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 7(1), 90–98 (1986) 9. J. Vanualailai, B. Sharma, S. Nakagiri, An asymptotically stable collision-avoidance system. Int. J. Non-Linear Mech. 43(9), 925–932 (2008)

Path Planning of Multiple Mobile Robots in a Dynamic 3D Environment Jai Raj, Krishna Raghuwaiya, Jito Vanualailai, and Bibhya Sharma

Abstract In this paper, we present a theoretical exposition into the application of an artificial potential field method, that is, the Lyapunov-based control scheme. A motion planner of mobile robots navigating in a dynamic environment is proposed. The dynamic environment includes multiple mobile robots, fixed spherical and cylindrical-shaped obstacles. The motion planner exploits the minimum distance technique for the avoidance of the cylindrical obstacles. The mobile robots navigate in a bounded environment, avoiding all the obstacles and each other whilst enroute to its target. The effectiveness of the suggested nonlinear velocity governing laws is verified by a computer simulation which proves the efficiency and robustness of the control technique. Keywords Cylindrical obstacles · Minimum distance technique · Stability

1 Introduction Autonomous navigation is an active and functioning exploration domain and has been so far over the most recent few decades. When contextualised with mobile robots, this motion planning and control (MPC) problem involves the planning of a collision-free path for a given mobile robot to reach its final configuration in a designated amount of time [1]. In comparison with single agents, multi-agent also needs to avoid each other whilst synthesising a robots motion subject to the kinodynamic constraints associated with the robotic system [2, 3]. Multi-agent research is always favoured over single agents. The swarm of robots is able to carry out tasks such as surveillance, transportation, healthcare and mining, resulting in a high rate of system effectiveness [4, 5]. The workspace for multi-agent research is no longer static but dynamic, hence, devising motion planning algorithms becomes inherently difficult. J. Raj (B) · K. Raghuwaiya · J. Vanualailai · B. Sharma The University of the South Pacific, Suva, Fiji e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_19

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There are numerous algorithms that addresses the MPC problem of mobile robots [2]. In this research, we utilise the artificial potential field (APF) method, classified as the Lyapunov-based control scheme (LbCS) for the control of point-mass mobile robots [6]. The LbCS provides a simple and effective methodology of extracting control laws for various systems. The advantage of the control scheme lies in the flexibility to consider system singularities, for example, workspace limitations, velocity constraints and obstacles [7]. We use the concept of the minimum distance technique (MDT) [8] to develop an algorithm for cylindrical obstacle avoidance. Using the architecture of the LbCS, we propose a motion planner for point-mass robots traversing in the presence of obstacles in a given workspace. Based on the APF method, the stability of the system is considered via the direct method of Lyapunov. This research article is organised as follows: Sect. 2 represents the kinematic model of the point-mass system; in Sect. 3, the APF functions are defined; in Sect. 4, the design of the Lyapunov function is considered and the robust continuous nonlinear control laws for the point-mass mobile robot is extracted; in Sect. 5, the stability analysis of our system is performed; in Sect. 6, the effectiveness and robustness of the derived controllers are simulated; and finally, the conclusion and future work are given in Sect. 7.

2 Modelling of the Point-Mass Mobile Robot A simple kinematic model for mobile robots is presented in this section and frontier the development of velocity controls to address the multi-tasking problem of multiple point-mass in a dynamic environment. Figure 1 represents a two-dimensional schematic of a point-mass mobile robot. Definition 1 A point-mass, Pi is a sphere of radius rpi and centred at (xi (t), yi (t), zi (t)) ∈ R3 for t ≥ 0. Specifically, the point-mass represents the set   P i = (Z1 , Z2 , Z3 ) ∈ R3 : (Z1 − xi )2 + (Z2 − yi )2 + (Z3 − zi ) ≤ rpi2 At time t ≥ 0, let (vi (t) , wi (t) , ui (t)) = (˙xi (t) , y˙ i (t) , z˙i (t)) be the instantaneous velocities of Pi . Assuming the initial conditions at t = t0 ≥ 0, a system of first-order ODE’s for Pi is given below, : x˙ i = vi (t) , y˙ i = wi (t) , z˙i = ui (t) , xi0 := xi (t0 ) , yi0 := yi (t0 ) , zi0 := zi (t0 ) ,

 (1)

for i = 1, . . . , n, with the principle objective to steer and navigate Pi to its goal configuration in R3 .

Path Planning of Multiple Mobile Robots in a Dynamic 3D Environment

Z2

Target of

2

Target of

211

1

Obstacle

2

1

Z1 Fig. 1 2D representation of Pi in a two-dimensional plane

3 Deployment of the APF Functions In this section, we will present and outline a disposition for yielding collision-free motions of Pi in a well-defined, bounded but dynamic environment. For Pi , the velocity controllers vi (t), wi (t) and ui (t) will be designed using the LbCS. The ultimate goal for Pi is to navigate safely in the dynamic environment and converge to its target configuration. We begin by unfolding the target, the obstacles and the boundary limitations.

3.1 Attractive Potential Field Functions Target Attraction The target of Pi is a sphere with centre (τi1 , τi2 , τi3 ) and radius rτi . For the attraction of Pi to this target, the work utilises, in a candidate Lyapunov function to be proposed, a function for the convergence to the target of the form Vi (x) =

 1 (xi − τi1 )2 + (yi − τi2 )2 + (zi − τi3 )2 , 2

(2)

for i = 1, . . . , n. Auxiliary Function For Pi to converge to its designated target, an auxiliary function of the form (3) G i (x) = Vi (x) , for i = 1, . . . , n is proposed.

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3.2 Repulsive Potential Field Functions Workspace Boundary Limitations A really important consideration with any robot is the set of all possible points that it can reach. We refer to this volume as the workspace of the robot. Hence, we desire to confine the motion of Pi in a 3D framework of dimensions η1 × η2 × η3 . The boundary walls are treated as fixed obstacles. Hence, for the avoidance of these walls, we propose the following functions of the form ⎫ W Si1 (x) = xi − rpi , ⎪ ⎪ ⎪ W Si2 (x) = η2 − (yi + rpi ) , ⎪ ⎪ ⎪ ⎬ W Si3 (x) = η1 − (xi + rpi ) , (4) W Si4 (x) = yi − rpi , ⎪ ⎪ ⎪ ⎪ W Si5 (x) = zi − rpi , ⎪ ⎪ ⎭ W Si6 (x) = η3 − (zi + rpi ) , for i = 1, . . . , n, noting that they are positive within the rectangular cuboid. Moving Obstacles Every mobile robot turns into a moving deterrent for each other moving robot in the bounded environment. Therefore, for Pi to avoid Pj , we consider the following M Oij (x) =

2

2

2

2  1 xi − xj + yi − yj + zi − zj − rpi + rpj , 2

(5)

for i, j = 1, . . . , n, j = i. Spherical Obstacles Consider qa ∈ N spherical-shaped obstacles within a bounded environment which Pi needs to avoid. We consider the following definition: Definition 2 A stationary solid object is a sphere with centre (ol1 , ol2 , ol3 ) and radius rol . Thus,   Or = (Z1 , Z2 , Z3 ) ∈ R3 : (Z1 − ol1 )2 + (Z2 − ol2 )2 + (Z3 − ol3 )2  rol 2 . For Pi to avoid these spherical obstacles, we consider FOl (x) =

 1 (xi − ol1 )2 + (yi − ol2 )2 + (zi − ol3 )2 − (rpi + rol )2 , 2

(6)

where i = 1, . . . , n and l = 1, . . . , qa. Cylindrical Obstacles The surface wall of the cylinder are classified as fixed obstacles. Hence, the point-mass mobile robot Pi needs to avoid these walls. To begin, the following definition is made: Definition 3 The kth surface wall is collapsed into a cylinder in the Z1 Z2 Z3 plane with initial coordinates (ak , bk , ck1 ) and final coordinates (ak , bk , ck2 ) with radius rck . The parametric representation of the kth cylinder of height (ck2 − ck1 ) can be given as Cxk = ak ± rck cos χk , Cyk = bk ± rck sin χk and Czk = ck1 + λk (ck2 − ck1 ) where χk : R → (− π2 , π2 ) and λk : R2 → [0, 1].

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In order to facilitate the avoidance of the surface wall of the cylinder, we adopt the MDT from [8]. We compute the shortest distance between the centre of Pi and the surface of the kth cylinder. This results in the avoidance of the resultant point of the surface of the kth cylinder. The coordinates of this point can be expressed cos χik , Cy as Cxik = ak ± rck  ik = bk ± rck sin χik and Czik = ck1 + λik (ck2 − ck1 ) y − b 1 i k and λik = (zi − ck1 ) , and the saturation where χik = tan−1 xi − ak ⎧ (ck2 − ck1 ) ⎨ 0 , if λik < 0  π π . functions are given by λik = λik , if 0 ≤ λik ≤ 1 and χik = − , ⎩ 2 2 1 , if λik > 1 Therefore, for Pi to circumvent past the closest point on the surface wall of the kth cylinder, we consider the following function COik (x) =

 1 (xi − Cxik )2 + (yi − Cyik )2 + (zi − Czik )2 − (rpi )2 , 2

(7)

where k = 1, . . . , m and i = 1, . . . , n.

4 Design of the Nonlinear Controllers Next, we design the Lyapunov function and extract the governing laws pertaining to system (1).

4.1 Lyapunov Function Using the tuning parameters and for i, j = 1, . . . , n, (i) (ii) (iii) (iv)

℘is > 0, s = 1, . . . , 6; ϑil > 0, l = 1, . . . , qa; ζik > 0, k = 1, . . . , m; βij > 0, j = i,

the Lyapunov function for system (1) is: ⎞⎤ qa  ℘is ϑil + + ⎟⎥ ⎢ ⎜ n ⎢ ⎜ W Sis (x) FOil (x) ⎟⎥  ⎢ ⎜ s=1 ⎟⎥ l=1 m n L (x) := ⎢Vi (x) + G i (x) ⎜  ⎟⎥.  βij ζik ⎢ ⎜ ⎟⎥ i=1 ⎣ + ⎝ ⎠⎦ COik (x) M O (x) ij j=1 k=1 ⎡



6 

j =i

(8)

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4.2 Nonlinear Controllers By differentiating the different components of L (x) along t, we then extract the control laws for Pi . Using the convergence parameters αi1 , αi2 , αi3 > 0, the constituents of the control inputs are: ⎞ qa 6 m n     βij ⎟ ℘is ϑil ζik ⎜ + + + fi1 = ⎝1 + ⎠ (xi − τi1 ) W Sis FOil COik M Oij j=1 s=1 ⎛

l=1

k=1

j =i

qa  ℘i1 ℘i3 ϑil −G i + Gi − Gi (xi − ol1 ) 2 2 (W Si1 ) (W Si3 ) (FOil )2 l=1 n 

βij −2G i xi − xj M Oij j=1 j =i  ⎞ ⎛ (yi − bk ) 1 ∓ r − Cx sin χ m (x ) ik k ik  ζik ⎜ i 2 ⎟ (xi − ak )2 + (yi − bk ) ⎟, ⎜  −G i ⎠ (yi − bk ) COik ⎝ ± (y − Cy ) r cos χ k=1 i ik k ik 2 2 (xi − ak ) +⎞(yi − bk ) ⎛

 ℘is  ϑil  ζik  βij ⎟ ⎜ fi2 = ⎝1 + + + + ⎠ (yi − τi2 ) W Sis FOil COik M Oij j=1 s=1 6

qa

m

l=1

k=1

n

j =i

qa  ℘i2 ℘i4 ϑil +G i − G − G (yi − ol2 ) i i 2 2 (W Si2 ) (W Si4 ) (FOil )2 l=1 n 

βij −2G i yi − yj M Oij j=1 j =i   ⎞ ⎛ (xi − ak ) ± (xi − Cxik ) rk sin χik m  ⎟ ζik ⎜ (xi − ak )2 + (yi − bk )2 ⎜  ⎟, −G i ⎠ ⎝ − a (x ) i k COik + (y − Cy ) 1 ∓ r cos χ k=1 i ik k1 ik 2 2 (xi − ak ) + (yi − bk )

⎞ qa 6 m n     βij ⎟ ℘is ϑil ζik ⎜ fi3 = ⎝1 + + + + ⎠ (zi − τi3 ) W S FO CO M Oij is il ik j=1 s=1 ⎛

l=1

k=1

j =i

 ϑil ℘i5 ℘i6 −G i + G − G (zi − ol3 ) i i (W Si5 )2 (W Si6 )2 (FOil )2 l=1 n 

βij −2G i zi − zj , M Oij j=1 qa

j =i

for i = 1, . . . , n.

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Theorem 1 Let the motion of the mobile robot, Pi be controlled by the ODE’s in system (1). The overall objective is to navigate Pi in a dynamic workspace and reaching its final configuration. The subtasks include: convergence to predefined targets, avoidance of the fixed spherical and cylindrical obstacles, avoidance of the walls of the boundaries and avoidance of other moving point-mass mobile robots. To guaranty stability in the sense of Lyapunov of system (1), we consider the following continuous velocity control laws: vi = −

1 1 1 fi1 , wi = − fi2 , ui = − fi3 , αi1 αi2 αi3

(9)

for i = 1, . . . , n.

5 Stability Analysis We begin with the accompanying theorem for stability analysis: Theorem 2 Let (τi1 , τi2 , τi3 ) be the position of the target of the point-mass mobile robot, Pi . Given a stable equilibrium point for system (1), xe ∈ D(L(x)), where xe := (τi1 , τi2 , τi3 , 0, 0, 0) ∈ R6 . Proof For i = 1, . . . , n: 1. Over the space D(L(x)) = {x ∈ R6n : W Sis (x) > 0, s = 1, . . . , 6; FOil (x) > 0, l = 1, . . . , qa; COik (x) > 0, k = 1, . . . , m; MOij (x) > 0, j = i}, L(x) is positive and continuous; 2. L(xe ) = 0; 3. L(x) > 0 ∀x ∈ D(L(x))/xe . Then, along a solution of system (1), we have: L˙ (1) (x) =

n    fi1 x˙ i + fi2 y˙ i + fi3 z˙i . i=1

Using (9), we have the following time derivative of L(x)semi-negative definite function for system (1) L˙ (1) (x) = −

n    αi1 vi2 + αi2 wi2 + αi3 ui2 ≤ 0. i=1

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Therefore, L˙ (1) (x) ≤ 0 ∀x ∈ D(L(x)) and L˙ (1) (xe ) = 0. Moreover, L(x) ∈ C 1 (D(L(x))), hence, for system (1), L(x) is classified as its Lyapunov function and xe is a stable equilibrium point. The above result does not contradict Brockett’s theorem [9] since we are only proving stability. 

6 Simulation Results To demonstrate the effectiveness and robustness of our proposed scheme, we simulate a virtual scenario. The stability results obtained from the Lyapunov function are verified numerically. We consider the motion of point-mass mobile robots with fixed spherical and cylindrical obstacles in its path. The mobile robots navigate towards its designated targets whilst ensuring collision-free manoeuvers with any obstacle. Figure 2 shows the default 3D-view and Fig. 3 shows the top 3D view of the motion of the mobile robots. The values of the initial conditions, constraints and different parameters utilised in the simulation are provided in Table 1. The behaviour of the Lyapunov function and its time derivative are shown in Fig. 4.

Fig. 2 Default 3D motion of the point-mass mobile robots at t = 0, 22, 101, 500 units

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Fig. 3 Top 3D motion of the point-mass mobile robot at t = 0, 22, 101, 500 units Table 1 Parameters utilised in the numerical simulation. There are 2 point-mass mobile robot, (n = 2), 4 spherical-shaped obstacles (qa = 4) and 3 cylindrical obstacle, (m = 3) Description

Value

Initial state of the point-mass mobile robot Workspace Rectangular position

η1 = 500, η2 = 200, η3 = 100 (x1 , y1 , z1 ) = (30, 100, 20) (x2 , y2 , z2 ) = (30, 150, 50)

Radius of point-mass

rp1 = rp2 = 5

Constraints Target centre, radius

(τ11 , τ12 , τ13 ) = (400, 130, 75), rτ1 = 5 (τ21 , τ22 , τ23 ) = (400, 50, 50), rτ2 = 5

Centre of cylinder, radius and height

(a1 , b1 , c11 ) = (250, 110, 0), rc1 = 50 , c12 = 70 (a2 , b2 , c21 ) = (350, 70, 0), rc2 = 30 , c22 = 85 (a3 , b3 , c31 ) = (350, 110, 0), rc3 = 30 , c32 = 90

Sphere centre and radius

(o11 , o12 , o13 ) = (100, 120, 5), ro1 = 20 (o21 , o22 , o23 ) = (120, 160, 20), ro2 = 20 (o31 , o32 , o33 ) = (110, 50, 70), ro3 = 20 (o41 , o42 , o43 ) = (160, 70, 50), ro4 = 20

Control and convergence parameters Avoidance of spherical obstacles

ϑil = 10, for i = 1, 2, l = 1, . . . , 4

Avoidance of cylindrical obstacles

ζik = 0.5, for i = 1, 2, k = 1, . . . , 3

Avoidance of workspace

℘is = 50 for i = 1, 2, s = 1, . . . , 6

Inter-individual collision avoidance

βij = 20 for i = j = 1, 2, j  = i

Convergence

αi1 = αi2 = αi3 = 0.05, for i = 1, 2

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Fig. 4 Behaviour of L˙ (1) (x) and L(x)

7 Conclusion In this paper, for a point-mass mobile robot, we explore its motion in a dynamic environment. This environment includes spherical and cylindrical obstacles and interindividual collision avoidance amongst the mobile robots. To do so, the robust, velocity control inputs were derived from the LbCS, which produced feasible trajectories of the mobile robots to navigate safely in the dynamic environment, whilst ensuring collision avoidance with any obstacles. To show the effectiveness and robustness of our scheme, a virtual scenario is simulated for the mobile robots. Future research will address having tunnel passing manoeuvers in a 3D-space using hollow cylinders.

References 1. J. Vanualailai, J. Ha, S. Nakagiri, A solution to the two-dimensional findpath problem. Dynamics Stability Syst. 13, 373–401 (1998). Dec. 2. B.N. Sharma, J. Raj, J. Vanualailai, Navigation of carlike robots in an extended dynamic environment with swarm avoidance. Int. J. Robust Nonlinear Control 28(2), 678–698 (2018) 3. J. Raj, K. Raghuwaiya, S. Singh, B. Sharma, J. Vanualailai, Swarming intelligence of 1-trailer systems, in Advanced Computer and Communication Engineering Technology, ed. by H. A. Sulaiman, M. A. Othman, M.F.I. Othman, Y.A. Rahim, N.C. Pee (Springer International Publishing, Cham, 2016), pp. 251–264 4. A. Prasad, B. Sharma, J. Vanualailai, A new stabilizing solution for motion planning and control of multiple robots. Robotica 34(5), 1071–1089 (2016) 5. K. Raghuwaiya, B. Sharma, J. Vanualailai, Leader-follower based locally rigid formation control. J. Adv. Transport. 1–14, 2018 (2018) 6. B. Sharma, J. Vanualailai, A. Prasad, Trajectory planning and posture control of multiple mobile manipulators. Int. J. Appl. Math. Comput. 2(1), 11–31 (2010) 7. J. Vanualailai, B. Sharma, A. Ali, Lyapunov-based kinematic path planning for a 3-link planar robot arm in a structured environment. Global J. Pure Appl. Math. 3(2):175–190 (2007)

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8. B. Sharma. New Directions in the Applications of the Lyapunov-based Control Scheme to the Findpath Problem. PhD thesis, University of the South Pacific, Suva, Fiji Islands, July 2008 9. R.W. Brockett, Differential geometry control theory, Asymptotic Stability and Feedback Stabilisation (Springer, Berlin, 1983), pp. 181–191

Autonomous Quadrotor Maneuvers in a 3D Complex Environment Jito Vanualailai, Jai Raj, and Krishna Raghuwaiya

Abstract This paper essays collision-free avoidance maneuvers of a quadrotor aircraft. We use the artificial potential field method via a scheme, known as the Lyapunov-based control scheme to extract the inputs of the control laws that will be utilized to govern the autonomous navigation of the quadrotor. The hollow cylinder, which becomes an obstacle for the quadrotor, is avoided via the minimum distance technique. The surface wall of the cylinder is avoided whereby we compute the minimum Euclidean distance from the centre of the quadrotor to the surface wall of the cylinder and then avoid this resultant point. The quadrotor autonomously navigates itself past the obstacle to reach its target. The effectiveness of the proposed nonlinear control inputs are demonstrated of a virtual scenario via a computer simulation. Keywords Cylindrical obstacle · Quadrotor · Minimum distance technique

1 Introduction Autonomous navigation of unmanned aerial vehicles (UAVs) has been an active area of research in the past decade and has attracted significant interest from both academic researchers and commercial designers. Among all the other types of UAVs, quadrotor UAVs are the most common and favoured. In essence, this is due to their advanced characteristics, simple structure and ease to assemble, and its vertical take-off and landing (VTOL) capabilities [1]. Despite the quadrotor being popular, there still exists problems at large that needs to be solved. The quadrotor is an under actuated system which has only four control inputs and six outputs. In addition, the attitude dynamics and position dynamics of a quadrotor are strongly coupled. To solve these problems, several control solutions have been proposed by researchers, including PID controllers, fuzzy PID, sliding mode control, linear quadratic regulator (LQR), and Lyapunov method [2]. J. Vanualailai (B) · J. Raj · K. Raghuwaiya The University of the South Pacific, Suva, Fiji e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_20

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In this research, we will employ the Lyapunov-based control scheme (LbCS) [3, 4], which is an artificial potential field (APF) method for the autonomous control of quadrotor, which is an intelligent vehicle system (IVS). The governing principle behind the LbCS revolves around the construction of functions for target attraction and obstacle avoidance. With respective to the APF functions, the attractive functions are contemplated as attractive potential field functions and the obstacle avoidance functions as repulsive potential field functions. The rational functions are then constructed with positive tuning parameters in the numerator of the repulsive potential field functions [4, 5]. The advantage of employing the APF method lies in its simplicity and elegance to construct functions out of system constraints and inequalities, favourable processing speed, decentralization, and stability features [6, 7], although it inherently involves the existence of local minima. The workspace for the quadrotor is immersed with positive and negative fields, with the direction of motion facilitated via the notion of steepest decent. The environment in which the quadrotor will be exposed to is a complex and dynamic environment. First of all, the dynamics of the quadrotor itself is complex, hence making the motion planning and control (MPC) problem a challenging, computer intensive, yet an interesting problem. These results in the dynamic constraints are to be considered as repulsive potential filed functions. We will also introduce obstacles in the workspace, in this case cylindrical obstacles, whereby the minimum distance technique (MDT) [8] will be employed for a smooth collision-free path by the quadrotor. Using the composition of the LbCS, we propose a motion planner for quadrotor aircraft navigating in a workspace cluttered with obstacles. The remainder of this research article is organized as follows: Sect. 2 represents the dynamic model of the quadrotor UAV; in Sect. 3, the APF functions are defined; in Sect. 4, the Lyapunov function is constructed, and the robust continuous nonlinear control inputs for the quadrotor are extracted; in Sect. 5, we demonstrate the effectiveness of the proposed controllers via a computer simulation; and finally, the conclusion and future work are given in Sect. 6.

2 Dynamic Model of a Quadrotor In this section, we model a dynamic model of a quadrotor UAV. Its dynamics are shown in Fig. 1. The dynamic model of the quadrotor can be expressed by the following set of nonlinear differential equation:

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Fig. 1 Schematic structural configuration of a quadrotor UAV

x˙ i Ui1 (cos φi sin θi cos ψi + sin φi sin ψi ) − κi1 , mi mi y˙ i Ui1 y¨ i = (cos φi sin θi sin ψi − sin φi cos ψi ) − κi2 , m mi z˙i Ui1 z¨i = cos φi cos θi − g − κi3 mi mi ˙ φi θ˙i ψ˙ i li l 1 φ¨ i = Ui2 − li κi4 , θ¨i = Ui3 − li κi5 , ψ¨ i = Ui4 − κi6 . Iix Iix Iiy Iiy Iiz Iiz x¨ i =

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

(1)

For the ith quadrotor UAV, (xi , yi , zi ) represents position of the quadrotor, (φi , θi , ψi ) are the three Euler angles, namely the roll, pitch, and yaw angles, respectively, g is the gravitational acceleration, mi is the total mass of the quadrotor structure, li is the half length of the quadrotor, Iix,iy,iz are the moments of inertia, κiι , ι = 1, . . . , 6 are the drag coefficients, and Uiς , ς = 1, . . . , 4 are the virtual control inputs. To remove and solve the under-actuation of the quadrotor system, we introduce the control inputs 

2 2 2 + Ui1y + Ui1z . The Ui1x , Ui1y , and Ui1z to replace Ui1 . Consequently, Ui1 = Ui1x set of first-order ODE’s for the quadrotor model is hence:

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x˙ i = vi ,

y˙ i = wi , z˙i = ui ,

vi Ui1x − κi1 , mi mi Ui1y wi = (cos φi sin θi sin ψi − sin φi cos ψi ) − κi2 , mi mi ui Ui1z = cos φi cos θi − g − κi3 , mi mi ˙ ˙ = qi , θi = pi , ψi = ri , qi pi ri li li 1 = Ui2 − li κi4 , p˙ i = Ui3 − li κi5 , r˙i = Ui4 − κi6 . Iix Iix Iiy Iiy Iiz Iiz

v˙i = (cos φi sin θi cos ψi + sin φi sin ψi ) w˙ i u˙ i φ˙ i q˙ i

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

(2)

3 Deployment of the APF Functions In this section, we formulate collision-free trajectories of the quadrotor UAV using control laws extracted via the LbCS. We design attractive potential field function for attraction to the target and repulsive potential field functions for repulsion from obstacles. We enclose the ith quadrotor UAV, Ai with a spherical protective region of radius li .

3.1 Attractive Potential Field Functions Attraction to Target The designated target of Ai is a sphere with center (τi1 , τi2 , τi3 ), and radius rτi . For the attraction of the quadrotor UAV to this target, we design the target attractive potential field function of the form  1 (xi − τi1 )2 + (yi − τi2 )2 + (zi − τi3 )2 + vi2 + wi2 + ui2 + qi2 + pi2 + ri2 2 (3) for i = 1, . . . , n. The function is not only a measure of the distance between the quadrotor and its target, but also a measure of convergence to the target. Auxiliary Function In order to achieve the convergence of the quadrotor aircraft to its designated target and to ensure that the nonlinear controllers vanish at this target configuration, we consider the auxiliary function of the form Vi (x) =

G i (x) =

 1 (xi − τi1 )2 + (yi − τi2 )2 + (zi − τi3 )2 , 2

(4)

for i = 1, . . . , n. This auxiliary function is then multiplied to each of the obstacle avoidance functions to be designed in the following subsection.

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3.2 Repulsive Potential Field Functions Modulus Bound on the Angles The roll (φi ), pitch (θi ), and yaw (ψi ) angles of Ai are limited and bounded to avoid the quadrotor UAV from flipping over. These are treated as artificial obstacles, and for their avoidance, we construct obstacle avoidance functions of the form 1 (φmax − φi ) (φmax + φi ) , 2 1 Si2 (x) = (θmax − θi ) (θmax + θi ) , 2 1 Si3 (x) = (ψmax − ψi ) (ψmax + ψi ) , 2 Si1 (x) =

(5) (6) (7)

where i = 1, . . . , n, φmax is the maximum pitching angle, φmax = π2 , θmax is the maximum rolling angle, θmax = π2 , and ψmax is the maximum yawing angle, ψmax = π. Modulus Bound on the Velocities In order to avoid fast solutions and for safety reasons, the translational and angular velocities of the quadrotors are bounded. Again, treated as artificial obstacles, for their avoidance, we construct obstacle avoidance functions of the form Di1 (x) = Di2 (x) = Di3 (x) = Di4 (x) = Di5 (x) = Di6 (x) =

1 (vmax − vi ) (vmax + vi ) , 2 1 (wmax − wi ) (wmax + wi ) , 2 1 (umax − ui ) (umax + ui ) , 2 1 (qmax − qi ) (qmax + qi ) , 2 1 (pmax − pi ) (pmax + pi ) , 2 1 (rmax − ri ) (rmax + ri ) , 2

(8) (9) (10) (11) (12) (13)

where i = 1, . . . , n. Cylindrical Obstacles The surface wall of the cylinder are classified as fixed obstacles. Hence, the quadrotor UAV Ai needs to avoid these walls. To begin, the following definition is made: Definition 1 The kth surface wall is collapsed into a cylinder in the Z1 Z2 Z3 plane between the following coordinates, (ak , bk , ck1 ) and (ak , bk , ck2 ) with radius rck . The parametric representation of the kth cylinder of height (ck2 − ck1 ) can be given as Cxk = ak ± rck cos χk , Cyk = bk ± rck sin χk and Czk = ck1 + λk (ck2 − ck1 ) where χk : R → (− π2 , π2 ) and λk : R2 → [0, 1].

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In order to facilitate the avoidance of the surface wall of the cylinder, we adopt the architecture of the MDT from [8]. We compute the minimum Euclidian distance from the center of Ai to the surface of the kth cylinder and avoid this resultant point of the surface of the cylinder. From geometry, the coordinates of this point can be given cos χik , Cy as Cxik = ak ± rck

ik = bk ± rck sin χik and Czik = ck1 + λik (ck2 − ck1 ) y − b 1 i k and λik = (zi − ck1 ) , and the saturation where χik = tan−1 xi − ak ⎧ (ck2 − ck1 ) ⎨ 0 , if λik < 0  π π . functions are given by λik = λik , if 0 ≤ λik ≤ 1 and χik = − , ⎩ 2 2 1 , if λik > 1 Therefore, for Ai to avoid the closest point on the surface of the kth cylinder, we construct repulsive potential field functions of the form COik (x) =

 1 (xi − Cxik )2 + (yi − Cyik )2 + (zi − Czik )2 − rτi2 , 2

(14)

for i = 1, . . . , n and k = 1, . . . , m.

4 Design of the Nonlinear Controllers In this section, the Lyapunov function will be proposed, and the nonlinear control laws for system 2 will be designed.

4.1 Lyapunov Function First, for i = 1, . . . , n, we design the Lyapunov function by introducing the following tuning parameters that will be utilized to form the repulsive potential field functions: (i) ξis > 0, s = 1, . . . , 3, for the avoidance of sth artificial obstacles from the dynamic constraints of the angles; (ii) γid > 0, d = 1, . . . , 6, for the avoidance of the d th artificial obstacles from dynamic constraints of the translational and rotational velocities; (iii) ζik > 0, k = 1, . . . , m, for the avoidance of the surface wall of the kth cylinder. Using these tuning parameters, we now propose the following Lyapunov function for system (2): L (x) :=

n  i=1

 Vi (x) + G i (x)

 3  s=1

 γid  ξis ζik + + Sis (x) Did (x) COik (x) d =1 k=1 6

m

 . (15)

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4.2 Nonlinear Controllers To extract the control laws for the quadrotor, we take the time derivative of the various components of L (x), which upon suppressing x is: L˙ (2) (x) =

fi1 vi + fi2 wi + fi3 ui + fi4 vi v˙i + fi5 wi w˙ i + fi6 ui u˙ i +gi1 qi + gi2 pi + gi3 ri + gi4 qi q˙ i + gi5 pi p˙ i + gi6 ri r˙i ,

 (16)

where 

 3 6 m   ξis  γid ζik fi1 = 1 + + + (xi − τi1 ) S Did COik s=1 is ⎛d =1 k=1

⎞ (yi − bk ) 1 ∓ rc − Cx sin χ m (x ) i ik k ik  ζik ⎜ 2 ⎟ (xi − ak )2 + (yi − bk )

⎟, ⎜ −G i ⎠ ⎝ − b (y ) i k COik ± (y − Cy ) rc cos χ k=1 i ik k ik 2 2 (xi − ak ) + (yi − bk )   3 6 m    ξis γid ζik fi2 = 1 + + + (yi − τi2 ) S D CO is id ik s=1 d =1 k=1

⎞ ⎛ (xi − ak ) rc ± − Cx sin χ m (x ) i ik k ik  ζik ⎜ ⎟ (xi − ak )2 + (yi − bk )2 ⎜

⎟, −G i ⎠ ⎝ (xi − ak ) COik + (y − Cy ) 1 ∓ rc cos χ k=1 i ik k ik 2 2 (xi − ak ) + (yi − bk )   3 6 m    ξis γid ζik γi1 fi3 = 1 + + + (zi − τi3 ) , fi4 = 1 + 2 , S D CO D id ik i1 s=1 is d =1 k=1 γi2 γi3 ξi1 ξi2 ξi3 , gi2 = , gi3 = , fi5 = 1 + 2 , fi6 = 1 + 2 , gi1 = Si1 Si2 Si3 Di2 Di3 γi4 γi5 γi6 gi4 = 1 + 2 , gi5 = 1 + 2 , gi6 = 1 + 2 . Di4 Di5 Di6 First, we introduce the convergence parameters δi > 0,  = 1, . . . , 6. Then, we utilize the concept fi1 v + fi4 v v˙ = −δi1 vi2 and make necessary substitutions to derive the control inputs. Utilizing these for the other cases, the following nonlinear control inputs are generated for system (2):

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Ui1x = Ui1y = Ui1z = Ui2 = Ui3 = Ui4 =

−mi (fi1 + δi1 vi ) + fi4 κi1 vi , fi4 (cos φi sin θi cos ψi + sin φi sin ψi ) −mi (fi2 + δi2 wi ) + fi5 κi2 wi , fi5 (cos φi sin θi sin ψi − sin φi cos ψi ) −mi (fi3 + δi3 ui − fi6 g) + fi6 κi3 ui , fi6 (cos θi cos φi ) −Iix (gi1 + δi4 qi ) + gi4 li κi4 qi , gi4 li −Iiy (gi2 + δi5 pi ) + gi5 li κi5 pi , gi5 li −Iiz (gi3 + δi6 ri ) + gi6 κi6 ri . gi6

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭

(17)

5 Simulation Results In this section, we exhibit, via a virtual scenario the effectiveness of our proposed control scheme. The quadrotor begins its flight from its initial position. Initially, the quadrotor pitches in order to initiate the movement. While in motion to its target, it comes across a hollow cylinder. It tries to navigate its motion past the cylindrical obstacle by going from its side; however, to minimize the time and distance, it flies avoiding the curve surface of the cylinder and goes over it. Since the cylinder is hollow, it pitches inside and, however, encounters the inside wall of the cylinder. It

Fig. 2 Default 3D motion view of the quadrotor at t = 0, 165, 260, 315, 450 units

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Fig. 3 Top 3D motion view of the quadrotor at t = 0, 165, 260, 315, 450 units

Fig. 4 Front 3D motion view of the quadrotor at t = 0, 165, 260, 315, 450 units

then moves out of the cylinder and moves towards its target destination. In essence, the A1 , while in flight motion to its target, avoids the cylindrical-shaped obstacle. Figure 2 shows the default 3D view, Fig. 3 shows the top 3D view, and Fig. 4 shows the front 3D view of the motion of the point quadrotor UAV. Table 1 provides all the values of the initial conditions, constraints, and different parameters utilized in the simulation.

6 Conclusion In this paper, we present a set of robust, nonlinear control laws, derived using the LbCS for the control of motion of quadrotors. The quadrotor, whilst in motion to its target, needs to avoid the cylindrical obstacle and undergo a collision-free navigation. The effectiveness of our proposed control laws is exhibited using a virtual scenario via a computer simulation. The derived controllers for the control input for the

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Table 1 Parameters of the quadrotor UAV Description Value Initial state of the quadrotor Position Angles Translational velocities Rotational velocities Constraints Mass Length Gravitational acceleration Moments of inertia Drag coefficient Target position Maximum translational velocities Maximum rotational velocities Parameters for the quadrotor Dynamic constraints on the angles Dynamic constraints on the velocities Avoidance of cylindrical obstacles Convergence

(xi , yi , zi ) = (0, 0, 0) φi = θi = ψi = 0 vi = wi = ui = 0.5 qi = pi = ri = 0 mi = 2 li = 0.2 g = 9.8 Iix = Iiy = 4.856 × 10−3 , and Iiz = 4.856 × 10−2 κiι = 0.01, for ι = 1, . . . , 6 (τi1 , τi2 , τi3 ) = (450, 120, 145) vmax = wmax = umax = 1 qmax = pmax = rmax = 0.5

Units

rad m/s rad/s kg m m/s2 kg m2 Ns/m m/s rad/s

ξis = 100 for s = 1, . . . , 3 γi1 = 1, γi2 = 2, γi3 = 0.5, γi4 = γi5 = γi6 = 100 ζik = 1, for k = 1 δi1 = 0.1, δi2 = 0.2, δi3 = 0.05, δi4 = 2, δi5 = 1 and δi6 = 0.5

There is one quadrotor, i = 1

quadrotor ensured feasible trajectories and a nice convergence while satisfying all the constraints tagged to the quadrotor system. Future work on quadrotors will address the MPC of multiple quadrotors in a dynamic environment.

References 1. K.D. Nguyen, C. Ha, Design of synchronization controller for the station-keeping hovering mode of quad-rotor unmanned aerial vehicles. Int. J. Aeronaut. Space Sci. 20(1), 228–237 (2019) 2. H. Shraim, A. Awada, R. Youness, A survey on quadrotors: configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control. IEEE Aerospace Electron. Syst. Mag. 33(7), 14–33 (2018). July 3. K. Raghuwaiya, S. Singh, Formation types of multiple steerable 1-trailer mobile robots via split/rejoin maneuvers. N. Z. J. Math. 43, 7–21 (2013) 4. B.N. Sharma, J. Raj, J. Vanualailai, Navigation of carlike robots in an extended dynamic environment with swarm avoidance. Int. J. Robust Nonlinear Control 28(2), 678–698 (2018) 5. JJ. Raj, K. Raghuwaiya, S. Singh, B. Sharma, J. Vanualailai, Swarming intelligence of 1-trailer systems, in Advanced Computer and Communication Engineering Technology, ed. by H. A.

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Sulaiman, M. A. Othman, M.F.I. Othman, Y.A. Rahim, N.C. Pee (Springer International Publishing, Cham, 2016), pp. 251–264 6. J. Vanualailai, J. Ha, S. Nakagiri, A solution to the two-dimensional findpath problem. Dynamics Stability Syst. 13, 373–401 (1998) 7. K. Raghuwaiya, B. Sharma, J. Vanualailai, Leader-follower based locally rigid formation control. J. Adv. Transport. 1–14, 2018 (2018) 8. B. Sharma. New Directions in the Applications of the Lyapunov-based Control Scheme to the Findpath Problem. Ph.D. thesis, University of the South Pacific, Suva, Fiji Islands, July 2008

Tailoring Scrum Methodology for Game Development Towsif Zahin Khan, Shairil Hossain Tusher, Mahady Hasan, and M. Rokonuzzaman

Abstract The closest comparison of video game development would be other types of software development such as application software development or system software development. Yet these comparisons do not do it any justice. A video game is more than a software or the sum of its parts, unlike other types of software. In video games there is a much larger emphasis on performance, system requirements are often subjective. Designing a video game also tends to be a lot harder due to the complex interactions between the objects and the end users. The same is true when it comes to testing video games. Whereas other software developers can get away with functional and meeting all the requirements, in due time, a video game also has to provide the target audience with entertainment value. Keywords Game development · Development methodology · Scrum methodology · Agile methodology · Tailoring

1 Introduction The video game industry is a relatively young industry while at the same time is fast growing and ever changing and is always at the forefront of technological innovation. As such, research for or about the video game development is in its infancy. T. Z. Khan (B) · S. H. Tusher · M. Hasan Department of Computer Science and Engineering, Independent University, Bangladesh, Dhaka, Bangladesh e-mail: [email protected] S. H. Tusher e-mail: [email protected] M. Hasan e-mail: [email protected] M. Rokonuzzaman Department of Electrical & Computer Engineering, North South University, Dhaka, Bangladesh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_21

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Most video game research attempts to look at video games from the outside, as a foreign entity and how it may harm, benefit or simply change or affect others, but comparatively very few are trying to work with the industry to find solutions to problems that have been plaguing both the veterans and novices to the industry since its inception. For example, there are no proposed or researched general development methodologies or guidelines specifically for the video game development addressing its many unique challenges and pitfalls. Figuring out a development methodology wholly unique to video games would be very costly. On the other hand, a much more feasible option would be to adopt guidelines or frameworks from the well-researched software engineering domain [1–3] and altering it to best suit the needs for specific work. In this case study, we attempt to adopt and tailor the well-known agile development methodology from the software engineering domain, and use it to development an actual video game and observe the effectiveness of this particular methodology for this particular project. We envision this paper being used to compare and inspire similar research efforts in the near future, leading to improved practices that will eventually be adopted by the industry itself.

2 Literature Review Currently, the library for video game research consists of mostly on video game violence [4], video game addiction [5], video game industry [6], video game effects [7], video game cultures [8, 9], video game benefits [10, 11], etc. Software development methodologies are different frameworks used to manage and plan the development process of an IT-based work. Research has shown that the implementation of software development methodology is actually very limited. Even when methodologies are used they are not used in their entirety as they are, rather different combinations and parts of methods are cherry picked to benefit a particular work in its particular context. Agile development methodology in itself is not a particular process to follow, but rather a guideline constructed to bring agility to the software development process. According to the context of the work, agility can be more beneficial in reaching the project goals rather than a rigorous development process. These contexts may consist of requirements not being fully realized during development, the context may change during development, the need to deliver working software iterations mid-development, etc. The four core factors of agile methodology are [12]: • • • •

Individuals and interactions over processes and tool. Working software over comprehensive documentation. Customer collaboration over contract negotiation Responding to change over following a plan.

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3 Problem Statement The development methodology contributes to many of the problems that are currently plaguing the video game industry. Big budget game developments consisting of teams made up of hundreds of developers are still using development methodologies which were popular when a team made up of ten developers was considered a big team [13, 14]. Some common and reoccurring video game development problems are discussed below: Technology: Video games are technology dependent. Releasing games for any specific technology, for example, dedicated game consoles, means getting over the initial learning curve and finding work abounds for the shortcomings of that specific technology [13]. New generation of video game consoles is being introduced closer to the release of the previous generation of consoles with time [15]. This means that developers will have to face these issues even more frequently in the future [16]. Collaboration and Team Management: A game development team consists of people from many different career backgrounds such as programmers, plastic artists, musicians, scriptwriters and designers. Communication between people from so many different career backgrounds is often as hard as it is critical to successfully complete a video game project [13]. Nonlinear Process: Game development is never a linear process and requires constant iterations. Often the waterfall model with some enhancements to allow for iterations is used [13]. Schedule: Projects may face delays because proper targets or deliverables were not established. Even when estimated, estimates are often calculated wrong because of the time needed for communication, emergent requirements or lack of documentation. Since the game development process consists of teams made up of different disciplines and each team is dependent on the delivery of one or more other teams the required development time may be hard to estimate [13]. It is also hard to estimate deadlines and schedules as there are less historical data to look back on [16, 17]. Crunch Time: Crunch time is a term in the game industry to address the period of time when the whole development team has to overwork to get tasked finished and read before validation or project delivery deadlines. Crunch time may last 6–7 days and the work hours for per day may be over 12 h [13, 17]. Scope and Feature Creep: Feature creep is a term in the game addressing new functionality that is added during the development process which in turn increases the workload, and hence negatively impacts the schedule and deadlines [17]. Any new feature needs to be evaluated effectively as unmanaged feature creeps may increase errors, possible defects and ultimately the chances of failure. However, feature creeps are not completely avoidable during game development as video games are an interactive medium and things simply have to be changed mid-development to make it more enjoyable or entertaining for the end users.

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Inadequate Documentation: Documentation helps avoid “feature creeps” and estimate the scope and time of the work. How much documentation is required depends on how complicated the work is [13].

4 Methodology In this section, we describe the scrum development process. Following are the roles required for scrum development methodology. Product Owner: Product owner is a single entity who is responsible to decide the priorities of the features and functionalities that are to be built. Scrum Master: A single entity who is responsible to teach the scrum values and processed to the team and actively works on improving the scrum experience on an extremely contextual basis. Development Team: In scrum, the development team is simply a cross-functional collection of individuals who together has the skills, resources and capabilities to design, build and test the product. Usually, the development team is made up of five to nine people who are self-organized and self-responsible to find out and implement the best ways to fulfill the goals set by the product owner [18]. The detail scrum framework is explained below [18]: • A sprint usually consists of an entire calendar month. • Product owner has a vision of the complete product. “The Big Cube” is to be made. • Then, the Big Cube is broken down into a prioritized list of features, “The Product Backlog.” • During “Sprint Planning” the product owner provides the team with the top most features from the “The Product Backlog.” • The team then selects the features they can complete during the sprint and breaks them down into the “Sprint Backlog” which is a list of tasks that is needed to complete the features (Fig. 1). • The “Sprint Execution” phase starts which lasts for mostly the entire sprint. • A daily short-meeting is held called the “Daily Scrum.” The development team and also the product owner must be present during the Daily Scrum to put into context what has already been made, what needs to made next and to make sure that the team is not deviating from the goals and requirements of the final product. • At the end of the sprint, the team by should have a portion of the final product that has the potential to be shipped and completely functional on its own. • Then, the “Sprint Review” starts where the development team and stakeholders simply discuss the product being built. • The sprint ends with the “Sprint Retrospective.” The development team tries to answer three key questions about the current sprint. “What the team did well”, “What the team should change” and “What the team should stop doing altogether.”

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Fig. 1 Scrum framework

• After the end of one sprint, the whole process starts again from the Product Backlog. • The process continues until the deadline or completion of the requirements.

5 Solution Different development methodologies are better for different development contexts. If a project, for example, has very well-defined requirements which have a very low chance of changing during development and the means or skills to reach those requirements are well known then methodologies such as the tried and true, linearsequential design with extensive documentation such as the “Waterfall Model” would be best. As such the first step would be to understand the project we are working on.

5.1 Background of the Experiment Elevator Pitch and Tool used: Single-device two-player game for Android smartphones made up of multiple mini-games (as shown in Table 1). Table 2 lists down the tools used for the development purpose. Team and Skills: The team is made up of four undergraduate students of Computer Science at Independent University, Bangladesh. Although the team is made up of hobbyist artists, musicians and even game designers and developers this research

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Table 1 Pitch breakdown Keywords

Description

Two players

The games are meant to be played by two players at the same time either competing with each other or playing as a team

Single-device The game is meant to be played on the same device which means external connections or peripherals such as Internet connectivity is not required Mini-games

A collection of small games that can be played in short bursts

Android

Although due to the flexibility of the game engine, Unity, being used the product can easily be built for iOS smartphones we decided to focus and test it only on Android smartphones

Smartphones All interactions with the game or app will be done through hand gestures

Table 2 Tools used Tools

Description

Unity

A very well-known and widely used game engine free to use for hobbyist game developers, arguably has the most amount of online resources compared to other game engines

Adobe Photoshop PS One of the oldest and most popular digital art and graphics editor software by Adobe Systems MediBang Paint Pro

Lightweight and free digital art and graphics designing software

is the first time they will be using these particular set of tools. Being full time students first the amount of time each individual can contribute to this research at any given time will vary extremely due to exam dates, family responsibilities, etc. Each individual will mostly work remotely from home. Time: The research is part of the senior project/thesis of two undergraduate students of the Independent University, Bangladesh and as per university rules this research has a production time of seven months. Stakeholders: The stakeholders were the individual members of the development team and the university faculty supervising this research. The faculty, in particular, had one requirement for the research. The requirement was that the team had to show significant progress on a weekly basis. The chosen methodology has to promote experimentation and exploration as the team is using tools that they have never used before. The work requirements will inevitably change as the team gains more knowledge on the tools and gains more understanding of the scope of the work and the effort and time required to complete it. The time and deliverables have to be flexible enough to account for the extremely varying availability of individual members of the research.

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5.2 Tailoring Scrum Methodology Backlogs: Instead of keeping multiple backlogs for a very small team (smaller than the usual scrum team of five), it would inevitably and unnecessarily increase the development time, and the team has opted to simplify the multiple backlogs into one online spreadsheet. Below we describe simplified backlog: • A task code starting with the character “A” depicts an art asset, while “C” depicts a code asset. “M” depicts the integration of multiple assets into the main game. “Undefined” represents a feature that is yet to be broken down into smaller tasks. • The “Not Started” list is prioritized. • The heading for each column (“Not Started”, “Started” and “Done”) simply lists the tasks in each phase of development as the name suggests. “Done Done” depicts a task that was tested and the team is very sure will not go through any more iterations. • This list was updated every week at the end of every sprint (Table 3). Daily Scrum: The team could not work on the experiment full time and also worked remotely. Instead of daily sprint, the team opted to simply contact each other and provide support to each other whenever it was necessary during the Weekly Sprint. Potentially Shippable Product Increment: Due to the extremely varying amount of time each individual had to work on this research per week the team decided that it would be too difficult to set a goal to have a Shippable Increment of the product each week. Instead, the goal was set to build a “Product Increment Showing Progress” every week. This concept was also in line with the weekly update requirement of the faculty (product owner) who was overseeing this research. Finally, the process model of tailored scrum development if provided below (Fig. 2). Table 3 Tailored backlogs Not started

Started

Done

A2

Minigam1 character “Pink” animation

C2

Game select start button

A3

Game select button and start button

A1

Minigame1 character “Blue” animation

M1

A1 + C2

Undefined

Game over state

C1

Done Done Slide controls

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Fig. 2 Tailored scrum methodology

6 Experiment Results Team Composition: Early in development the team realized that two of its members were unable to deliver as was needed by them. They were unable to make time for the research due to various reasons. After a few days of discussions, the aforementioned two members had quit the research, rendering the development team size only half as big as before. Due to the extremely flexible nature of the methodology, the team was able to re-scope and redistribute its resources and successfully finish development before the due date. Team Skills: In the beginning, the workflow was very slow and the deliverables per week were also very small in scope. At different points in the development, the team had reached different skill levels, at which point the team often rebuilt the most recent increment from scratch to increase modifiability or quality. Improvement in Programming Skills and Code Assets: In the beginning, the team had difficulties with even the most basic modules. The team was more concerned with showing progress at the end of the sprint rather than thinking about long time modifiability or reusability or testability. A particular mini-game initially used two code files to get everything done and was not reusable elsewhere in the project. The team gradually gained programming and development expertise in the language (C#) and the engine (Unity), respectively. Later, these codes were reworked in a completely object-oriented approach which increased future modifiability, testability and reusability greatly.

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Nine out of the thirteen code files written for Game1 was reused elsewhere within the larger application. Approximately 70% of the code files were reused. As expected the sprint deliverables increased drastically in scope and ultimately benefited the work even though there were some overhead costs of these changes. Improvement in Graphics Art Skill and Art Assets: In the beginning, the team decided on the theme ponds and creatures found around ponds. This idea turned out to be very costly. Firstly, the team had to relate the game-play with the creatures and backdrop. Each art asset took a long time to create as these creatures and backgrounds had to be accurate enough to convey the idea, for example, a bee had to look like a bee to the players. When all the art assets were put together in the game things did not look very attractive or intuitive to play. Mid-development the team decided to redo the art assets for all the mini-games with a few simple concepts in mind: • Use few colors through the whole game. • Use two characters to represent the two players for each game. • Use bubbles of two different colors to represent different game objects (other than the player characters) through the whole game. • Make the two characters simple and cartoony enough so that they can be placed in a variety of scenarios and be easily animated for all the different games in different ways. • Use a simple color for the backdrop for all games that simply contrasts with all other game objects. • Similarly, to the improvements in code these decisions made it far easier and faster to create reusable graphics that were also better in quality and more intuitive for play (Fig. 3). Game-Play: The final product has three mini-games while the team has tested twice as many mini-games and has talked about thrice as much. During the early tests among students and faculties from different departments of Independent University, Bangladesh, a few things were clear. The games were extremely un-intuitive. This was due to both the game-play (and rule set) as well as the graphics. From the testers feedbacks, the team later decided to completely redo the graphics and set up some guidelines for the mini-games to make them easier to learn, remember and even teach to others. Below the game-play guidelines are provided: • Number and complexity of the game rules must be simple enough to explain in one or two sentences. • The “win condition” must remain consistent throughout the game. Changing attributes such as increasing the speed of play with time to increase the chances of meeting the game over condition, as long as the game rules still remain the same. • Each game will be played while holding the device in portrait orientation. Top half will always be the play area for Player1 while the bottom half will always be the play area for Player2.

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Fig. 3 Graphic asset comparison. Initial versus final

• The rapid development cycle and flexibility of the methodology allowed for a random selection of testers to give quick feedback while the team was able to work on those feedbacks just as fast.

7 Conclusion The success of the research by a large part can be attributed to the methodology used which was extremely flexible yet disciplined process. The team was able to quickly adapt to new knowledge and unpredictable circumstances and take drastic but successful decisions relatively. The development process would have been far more efficient in the long run if a scrum master was included. On rare occasions, too much flexibility has led to the team becoming less effective, when they were de-motivated as the team could easily promise to deliver less than they were capable of for the next sprint. The methodology used was tailored particularly for this work and could be reused for similar small-scale work and an inexperienced development team, depending on the context, but will definitely not be suitable for work of larger scales and larger team sizes. Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interests.

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Ethical Approval This chapter does not contain any studies with human participants or animals performed by any of the authors. Informed Consent “Informed consent was obtained from all individual participants included in the study.”

Bibliography 1. L.N. Raha, A.W. Hossain, T. Faiyaz, M. Hasan, N. Nahar, M. Rokonuzzaman, A guide for building the knowledgebase for software entrepreneurs, firms, and professional students, in IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), Kunming (2018) 2. S. Tahsin, A. Munim, M. Hasan, N. Nahar, M. Rokonuzzaman, Market analysis as a possible activity of software project management, in 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), Kunming (2018) 3. M.M. Morshed, M. Hasan, M. Rokonuzzaman, Software Architecture Decision-Making Practices and Recommendations, in Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, Singapore (2019). 4. C.A. Anderson, A. Shibuya, N. Ihori, E.L. Swing, B.J. Bushman, A. Sakamoto, H.R. Rothstein, M. Saleem, Violent video game effects on aggression, empathy, and prosocial behavior in Eastern and Western countries: a meta-analytic review. Psychol. Bull. 136(2), 151 (2010) 5. D.L.K. King, D.H. Paul, F.D. Mark, Video Game Addiction (2013), pp. 819–825 6. A. Gershenfeld, M. Loparco, C. Barajas, Game Plan: The Insider’s Guide to Breaking in and Succeeding in the Computer and Video Game Business (St. Martin’s Griffin, 2007) 7. N.L. Carnagey, C.A. Anderson, B.J. Bushman, The effect of video game violence on physiological desensitization to real-life violence. J. Exp. Soc. Psychol. 43(3), 489–496 ( 2007) 8. A. Shaw, What is video game culture? Cultural studies and game studies. Games Culture 5(4), 403–424 (2010) 9. Y. Aoyama, H. Izushi, Hardware gimmick or cultural innovation? Technological, cultural, and social foundations of the Japanese video game industry. Res. Policy 32(3), 423–444 (2003) 10. D.E. Warburton, S.S. Bredin, L.T. Horita, D. Zbogar, J.M. Scott, B.T. Esch, R.E. Rhodes, The health benefits of interactive video game exercise. Appl. Physiol. Nutr. Metab. 32(4), 655–663 (2007) 11. K. Squire, Video Games and Learning: Teaching and Participatory Culture in the Digital Age. Technology, Education–connections (the TEC Series) (Teachers College Press, 2011), p. 272 12. K. Bect, M. Beedle, A.V. Bennekum, A. Cockburn, W. Cunningham, M. Fowler, J. Grenning, J. Highsmith, A. Hunt, R. Jeffries, Manifesto for Agile Software Development (2001) 13. R. Al-Azawi, A. Ayesh, M.A. Obaidy, Towards agent-based agile approach for game development methodology, in World Congress on Computer Applications and Information Systems (WCCAIS) (2014), pp. 1–6 14. R. McGuire, Paper burns: game design with agile methodologies, in Gamasutra—The Art & Business of Making Games (2006) 15. A. Marchand, H.-T. Thorsten, Value creation in the video game industry: industry economics, consumer benefits, and research opportunities. J. Interactive Market. 27(3), 141–157 (2013) 16. J.P. Flynt, O. Salem, Software Engineering for Game Developers, 1st edn. (Course Technology PTR, 2005) 17. F. Petrillo, M. Pmenta, F. Trindade, C. Dietrich, Houston, we have a problem …: a survey of actual problems in computer games development, in Proceedings of the 2008 ACM Symposium on Applied Computing (2008) 18. K.S. Rubin, Essential Scrum: A Practical Guide to the Most Popular Agile Process (AddisonWesley, 2012)

Designing and Developing a Game with Marketing Concepts Towsif Zahin Khan, Shairil Hossain Tusher, Mahady Hasan, and M. Rokonuzzaman

Abstract The video game industry is still in its infancy and lacks academic research concerning the actual process of developing a video game. Even though many aspiring video game developers dream of becoming a professional video game developer, someday reaching that goal is mostly bolstered by many self-experience failures as more formal academic resources are lacking. In this research, we have attempted to combine the well-established basic marketing concepts with the development process itself in an effort to bring some form of formal education into the video game development process. Keywords Game development · Marketing · Game design · Software design · Software development

1 Introduction The video game industry is still in its infancy. Yet the industry has a net worth of $118.6 billion [1]. Game such as “Clash of clans” [2] or “flappy bird” [3] earns a daily revenue of $50,000. Most papers on video games attempt to look at video games from the outside, as a foreign entity and how it may harm, benefit or simply change or affect others, T. Z. Khan (B) · S. H. Tusher · M. Hasan Department of Computer Science and Engineering, Independent University, Bangladesh, Dhaka, Bangladesh e-mail: [email protected] S. H. Tusher e-mail: [email protected] M. Hasan e-mail: [email protected] M. Rokonuzzaman Department of Electrical & Computer Engineering, North South University, Dhaka, Bangladesh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_22

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but comparatively very few are trying to work with the industry to find solutions to problems that have been plaguing both the veterans and novices of the industry alike. For example, there is no proposed or researched general development methodology or guidelines specifically for the video game development, addressing its many unique challenges and pitfalls. In this paper, we propose using some basic marketing concepts starting from the very development of the game or the product, so that in the long-run marketing the game can be more fruitful. In an attempt to do so, we have developed a video game with said concepts in mind and observe the final results and respective effects. We envision that this paper will both be used to compare and inspire similar research efforts in the future, leading to better practices that will eventually get adopted by the industry itself so that video games can be made in a more marketable manner with respect to traditional marketing concepts.

2 Literature Review Video Game Research Library: Currently, the library for video game research consists of mostly on video game violence [4, 5], video game addiction [6, 7], video game industry [8, 9], video game effects [4, 10], video game cultures [11, 12], video game benefits [13], etc. Marketing and Software Engineering: Although this paper is the first of its kind as per our knowledge, in recent times, a lot of research is being published with the goal of integrating marketing concepts with concepts from the Software Engineering Domain in an effort to enhance the latter domain [14, 15]. Aforementioned papers approach the research from mostly a theoretical point of view, such as the possibility of incorporating market research as a component of Software Project Management [16].

3 Problem Statement Ineffective Communication: Communication between the developers of any product and its end users is very important regardless of the medium or type of product [17]. The same concepts of communication can be applied to the game industry concerning the video game developers and video game players, respectively. Misunderstood Role of Marketing: Proper marketing begins even before the product has been developed in contrast to simply promoting a product postdevelopment. Developers can get better results by being more mindful of the marketing concepts through pre-production, production and post-production.

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Unaligned Strategies: Marketing and development are often treated as separate entities in the game development. As a result, often the game is marketed toward audiences for whom the game was not meant for, diminishing returns. Undefined Game Portfolio: Game or product portfolio is the mission and vision statement that defines how the game developers or sellers of the product want the product to be ultimately envisioned by the customers. Vague or nonexistent portfolios give rise to contradictory marketing efforts that reduce the effectiveness of marketing and fail to create a focused image of the product in the consumer’s mind. Ambiguous Market Plan: An ambiguous or nonexistent marketing plan will have reduced success rate or reduced effectiveness. Proper marketing plans are mapped out even before the product has been announced to the consumers according to the game or product portfolio.

4 Solution 4.1 Effective Marketing Communication Definition: Marketing communication is effective when the proper feedback from the consumers is taken into account all throughout development and even after launch so that the products can be further developed or marketed accordingly. An effective marketing communication can be modeled with keeping these ideas in the mind: • • • • •

Understanding the target audience. Uncover your unique selling proposition. Sharpen or associate your brand look. Consistency in messaging. Choose a perfect media mix.

Demonstration: According to the basic marketing concepts, there are a few tried and true sequence of processes for effective marketing communication. Pre-development—market research: • • • •

Market segmentation Target market Target market analysis SWOT analysis During development—brand development:

• Brand positioning • Brand identity

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4.2 Market Research Market analysis is a thorough analysis of the current and predictive marketplace so as to take better decisions regarding the development of new products and improvements of old products. A basic market research consists of market segmentation, target market and SWOT analysis. Market Segmentation Definition: Group of consumers on the basis of certain traits such as age, income, sex and usage. Consumers who belong to a certain group will respond similarly to marketing strategies. Demonstration: We segmented our market, according to the medium or platform the games are developed for. Our segments namely contain smartphones, tablet, handhelds, TV/consoles, browser based and personal computer games. According to the secondary data analysis (Fig. 1), the market share for smartphone games is steadily increasing, while the market share of other platforms is either staying consistent or steadily decreasing. Target Market Definition: A certain segment, to whom the product and marketing strategies are aimed for, is chosen from segments found in the market segmentation phase. Demonstration: Further secondary data analysis on the market share of different Operating Systems (Fig. 2) shows that the Android OS has a significant market share compared to its competitors. Android and iOS are used primarily by Android smartphones and Apple smartphones, respectively. Accordingly, we can conclude

2016-2019 Global Games Market 40

Market Share

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32

30

28

27

26

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25 20 15

10

5

11

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10 2

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5 1

0 2016 Smart Phone

2017 Tablet

Fig. 1 Global games market [1]

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Handheld

1

Year

2018

Tv/Console

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2019 Casual Web Games

PC

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Market Share of Different OS 100.00%

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80.00% 60.00% 40.00% 17.90%

20.00%

0.30%

0.00%

0.10%

Windows

BlackBerry

Others

0.00% Android

iOS

Fig. 2 Game’s market share based on OS platforms [18]

that Android smartphones have a much larger market share within the smartphone industry. Picking up the perfect game genre is another challenging task. After analyzing secondary data (Fig. 3) and some games [6] in the market, we have found that our idea, scope and market research drive us to the single-device multiplayer. Target Market Analysis Definition: Once the target market has been selected, the target market has to be further defined to guide both the development and marketing efforts to cater effectively to this particular segment.

Game genre's market

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Number

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0 Monthly Downloads Monthly Active Users Time Spent per month Aracde

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SimulationCasual

Casual

Strategy

Fig. 3 Game’s market share based on game genre [19]

Monthly Revenue

Role Playing

Racing

Dice

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Table 1 SWOT analysis Strengths

Weakness

New, innovative and unique idea Multi-disciplinary teams of programmers, game directors, sound designer and graphic artists who have years of experience in their own dedicated domains The game assets can be reused to develop similar games for non “touch” devices

Lack experience developing software or games for profit Lack marketing experience Specifically designed for “touch” devices, and hence, the fundamental of the game play will need to be redesigned if we wish to port to other type devices

Opportunities

Threats

Recent government initiatives to support and nurture local game development with training and/or funding Applicable methods to create “buzz” such as bonuses for sharing the game with friends over social media

Quick cloning of the game after release Overshadowing of the game by other games with stronger marketing and/or appeal parallel release

Demonstration: Casual games are defined as games for casual gamers. From the consumer perspective, these games are usually having simple rules, demand less time or attention and require less learned skills. From the developer’s perspective, these games require less production and distribution cost. SWOT Analyses Definition: SWOT Analysis stands for Strength, Weakness, Opportunity and Threats for a particular product. Demonstration: Table 1 demonstrates the SWOT analysis.

4.3 Brand Development Brand development is the act of creating a distinctive image of a particular product or brand in the eyes of the consumers in an effort to stand out when compared to its competitors. The basic branding consists of brand identities and brand positioning. Brand Positioning Definition: Brand positioning using points of parity (POP) and points of differences (POD) is a tool for brand development (Table 2). Brand Identity Definition: Brand identities are disparate elements of the brand purposefully planned and developed to give a complete and focused image or identity for the entire brand (Table 3).

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Table 2 POP and POD analysis Tools

Description

POP

Can be played on a single device such as a tablet or smartphone Two players can share the same screen between them

POD

Brand association with the two distinctive characters “Bloop” & “Ploop” Multiple game levels in a single-device multiplayer game

Table 3 Brand identities Identity

Description

Logo

Different colors to associate with each game character and players Memorable Meaningful enough to be word of mouth Distinctive

Characters

Two distinctive colors represent two different player characters Two distinctive colors of these two game characters are easily associated with the brand name “Bloop Ploop”

Image

5 Conclusion The project is not market tested, and we cannot infer by any means that by using these concepts to build a new game, the developers are guaranteed to see significant returns on investment. Regardless the research puts forth a concept of harmonizing traditional marketing concepts with a new age media development, namely video games. Along with the concept, we have also demonstrated how to go about using these marketing concepts in the actual game development process by developing a small-scale video game ourselves named “Bloop Ploop.” More research is needed to refine the methods in which traditional marketing concepts can be used in designing a video game. In this way, we can be mindful of how and to whom we are going market the product to. The degree of effectiveness of this concept can only be evaluated after collecting the related real-world data, post-launch. Hence, even more research is needed on the effect of these concepts post-launch as opposed to only during development.

Bibliography 1. The Global Games Market Reaches $99.6 Billion in 2016, Mobile Generating 37%, [Online]. Available: https://newzoo.com/insights/articles/global-games-market-reaches-99-6billion-2016-mobile-generating-37/

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2. Clash of Clans, [Online]. Available: https://www.similarweb.com/app/google-play/com.superc ell.clashofclans/statistics 3. A. Dogtiev, in Flappy bird revenue—How much did Flappy Bird make? [Online]. Available: http://www.businessofapps.com/data/flappy-bird-revenue/ 4. N.L. Carnagey, The effect of video game violence on physiological desensitization to real-life violence. J. Exp. Soc. Psychol. 43(3), 489–496 (2007) 5. K.E. Dill, D.C. Jody, Video game violence: a review of the empirical literature. Aggress. Violent. Beh. 3(4), 407–428 (1998) 6. D.L.K. King, D.H. Paul, F.D. Mark, Video Game Addiction (2013), pp. 819–825 7. A.J.V. Rooij, T.M. Schoenmaker, A.A. Vermulst, R.J.V.D. Eijinden, D.V.D. Mheen, Online video game addiction: identification of addicted adolescent gamers. Addiction 106(1), 205–212 (2010) 8. B.L. Bayus, V. Shankar, Network effects and competition: an empirical analysis of the home video game industry. Strateg. Manag. J. 24(4), 375–384 (2002) 9. Y. Aoyama, H. Izushi, Hardware gimmick or cultural innovation? Technological, cultural, and social foundations of the Japanese video game industry. Res. Policy 32(3), 423–444 (2003) 10. C.A. Anderson, A. Shibuya, N. Ihori, E.L. Swing, B.J. Bushman, A. Sakamoto, H.R. Rothstein, M. Saleem, Violent video game effects on aggression, empathy, and prosocial behavior in Eastern and Western countries: a meta-analytic review. Psychol. Bull. 136(2), 151 (2010) 11. A. Shaw, What is video game culture? Cultural studies and game studies. Games Culture 5(4), 403–424 (2010) 12. K. Squire, Video Games and Learning: Teaching and Participatory Culture in the Digital Age. Technology, Education–Connections (the TEC Series) (Teachers College Press, 2011), p. 272 13. D.E. Warburton, The health benefits of interactive video game exercise. Appl. Physiol. Nutr. Metab. 32(4), 655–663 (2007) 14. L.N. Raha, A.W. Hossain, T. Faiyaz, M. Hasan, N. Nahar, M. Rokonuzzaman, a guide for building the knowledgebase for software entrepreneurs, firms, and professional students, in IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), Kunming (2018) 15. M.M. Morshed, M. Hasan, M. Rokonuzzaman, Software architecture decision-making practices and recommendations, in Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, Singapore (2019) 16. S. Tahsin, A. Munim, M. Hasan, N. Nahar, M. Rokonuzzaman, Market analysis as a possible activity of software project management, in 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), Kunming (2018) 17. K.L. Keller, Strategic Brand Management: Building, Measuring and Managing Brand Equity (Pearson Esucation Limited, 1997) 18. J. Vincent, in 99.6 percent of new smartphones run Android or iOS While BlackBerry’s market share is a rounding error, [Online]. Available: https://www.theverge.com/2017/2/16/ 14634656/android-ios-market-share-blackberry-2016 19. M. Sonders, in New mobile game statistics every game publisher should know in 2016, SurveyMonkey Intelligence, [Online]. Available: https://medium.com/@sm_app_intel/new-mobilegame-statistics-every-game-publisher-should-know-in-2016-f1f8eef64f66

Some Variants of Cellular Automata Ray-Ming Chen

Abstract Cellular Automata (CA) shed some light on the mechanisms of evolution. Typical cellular automata have many assumptions, simplifications and limitations. Most of them have been extended or generalized or even lift. In this chapter, I introduce more other cellular automata from different perspectives with the aim at enriching the research scope and applications of CA. Keywords Cellular automata · Transition · Interactive CA

1 Introduction The concept of CA [1] provides an approach to delving into how the physical and social worlds work and evolve. Though the settings and concepts [2, 3] of CA are easy to comprehend, they have a far-reaching power to simulate real or imaginary objects and their lives. Since it had been introduced, it has snowballed into a new subject of research. Many different generalizations of CA have emerged and developed. In this chapter, I look into CA—in particular, their transition rules—from other perspectives. Later on, I would design some CA to capture these perspectives. First of all, in Sect. 2, I look into the state-dependent transition rule. Unlike a universal transition rule for all the states, the transition rules will depend on the states of the cells. In Sect. 3, I study the transition rules based on cellular automata with cells whose states are the rules. There are already some research on transition rules which take environment into consideration. I will also focus on the mutation of states and the behaviour of camouflage induced by environment. With or without explicit transition rules, evolution of states consumes resources. Hence, resources per se become implicit transition rules. This mechanism will be also explored. In a

R.-M. Chen (B) School of Mathematics and Statistics, Baise University, 21, Zhongshan No. 2 Road, Baise, Guangxi Province, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_23

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dynamical system, the behaviour of one set of cellular automata might depend on other cellular automata or mutually dependent. I will study such mechanisms. Lastly, I will investigate some combinations of the above mechanisms.

2 State-Dependent Transition In this paper, we use CA to denote either one-dimensional cellular automaton or cellular automata. It depends on the context. A state might play an active role in forming or choosing its neighbourhood or rules. All the neighbourhood and transition rules are not predetermined. It depends on the value of the state and evolves accordingly.

2.1 State-Dependent Neighbourhoods The concept of neighbourhood of a state capsules the degree a state is influenced by other related states. A fixed radius assumes that the range of influence depends only on a fixed number of position and makes no distinction of dependency between different states. This simplification might overlook the dependency of the cells. To distinguish different weight on each cell, a mechanism is introduced. Each array of cells at time tn decides the neighbourhoods at time tn+1 and which activates the related rule to decide the results of next array of cells at time tn+1 . Example 1 Let k be the length of a lattice of an one-dimensional CA. Let the lattice of cells or an array of cells be C = (C1 , C2 , . . . , Ck ). Let v tn (Ci ) denote the value (or state) in the cell Ci at time tn and let the array s(tn ) = (v tn (C1 ), v tn (C2 ), . . . , v tn (Ck )). Define Right s(tn ) (Ci ) := h, the order of the first element at C in the right of Ci such that v tn (Ch ) = v tn (Ci ), if there exists such h and Right s(tn ) (Ci ) := i, otherwise. Define Left s(tn ) (Ci ) := m, the order of the first element at C in the left of Ci such that v tn (Cm ) = v tn (Ci ), if there exists such m and Left s(tn ) (Ci ) := i, otherwise. Define the rule for the neighbourhood of cell Ci , N bs(tn ) (Ci ) := {Cn : Lefts(tn ) (Ci ) ≤ n ≤ Rights(tn ) (Ci )}. For example, assume that s(tn ) = (0, 1, 1, 0, 1, 0, 1, 0). Based on the above definitions, one computes each neighbourhood of each cell as follows: N bs(tn ) (C1 ) = {C1 , C2 , C3 , C4 }, N bs(tn ) (C2 ) = {C2 , C3 }, N bs(tn ) (C3 ) = {C2 , C3 , C4 , C5 }, N bs(tn ) (C4 ) = {C1 , C2 , C3 , C4 , C5 , C6 }, and N bs(tn ) (C5 ) = {C3 , C4 , C5 , C6 , C7 }, N bs(tn ) (C6 ) = {C4 , C5 , C6 , C7 , C8 }, and N bs(tn ) (C7 ) = {C5 , C6 , C7 }, N bs(tn ) (C8 ) = {C6 , C7 , C8 }. Now suppose the transition rule is: v tn+1 (Ci ) = min{v tn (D) : D ∈ N bs(tn ) (Ci )}. Then one has the transited state s(tn+1 ) = (0, 1, 0, 0, 0, 0, 0, 0).

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2.2 State-Dependent Rules The transition rule of a state is not predetermined, but it depends on the array of the states. It indicates the transition routes highly rely on the values of states per se. Example 2 Assume C = (C0 , C1 , C2 , . . . , C7 ) is a lattice of cells of an one-dimensional CA. Each cell has only two states: 0 and 1 and the radius of its neighbourhood is 1. Let the value of a cell C at time tn be denoted by v tn (C). Assume the chosen transition rule from an array s(tn ) to an array s(tn+1 ) is v tn (C0 ) × 20 + v tn (C1 ) × 21 + v tn (C2 ) × 22 + · · · + v tn (C7 ) × 27 . Now suppose that s(tn ) = (1, 1, 0, 1, 0, 1, 0, 1) and N bs(tn ) (C0 ) = (1, 1, 0), and N bs(tn ) (C1 ) = (1, 1, 0), N bs(tn ) (C2 ) = (1, 0, 1), N bs(tn ) (C3 ) = (0, 1, 0), N bs(tn ) (C4 ) = (1, 0, 1) and N bs(tn ) (C5 ) = (0, 1, 0), N bs(tn ) (C6 ) = (1, 0, 1), N bs(tn ) (C7 ) = (1, 0, 1). Then s(tn+1 ) = R171 (s(tn )) = (0, 0, 1, 0, 1, 0, 1, 1), where R171 (s(tn )) means the Rule 171 applies to the array s(tn ).

3 Automated-Rule-Dependent Transition A fixed predetermined transition rule is not always the best strategy for an evolution. Transition rules might evolve as well. Here we devise a mechanism to automate such evolution. Let L a lattice of cells with length k + 1 of a CA. Let time scale be a time scale of the evolution. Let h be the size of the neighbourhood of each cell. Let S be a set of states and s(tn ) be the array of states at time tn . Let si,j denote the value in the cell j at time ti . Let R(S, h) be the potential transition rules, for example, the one-dimensional CA with the size of a neighbourhood h = 3 (i.e. the radius is 1) and S = {0, 1} has R({0, 1}, h = 3) = Now we construct another CA, say CA , to automate the potential transition rules. Let L be another lattice of cells with length k + 1. Let T  be the same time scale as T . Let h be the size of each neighbourhood of a cell. Let S = R(S, h) be a set of states and r (tn ) be the array of states at time tn . Let ri,j denote the value (a rule) in the cell j at time ti . Let  be its transition rule for CA . Now the activated transition rule for each state si,j is ri,j . This mechanism is shown in Fig. 1: Example 3 Let CA be a cellular automaton with only two states {0, 1}. Let the size of the neighbourhood of each cell h = 3 (i.e. radius 1). Let N btn (C) denote the set of neighbourhood of the cell C at time tn . Suppose the length of the lattice is 6. Suppose the array of states of CA at time t0 is s(tn ) = (1, 0, 0, 0, 1, 1). Suppose that the ordered set of all the neighbourhoods at time tn is N (s(tn )) ≡ (N bs(tn ) (C0 ), N bs(tn ) (C1 ), N bs(tn ) (C2 ), N bs(tn ) (C3 ), N bs(tn ) (C4 ), N bs(tn ) (C5 ))=((1, 0, 0), (1, 0, 0), (0, 0, 0), (0, 0, 1), (0, 1, 1), (0, 1, 1)). Let a • b be the inner product  Similarly, let CA have the same length of lattice of cells as CA and of vectors a and b.

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Fig. 1 Transited states

S = R({0, 1}, h = 3) = {Rule 0, Rule 1, Rule 2, . . . , Rule 255}. Let the size of the neighbourhood of each cell h = 3 (i.e. radius 1). Suppose  is Rule 189889 and the array of rules (or states) at time tn is r (tn ) = (9, 231, 20, 0, 20, 255). Let the ordered set of all the neighbourhoods N (r (tn )) ≡ (N br (tn ) (C0 ), N br (tn ) (C1 ), N br (tn ) (C2 ), N br (tn ) (C3 ), N br (tn ) (C4 ), N br (tn ) (C5 )) = ((9, 231, 20), (9, 231, 20), (231, 20, 0), (20, 0, 20), (0, 20, 255), (0, 20, 255)). Assume e = (2550 , 2551 , 2552 ). Define N ∗ (r (tn )) := (b0 ≡ N br (tn ) (C0 ) • e, b1 ≡ N br (tn ) (C1 ) • e, b2 ≡ N br (tn ) (C2 ) • e, b3 ≡ N br (tn ) (C3 ) • e, b4 ≡ N br (tn ) (C4 ) • e, b5 ≡ N br (tn ) (C5 ) • e). Suppose k is a fixed positive integer and Y is an arbitrary set. For any non-negative integer n = c0 × |Y |0 + c1 × |Y |1 + c2 × |Y |2 + · · · + ck−1 × |Y |k−1 , define RulenS : {0, 1, 2, . . . k − 1} → {c0 , c1 , c2 , . . . , ck−1 } by RulenY (m) := cm , where all the coefficients are non-negative integers lying between 0 and |Y | − 1 and where 0 ≤ m ≤ k − 1. Then s(tn+1 ) = (R9 (1 × 20 + 0 × 21 + 0 × 22 ), R231 (1 × 20 + 0 × 21 + 0 × 22 ), R20 (0 × 20 + 0 × 21 + 0 × 22 ), R0 (0 × 20 + 0 × 21 + 1 × 22 ), R20 (0 × 20 + 1 × 21 + 1 × 22 ), R255 (0 × 20 + 1 × 21 + 1 × 22 )) = (R9 (1), R231 (1), R20 (0), R0 (4), R20 (6), R255 (6)) = (0, 1, 0, 0, 0, 1), where Rn (m) denotes the state after applying the Rule n to the coded neighbourhood number m. Furthermore, N (s(tn+1 )) = ((0, 1, 0), (0, 1, 0), (1, 0, 0), (0, 0, 0), (0, 0, 1), (0, 0, 1)).

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S S Moreover, it follows r (tn+1 ) = (u0 ≡ Rule189889 (b0 ), u1 ≡ Rule189889 (b1 ), u2 ≡ S S S S Rule189889 (b2 ), u3 ≡ Rule189889 (b3 ), u4 ≡ Rule189889 (b4 ), u5 ≡ Rule189889 (b5 )). Hence, s(tn+2 ) = (Ru0 (0 × 20 + 1 × 21 + 0 × 22 ), Ru1 (0 × 20 + 1 × 21 + 0 × 22 ), Ru2 (1 × 20 + 0 × 21 + 0 × 22 ), Ru3 (0 × 20 + 0 × 21 + 0 × 22 ), Ru4 (0 × 20 + 0 × 21 + 1 × 22 ), Ru5 (0 × 20 + 0 × 21 + 1 × 22 )) = (Ru0 (2), Ru1 (2), Ru2 (1), Ru3 (0), Ru4 (4), Ru5 (4)).

4 Environment-Dependent Transition In the strict sense, there exists no closed system. Everything is somehow connected. If the effect from the environment is very small, then one could consider the whole system as an isolated system. Nonetheless, if the environment interacts with the system closely and has high impact on the system, then one needs to design some rules to capture their interactions. Here I only describe some mechanisms for mutation and camouflage since there are already some research on CA with environment factor. Mutation is common and occurs very often—for example, nuclear radiation creates some mutations to living creatures or some unpredictable swerve of a social structure from tradition, while camouflage is also prevalent among some natural or social mechanisms.

4.1 Mutation Under normal conditions or a closed system, a set of states is expected to remain the same under the whole course of evolution. However, when the environment has a high impact on the system, then the course of the evolution would change accordingly. For example, under harsh environment, the offspring of one species would change their genes or behaviours to adapt to the environment. Here I consider the mutation of the set of states. Let SE be a set of states under the environment E. Now when the environment changes to E  , the set of states would change to SE  . Such changes could be specified by some threshold rules. These rules might be derived from some other mechanisms too. Example 4 Let E(tn ) be the degree of temperature in an environment at time tn . Suppose s(tn ) =(yellow, blue, white) and E(tn ) = 40 ◦ F. The transition rule is: if the temperature shifts from 30–50 ◦ F range to 390–600 ◦ F range, then yellow will turn black, blue will remain blue and white will turn transparent (a mutated state). Now suppose the temperature at time tn+1 is E(tn+1 ) = 508 ◦ F. Then one has s(tn+1 ) =(black, blue, transparent). Such mutation is still under the realm of predictability, since it could still be specified by the threshold rules. For a random mutation, one could add more threshold rules for a single situation or introduce probability into the transition rule.

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4.2 Camouflage In the case of camouflage, when the environment changes, CA would evolve as close to the environment as possible. This enables them to assimilate into the whole system and save themselves from being exposed to the enemies. Example 5 Suppose a CA has only two states: black and white and its environment has three states: brown, red and blue. Assume its array of states at tn is s(tn ) = (white, black) and assume the state of its environment at tn is brown. When CA observes the difference between itself and the environment, it transits from the array s(tn ) to s(tn+1 ) = (black, black) since black is a state closer to brown. If one considers a simultaneous transition, i.e. no time lag between the environment and CA, then CA needs some estimation or prediction of the outcome of the environment and acts accordingly.

5 Resource-Dependent Transition The evolution of CA does not come for free. It consumes resources. The form of resources could be any physical resources: electricity, gas, money, …, etc or nonphysical ones: knowledge, information, love, …, etc. No matter whether there exist an explicit transition rule or not, the transition is also implicitly constrained by the resources provided. In the case with no explicit rules, resource constraint will act as an implicit rule for the transition. There are many aspects that would involve the consumption of resources.

5.1 Length of Lattice To create each cell to bear states, it needs space or capacity, for example, memory of a computer or the strength of power supply. An infinite length of a lattice is too idealistic for real applications. It is conceptual. To implement it, one needs some capacity or resources. Suppose one lattice is constructed based on power supply. The more power you provide, the longer lattice you have. Then the amount of power in time t would decide the length of a lattice. One needs to specify the transition rules for the states from one length of lattice to another one. In the following, I give a very simple example. Example 6 Let PWtn denote the amount of power available at time tn . Let Lentn (PWtn ) denote the length of lattice supplied by power at time tn . Suppose each megawatt electrical output creates exactly one cell. Assume PWtn = 8 megawatts (MW). Then one has Lentn (PWtn ) = 8. Suppose that s(tn ) = (1, 1, 0, 1, 0, 1, 1, 0) and the transition rule is to copy its previous state from left to right repeatedly. Suppose the power sup-

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plied at time tn+1 is 14 MW, then one has s(tn+1 ) = (1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1). Suppose at time tn+2 there is a power shortage and PWtn+2 = 5 MW, then one has s(tn+2 ) = (1, 1, 0, 1, 0).

5.2 Cell-Powered Transition Suppose each cell is assigned or distributed with a specific amount of resources. The transition rule for each cell will be guided by the amount of the resources assigned or distributed. Example 7 Let s(tn ) = (1, 0, 0, 0, 1, 1). Suppose each cell is equipped with a circuit with functionality (or states): on and off. If it is on, then the resource is provided and off, otherwise. Suppose the transition rule is: when the resource at time tm is supplied, the state from time tm changes to its opposite one at time tm+1 and stays the same, otherwise. Let Cirt (C) be the state of the Circuit of the cell C at time t. Now assume Cirtn (C1 ) =on, Cirtn (C2 ) =off, Cirtn (C3 ) =on, Cirtn (C4 ) =on, Cirtn (C5 ) =off, Cirtn (C6 ) =off. Then one has s(tn+1 ) = (0, 0, 1, 1, 1, 1). This mechanism also entitles each cell to have its own set of states based on the resources it obtains. Example 8 Let L = (C1 , C2 , C3 ) be a lattice of cells. Let the value of a cell C at time tn be denoted by v tn (C). Let the resource-dependent set of states go as follows: SR1 = {1, 8, 18, 29}, the set of states given the resource level R1 . Similarly, SR2 = {2, 11, 12, 219}, SR3 = {12, 22, 25, 69}, SR4 = {41, 44, 65, 69}, SR5 = {0, 4, 51, 91}. Assume the resources assigned to each cell at time tn and tn+1 are: Restn (C1 ) = R2 , Restn (C2 ) = R1 , Restn (C3 ) = R4 ; Restn+1 (C1 ) = R5 , Restn+1 (C2 ) = R4 , and Restn+1 (C3 ) = R3 . Assume s(tn ) = (5, 19, 55). Suppose the transition rule is to choose the maximal state among all the states that maximize the distance between the to-be-transited state and potential transited state, i.e. v tn+1 (C) = max{argmax{|v tn (C) − d | : d ∈ SRestn (C) }}. Then s(tn+1 ) = (219, 1, 69) and s(tn+2 ) = (0, 69, 12).

5.3 Array-Powered Transition Unlike cell-powered transition, there is no resource designated for each cell. Instead, the resource is provided to the lattice as a whole. Then there are many situations to be considered. Are the cells themselves competing for resources? Or are they acting as a whole? Here I only consider the latter situation, i.e. the transition rule is not based on each individual cell, but on the whole lattice. These act more like team-work cellular automata. Example 9 Let the time scale T = {t0 , t1 , t2 , t3 , t4 }. Let L = (C0 , C2 , . . . , C6 ) be a lattice of cells. Given a state of resource, suppose the transition rule is to maximize the

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distance between the transition s(tn ) and s(tn+1 ). Let RS = {RS(t) : t ∈ T } be a set of resource states in which each is measured in terms of the amount of information. Suppose at time tn , the state is s(tn ). Let us define the distance between two states in 6   := terms of the amount of information. For any α , β ∈ D ≡ {0, 1}, define dis( α , β) 6  |α(j)−β(j)|   are the j’th elements in α and β,  respectively. 2 , where α (j) and β(j) j=0

The transition rule is s(tn+1 ) = argmax{dis(s(tn ), α ) : dis(s(tn ), α ) ≤ RS(tn+1 )}. Let α ∈D

RS = {RS(t0 ) = 100, RS(t1 ) = 27, RS(t2 ) = 0, RS(t3 ) = 31}. Let the initial state, s(t0 ) = (1, 1, 0, 0, 1, 0, 1). Thus s(t1 ) = (1, 1, 1, 0, 1, 1, 0), s(t2 ) = (0, 0, 1, 1, 0, 1, 0), s(t3 ) = (0, 0, 1, 1, 0, 1, 0) and s(t4 ) = (1, 1, 0, 0, 1, 1, 0). Resource constraints also provide a natural way to design a beginning or an ending to cellular automata. If the resources are presented, it starts to evolve according to its transition rules; if not, it decays or reverses or ceases to proceed. Resource constraints could also serve as a way to specify the boundary and its transition rule of CA. In addition to that, they could also serve as a time scale.

6 Cellular-Automata-Dependent Transition The transition rules of one CA depend on the transition rules of another CA. This is also a dynamical-transition-rule setting as there is no predetermined and fixed rules. In a simple situation, there is a leading cellular automaton, say CAL , whose actions contribute to the transition rules of his following cellular automaton, say CAF . Suppose the transition rules of CAL are observable to both CAL and CAF . In a more complicated situation, the transition rules of the CAL are only known to the leading CA, but unknown to the following CA. Then the following CA must learn the leading CA’s transition rules via observing the transition of its states. The mechanism is described in Sect. 6.1. If there is no leading or following cellular automaton, but only mutual dependent transitional rules (i.e. the transition rules of CA1 depend on the ones of CA2 and vice versa), then a dynamical process of observation or learning would then decide the their transition rules. The mechanism is described in Sect. 6.2.

6.1 Leading and Following CA There is a time lag and the performance of one cellular automaton will specify or decide the rules for another cellular automaton. This situations occur virtually everywhere, for example, a food chain. We call them a leading and following cellular automaton, respectively. There are at two aspects to be considered:

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1. Transited states: the following CA observes or estimates the states of its leading CA and acts afterwards. 2. Transition rules: the following CA observes or estimates the transition rules of the leading CA and acts afterwards.

6.1.1

Direct Observation: Leading States

The following CA will observe the states of the leading CA at time tn and then transit at tn+1 accordingly. Example 10 (Aspect One: State) Suppose the lengths of lattice of cells of the leading CAL and following CAF are the same. Suppose there are only two states: 0 and 1 for both CA. Assume the transition rule for the following CAF is: it will change the value in a cell C to the opposite value when its state in the cell C is the same as CAL in its cell C. Suppose the arrays of states of CAL and CAF at time tn are sL (tn ) = (1, 0, 0, 1, 0, 0, 1, 1, 0) and sF (tn ) = (0, 0, 1, 0, 0, 0, 1, 0, 1), respectively. Then sF (tn+1 ) = (0, 1, 1, 0, 1, 1, 0, 0, 1). Furthermore, if sL (tn+1 ) = (1, 1, 1, 0, 0, 1, 1, 0, 0), then sF (tn+2 ) = (0, 0, 0, 1, 1, 0, 0, 1, 1).

6.1.2

Direct Observation: Leading Transition Rules

The following CA will observe the transition rules of the leading CA at time tn and then make its transition at time tn+1 . Example 11 (Aspect Two: Transition Rule) Suppose there are only two states: 0 and 1 for both CA. Let sF (tn+1 ) = (0, 0, 1, 0). Assume there are two options of transition rules for CAL : rule a and rule b. The transition rule for the following CA is: if the leading CA adopt rule a, then CAF will keep its states unchanged and change to the opposite if rule b is adopted. Suppose the following CA observes that rule b is adopted by CAL for the transition from sL (tn ) = (0, 1, 1, 1) to sL (tn+1 ) = (1, 1, 0, 1). Then CAF acts accordingly and has sF (tn+2 ) = (1, 1, 0, 1).

6.1.3

Following CA’s Learning

When direct observation of the states or transition rules of the leading CA is not possible, then the following CA must design some learning mechanisms or devise some approaches to approximate or extract the leading CA’s states or transition rules. Here we omit the unknown state part, and only focus on the unknown transition rules, i.e. we assume the states of the leading CA are always observable by the following CA. In addition, if there exist several solutions for such estimation, then the following CA must also devise a mechanism to make an optimal choice.

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Example 12 Let us continue Example 11, but which rule among rule a and rule b is adopted by the leading CA is unknown to the following CA. Now suppose the following CA devises a mechanism: if the total number of state 1 in sL (tn+1 ) is greater or equal to the total number of state 1 in sL (tn ), then rule a is adopted by the leading CA and rule b, otherwise. Based on this mechanism, the following CA jumps to the conclusion that rule a is adopted by the leading CA and transits to sF (tn+2 ) = (0, 0, 1, 0), accordingly.

6.2 Interactive CA Suppose there are two CA: CA1 and CA2 . and there is no leading or following cellular automaton. CA2 will base its transition from its array s2 (tn ) to array s2 (tn+1 ) by either the state s1 (tn ) or the transition rule from CA1 ’s array of states s1 (tn−1 ) to array s1 (tn ) and vice versa.

6.2.1

Direct Observation: Mutual States

Both CA base their transition rules on the states of the other CA and evolve interactively. Example 13 Let CA1 and CA2 be two CA whose sets of states are both {0, 1}. Suppose the arrays of states of CA1 at time t0 is s1 (t0 ) = (1, 0, 1, 0, 1) and CA2 is s2 (t0 ) = (1, 1, 1, 0, 0). The transition rule for CA1 is: if the sum of the values of s2 (tn ) is greater than or equal to 3, then it changes the values in the last two cells to the opposite ones; if not, it changes the values in the first two cells to the opposite ones. The transition rule for CA2 is: if the values in the cell 1 and cell 3 of s1 (tn ) are the same, then it changes the values in its first and second cells to the opposite ones; if not, then it changes the values in the second and third cells to the opposite ones. The the transition could be shown as in Table 1: Observe that this transition is stable and periodic with a length of 4.

Table 1 Interactive transition Time scale t0 t1 t2 t3

Cellular automaton one

Cellular automaton two

s1 (t0 ) = (1, 0, 1, 0, 1) s1 (t1 ) = (1, 0, 1, 1, 0) s1 (t2 ) = (0, 1, 1, 1, 0) s1 (t3 ) = (0, 1, 1, 0, 1)

s2 (t0 ) = (1, 1, 1, 0, 0) s2 (t1 ) = (0, 0, 1, 0, 0) s2 (t2 ) = (1, 1, 1, 0, 0) s2 (t3 ) = (1, 0, 0, 0, 0)

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Direct Observation: Mutual Transition Rules

Both CA base their transition rules on the transition rules of the other CA and evolve interactively. The whole mechanism is similar to Examples 11 and 13.

6.2.3

Mutual Learning

When the states or the transition rules of either CA are unknown to the other one, then both CA must come up with some mechanisms or methods to obtain the transition rules of the other CA. All the mechanisms are similar to Example 12.

7 Mixed Systems So far, we have learned many different transition rules from various perspectives. Based on this, one can start to design some more complicated cellular automata systems which might involve many different set of transition rules. Example 14 Suppose the transition rule of a leading CAL is predetermined while the one for its following CAF depends on the resources produced by the transition of CAL . Then, this combination contributes to a composite cellular automata system.

8 Conclusions and Future Work I have introduced various transition rules for cellular automata from different points of view: state-dependent, environment-dependent, resource-dependent and cellularautomata-dependent. Based on these mechanisms, one could start to put forward some theories or implementation of them. In this paper, I give only some guidelines of designing some cellular automata that would much more applicable to real situations. In view of uncertainty, one could also couple probability or entropy with the models or calculations to settle the problem of optimal choices. In addition, one can also combine these cellular automata with other generalized or non-conventional cellular automata, if the real situations are much more complicated.

References 1. S. Wolfram, Cellular Automata and Complexity (Westview Press, 1994) 2. A. Ilachinski, Cellular Automata—A Discrete Universe (World Scientific Publishing Co., 2001) 3. A. Adamatzky, Game of Life Cellular Automata (Springer, London Limited, 2010)

An Exchange Center Based Digital Cash Payment Solution Yong Xu and Jingwen Li

Abstract The paper proposes a digital cash (Dcash) online payment system based on exchange center that represents central bank to manage and monitor issuance, payment and refund of Dcash. Difference Dcash could be exchanged when its issuer registered at the exchange center. Then it introduces architecture of the system and a series of transaction procedures. This paper models the payment activity using Color Petri Nets. At last, by analyzing the state space reports of the model, we can verify and check this system and get the results that the flow charts of these entities are controllable, realizable and feasible. Keywords Digital cash · Online payment · Exchange center · Color petri nets

1 Introduction Usually the payment tools used in e-commerce are named electronic money (emoney) [1]. There are two kinds of e-money systems that one is online system and another is offline system [2, 3]. In this paper, we only focus on online payment systems. Online systems are divided into two kinds [3]: (1) the traceable systems, such as: First Virtual, Cyber Cash,Netbill, Minipay and so on [4–6]; (2) the untraceable systems, such as: NetCash [7]. However, there are many problems in especial two problems listed below in these solutions [7–9]. Lack of unified and standard security mechanisms. Most of e-money systems can be used in specified narrow scope, such as one game website or one e-commerce website. They cannot be universal. And they cannot be exchanged. It means that one kind of e-money cannot be used in other systems and vice versa. The lack of one Y. Xu (B) · J. Li School of Economics and Commerce, South China University of Technology, 510006 Guangzhou, China e-mail: [email protected] J. Li e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_24

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unified e-money system may result in waste of resources. Then a lot of distributed applications will be necessary to establish many e-money systems. And also it is not conducive to the development of e-money related technologies and standards. Lack of Supervisory. With the development of electronic payment, e-money systems as one kind of electronic payment systems have been used in a wide range of applications. Its influence is also growing. But e-money’s supervisory laws and means are not yet legible. To overcome these two problems, this paper presents a solution to build a unified online digital cash system. In this paper, we will focus on digital cash’s business process, not on digital cash technologies such as security, anonymity, and signature algorithm. But the system can adopt these mature technologies if needed. The main contributions of this work are as follows: (1) We define a unified digital cash payment model as an online payment system based on exchange center and a series of transaction procedures for the model. (2) We model the solution using CPNs (Colored Petri Nets). By analyzing the state space report of the CPNs model, we know that the payment business process of the system is controllable, realizable and feasible. The remainder of the paper is organized as follows. Section 2 discusses the related work. Section 2.1 proposes the scheme of a unified digital cash payment system. Section 3 analyzes the model verification and checking of the payment CPNs model. Finally, Sect. 4 provides some concluding remarks.

2 Related Work 2.1 Researches on E-Cash 2.1.1

Research on Online E-cash System

D. Chaum presented an online anonymous e-cash system. He was the first researcher to apply blind signature algorithm to perform e-cash systems. In his solution, banks issue e-cashes that are anonymous and record used e-cash to avoid e-cash being recycled [9]. Damgard proposed an e-cash system that is provable security using cryptogram protocols and zero-knowledge proof algorithm [16]. Many Internet payment systems are online systems: • Credit-card payment: First Virtual, CyberCash. First Virtual is a system for making credit card payments over the Internet without exposing the credit card number to the merchant. It required no special software for a customer to make a purchase [5]. CyberCash is a system that allows customers to pay by a credit card without revealing the credit card number to the merchant [4]. The main point of this scheme was to prevent merchant’s fraud, and thus allow customers to do business with more merchants without fear of scam. However, CyberCash is not able to find the market.

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• Micropayments: NetBill, Millicent and NetCash NetBill is a system for micropayments for information goods on the Internet. A NetBill transaction transfers information goods from merchant to customer, debiting the customer’s NetBill account and crediting the merchant’s account for the value of the goods [6]. Millicent was designed by Digital Equipment’s Systems Research Center to provide a mechanism for secure micropayments. The NetCash research prototype is a framework for electronic currency developed at the Information Sciences Institute of the University of Southern California [10]. • Multi-banks e-cash system based on group signature. Brands proposed an e-cash system based on many banks that each bank issues different e-cash signed by different public key and private key and cleared by accounting center [11–13]. Chaum and Camenisch respectively put forward different group signature solutions and algorithms [14, 15]. • Multi-banks e-cash system based on proxy signature. Zhou presented a multi-banks’ e-cash system based on proxy signature that center bank managed merchant banks allying the trusted third party with proxy signature [16]. A number of recent contributions [17, 18] confirm an ongoing interest in the development of new payment systems.

2.2 Model Verification, Checking and Colored Petri Nets CPNs are widely applied to model, verify and analyze communication protocols and network protocols [19–21]. CPN Tools have been used to model check the absence of deadlocks and livelocks and the absence of unexpected dead transitions and inconsistent terminal states [22]. Aoyang. C. used CPNs to verify and analyze the Internet Open Trading Protocol [23].

3 Activities of the Model The improvement of this scheme is based on proxy signature of Schnorr which adds the identity information of Digital Cash Exchange Center (DCEC) to the signature. Similar to the bill clearing house of central bank and the bank card switching center, DCEC is able to build a unified digital cash signatures, management, communication methods as well as clearing and regulatory modes easily. The model is shown in Fig. 1. There are five kinds of entities, such as customer, merchant, bank of customer (BankC), bank of merchant (BankM) and DCEC in this solution.

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Fig. 1 Architecture of unified Dcash system

3.1 Authority of Agency and Authority of Issue Based on the authorization of central bank, the functions of DCEC includes: managing Dcash, undertaking e-signature as the agent of Central Bank, clearing for commerce banks and supervising to banks, maintaining the Dcash database. Commercial banks should make an application to central bank for authority of issue and achieve their functions such as Dcash issue, customer register, deposit, withdraw and so on. Register There are three kinds of registers must be completed before using Dcash: 1. Issue banks register in DCEC and provide IDBANKi, mwi and signature algorithms, the structures of Dcash. 2. Merchants register in BANKj and provide their certificates. 3. Customers register in BANKk and provide their certificates. Issue The issuance of Dcash in our program is divided into two stages. (1) Banks issue the Dcash. (2) Customers deposit. In this section, we will introduce how the banks issue the Dcash. In the follow section, we will introduce the process that how customers obtain Dcash.

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As we know, the existing Dcash issued by different organizations has different formats. The different data formats in different Dcashes are compatible by registering in DCEC. The denomination in unified Dcash is formulated as the principle in existing paper currency. DCEC and banks are able to make batch digital signatures of Dcashes.And the Dcash can be produced without the customers’ application so that the requirement for networks is reduced. Withdraw Withdraw means customers get Dcash from their registered banks by pre-payment. In this solution it will completely be changed for the issue mode of Dcash. Each time the value of issued Dcash will become multiples of some fixed denomination or the combination of some fixed denomination so that Dcash does not have to be produced temporarily based on the value of the user’s requirements. It will be more convenient for customers’ withdraw. (1) Dcash does not have to be produced temporarily based on the value of the customer’s requirements. (2) the process of withdraw just relates to the customers and their registered bank without making real-time interaction with DCEC. Payment The process of Dcash payment is as follows: (1) Customer U selects goods on the merchant’s site and produces original order. (2) Merchant checks the request and generates the final order. Then it is sent to U, who confirms the order. After that, U selects Dcash payment among several payment options. (3) U uses Dcash client to combine or split Dcash into series of Dcash C  in accordance with the number of payments SUM. Then C  is sent to the trade company. (4) Merchant D receives Dcash C  and makes a digital signature of information about IDD, order number, the total amount to pay and so on. Then D sends the information to registered bank which means Dcash authorization request. (5) BANKj decrypts them and gets IDD, C  , SINGD(IDD, C  )and so on. BANKj verifies the merchant’s signature and gets Dcash according to the combination or spin-off of C  .BANKj makes a digital signature of C  and IDBANKj. Then BANKj sends the signature to DCEC. (6) DCEC verifies the BANKj’s signature. It verifies itself signature in Dcash original information. If the verification of signature isn’t proved to be true, the authorization will be failed. Otherwise, DCEC obtains the signature signed by issuing bank which is the BankC. Then DCEC verifies this signature. If the signature is proved to be true, DCEC will check the Dcash database to confirm whether the corresponding serial number of received Dcash still in the available Dcash database. If Dcash is available, the combination or spin-off of series of Dcash pass the authorization verification successfully. When the issue bank’s signature or the DCEC’s signature isn’t proved, the authorization of Dcash will fail. Only when all the Dcash serial number of C  is

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available and the portion of spin-off of Dcash is verified, the whole authorization is completed. Otherwise the authorization fails. Then DCEC will send the failed information back to BANKj. BANKj will send the failed information to both D and BankC. Then customer receives the failed information sent BankC. If C  is a combination of several Dcashes, DCEC will mark the Dcashes “used” to prevent these serial number of Dcashes retransmission reuse. If there are Dcashes split in C  , DCEC will mark these serial number of Dcashes “split”. What’s more, DCEC marks the split Dcashes which are used to pay “used”. DCEC produces new time stamp and makes a digital signature of marked Dcashes C  . (7) BANKj stores both C  and the signature which DCEC made of C  in the merchant’s ACCOUNTD. Meanwhile BANKj sends the information about successful authorization to merchant, which means the payment has been successful. Merchant is able to deliver to customer. After receiving combination of original Dcashes of C  , BANKj makes corresponding deduction from ACCOUNTU. Meanwhile, BANKk sends the information about successful authorization to customer, which means the payment is successful. Then customer is able to check the merchant’s delivery information. Refund Refund means merchants or customers make an application to their registered banks for converting their Dcashes to corresponding value of the existing currency. When using Dcash online, both merchants and customers manage their Dcash accounts through their registered banks. Therefore, the process of refund is easier.

3.2 Analysis of the Activities According to the process mentioned previously, the activities involved in the whole process of using Dcash are showed in Table 1.

3.3 The CPNs Model of the Payment Activity Assumptions To simplify the model, we give three assumptions. 1. Both customer and merchant accept Dcash as payment tool. 2. All communication channels are reliable. All five roles are reliable. So we do not need CA (Certificate Authority) in whole trade process when the Dcash system is running. 3. We do not consider particular contents and formats of all messages, responses and verified result.

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Table 1 Relationship between entities and activities Role

Customer

Merchant

BankC

BankM

√a



Activity Authority of agency Authority of issue Registerc Inquiryd Issue Withdraw Payment Refund Clearing Logistics

DCEC

Central Bank



√ √

√b



























































√ The symbol stands for that the entity will attend at the step. For example, the role BankC, BankM and DCEC will appear in the activity Issue b The symbol √ stands for that the role or entity may attend at the step. It depends on the activity. For example, when Inquiry is that Customer inquiries his Dcash account, the role Customer, BankC and DCEC will attend in the activity Inquiry c As mentioned above, registration is divided into three different roles register: (1) Customers register involves customer and bank of customer; (2) Merchants register involves merchant and bank of merchant; (3) The issue banks register in DCEC, which involves DCEC and issue bank d There are two kinds of inquiry: (1) Customer’s inquiry involves customer, BankC and DCEC; (2) Merchant’s inquiry involvers merchant, BankM and DCEC a

4. We only model the payment activity, because payment is most complex activity in all activities of the Dcash system. In this activity, there are five entities to be considered: customer, merchant, BankC, BankM and DCEC.

4 State Space Analysis of the CPN Model This part analyses the state space report of the CPNs model. The state space (SS) report is generated by the CPNs Tools. Part of report is showed in Table 2. It shows that there are 187 nodes and 668 arcs in the state space report and the number of nodes and arcs in SCC Graph is the same as in the SS report.This implies that there is no cycles in this SS report. It means that there no livelocks in the model of payment activity. The table also shows that there is no dead transition instance. So the model of payment activity has no dead nodes. In the table we find that there are 3 Dead Markings. All three dead markings are listed in Table 3. From the table they are different characteristic. We will discuss them in detail.

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Table 2 Part of the state space report Statistics

State space

SCC graph

Liveness properties

Nodes

187

Arcs

668

Secs

0

Status

Full

Node2

187

Arcs

668

Secs

0

Dead markings

3[33,103,187]

Dead transition instances

None

Live transition instances

None

Table 3 Type of dead markings Type

Dead markings

Characteristic

1

33

2

103

Payment result is Success.

The number of Invalid Order is up to limit (3)

3

187

The number of that payment verified result is Failure is up to 3

4.1 Type 1: When the Number of Invalid Order Verified by Merchant Is up to 3 In this situation the dead markings is markings 33. From the CPNs tools, we get the values of all of places. All states of others place in this dead marking 33 are empty or 1‘[]. Due to this, we can conclude that the dead marking is expected.

4.2 Type 2: When Payment Verified Result Is Success In this situation, the dead marking is marking 103. Payment is verified to be Success. It means that customer has finished the payment for his order and will get his goods. At the same time, the merchant has gotten Dcash and should deliver goods to customer. All entities have finished their Flow Chart and go to the terminate state. All the places of counters return to their initial value its initial state or empty or 1 []. Due to this analysis all states of all places, we can conclude that the dead marking 103 is expected.

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4.3 Type 3: Payment Verified Result Is Failure In this situation, the dead marking is node 187. The Characteristic of this type is the number of that payment verified result is Failure is up to 3. That means that any of payment message (or Dcash) is invalid or is no sufficient for paying. From the CPNs Tools we get the values of all of places. Similarly, due to this analysis all states of all places, we can conclude that the dead marking 186 is expected. Obviously, the length of this path from node 1 to node 187 is longer than that of the path from node 1 to node 103.

5 Conclusions We present a unified Dcash payment system based on exchange center, which is modeled using CPNs tools and analyze state space report of the model.The contributions of the paper are summarized as below. (1) Definition of the unified Dcash payment model. We propose a unified model and a series of transaction procedures for the model, such as system preparation phase including system initialization, application for DCEC’s agency agreement of signature from central bank and authority of commerce banks’ issue, registering phase, inquiry phase, issuing phase, withdraw phase, payment phase, refund phase and clearing accounts phase in detail. We analyze the relationships among entities and activities in the Dcash system. From a form of the relationship, we know that not all five entities will present in all activities. But they will be in payment phase or payment activities and play important roles respectively. We therefore focus on the payment phase. Finally, we refine the detailed definitions of flow charts (or state machine) for five entities. (2) Modeling the payment activity by CPN Tools. To simplify the model, we give a series of assumptions. Then we take five entities into consideration for the core places. We declare their states as color sets. Base on these points and workflows, we get the CPN model. (3) Verification of the payment activity. By analyzing the state space of CPN model, we know that the payment activity has no livelock or dead code and that all three dead markings are desirable terminal states. There is not unexpected marking in this model. So the payment work flows of five entities all are controllable, realizable and feasible.

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References 1. http://econc10.bu.edu/Ec341_money/Papers/papers_frame.htm 2. N. Asokan, A. Phillipe, J. Michael, S.M. Waidner, The state of the art in electronic payment systems. Computer 30(9), 28–35 (1997) 3. P. Anurach, Money in electronic commerce: digital cash, electronic fund transfer, and Ecash. Commun. ACM 39(6), 45–50 (1996) 4. J.D Tygar, Atomicity in Electronic Commerce. in Proceedings of the ACM Symposium on Principles of Distributed Computing’96 (Philadelphia, PA, USA, 1996), pp. 8–26 5. N. Borenstein, Perils and pitfalls of practical cyber commerce: the lessons of first victual’s first year. in Presented at Frontiers in Electronic Commerce (Austin, TX, 1994) 6. B. Cox, J.D. Tygar, M. Sirbu, NetBill security and transaction protocol. in Proceedings of the First USENIX Workshop on Electronic Commerce (1995) pp. 77–88 7. Y. Xu, Q.Q. Hu, Unified electronic currency based on the fourth party platform integrated payment service. Commun. Comput. Inform. Sci. 135, 1–6 (2011). 1 8. Y. Xu, C.Q Fang, A theoretical framework of fourth party payment. in The International Conference on E-Business and E-Government (iCEE, 2010), pp. 5–8 9. B.D. Chaum, Lind signatures for untraceable payments. in Advances in cryptology-rypto’82, (1983) pp. 199–203 10. G. Medvinsky, B.C. Neuman, Electronic currency for the internet, electronic markets. 3(9/10), 23–24 (1993) (invited); Also appeared in connexions 8 (6), 19–23 (1994, June) 11. I.B. Damgard, Payment systems and credential mechanisms with provable security against abuse by individuals. in Proceedings of Cryptology (1988). pp. 328–335 12. S. Garfinkel, Web Security, Privacy and Commerce, 2 ed. (O’Reilly Media Inc, 1005 Gravenstein Highway North, Sebastopol, CA, 2001) 95472.2002.1 13. T. Okamoto, K. Ohta, Universal electronic cash. in Advances in Cryptology-Crypto’91. Lecture Notes in Computer Science (Springer, 1992), pp. 324–337 14. D. Chaum, E. van Heyst, Group signatures. in Proceedings of EUROCRYPT’91. Lecture Notes in Computer Science (Springer, 1991) pp. 257–265 15. J. Camenisch, M. Stadler, Efficient group signatures for large groups. in Proceedings of CRYPTO’97. Lecture Notes in Computer Science 1294 (Springer, 1997) pp. 410–424 16. H.S. Zhou, B. Wang, L. Tie, An electronic cash system with multiple banks based on proxy signature scheme. J. Shanghai Jiaotong Univ. 38(1), 79–82 (2004) 17. Y. Mu, K.Q. Nguyen, V. Varadharajan, A fair electronic cash scheme. in Proceedings of ISEC 2001. Lecture Notes in Computer Science 2040 (Springer, 2001), pp. 20–32 18. A. Nenadic, N. Zhang, S. Barton, A security protocol for certified e-goods delivery. in Proceedings of the International Conference on Information Technology: Coding and Computing (IEEE Computer Society, 2004) 19. K. Jensen, in Coloured Petri Nets. Basic Concepts, Analysis Methods And Practical Use. Basic Concepts. Monographs in Theoretical Computer Science, vol 1 (Springer, 1997). 2nd corrected printing. ISBN: 3–540-60943-1 20. K. Jensen, in Coloured Petri Nets. Basic Concepts, Analysis Methods And Practical Use. Analysis Methods. Monographs In Theoretical Computer Science, vol 2 (Springer, 1997). 2nd corrected printing. ISBN: 3–540-58276-2 21. K. Jensen, in Coloured Petri Nets. Basic Concepts, Analysis Methods And Practical Use. Practical Use. Monographs In Theoretical Computer Science (Springer, 1997). ISBN: 3–54062867-3 22. J. Billington, G.E. Gallasch, B. Han, A coloured petri net approach to protocol verification. ACPN 2003, LNCS 3098 (2004) pp. 210–290 23. C. Ouyang, J. Billington, Formal analysis of the internet open trading protocol. in FORTE 2004 Workshops, LNCS 3236 (2004) pp. 1–15

Design and Implementation of Pianos Sharing System Based on PHP Sheng Liu, Chu Yang, and Xiaoming You

Abstract In order to realize the concept of sharing economy, enrich our public culture,decrease the idling rate of piano, in this paper, guided by the Yii2.0 framework, B/S architecture and MAMP is used as the system structure and integrated development environment respectively. PHP, Ajax and Boostrap are used as key development technologies; a sharing piano system based on PHP is designed and implemented to meet the actual demand of society. Through this platform, the requirements of both suppliers and demanders for using piano can be quickly got, so as to meet users’ multi-level and personalized using piano trading objectives. Keywords Sharing piano · PHP · MySQL · Yii2.0 framework

1 Introduction With the maturity of Internet technology, the concept of “sharing economy” is becoming more and more popular. After sharing bicycles, electric cars and charging treasures, the concept of “sharing culture” has begun to penetrate into people’s lives from all aspects. Especially with the improvement of people’s living standard, music has more and more influence on people. Because of the high cost of piano and its idleness, it is easy to cause resources not to be fully utilized and even wasted. Therefore, the demand for “sharing piano” is also increasing. Most of the existing sharing pianos are based on the piano company. It has not fully solved the problem of family piano idleness. How to rely on the development of “Internet +” technology to realize the piano service trading opportunities for users

S. Liu · C. Yang · X. You (B) School of Management, Shanghai University of Engineering Science, 201620 Shanghai, China e-mail: [email protected] S. Liu e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_25

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and customers based on sharing economy has become a real problem that has to be considered [1]. At present, there are related research on sharing management system such as sharing bicycle and sharing car management system, but there are few reports about the design and implementation of piano sharing system [2, 3]. Based on this, a piano sharing system is designed and developed in this paper. The system is guided by Yii 2.0 framework, B/S architecture and MAMP is used as the system structure and integrated development environment respectively. PHP, Ajax and Boostrap are used as key development technologies [4–6], and development tools such as Sublime Text 3, Navicat Premium are used to develop a flexible PHP-based piano sharing system to meet the requirement of sharing pianos [7, 8].

2 System Business Process Analysis This system will play an intermediary role, providing a secure trading platform for both parties who have idle pianos and need to use pianos. From the piano demand side, some families do not have the ability to buy piano, but there is a need to learn the piano. From the standpoint of piano idle, high maintenance costs and loss of piano idle will have a certain impact on the economy. Through this platform, the piano can be paid to more people who need to use it. The business process of the system is shown in Fig. 1. Sharing piano users can view the piano information nearby through the piano map and select a piano to make a time reservation. After checking out the relevant information, the system will give the estimated order price according to the user’s credit score, piano score and the duration of the reservation. After the reservation is completed, the sharing piano users will wait for the confirmation of the piano owner. Upon the confirmation of the piano owner, the user can play the piano at the appointed place at the appointed time and pays for the piano by sweeping the code offline. Then user can comment on the playing situation online, including the feeling of playing, the environment and the evaluation of the piano owner. The piano owner can provide piano information and personal information, improve the two-dimensional code of the receipt, and then conduct piano rental after online and offline audits. In the piano Information Interface, you can see the rental price per hour after the evaluation is completed. After received the piano reservation information, the information of the sharing piano users can be checked. If the credit score of the user is low, the owner has the right to refuse the reservation request made by the user. If the reservation is accepted, an offline collection will be made after the piano is hired out. At the end of the hiring service, the piano owner can be evaluated in the piano sharing system. If the evaluation is less than the required level, according to comments, the administrator can deduct piano owner credit points after auditing in the background.

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Begin

Visitors

N Whether or not the user

register

Y log-in

N Query Piano Map

Y

Whether or not the owner

Providing Piano and Information to Improve Personal Receipt Information

Receiving Piano Reservation Information

Choose Piano to View Piano Information Re order

View user information Reservation time and place This order is over, please reserve it again.

Waiting for the confirmation time of the piano owner

whether or not Success

N

Whether or not agree to an appointment Y Off-line collection

N

Administrator's background audit, deducting the corresponding credit score

Y Off-line use of piano off-line payment

Evaluate users

Y

Comments on the Use of Piano

Is it less than 2 stars? N

Whether to apply for repair or not Y

N

Administrators decide to temporarily shut down Piano Service Based on information provided

End

Notify the piano owner

Fig. 1 System flowchart

3 System Design 3.1 System Overall Function Design The whole system includes three types of users: administrators, piano owners and sharing piano users. The functional module diagram of the system is shown in Fig. 2.

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Sharing Piano User

Administrators

Piano Owner

System feedback

View piano assessment results

Evaluating the piano user

View reviews

View reservation information

System feedback

Order details

Evaluating the piano owner

View reservation information

Reservation for piano

View piano map

Personal information

Feedback mailbox management

Credit score management

View repair information

Evaluating the piano

Fig. 2 System function module diagram

3.2 System Database Design The database is the core and foundation of the system and its design directly affects the quality of the whole system. (1) Conceptual Structural Design From the data requirement analysis, there are four entities in the system, namely, user, administrator, piano and piano owner. Through the integration of the dependence relationship between entities, the main E-R diagram of the system is designed as shown in Fig. 3. Because the system is a platform for individual piano rental, by default, a piano owner will provide a piano. So the relationship between the piano owner and the piano is one-to-one and the relationship between other entities is many-to-many. (2) Logic Structure Design According to the E-R diagram of the system, the design of logical structure for the database of the system is carried out. That is, the field names, types and lengths of the main tables in the database are described in detail. (a) user (user ID char (10), name char (8), sex char (2), age int, credit score int, status char (10), mailbox char (15), telephone char (11), identity card number char (20)) (b) piano owner (owner ID char (10), name char (8), sex char (2), age int, credit score int, status char (10), piano ID char (10), mailbox char (15), telephone char (11), ID card number char (20), payment code varchar (100)) (c) Reservation (Reservation ID char (10), Piano ID char (10), User ID char (10), Reservation Start Time Datetime, Reservation End Time Datetime, Reservation Status Char (10), Reservation Date Datetime, Reason varchar (100))

Design and Implementation of Pianos Sharing System Based on PHP Fig. 3 System E–R diagram

n

Piano owner

279 m

Administration

Administrators

n n evaluate

m Assess

l

User

n m

Reserve

m

provide

l

Piano

(d) Assessment (piano ID char (10), timbre int, handle int, stability int, environment int, administrator ID char (10)) (e) Piano (Piano ID Char (10), Hour Price int, Piano Picture Long Blob, Piano Age int, Piano Score int, Detailed Address varchar (50), Longitude Decimal, Latitude Decimal) (f) Evaluation (Evaluation ID Char (10), User ID Char (10), Owner ID Char (10), Evaluation Time Datetime, Evaluation Content varchar (100), Score int).

4 Implementation of the System In the system, Yii 2.0 framework is applied, MVC mode (Model, View and Controller) is followed, and business logic, data and interface display is separated to write code. In addition, business logic is putted into a background folder, while compiling personalized beautiful interface and improving user interaction experience, it can be reused. Model is the core of the application, View is used to display data, Controller is used to process data input. From the perspective of database system, Model is responsible for reading and writing database. View is used to display data read from database in controller, it is usually created based on model data. Controller processes and controls database data to cooperate with business operation, that is, it handles the parts which interact with the users [9, 10].

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4.1 Implementation of Piano Map Module This module is displayed in the piano user system, after entering the system, the piano user can click on the piano map to view the piano in the sub-menu under the piano order in the navigation bar. This map assists the piano user to hire the piano. On this map, the user can see the piano nearby (as shown in Fig. 4). After clicking on the blue bubbles, user can see the ID, detailed geographical location and piano score of the selected piano. This module uses Golden Map API and corresponding interface classes. The piano information and coordinate information are inquired from the background database, and transmitted to the front desk using json array. The front desk uses Ajax technology to receive data, after receiving data, AMap. Marker method in API is used to mark points on the map. The key codes are as follows: var infoWindow = new AMap.InfoWindow({offset:new AMap.Pixel(0,-30)}); for(var i= 0,marker;i na , then D[m, n a ] + dis({x[m + 1], x[m + 2], · · · x[M]}, {y[n a + 1], y[n a + 2], · · · y[N ]}) ≤ D[m, n a ] + dis(− , {y[n a + 1], y[n a + 2], · · · y[n b ]}) + dis({x[m + 1], x[m + 2], · · · x[M]}, {y[n b + 1], y[n b + 2], · · · y[N ]}) = D[m, n a ] + n b − n a + dis({x[m + 1], x[m + 2], · · · x[M]}, {y[n b + 1], y[n b + 2], · · · y[N ]}) ≤ D[m, n b ] + dis({x[m + 1], x[m + 2], · · · x[M]}, {y[n b + 1], y[n b + 2], · · · y[N ]})

(7)

where “–” means an empty set. Therefore, from (5) and (7), one can see that skipping D[m, nb ] does not affect the result of edit distance computation. In the case where nb < na and D[m, nb ] − D[m, na ] ≥ na − nb , since one can always find an ma such that m ≤ ma ≤ M and dis({x[m + 1], x[m + 2], . . . x[M]}, {y[n b + 1], y[n b + 2], . . . y[N ]}) = dis({x[m + 1], x[m + 2], . . . x[m a ]}, {y[n b + 1], y[n b + 2], . . . y[n a ]}) + dis({x[m a + 1], x[m a + 2], . . . x[M]}, {y[n a + 1], y[n a + 2], . . . y[N ]}), (8)

Fast Algorithm for Sequence Edit Distance Computation

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from the fact that dis({x[m + 1], x[m + 2], . . . x[m a ]}, {y[n b + 1], y[n b + 2], . . . y[n a ]}) ≥ Max(m a − m − n a + n b , 0) ≥ m a − m − (n a − n b )

(9)

D[m, n b ] + dis({x[m + 1], x[m + 2], . . . x[m a ]} , {y[n b + 1], y[n b + 2], . . . y[n a ]}) ≥ D[m, n b ] + m a − m − (n a − n b ) ≥ D[m, n a ] + m a − m,   D m, n b + dis({x[m + 1], x[m + 2], . . . x[M]}, {y[n b + 1], y[n b + 2], . . . y[N ]}) ≥ D[m, n a ] + m a − m + dis({x[m a + 1], x[m a + 2], . . . x[M]} , {y[n a + 1], y[n a + 2], . . . y[N ]}) ≥ D[m, n a ] + dis({x[m + 1], x[m + 2], . . . x[M]}, {y[n a + 1], y[n a + 2], . . . y[N ]})

(10) Therefore, from (5) and (10), one can see that skipping D[m, nb ] does not affect the result of edit distance computation (Fig. 2). In other words, if (4) is satisfied, D[m, nb ] can be ignored no matter whether nb is larger or smaller than na . The slope rule is very helpful for removing the computation redundancy in the DP process. There are some rules that can be viewed as an extension of the slope rule and are also helpful for efficiency improvement. (a) First Row Rule. That is, instead of (2), only one of the entry in the first row has to be initialized: D[0, 0] = 0

(11)

and other entries in the first row can be ignored. This is due to that D[0, n] − D[0, 0] = n ≥ |n − 0|.

(12)

Therefore, other entries in the first row are bound to be removed by the slope rule, (b) Same Entry Rule. Suppose that D[m, n] has been determined. If x[m + 1] = y[n + 1], then D[m + 1, n + 1] = D[m, n]

(13)

must be satisfied. Moreover, one has not to determine D[m + 1, n] from D[m, n] + 1. This rule can be proven from the fact that, if D[m, n + 1] does not delete from the slope rule, then D[m, n + 1] = D[m, n] should be satisfied. Thus, D[m, n + 1] + 1 =

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D[m, n] + 1 is larger than D[m, n] + dif (x[m + 1], y[n + 1]) = D[m, n] and (3) is simplified as D[m + 1, n + 1] = min{D[m + 1, n] + 1, D[m, n]}.

(14)

Also note that D[m + 1, n] = min D[m, τ ] + dis(x[m + 1], {y[τ + 1] . . . y[n]}) τ =0,1,...,n  D[m, τ ] + dis(x[m + 1], {y[τ + 1] . . . y[n]}), = min min τ =0,1,...,n−1

D[m + 1, n] + 1)

(15)

If D[m, n] is not deleted by the slope rule, then D[m, n − 1] < D[m, τ ] + n – 1 − τ where τ < n − 1. Therefore, D[m, τ ] + dis(x[m + 1], {y[τ + 1] . . . y[n]}) > D[m, n] − (n − τ ) + dis(x[m + 1], {y[τ + 1] . . . y[n]}) ≥ D[m, n] − (n − τ ) + n − τ − 1, D[m + 1, n] > D[m, n] − 1, D[m + 1, n] ≥ D[m, n]

(16) (17)

From (14) and (17), we can conclude that (13) must be satisfied. Moreover, since D[m + 1, n + 1] = D[m, n], if D[m + 1, n] is not omitted by the slope rule, then D[m + 1, n] = D[m + 1, n + 1] must be satisfied and D[m + 1, n] cannot be equal to D[m, n] + 1. (c) Different Entry Rule. Suppose that D[m, n] has been determined. If x[m + 1] = y[n + 1], then (i) If D[m, n + 1] is not empty, then only D[m + 1, n] = D[m, n] + 1

(18)

should be determined and one has not to determine D[m + 1, n + 1] from D[m, n]. This is due to that, if both D[m, n] and D[m, n + 1] does not delete by the slope rule, then D[m, n] = D[m, n + 1] should be satisfied and D[m, n] + di f (x[m + 1], y[n + 1]) = D[m, n] + 1 = D[m, n + 1] + 1. (19) (ii) If D[m, n + 1] is empty, then one has to compute D[m + 1, n] = D[m + 1, n + 1] = D[m, n] + 1.

(20)

Fast Algorithm for Sequence Edit Distance Computation

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(iii) Suppose that x[m + 1] = y[n + τ ] but x[m + 1] = y[n + k]where k = 1, 2, . . . , t − 1, (21) if D[m, n + k] where k = 1, 2, …, τ − 1 are all empty, then we determine D[m + 1, n + k] = D[m, n] + k − 1.

(22)

If one of the entries among D[m, n + 1], D[m, n + 2], …, D[m, n + τ − 1] is active, then (22) is unnecessary to be computed. With these extension rules, the DP process can be further simplified.

2.3 Magic Number Rule The magic number rule is to delete the entry in the DP matrix that is impossible to achieve the minimal edit distance. Remember that the edit distance can be expressed by (5). Note that, if M − m ≥ N − n and x[m + 1] = y[n + 1], x[m + 2] = y[n + 2], …, x[m + N − n] = y[N], then dis({x[m + 1], x[m + 2], . . . x[M]}, {y[n + 1], y[n + 2], . . . y[N ]}) = M − m − N + n.

(23)

If N − n > M − m and x[m + 1] = y[n + 1], x[m + 2] = y[n + 2], …, x[M] = y[n + M − m], then dis({x[m + 1], x[m + 2], . . . [M]}, {y[n + 1], y[n + 2], . . . y[N ]}) . = N −n−M +m

(24)

Moreover, if none of the elements among {x[m + 1], x[m + 2], …, x[M]} is equal to {y[n + 1], y[n + 2], …, y[N]}, then dis({x[m + 1], x[m + 2], · · · x[M]}, {y[n + 1], y[n + 2], · · · y[N ]}) = max(N − n, M − m)

(25)

Therefore, from (23) to (25), we have |N − n − M + m| ≤ dis({x[m + 1], x[m + 2], . . . x[M]}, . {y[n + 1], y[n + 2], . . . y[N ]}) ≤ max(N − n, M − m) From (26), we can derive the magic number rule as follows:

(26)

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[Magic Number Rule] If D[m 0 , j] + Max(N − j, M − m 0 ) ≤ D[m 0 , j0 ] + |N − j0 − M + m 0 |,

(27)

then the entry D[m0 , j0 ] can be deleted. Note that, if (27) is satisfied, then from (26), D[m 0 , j] + dis({x[m 0 + 1], x[m 0 + 2], . . . x[M]}, {y[ j + 1], y[ j + 2], . . . y[N ]}) ≤ D[m 0 , j0 ] + dis({x[m 0 + 1], x[m 0 + 2], . . . x[M]}, {y[ j0 + 1], y[ j0 + 2], . . . y[N ]}) (28) must be satisfied. Therefore, from (5), deleting the entry D[m0 , j0 ] does not affect the result of edit distance computation. For example, in Fig. 1b (M = 5 and N = 6), D[2, 3] = D[3, 3] = D[3, 4] = 1. However, since D[3, 4] + Max(N − 4, M − 3) = 1 + 2 = 3, D[2, 3] + | N – 2 − M +3| = 1 + 2 ≥ 3, from the magic number rule in (27), D[2, 3] can be deleted. Moreover, in the next row, D[3, 4] = 2 and D[4, 5] = 1. Since D[4, 5] + Max(N − 5, M − 4) = 2 and D[3, 4] + | N −3 – M +4| = 4 ≥ 2, from (27), D[3, 4] can also be deleted (Fig. 3).

Fig. 2 Illustration of the slope rule

Fig. 3 Illustration of the magic number rule

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Fig. 4 Flowchart of the proposed fast sequence edit distance computation algorithm

As the slope rule, the magic number rule is also very helpful for improving the computation efficiency for edit distance computation. The overall flowchart of the proposed efficient edit distance computation, which applies the slope rule, the magic number rule, and the rules extended from the slope rule in (11)–(22), is shown in Fig. 4.

3 Simulations To evaluate the performance of the proposed algorithm, we coded it in Matlab and performed on Intel i5-7400 4 core CPU at 3.00 GHz with 24 GB RAM. We randomly generate DNA sequences with different lengths. Then, these sequences are mutated (including insertion, deletion, and replacement) with different ratios from 5 to 50%. In Figs. 5 and 6, we compare the running time of the traditional DP method and the proposed algorithm in different sequence lengths and different mutation ratios. In Fig. 5, we show the case where the mutation ratio is 10%. In Fig. 6, we show the ratios of the computation time of the original DP method to that of the proposed fast algorithm under different sequence lengths and different mutation ratios. One can

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Fig. 5 The computation times of the proposed fast algorithm and the original DP method when the mutation ratio is 10%

Fig. 6 The ratios of the computation time of the DP method to that of the proposed fast algorithm

see that the proposed algorithm is much more efficient than the original DP method, especially for the long sequence length case and the high similarity case. Note that, when the mutation ratio is 0.05, the computation of the proposed algorithm is 1/6 of that of the original DP method when the sequence length is 2000. Moreover, when the sequence length is about 15,000, the computation of the proposed algorithm is 1/18 of that of the original DP method. In Fig. 7, we show how the proposed algorithm simplifies the computation of the DP matrix (the entries that are processed are marked by blue color and the entries that are not processed are blank). We can see that, compared to other algorithms, when using the proposed algorithm, only a small part of entries is processed. Therefore, the proposed algorithm has much less computation redundancy than other edit distance computation methods. In Table 1, the computation times of the

Fast Algorithm for Sequence Edit Distance Computation

(a) Original DP

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(b) Ukkonen’s method (c) Davidson’s method

(d) Proposed

Fig. 7 The entries that should be processed using different algorithms

Table 1 Comparison time of the proposed algorithm and other methods for edit distance computation Sequence length 600

DP method (ms) 5.66

Ukkonen’s method (ms) 2.24

Davidson’s method (ms) 2.47

Proposed algorithm (ms) 1.03

1000

15.72

6.60

7.33

2.66

2000

63.83

27.36

34.52

10.49

4000

256.30

113.61

163.10

41.17

8000

1033.51

463.86

764.24

166.99

original DP method, Ukkonen’s method [7], Davidson’s method [8], and the proposed algorithm are shown. The results show that the proposed algorithm requires much less computation time and can compute the edit distance between two sequences efficiently.

4 Conclusion In this paper, a very efficient algorithm to compute the edit distance between two DNA sequences is proposed. Several techniques, including the slope rule, the magic number rule, the first row rule, the same entry rule, aa the different entry rule, were proposed to remove the computation redundancy. With these rules, only a very small part of the entries in the DP matrix should be computed. It can much reduce the time for determining the similarity between two DNA sequences and will be very helpful for biomedical signal processing.

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References 1. M.S. Waterman, Introduction to Computational Biology: Maps, Sequences and Genomes (Chapman and Hall/CRC, London, 2018) 2. J.M. Bower, H. Bolouri, Computational Modeling of Genetic and Biochemical Networks (MIT press, 2004) 3. P. Pevzner, Computational Molecular Biology: An Algorithmic Approach (MIT press, 2000) 4. W.R. Pearson, Using the FASTA program to search protein and DNA sequence databases, in Computer Analysis of Sequence Data (Humana Press, 1994), pp. 307–331 5. J. Ye, S. McGinnis, T.L. Madden, BLAST: improvements for better sequence analysis. Nucleic Acids Res. 34(2), 6–9 (2006) 6. N. Bray, I. Dubchak, L. Pachter, AVID: A global alignment program. Genome Res. 13(1), 97–102 (2003) 7. E. Ukkonen, Algorithms for approximate string matching. Inf. Control 64, 100–118 (1985) 8. A. Davidson, A fast pruning algorithm for optimal sequence alignment, in IEEE International Symposium Bioinformatics and Bioengineering Conference (2001), pp. 49–56

Predicting Student Final Score Using Deep Learning Mohammad Alodat

Abstract The purpose of this paper is to create a smart and effective tool for evaluating students in classroom objectively by overcoming human subjectivity resulting from lack of experience of instructors, and students’ over-trust in themselves. We had provided instructors in Sur university with the “Program for Student Assessment (PISA)” tool to assess it’s positive impact on academic performance, self-regulation, and improvement on their final exam scores. The study sample included in this study was the students enrolled at Sur University College at the time of data collection in the 2018/2019 semester. In the purpose of testing the efficiency of four models in predicting students’ final scores based on their mark in the first exam. The four tested algorithms were: Multiple Linear Regressions (MLP), K-mean cluster, Modular feed for-ward neural network and Radial Basis Function (RBF) (De Marchi and Wendland, Appl Math Lett 99:105996, 2020 [3], Niu et al, Water 11(1):88, 2019 12]). After comparing the four models’ effectiveness in predicting the final score, results show that RBF has the highest average classification rate, followed by neural network and K-mean cluster, while Multiple Linear Regressions was the worst at performance. RBF has been used to create the Instructor Program for Student Assessment (PISA).predicting student performance early will help students to improve their performance and help instructors modify their teaching style to fit their student’s needs. Keywords Euclidean dissimilarity · K-mean cluster · PISA · RBF · Neural network · Learn the deep machine

1 Introduction Traditional assessment of students is either the student assesses himself or instructor’s assessment for students [14]. However, Traditional assessment is a subjective way of M. Alodat (B) IST Department, Sur University College, Sur, Oman e-mail: [email protected]; [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_39

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assessment that depends on self-awareness which might increase a problem of SelfCognitive bias (Over-Trust) [8]. Self-Cognitive bias appears clearly when weaker and less mature individuals tend to overrate themselves, they tend to support positive results and reject negative outcomes. Weak assessment tools will mislead the learning process; for Instance, instructors when mistaken in expectations of the final exam score they write in recommendations low student effort, low student capability, and students comment that the reason is hard exams and incompetent teachers [2]. In order to increase the quality and chances of success after the first test of the course, it was necessary to predict the degree of students currently enrolled. The benefits that reflect the public (instructors, students, and administrator) after predicting the degree of students currently enrolled are as follows: (1) help students critically evaluate and more self-awareness in their studies, and create plans of action to improve their performance, which leads to academic accuracy and quality of education. (2) The administrator helps when to know expected grades in class room management and student maintaining. (3) Helps instructors to reduce effort and workload because instructors get feedback for every student [6].

2 Distance Numerical Measure 2.1 Euclidean Dissimilarities Euclidean dissimilarities: Distance numerical measure between objects and when the amount of difference is as small as possible are more alike [10, 13]. The degree of variation of Euclidean dissimilarities is extracted through averaging or by using the minimum or the maximum of the two values; In general, the distance value is non-negative number. Euclidean dissimilarities benefit to find a preference between prediction models, separation of classes (Good for Observation), and creation of good classifiers (compare). Euclidean dissimilarities Semi-Symmetric belongs to the same class is Euclidean dissimilarities symmetric, when values closer to zero and not a problem if it is smaller or equal to 0.5 as long as it is the smallest distance possible for that object but Interval between [1.5, 4] refers to a lousy, non-proper distance measure. The equation for the Euclidean dissimilarities (Interval, Ratio) is as follows: E D = | (Max P x − Min P x − 1) |

(1)

The degree of variation was divided into (1) Euclidean dissimilarities matched, they are two types (a) Euclidean dissimilarities symmetric is the degree of variation is complete. (b) Euclidean dissimilarities Semi- Symmetric (EDSS) is Close to match and start from 0.5 and close to zero. (2) Euclidean dissimilarities no matched, they are two types (a) Cut Dissimilarity (CD) is over the period [0.5.1.5] degrees. (b) Euclidean Dissimilarity of Wide (DDW) is over the period [1.5.4] degrees.

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2.2 Kernel Methods Kernel K-mean cluster is an unsupervised learning algorithm to classifying data into groups n objects based on attributes into k partition, and you want to group them and as the name suggests you want to put them into clusters, where k < n and k is the number of groups or data points within a homogeneous and heterogeneous group, which means objects that are similar in nature similar in characteristics need to be put together. Its advantages are that it is the easiest algorithm of clustering and has the advantage of speed, strength, and efficiency and gives the best results in cases of core clustering from hierarchical clustering.

2.3 Multiple Linear Regressions Multiple Linear Regressions is one of the most populate machine learning algorithms, it is fast, simple, primary and easy to use, By installing the best line to include all data points, this line is known as linear regressions and is represented by the following linear equation: Y = α + a0 X 1 + a1 X 2 + a2 X 3 .

(2)

They are used to assess the real values based on variables and the relationship between independent and non-independent variables. Its disadvantages are that it is limited to linear relationships and relies on independent data, thus rendering it incapable of complex relationships [5, 11].

3 Deep Machine Learning 3.1 Neural Network It is used in deep machine learning and fall within the two types are supervised models and unsupervised models. It is designed to mimic the brain’s human brain through massive processing and is distributed in parallel and has many applications. Consisting of simple processing units and these units are only computational elements called neurons or nodes neurons. The neurons have a neurological characteristic; they store practical knowledge and experimental information to make them available to the user by adjusting the weights. In order to get an acceptable output response, all ANNs go through the following phases: (1) training, (2) cross-validation, and (3) testing. Training a neural network means feeding it with teaching patterns and letting it to adjust its weights in the nodes and passing them to other classes for training and output results. It distinguishes them in the detection of complex nonlinear

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relationships between independent variables, and their disadvantages are difficult to follow and correct [1, 4, 7, 9].

3.2 Radial Basis Function (RBF) Radial basis functions (RBFs) is a nonlinear hybrid network and popular as a result of its computational advantages that typically contains a single hidden layer of processing elements. It can be applied for data interpolation and approximation plus it works on appropriate for large data and noisy data points in comparison to the other methods. RBF can be used to produce smoother curves or surfaces from a large number of data points in comparison to the other fitting methods and to deal with large scattered data points (unorganized) and able to be extended to high dimensional space[4, 7].

4 Results and Discussion The methodology used in this study, relies on deep learning to predict students’ performance. Effectiveness was tested a method Radial basis Function (RBF), by creating a quantity of data points and it is compared with three other models, and include: Neural Network, K-mean cluster, Multiple Linear Regressions. We discuss some differences among the predict students’ performance obtained by instructors from four models and each user model is represented as follows neural network (P1), K-mean cluster (P2), Radial basis Function (P3) and Multiple Linear Regressions (P4). We Excluded degrees Euclidean Dissimilarity of Wide (DDW) and degrees Euclidean dissimilarities matched less than 90% and the greater values of the Euclidean distance, as in Table 1. The best prediction was among the methods for the grade of the student in the final exam score of a course after the first exam, as in Table 2. Table 1 Excluded worst degrees Euclidean Variance

P1, P2, P3, P4

P1, P2

P2, P3

P2, P4

symmetric (0)

0.10

0.16

0.17

0.30

Semi-symmetric (0.5)

0.16

0.22

0.19

0.30

CDD (1.0)

0.36

0.33

0.34

0.21

CDD (1.5)

0.24

0.17

0.17

0.11

DDW (2.0)

0.02

0.09

0.01

0.02

DDW (2.5)

0.12

0.03

0.12

0.06

20.61

17.69

19.33

15.62

Euclidean distance

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Table 2 Best predictive methods Method Variance

P3, P4 No.

P1, P4 %

P1, P3

No.

%

No.

%

symmetric (0)

126

0.52

120

0.50

0.44

106

Semi-symmetric (0.5)

94

0.39

101

0.42

0.45

108

Total

220

0.91

221

0.92

0.89

214

Euclidean distance

7.35

7.38

7.35

The degree of variation in Euclidean dissimilarities symmetric is the best with model P1, P3, and obtained a match in predicting students’ final scores are 126 students. The degree of variation in Euclidean dissimilarities Semi-Symmetric (EDSS) is the best with model P3, P4, and obtained a semi-match is 108 students. The degree of variation in Euclidean dissimilarities matched is the best with model P1, P4, and got 92%. Equal Euclidean distance between model P1, P3 with P3, P4, and the worst model is P1, P4. Help the degree of variation between models to extract the status of the material currently registered for each student at the University College in the semester 2018/2019, such as violating, unknown, failure, withdrawn, incomplete, and transferred, as in Table 3. Students transferred to the college extracted from model P1, P3, Harvest the largest number of 13 students. Students violated by the currently registered material extracted from the model P3, P4, Harvest the largest number of seven students. Students who are failure, withdrawal, and incomplete, extracted from the model P3, P4, Harvest the largest number of 3 students. Students who are unknown there has been a sudden change in their lives, such as psychological, family, or financial problems, extracted from model P1, P3, Harvest the lowest number of four students. Table 3 The status of the material currently registered Method

P1, P3

P1, P4

P3, P4

Violating

2

1

7

Unknown

4

5

17

Failure

1



Withdrawn

1

Incomplete



1

Transferred

5

6



Withdrawn



1



Transferred

8

6



21

20

27

Variance 1

1.5 Total

1 1 1

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4.1 Indicators in the Status of the Materials The status of each student in the previous requirement for each registered subject indicates, as follow: (1) Finding Transferred students is due to the ease of graduation at the college from among the colleges located in the Sultanate or providing students with comfort in the access and reputation of the college. (2) Finding unknown students indicates a change for better or worse in surrounding the students, such as differences in the teaching style of teaching staff, and provision of comfort management for students. The degree of variation between models to extract the status of the materials currently registered for each unknown students, as in Table 4. Table 4 shows that all students in the models will receive a high score at the end of the semester. This indicates a rise in study levels, time management, management support, and academic guidance, which will result in fewer withdrawals and an increase in the overall rate. The degree of variation between models was used to extract the status of the materials currently registered for each student violated of the study plan, Table 5 shows that all students who violators of the study plan will receive a lower score at the end of the semester. This indicates the low level of scientific and practical students, and weak follow-up guidance and support by instructors and administration, which will lead to an increase in the number of withdrawals and decrease in the overall rate resulting in an increase in warnings, dismissal, and failure of the study. In order to solve this problem, it is necessary to stop the excesses of the study plan by solving the gap in the registration system and activating the role of the instructors. Table 4 The status of the materials related to Unknown students Status

Materials

P3, P4

Unknown

Business systems

2

Up Data structures

1

Decision support

2

E-commerce

3

Down

Down

Up

Down

1 1

2

1

1

1

Object-oriented

2

Strategic information

1

17

P1, P3

1

Knowledge management

14

Up 1

IS innovation

Enterprise architecture Total

P1, P4

1

1

1

1 1

2

3

5

3

5

4

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Table 5 The status of the materials related to students violated of the study plan Materials

Violating P1, P3

P1, P4

P3, P4

Up Down Up Down Up Down Business systems

1

2

E-commerce IS innovation

1

1

Enterprise architecture systems, IT audit and control and java programming

1

Total

2 2

1 1

1

6

7

5 Conclusion The Radial Basis Function (RBF) is one of the four models and was chosen to predict the student’s final exam mark. In comparison to students’ own expectations of their final exam score; RBF was more accurate and objective, which would assist decisionmakers (instructors and psychologists) in achieving student retention and increase profits, and assist students early to work harder during the semester. The results of the study indicate that both students’ and instructor’s perceive Instructor Program for Student Assessment (IPSA) as very satisfying and objective when compared with self-assessment or when evaluated by instructors. Future research should focus on investigating machine learning algorithms to predict the student’s performance by considering prerequisite and the place of training that suits his abilities and also extending the coverage of the dataset used in this paper.

6 Compliance with Ethical Standards The author declares that there is no conflict of interest and no fund was obtained. Institution permission and Institution Review Board (IRB) was taken from Sur University College. Written informed consent was obtained from all individual participants included in the study. The researcher explained the purpose and the possible outcomes of the research. Participation was completely voluntary and participants were assured that they have right to withdraw at any time throughout the study and non-participation would not have any detrimental effects in terms of the essential or regular professional issues or any penalty. Also, participants were assured that their responses will be treated confidentially. Ethical approval: This research paper contains a survey that was done by students’ participants as per their ethical approval. “All procedures performed in studies involving human participants were in accordance with the ethical standards

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of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.” Acknowledgements I would like to thank the management of Sur University College for the continued support and encouragement to conduct this research.

References 1. E. Akgün, M. Demir, Modeling course achievements of elementary education teacher candidates with artificial neural networks. Int. J. Assess. Tools Educ. 5(3), 491–509 (2018) 2. A.A. Darrow, C.M. Johnson, A.M. Miller, P. Williamson, Can students accurately assess themselves? Predictive validity of student self-reports. Update Appl. Res. Music Educ. 20(2), 8–11 (2002). 3. S. De Marchi, H. Wendland, On the convergence of the rescaled localized radial basis function method. Appl. Math. Lett. 99, 105996 (2020) 4. M. Gerasimovic, L. Stanojevic, U. Bugaric, Z. Miljkovic, A. Veljovic, Using artificial neural networks for predictive modeling of graduates’ professional choice. New Educ. Rev. 23(1), 175–189 (2011) 5. Z. Ibrahim, D. Ibrahim, Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression, in 21st Annual SAS Malaysia Forum, 5th September (2007) 6. B.A. Kalejaye, O. Folorunso, O.L. Usman, Predicting students’ grade scores using training functions of artificial neural network. Science 14(1) (2015) 7. K. Kongsakun, C.C. Fung, Neural network modeling for an intelligent recommendation system supporting SRM for Universities in Thailand. WSEAS Trans. Comput. 11(2), 34–44 (2012) 8. K. Leithwood, S. Patten, D. Jantzi, Testing a conception of how school leadership influences student learning. Educ. Admin. Quart. 46(5), 671–706 (2010) 9. I. Lykourentzou, I. Giannoukos, G. Mpardis, V. Nikolopoulos, V. Loumos, Early and dynamic student achievement prediction in e-learning courses using neural networks. J. Am. Soc. Inform. Sci. Technol. 60(2), 372–380 (2009) 10. Z. Miljkovi´c, M. Gerasimovi´c, L. Stanojevi´c, U. Bugari´c, Using artificial neural networks to predict professional movements of graduates. Croatian J. Educ. 13, 117–141 (2011) 11. M.F. Musso, E. Kyndt, E.C. Cascallar, F. Dochy, Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learn. Res. 1(1), 42–71 (2013) 12. W.J. Niu, Z.K. Feng, B.F. Feng, Y.W. Min, C.T. Cheng, J.Z. Zhou, Comparison of multiple linear regression, artificial neural network, extreme learning machine, and support vector machine in deriving operation rule of hydropower reservoir. Water 11(1), 88 (2019) - c, A neural network model for predicting children’s 13. M. Pavlekovi´c, M. Zeki´c-Sušac, I. Ðurdevi´ mathematical gift. Croatian J. Educ. Hrvatski cˇ asopis za odgoj i obrazovanje 13(1), 10–41 (2011) 14. K. Struyven, F. Dochy, S. Janssens, Students’ perceptions about evaluation and assessment in higher education: a review. Assess. Eval. Higher Educ. 30(4), 325–341 (2005)

Stance Detection Using Transformer Architectures and Temporal Convolutional Networks Kushal Jain, Fenil Doshi, and Lakshmi Kurup

Abstract Stance detection can be defined as the task of automatically detecting the relation between or the relative perspective of two pieces of text- a claim or headline and the corresponding article body. Stance detection is an integral part of the pipeline used for automatic fake news detection which is an open research problem in Natural Language Processing. The past year has seen a lot of developments in the field of NLP and the application of transfer learning to it. Bidirectional language models with recurrence and various transformer models have been consistently improving the state-of-the-art results on various NLP tasks. In this research work, we specifically focus on the application of embeddings from BERT and XLNet to solve the problem of stance detection. We extract the weights from the last hidden layer of the base models in both cases and use them as embeddings to train task-specific recurrent models. We also present a novel approach to tackle stance detection wherein we apply Temporal Convolutional Networks to solve the problem. Temporal Convolutional Networks are being seen as an ideal replacement for LSTM/GRUs for sequence modelling tasks. In this work, we implement models to investigate if they can be used for NLP tasks as well. We present our results with an exhaustive comparative analysis of multiple architectures trained on the Fake News Challenge (FNC) dataset. Keywords Stance detection · BERT · XLNet · Temporal convolutional networks · Fake news challenge

Kushal Jain and Fenil Doshi have made equal contributions to the work and Lakshmi Kurup was our supervisor. K. Jain (B) · F. Doshi · L. Kurup Dwarkadas J. Sanghvi College of Engineering, Mumbai 400056, India e-mail: [email protected] F. Doshi e-mail: [email protected] L. Kurup e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_40

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1 Introduction The modern media has become a home to a large number of misleading and manipulative content, from questionable claims, alternate facts to completely fake news. In the current scenario, the major ways in which information is spread is via social networking sites like Twitter and Facebook. To tackle such problems, many factchecking websites like PolitiFact, Snopes, etc. were introduced. However, by the time these websites debunk the truth of the fake news, it has already been read by millions of people. Therefore, in this information age, checking the authenticity of news and claims and quelling the spreading of fake news at its source has become a necessity. Moreover, manual fact-checking requires a lot of resources including dedicated and unbiased editors. Given the rapid advancements in Machine Learning and NLP, stance detection became an important part of automating this process of fact-checking. Since a lot of such polarizing content and information is disseminated through microblogging sites such as Twitter, [1] introduced a dataset that considered specific targets (usually a single word or phrase) and collected tweets about those targets. This dataset comprised of manually labelled the stance as well as the sentiment of tweets and was a task in Semantic Evaluation in 2016. In 2017, the Fake News Challenge (FNC-1) was launched along with a dataset containing headlines and articles to support or refute the headline. Furthermore, in 2017, Semantic Evaluation also included a task called RumorEval [2] that dealt with stance detection in Twitter threads. We make the following contributions in this research work: • Applying the recent state-of-the-art transformer and attention-based architecturesBERT and XLNet to the problem of stance detection. • Use the embeddings from pretrained language models to train multiple recurrent neural networks that employ conditional as well as bidirectional encoding of textual information and present an exhaustive comparative analysis of all the models. • Apply temporal convolutional networks to tackle the problem of stance detection and analyze whether they can replace LSTM/GRU based networks.

2 Related Work Previously, most of the work related to stance detection was centered around targetspecific stances where data was collected for a specific target and stance of different bodies were to be estimated in relation to the specified target. In the real world, not much data exists for a particular target word. Fake News Challenge [3] released a training dataset, the largest publicly available dataset for stance detection where the target was not a predefined single word but headlines with a series of words. The dataset was built upon the Emergent dataset [4].

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The common approaches for dealing with the problem were to use handcrafted feature engineering techniques or using neural networks for automated feature selection. The baseline provided by the FNC organizers used hand-engineered features namely word/n-gram overlap, word-polarity, and refutation. In [5], the authors used another set of hand-engineered features-bag of words representation (Term frequency and Inverse document frequency) and cosine similarity between the two in their work. Bhatt et al. [6] combined the two approaches and employed a combination of character n-grams, word n-grams, weighted TF-IDF, polarity and additionally used the component-wise product and the absolute difference between skip-thought vector representation of length 4800. The winning solution in the FNC-1 used a combination of deep convolutional neural networks and gradient boosted decision trees with lexical features [7]. The next best solution by team Athene [8] comprised of an ensemble of five multi-layer perceptron (MLP) with six hidden layers each and handcrafted features. With the success of Word Embeddings and Recurrent Neural networks for NLP tasks, many research works incorporated Glove [9] and word2vec [10] embeddings along with LSTM/GRU cells into the network for this task. Chaudhry [11] represented the words as trainable GloVe vectors of 50 dimensions and trained LSTM network over them followed by an attention mechanism. Zeng [12] tried a similar approach with non-trainable GloVe embeddings and compared results between Bilateral Multiple Perspective Matching (BiMPM) network and attention models using RNN with GRU cells. Conforti et al. [13] used word2vec embeddings as word representations and proposed two architectures—Double Conditional Encoding and Co-matching attention, both followed by self-attention. The progress in approaches to deal with stance detection closely resembles the way in which the field of NLP has developed. Researchers have steadily attempted to apply state-of-the-art approaches to stance detection. This can be seen with the application of word embeddings, convolutions and attention for stance detection and the subsequent improvement in results with novel architectures incorporating these techniques.

3 Proposed Approach Much of the previous methods used for stance detection, as discussed above are primarily based on static or context-free word embeddings. To the best of our knowledge, we are the first to apply deep contextual word embeddings extracted from transformer-based models: BERT and XLNet to the problem of stance detection. Moreover, we also propose a novel architecture that employs temporal convolutional blocks to infer the task. Using temporal convolution to solve stance detection has been unprecedented and we present our results on such a network for the first time. The structure of the paper from here on follows this pattern. In this section, we briefly introduce each of the techniques used namely BERT, XLNet and Temporal

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Convolutional Networks, and then explain how we use it for our models. In the next section, we provide exhaustive details about our experiments, implementation details and results. Finally, we conclude with an analysis of the obtained results and discuss about the future scope of our work.

3.1 BERT BERT which stands for Bidirectional Encoder Representations from Transformers [14] introduced a novel pre-training approach based on masked language modelling and reported state-of-the-art results on multiple NLP tasks. The pre-training strategy used in BERT is different than the traditional strategy of autoregressive language modelling employed by models thus far. Previously, bidirectional LSTM based language models trained a standard left-to-right language model or combined leftto-right and right-to-left models, like ELMO [15]. However, instead of predicting the next word after a sequence of words, BERT randomly masks words in the sentence and predicts them. More specifically, during pre-training, BERT masks 15% of the words in each sequence with a [MASK] token. It then attempts to predict the masked words based on the context provided by words present on both sides of the masked word, hence giving a bidirectional representation. For our experiments, we have used the base model which consists of 12 transformer blocks, 12 attention heads and 110 million parameters. We extracted the weights of the last layer from the pretrained model for all the tokens in the text sequences present in the dataset. To reduce the computational overhead, we calculated these embeddings beforehand and saved them to disk, so that these embeddings can be used directly while training the recurrent models (discussed in later sections). To calculate these embeddings, we use a PyPI package called pytorch-transformers [16].

3.2 XLNet XLNet [17] was recently published by Google AI and reported state-of-the-art results on multiple NLP tasks, outperforming BERT on 20 tasks [17]. The major problem with BERT was that it corrupted the input with [MASK] tokens which are used only during pre-training and do not appear when we finetune BERT on downstream tasks [17]. This leads to a significant pre-train finetune discrepancy. To deal with the limitations of BERT, XLNet was trained using permutation language modelling. Permutation language models are trained to predict one token given preceding context like the traditional language model, but instead of predicting the tokens in sequential order, it predicts tokens in some random order. Unlike BERT which consists of transformer blocks, XLNet is based on another novel architecture called TransformerXL [18].

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For our experiments, we used the base model of XLNet which consists of 12 Transformer-XL blocks and 12 attention heads. To extract the embeddings, we used a similar approach as we did for BERT.

3.3 Recurrent Architectures (LSTM) We use the embeddings extracted from the language models to train task-specific recurrent neural networks. We train three variants of architectures like in [19], which was, however, done on a different dataset • Independent Encoding: In this architecture, embeddings of headline and the article are fed independently to two different LSTM layers. The final hidden states of both the layers are concatenated and connected with a fully connected layer followed by a softmax transformation for predictions. • Conditional Encoding: Unlike independent encoding of inputs where the LSTM layers operated in parallel, here, they operate in sequence. First, the headline is passed through an LSTM layer. The final states of this layer are used as initial states for another LSTM layer through which the article is passed. The final states of this LSTM layer (article) are fed to a fully connected layer followed by a softmax layer for predictions. • Bidirectional Conditional Encoding: The architecture used here is like the previous one with the only difference being that bidirectional LSTMs are employed instead of the vanilla LSTM layers.

3.4 Temporal Convolutional Networks Temporal Convolutional Networks (TCN) are a relatively novel architecture for sequential tasks introduced in [20]. They have outperformed canonical recurrent models (with LSTM, GRU cells, and vanilla RNN) across multiple sequence modelling tasks [21]. TCN’s can be processed in parallel unlike RNN since convolutions can be performed in parallel [20]. Hence, compared to LSTM/GRU, these networks require a comparatively very low memory and are faster. TCN uses a combination of 1D Fully Convolution Network [22] and Causal Convolutions. These convolutions are dilated so that they can learn from bigger sequences for the tasks that need to remember longer history. Hence, we believe that a TCN would be able to capture important information from large sequences of text. Dilated convolutions enable an exponentially large receptive field, unlike usual convolutions which work on a linear receptive field. Causal convolutions are particularly useful for predicting the information related to future time steps since they ensure that there is no leakage of information from future to past. Since our task doesn’t necessarily obey the requirement of no leakage as the entire sequence can be used at a time to predict the output, we modified the architecture

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to use non-causal convolutions. This ensures that at a time step, the model learns from the forward as well as backward time steps. A single layer of TCN block is a series of 1D Convolution, Activation and Normalization layers stacked together with Residual connection. The convolution operation used is non-causal dilated convolution. Activation used is (Rectified Linear Units) ReLU [23] activation and weighted normalization are applied to convolution filters.

4 Experiments and Results 4.1 Training Dataset We perform our experiments on the FNC dataset. The key components of the dataset are the following: • Headline/Claim: A shorthand representation of an article, capturing only the essence of the article without any additional information. • Article Body: A detailed explanation of the headline or the claim. The article body might be related to the headline or be completely unrelated. • Stance: The output classes. The relation between the headline and the body can be classified into four classes: Agree, Disagree, Discuss, Unrelated. The total number of unique headlines and articles are around 1600 each. However, since each article is matched with multiple headlines, the total number of training examples reach up to 50,000.

4.2 Test Dataset The test dataset used in this work is the final competition dataset released by the organizers after the completion of the competition. This dataset was originally used to judge the submissions. The test dataset is structurally similar to the training set, however, the total number of tuples here, is around 25,000 with around 900 unique headlines and articles.

4.3 Pre-processing All the stop-words were removed from the text using NLTK [24]. Stemming or lemmatization was not performed on the text hoping that the embeddings from the pretrained models might capture important information from the text. Punctuations

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were removed from the text so that their embeddings do not corrupt the word vectors. These words were later fed to the trained model to calculate the embeddings.

4.4 Embeddings Calculation We use an open-source library called pytorch-transformers developed by Hugging Face for getting the BERT as well as XLNet embeddings [13]. We use the pretrained model bert_base_uncased and xlnet_base_cased that gave us 768 length vector representation for each word based on its context. We used the base models in both cases due to the lack of computational resources.

4.5 Training 4.5.1

LSTM Models

We trained both LSTM as well as TCN models on a batch size of 128 for a total of 49,972 tuples in the training. LSTM models were trained for 20 epochs and TCN models were trained for 30 epochs. To account for the unbalanced dataset, we used a weighted loss function. We calculated the class weights using a simple function from scikit-learn [25]. The hidden-size or the number of hidden units in the LSTM layers in all the models was chosen to be 128. After combining LSTM encodings in different ways (depending on the architectures discussed previously), a fully connected layer with 128 units was used before making final predictions. The ReLU was used as the activation function for all the models. We trained our LSTM models using Keras [26] and PyTorch [27]. The models were trained on an apparatus with a 16 GB RAM which had an NVIDIA 960 M GTX GPU. Some models were also trained on CPU.

4.5.2

TCN Model

We used 100-D GloVe embeddings [9] to represent the text sequences. To capture the relation between the headline and article texts, we simply concatenated them. The words in the concatenated sequence were then converted into tokens and fed to a pretrained embedding layer. These embeddings are then fed to a TCN layer. TCN network has 128 filters with kernel size of 12 and 1 stacked-layer with a sequence of dilations as (1, 2, 4, 8, 16, 32). Dropout rate used was 0.1 for regularization and ReLU activation was used. The output from this layer was then fed to dense layer which finally gave us the predictions. We use the Keras-TCN [28] PyPi package for our experiments (Fig. 1).

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Fig. 1 Architecture of TCN model

4.6 Results We performed multiple experiments and present an exhaustive analysis of our competitive results. We compare and report the training and testing accuracy of all the three models while using both BERT and XLNet embeddings to represent the text in the headlines and article. The test dataset was completely unseen to the model. Maximum test accuracy of 70.3% was given by the bidirectional model trained on BERT representations. Overall, BERT embeddings outperformed XLNet on all three fronts with bidirectional models giving the best training and test accuracy (Tables 1 and 2). Table 1 Our results that show training accuracy with a weighted loss function and test accuracy for LSTM architectures after training for 20 epochs Models

Accuracy

Independent

Conditional

Bidirectional

BERT

Training

93.3

85.7

84.5

Test

65.4

70.1

70.3

Training

80.7

82.0

83.2

Test

64.3

67.0

67.8

XLNet

Stance Detection Using Transformer Architectures … Table 2 Results obtained after training for 20 epochs

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Models

Accuracy

TCN

GLoVe

Training

83.64

Test

71.1

On the whole, TCN models trained on GLoVe gave the best accuracy compared to the recurrent models. Thus, it can be inferred that these networks are also a viable approach to obtain competitive results in NLP tasks. Our results also align with the findings in [20] that RNN architectures with LSTM/GRU units should not be the default go-to as is the case in many NLP related tasks.

5 Conclusions and Future Scope Our results show that BERT embeddings perform significantly better than those calculated using XLNet. While this seems anomalous, we must understand that we still do not completely understand what the neural network is learning and need to analyze the results to a greater extent before we reach any intuitive conclusion. As per our expectations, conditional and bidirectional conditional models perform better than independent models in most cases. We do see slightly anomalous results when it comes to training accuracy of BERT Independent models. Upon further investigation, we found that our model began to overfit after a certain point in time. The test accuracy, which is comparatively lower than subsequent models, however, confirms our hypothesis. We also experimented with Temporal Convolution Networks to see whether they can outperform canonical RNN architectures due to their larger receptive field. We conclusively infer that they gave more accurate results on the test set with lesser use of memory and training time. Our research and results are limited to the computing power and resources that we had access to. Further research with more resources needs to be performed to confirm the conclusions in [20] that TCN can be used to replace existing LSTM/GRU architectures for sequence modelling tasks. In this work, we used word embeddings for each token in the headline and article text. In future, we could try to use sentence-vectors to capture the meaning of the whole text into one fixed-size vector. This would reduce the computational resources required due to the reduced dimensionality of embeddings. Such models have recently become an integral part of fake news detection systems. TCN networks can also be trained with different sets of representations such as BERT, ELMo, etc. and comparative analysis can be done to give the best set of models. Models with greater accuracy and faster predictions will further help in improving these systems.

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References 1. S. Mohammad, S. Kiritchenko, P. Sobhani, X. Zhu, C. Cherry, SemEval-2016 Task 6: Detecting Stance in Tweets (2016). SemEval@NAACL-HLT 2. A. Zubiaga, G.W. Zubiaga, M. Liakata, R. Procter, P. Procter, Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS One (2016) 3. D. Pomerleau, D. Rao, Fake News Challenge (2017). http://www.fakenewschallenge.org/ 4. W. Ferreira, A. Vlachos, Emergent: a novel data-set for stance classification (2016). HLTNAACL 5. B. Riedel, I. Augenstein, G.P. Spithourakis, S. Riedel, A simple but tough-to-beat baseline for the fake news challenge stance detection task (2017). abs/1707.03264 6. G. Bhatt, A. Sharma, S. Sharma, A. Nagpal, B. Raman, A. Mittal, Combining Neural, Statistical and External Features for Fake News Stance Identification (2018). WWW 7. W. Largent, Talos Targets Disinformation with Fake News Challenge Victory. https://blog.tal osintelligence.com/2017/06/talos-fake-news-challenge.html 8. A. Hanselowski, P.V. Avinesh, B. Schiller, F. Caspelherr, D. Chaudhuri, C.M. Meyer, I. Gurevych, A retrospective analysis of the fake news challenge stance-detection task (2018). COLING 9. J. Pennington, R. Socher, C.D. Manning, Glove: global vectors for word representation. EMNLP (2014) 10. T. Mikolov, K. Chen, G.S. Chen, J. Chen, Efficient Estimation of Word Representations in Vector Space (2013). abs/1301.3781 11. A.K. Chaudhry, Stance Detection for the Fake News Challenge: Identifying Textual Relationships with Deep Neural Nets 12. Q.Q. Zeng, Neural Stance Detectors for Fake News Challenge (2017) 13. C. Conforti, N. Collier, M.T. Pilehvar, Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles (2019) 14. J. Devlin, M. Chang, K. Chang, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018). abs/1810.04805 15. M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, L.S. Zettlemoyer, Deep contextualized word representations (2018). abs/1802.05365 16. Hugging Face pytorch-transformers. https://github.com/huggingface/pytorch-transformers 17. Z. Yang, Z. Dai, Y. Yang, J.G. Carbonell, R. Salakhutdinov, Q.V. Le, XLNet: Generalized Autoregressive Pretraining for Language Understanding (2019). abs/1906.08237 18. Z. Dai, Z. Yang, Y. Yang, J.G. Carbonell, Q.V. Le, R. Salakhutdinov, Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (2019). abs/1901.02860 19. I. Augenstein, T. Rocktäschel, A. Vlachos, K. Bontcheva, Stance detection with bidirectional conditional encoding. EMNLP (2016) 20. S. Bai, J.Z. Kolter, V. Koltun, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (2018). abs/1803.01271 21. D. Paperno, G. Kruszewski, A. Lazaridou, Q.N. Pham, R. Bernardi, S. Pezzelle, M. Baroni, G. Boleda, R. Fernández, The LAMBADA dataset: word prediction requiring a broad discourse context (2016). abs/1606.06031 22. J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation. CVPR (2015) 23. A.F. Agarap, Deep Learning using Rectified Linear Units (ReLU) (2018). abs/1803.08375 24. E. Loper, S. Bird, NLTK: The Natural Language Toolkit (2002). cs.CL/0205028 25. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. VanderPlas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011) 26. F. Chollet, Keras, GitHub (2015). https://github.com/fchollet/keras

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27. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, A. Lerer, Automatic differentiation in PyTorch (2017) 28. P. Rémy, Philipperemy/keras-tcn (2019). Retrieved from https://github.com/philipperemy/ker as-tcn

Updated Frequency-Based Bat Algorithm (UFBBA) for Feature Selection and Vote Classifier in Predicting Heart Disease Himanshu Sharma and Rohit Agarwal

Abstract In modern society, mortality and morbidity are caused majorly by heart disease (HD), and in world, deaths are mainly caused by heart disease (HD). The detection of HD and prevention against death is a challenging task. Medical diagnosis is highly complicated and it is very important. It must be performed efficiently with high accuracy. The professionals in healthcare in heart disease diagnosis are assisted by using various techniques in data mining. In this work, heart disease prediction method with following steps is introduced. The steps are preprocessing technique, feature selection, and learning algorithm. Before that important features are selected via the use of the updated frequency-based bat algorithm (UFBBA). In the UFBBA algorithm, the frequency values are computed via the use of the features. If the features are most important, then the frequency is higher else the frequency is lower. A selected feature from the UFBBA is used for better accuracy results than the other classifiers. A feature selected from the algorithm is applied for classification (Vote). Experimentation dataset of the proposed system is collected from Irvine (UCI) Cleveland dataset, University of California dataset. The results are measured with respect to accuracy, f-measure, precision, and recall. Keywords Data mining · Knowledge discovery in data (KDD) · Cleveland dataset · Cardiovascular disease (CD)

1 Introduction In modern society, mortality and morbidity are caused majorly by heart disease (HD), and in world, deaths are mainly caused by heart disease (HD). The detection of HD and prevention against death is a challenging task [1]. The heart disease H. Sharma (B) · R. Agarwal Department of Computer Engineering & Applications, GLA University, Mathura 281406, India e-mail: [email protected] R. Agarwal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_41

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detection from “various factors or symptoms are a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects” [2]. The professionals in healthcare in heart disease diagnosis are assisted by using various techniques in data mining. The diagnosis process is made simple by collecting and recording the data of patient. The experience and knowledge of specialist who is working with the symptoms of same disease are used. In healthcare organization, service with less cost is required. Based on “valuable quality service denotes the accurate diagnosis of patients and providing efficient treatment”, poor clinical decisions may lead to disasters and hence are seldom entertained. The prediction of cardiovascular disease is done in a new dimension with the use of techniques in data mining. Efficient collection data and processing in computer are done in data mining. Sophisticated mathematical algorithms are used by data mining techniques to segment and to evaluate future events probability. Knowledge discovery in data is also termed as data mining [7, 8]. From large amount of data, in order to obtain, implicit results and important knowledge, data mining techniques are used. From massive data, new and implicit patterns are computed by user by using technology of data mining. In the domain of healthcare, diagnosis accuracy can be enhanced by discovering knowledge by medical physicians and healthcare administrators. This also enhances surgical operation goodness, and the effect of harmful drugs is reduced [3]. KDD is defined as: “The extraction of hidden previously unknown and potentially useful information about data” [4]. In data, hidden and novel patterns are computed by a user-oriented technique given by technologies in data mining. The quality of service is enhanced by using this discovered knowledge by healthcare administrators. The adverse effect of drug is reduced by using this discovered knowledge and alternative therapeutic treatment with less cost also suggested by this [5]. The issue in the prediction of heart disease can be solved by introducing various classification methods. The important features and data mining techniques are identified by this research for heart disease prediction. From Irvine (UCI) machine learning repository, University of California, the dataset is collected. Most commonly used dataset is Cleveland dataset due to its completeness [6]. Data mining classification techniques are used to create model, after feature selection. The model is named as Vote and it is a hybrid technique formed by combing logistic regression with Naïve Bayes. This chapter introduced updated frequencybased bat algorithm (UFBBA) for compared multiple features at a time. Then, classification is performed by Vote.

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2 Literature Review Wu et al. [9] studied data processing technique impacts and various classifiers are proposed. The heart disease of patient is classified by conducting various experimentations and tried to enhance accuracy data processing techniques are used. In high dimension dataset, highly accurate result can be produced by Naïve Bayes (NBs) and logistic regression (LR) techniques. Benjamin Fredrick David and Antony Belcy [10] developed a three data mining classification algorithms like RF, DT, and NBs. The algorithm giving high accuracy and is identified by this research. In the prediction of heart disease, better results are performed by RF algorithm. Haq et al. [11] used heart disease dataset to predict heart disease by machine learning-based diagnosis system. Seven most popular classifiers and three feature selection algorithms are used for selecting features which are important. This includes artificial neural networks (ANNs), k-nearest neighbor (kNN), logistic regression, support vector machine (SVM), RF, DT, NB, minimum redundancy maximum relevance (mRMR), relief, and least absolute shrinkage and selection operator (LASSO). The better performance with respect to execution time and accuracy is achieved by feature reduction techniques. The heart patient’s diagnosis can be assisted effectively by proposed machine learning-based decision support system. Kumari and Godara et al. [12] implemented repeated incremental pruning to produce error reduction (RIPPER) classifier using data mining techniques. Dataset of cardiovascular disease is analyzed using DT, SVM, and ANN. False positive rate (FPR), error rate, accuracy, sensitivity, true positive rate (TPR), and specificity are used to measure and compare performance. Thomas and Princy [13] predicted the heart disease by developing various classification techniques. The patient age, blood pressure, gender, and pulse rate are used compute the risk level. Data mining techniques like DT algorithm, kNN, and Naïve Bayes are used for classifying risk level of patient. More attributes are used for enhancing accuracy. Chadha and Mayank [14] extracted interested patterns to predict the heart disease using data mining techniques. This paper strives to bring out the methodology and implementation of these techniques such as ANNs, DT, and NBs and stress upon the results and conclusion induced on the basis of accuracy and time complexity.

3 Proposed Methodology The data mining process has following steps, preprocessing, feature selection, selection of various feature combination, and classifier model design. In this chapter, important features are selected via the use of the updated frequency-based bat algorithm (UFBBA). In the UFBBA algorithm, the frequency values are computed via

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the use of the features. If the features are most important, then the frequency is higher else the frequency is lower. A selected feature from the UFBBA is used for better accuracy results than the other classifiers. For very attribute combination, selection of feature and modeling process, record the performance of every model created by attribute selection. Record the performance of every model created by attribute selection and techniques in data mining. After the completion of whole process, results are displayed. Matrix laboratory (MATLAB) is used for implementation and it uses the UCI Cleveland heart disease dataset which is shown in Table 1 and workflow of proposed system is shown in (Fig. 1). Table 1 Description of attributes from UCI Cleveland dataset Attribute

Type

Description

Sex

Nominal

Patient gender(1 for male and 0 for female)

Age

Numeric

Patient age in years

Cp

Nominal

4 values are used to describe chest pain type Value 1 represents typical angina Value 2 represents atypical angina Value 3 represents non-anginal pain Value 4 represents asymptomatic

Fbs

Nominal

Fasting blood sugar >120 mg/dl; 1 if true and 0 if false

Chol

Numeric

Cholesterol in serum in mg/dl

Exang

Nominal

Angina induced by exercise

Trestbps

Numeric

Blood pressure is resting condition

Restecg

Nominal

3 values are resulted by resting electrocardiographic Value 0 defines normal Value 1 defines ST-T wave abnormality Value 2 defined probable or definite left ventricular hypertrophy by Estes’ criteria

Thalach

Numeric

Heart rate which is achieved as a maximum

Slope

Nominal

The slope of the peak exercise ST segment Value 1 is used to describe up sloping Value 2 is used to describe flat Value 3 is used to describe down sloping

Oldpeak

Numeric

ST depression which induced by exercise relative to rest

Num

Nominal

With 5 values, heart disease diagnosis is represented. Value 0 represents absence, and 1–4 represents presence of heart disease

Ca

Numeric

Major vessels count (0–3) which are colored by fluoroscopy

Thal

Nominal

3 values are used for describing heart status Value 3 is used to describe normal Value 6 is used to describe fixed defect Value 7 is used to describe reversible defect

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Fig. 1 Workflow of proposed system

3.1 Data Preprocessing The collected data is preprocessed. In Cleveland dataset, missing values are there in six records and they are removed to reduce the record count to 297 from 303. From multiclass value, transform the predict the heart disease presence value. The value 0 is used for absence and 1, 2, 3, 4 for presence representation and they are converted to binary values 0 for absence and 1 for presence of heart disease. The values of diagnosis ranging from 2 to 4 are converted into 1 by data preprocessing method. Resultant dataset is going to have only the values of 0 and 1. Absence of heart disease is represented by 0 and its presence is represented by 1. With 297 records, after reduction and transformation, 139 records are assigned with 1 and 158 records are assigned with 0.

3.2 Feature Selection In the prediction of heart disease, 13 features are used and it includes patient’s personal information like “sex” and “age.” From different examinations in medical field, balance 11 features are collected and they are clinical attributes. In classification experimentation, various feature combinations are selected. The classification model is named as Vote and it is a hybrid technique formed by combing logistic regression with Naïve Bayes. The lower bound is limited by applying Brute force method. This

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paper introduced updated frequency-based bat algorithm (UFBBA) for compared multiple features at a time.

3.2.1

Updated Frequency-Based Bat Algorithm (UFBBA)

First, bat algorithm is made of real encrypted bats. Second important notification is that for each binary encrypted bat fitness is computed in the hierarchical fashion. The candidate solution for binary bat is the amalgamation of preprocessing technique, feature selection, learning algorithm, and selecting adaptively the best possible solution for the UCI Cleveland dataset. It is represented as { p1 , . . . , pn , f 1 , . . . , f m , c1 , . . . , cl }, where p1 , . . . , pn are the preprocessing techniques out of which best performing technique is selected for the particular UCI Cleveland dataset. Each practical combination is an attributes of UCI Cleveland dataset. f 1 , . . . , f m are the total number of features among which only the optimal set of features are being selected. Each position in the UCI Cleveland dataset represents a feature. In the binary version of the bat algorithm, UCI Cleveland dataset consists of 0 and 1 values, position value having 1 means that particular feature is selected otherwise it is not selected. Furthermore, c1 , . . . , cl are the learning algorithms and each possible combination is the UCI Cleveland dataset. When the bat position is updated according to the traditional bat algorithm, it got real values in their next position. To produce its binary version, Gaussian transfer function is applied to the updated real values to bind them in between 0 and 1. Then, a random number is generated which is considered as the threshold value. It is represented in Eq. 1:  F(S) =

1 if 1/(1 + exp(−(Snew ))) > rand 0 otherwise

(1)

Initially, a random population is generated in the starting phase of the evaluation of bats; fitness computation of each bat is carried in the hierarchical fashion. The dataset is preprocessed; first n bits consist of preprocessing techniques out of which appropriate technique is selected, after cleaning the dataset feature, selection is done on that trained dataset to select the optimal number of features out of next m bits. Finally, from l learning algorithms, appropriate model is selected and then the fitness of each bat is calculated. Fitness function is represented in Eq. 2. The fitness function comprises of two major objectives, first to maximize the performance of classification, and second to reduce the number of features selected. Fitness = c1 ∗ CError + c2 ∗ (N Fs/N )

(2)

where C Error is the classification error. c1 and c2 are random numbers such that c1 + c2 = 1. NFs is number of features selected and total number of features is represented as N. After computing fitness value of every bat, bat with minimum fitness is saved

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for the further analysis. The positions and velocities of bats are updated within each timestamp t. It is shown in Eqs. 3–5:   f i = f min + f max − f min β

(3)

  vit = vit−1 + xit−1 − x∗ f i

(4)

xit = xit−1 + vit

(5)

Important features are selected via the use of the updated frequency-based bat algorithm (UFBBA). In the UFBBA algorithm, the frequency values are computed via the use of the features. If the features are most important, then the frequency is higher else the frequency is lower. For every bat in local searching, a new solution is generated by using random walk: xnew = xold + ε At

(6)

  n f i = n f min + n f max − n f min β

(7)

where random vector is represented by β ∈ [0, 1], current global best location is given by x ∗ and it is obtained by comparing all solutions of n bats. Scaling factor is given by ε ∈ [−1, 1]. Loudness parameter for bats is given by A. For every bat, velocity and positions are updated using the equations. Real encoded values are computed by this new velocity and positions. Gaussian transfer functions are used to binarize these real encoded values. In hierarchical manner, fitness is computed with new binarized solution. In all technique, every possible feature combination is tested. From 11 attributes, possible combination of 8 features is selected. Data mining technique is sued to test every combination.

3.3 Classification Modeling Using Data Mining Technique Data mining classification techniques are used to create model, after feature selection. The model is named as Vote and it is a hybrid technique formed by combing logistic regression with Naïve Bayes. The model performance is validated using a technique called tenfold cross-validation. Ten subsets are formed by dividing the entire dataset in this method and 10 times they are processed. Out of 10 subsets, for training 9 subsets used and for testing 1 subset is used. The results of 10 iterations are averaged to obtain final results. Stratified sampling is used to form subsets. In this, every subset is going to have same class ratio of main dataset.

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4 Results and Discussion Experimentation results are discussed in this section. Four performance evaluation parameters are used to measure the classification model performance. They are accuracy, f-measure, precision, and recall. Among all instances, correctly predicted instances define the accuracy. Precision and recall’s weighted mean defines fmeasure. For positive class, correct prediction percentage is given by precision and true positive value percentage defines recall. These performance measures are used for the identification of important features. Best performing model is created by identifying data mining techniques. The measured precision and accuracy values are used. Based on the performance of combined feature behavior, significant features are identified. Highly accurate models are created by analyzing techniques in data mining for the prediction of heart disease. The performance of the model is highly influenced by precision and accuracy. For future analysis, measure the performance of each classifier individually and they are recorded properly (Table 2). Figure 2 shows the performance results of precision metrics with respect to three classifiers like proposed NBs, Vote, and Vote + feature selection. The results demonstrate that the proposed Vote + feature selection classifier gives higher precision Table 2 Performance comparison metrics versus CD classification methods Methods

Metrics Precision (%)

Recall (%)

F-measure (%)

Accuracy (%)

NBs

83.33

87.76

85.54

75.91

Vote

89.15

88.46

88.0

81.47

Vote + feature selection

90.20

92.74

91.45

92.74

92

Precision (%)

90 88 86 84

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82

Vote

80

Vote+feature selection

78

NBs

Vote Methods

Vote+feature selection

Fig. 2 Precision results evaluation of CD classification methods

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value of 90.20%, the other existing methods such as NBs, Vote gives lesser precision value of 83.33%, 89.15%, respectively. Recall results metrics with respect to three classifiers like proposed NBs, Vote, and Vote + feature selection are shown in Fig. 3. From Fig. 3, it shows that the proposed Vote + feature selection classifier provides higher recall value of 92.74%, the existing methods such as NBs, Vote gives lesser recall value of 87.76%, 88.46%, respectively. F-measure comparison of three classification methods is given in Fig. 4. Those methods are NBs, Vote, and Vote + feature selection. The results disclose that the proposed Vote + feature selection classifier provides higher f-measure results of 91.45%, whereas other existing methods such as NBs, Vote gives of 85.54% and 88.00%, respectively.

Recall (%)

94 92 90

NBs

88

Vote

86

Vote+feature selection

84 NBs

Vote

Vote+feature selection

Methods Fig. 3 Recall results evaluation of CD classification methods

F-Measure (%)

92 90 88

NBs

86

Vote

84

Vote+feature selecƟon 82 NBs

Vote

Methods

Vote+feature selection

Fig. 4 F-measure results evaluation of CD classification methods

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100 90

Accuracy (%)

80 70 60

NBs

50

Vote

40 30 20 10 0 NBs

Vote

Vote+feature selection

Methods Fig. 5 Accuracy results evaluation of CD classification methods

The accuracy results comparison of the three classification method is shown in Fig. 5. It discloses that proposed Vote + feature selection classifier provides higher accuracy results of 92.74%, whereas other existing methods such as NBs, Vote gives of 75.91% and 81.47%, respectively.

5 Conclusion and Future Work In modern society, mortality and morbidity are caused majorly by heart disease (HD), and in world, deaths are mainly caused by heart disease (HD). The detection of HD and prevention against death is a challenging task. Medical diagnosis is highly complicated and it is very important. It must be performed efficiently with high accuracy. The professionals in healthcare in heart disease diagnosis are assisted by using various techniques in data mining. The raw data is analyzed by techniques in data mining. Highly accurate disease prevention can be achieved by new insights of data given by this analysis. The UFBBA algorithm, the frequency values are computed via the use of the features. If the features are most important, then the frequency is higher else the frequency is lower.

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A selected feature from the UFBBA is used for better accuracy results than the other classifiers. A feature selected by feature selection algorithm is used for classification. Experimentation dataset of proposed system is collected from Irvine (UCI) Cleveland dataset, University of California dataset. The results are measured with respect to accuracy, f-measure, precision, and recall.

6 Future Work (1) The heart disease can be predicted from collection of patient data from remote devises by using an efficient remote heart disease prediction system with high accuracy. (2) Various optimization and feature selection techniques can be utilized to enhance the performance of this prediction classifier with more experimentation.

References 1. M. Gandhi, S.N. Singh, Predictions in heart disease using techniques of data mining, in International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE) (2015), pp. 520–525 2. S. Oyyathevan, A. Askarunisa, An expert system for heart disease prediction using data mining technique: Neural network. Int. J. Eng. Res. Sports Sci. 1, 1–6 (2014) 3. Z. Jitao, W. Ting, A general framework for medical data mining, in International Conference on Future Information Technology and Management Engineering (FITME) (2010) 4. A.K. Sen, S.B. Patel, D.P. Shukla, A data mining technique for prediction of coronary heart disease using neuro-fuzzy integrated approach two level. Int. J. Eng. Comput. Sci. 1663–1671 (2013) 5. K. Srinivas, G.R. Rao, A. Govardhan, Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques, in 5th International Conference on Computer Science & Education (2010), pp. 1344–1349 6. M.S. Amin, Y.K. Chiam, K.D. Varathan, Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform. 36, 82–93 (2019) 7. J. Soni, U. Ansari, D. Sharma, S. Soni, Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–48 (2011) 8. S.S. Ubha, G.K. Bhalla, Data mining for prediction of students’ performance in the secondary schools of the state of Punjab. Int. J. Innov. Res. Comput. Commun. Eng. 4(8), 15339–15346 (2016) 9. C.S.M. Wu, M. Badshah, V. Bhagwat, Heart disease prediction using data mining techniques, in 2nd International Conference on Data Science and Information Technology (2019), pp. 7–11 10. H. Benjamin Fredrick David, S. Antony Belcy, Heart disease prediction using data mining techniques. ICTACT J. Soft Comput. 09, 1824–1830 (2018) 11. A.U. Haq, J.P. Li, M.H. Memon, S. Nazir, R. Sun, A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mob. Inf. Syst. (2018) 12. M. Kumari, S. Godara, Comparative study of data mining classification methods in cardiovascular disease prediction. Int. J. Comput. Sci. Technol. 2(2) (2011)

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13. J. Thomas, R.T. Princy, Human heart disease prediction system using data mining techniques, in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) (2016), pp. 1–5 14. R. Chadha, S. Mayank, Prediction of heart disease using data mining techniques. CSI Trans. ICT 4, 193–198 (2016)

A New Enhanced Recurrent Extreme Learning Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection Rohit Agarwal and Himanshu Sharma

Abstract The health and life of women’s are seriously threatened by breast cancer. In female diseases, morbidity breast cancer and mortality breast cancer are ranked as first and second. The breast cancer’s mortality can be reduced by effective lump detection in early stages. The early detection, diagnosis and treatment of breast cancer are enabled by a mammogram-based computer-aided diagnosis (CAD) system. But unsatisfied results are produced by available CAD systems. Feature fusion-based breast CAD method is proposed in this work, which uses deep features of convolutional neural network (CNN). Deep feature of CNN-based mass detection is proposed in the first stage. Clustering is performed by enhanced recurrent extreme learning machine (ERELM) method. Loads are forecasted using recurrent extreme learning machine (RELM) and gray wolf optimizer (GWO) is used to optimize the weights. Deep, morphological, density and texture features are extracted in the next stage. The malignant and benign breast masses are classified by developing fused feature set-based ERELM classifier. High value of efficiency and accuracy is produced by a proposed classification technique. Keywords Computer-aided diagnosis · Gray wolf optimizer · Mass detection · Deep learning · Recurrent extreme learning machine · Fusion feature

1 Introduction In women, death is most commonly produced by breast cancer. In 2017, 40,610 women in the USA are expected to die, as estimated by the statistics of American cancer society. In use, 3.1 million women are having the breast cancer in March 2017. R. Agarwal (B) · H. Sharma Department of Computer Engineering & Applications, GLA University, Mathura 281406, India e-mail: [email protected] H. Sharma e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_42

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The suspicious breast abnormalities are assessed using various imaging models, like breast ultrasound, dynamic contrast-enhanced magnetic resonance and diagnostic mammography in clinical diagnosis. The unnecessary biopsies are avoided by radiologists by interpreting these images. Computer-aided diagnosis (CADx) techniques are used to assist the radiologists in interpretation. These techniques enhance the suspicious breast region detection accuracy [1]. The challenges in breast cancer early detection are rectified by deep learning (DL) methods [2, 3]. This includes advances in computational technologies, image processing techniques and significant progress in machine learning. In recent CAD, DL and convolutional neural networks are used due to its advancements [4, 5]. The high accurate quantitative analysis is given by CNN when compared to CAD [6, 7]. The error rate is reduced about 85% by the recent advancement in DL methods [8]. Small breast cancers are identified by radiologists by recent models of CNN. Lesions description is generated by CNN and it helps the making accurate results by radiologist [9]. In the future, independent MG are reading can be done by radiologist using CNN advancements. Feature extraction is done by CNN and clustering is done by ERELM. Various sub-regions are formed by dividing mammogram. Fused set of features, morphological features, density features and texture features are used in classification stage. The process of selecting important features defines the accuracy of breast CAD system. Malignant and benign of breast mass are classified by a classifier, after feature extraction. The proposed work used ERELM as a classifier. Accurate results in classification of multi-dimensional feature are produced by ERELM classifier.

2 Related Work Zhang et al. [9] used rejection option in two-stage cascade framework which uses random subspace ensembles and various image descriptors. One class classifier is used ensemble the dataset in [10]. Bahlmann et al. [11] formed E and H intensity channels are formed by transforming color RGB image. Twenty-two features are extracted and used in classifier. Image classification is done by deep convolutional networks is designed to category machine learning [12]. The latest version of CNN and it requires high time in execution [13]. The artificial neural network is used for the prediction and classification of breast cancer in [14]. From wavelet transform, various techniques are derived for segmentation. Wavelet transform is used for breast cancer detection, its degree of localization varies with scale and produce images on different scales [15]. Other methods like C-mean clustering has been used and suggested that along with genetic algorithm it gives better results for segmentation efficiency of affected region’s extraction and detection [16]. State-of-the-art algorithms [17–20] are proposed by researchers for breast cancer detection.

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3 Proposed Methodology In the detection of breast cancer, following five steps are used by this work. They are pre-processing of breast image, detection of mass, extraction of features, generation of training data and training of classifier. The contrast between surrounding tissues and suspected masses is increased by employing contrast enhancement algorithm in this work. The ROI is localized by performing mass detection. From ROI, morphological, density, texture and deep features are extracted. From dataset of breast image, images are used to train the classifier in the process of training using extracted features with their labels. Well-trained classifier is used to identify mammogram. The entire process of diagnosis is shown in Fig. 1.

3.1 Breast Image Pre-processing The noise in original mammogram is eliminated by an adaptive mean filter algorithm. This is done to reduce the effect of noise in subsequent analysis. In the image direction, a sliding window with fixed size is used. The noise content in a sliding window is computed by calculating variance, mean and spatial correlation values. The mean value replaces the pixel in the selected window, if it noisy. The contrast between surrounding tissues and suspected masses is increased by employing contrast enhancement algorithm in this work. Uniformly distributed histogram of image is formed. Enlarge the image’s gray scale. This will enhance the contrast and clear image details will be produced.

Fig. 1 Flowchart of the proposed mass detection process

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3.2 Mass Detection From normal tissue, mass regions are extracted in mass detection. Accurate extracted features will produce precise mass segmentation. Deep features of CNN sub-domain are used to propose a mass detection method. It used US-ELM clustering technique. After pre-processing, from images, ROI is extracted. Sliding window is used to divide ROI which form various non-overlapping subregions. The successful traverse of sub-regions is determined. The deeps features are extracted from sub-regions if they are traversed successfully. Else deep features will be clustered. The process of mass detection is completed by obtaining boundaries of mass area.

3.3 Extract ROI There are huge amount of gray values with 0 value in mammogram and they are not any useful information in breast CAD. The area of mammogram has to be separated from mammograms ROI to enhance the efficiency of mammary image processing and to ensure diagnosis accuracy. The breast mass region is extracted by using an adaptive mass region detection algorithm in this work. The first and last non-zero pixel in every row and column is computed by scanning mammogram sequentially. They are denoted by xs , xd , ys and yd .

3.4 Partition Sub-region The techniques used to form several non-overlapping sub-regions by dividing ROI are proposed in this section. In a rectangular area [xs , xd , ys , yd ], from ROI, searching area to compute masses is fixed. W = xd − xs gives the searching rectangular length and H = yd − ys gives width of it. The sliding window with width h and length w are used to segment the area of searching window. With certain step size, sliding window is moved in rectangle window. Non-overlapping sub-regions with equal size of w × h are formed by splitting ROI. The feature extraction is done using these sub-regions. The 48 × 48 sliding window with step size of 48 is used in this work. N non-overlapping sub-region (s1 , s2 , . . . , s N ) is formed by dividing ROI.

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3.5 Extract Deep Features Using CNN From the sub-regions of ROI, deep features are extracted using CNN in this work. From previous steps, sub-region of image with dimension 48 × 48 is captured and given as an input to CNN. Input image with 48 × 48 × 3 dimension and 12 kernel is given as input to convolutional layer and 40 × 40 × 12 is obtained as an output. k Conv(i, j) =



W k,l (u, v) · input j (i − u, j − v) + bk,l

u,v

where kth kernel is represented W k,l and bias of kth layer is given by bk,l . The tanh is used as an activation function and it is lies in the range [−1, 1].   k k Out put(i, j) = tanh Conv(i, j) A max pooling layer is connected to first convolution layer’s output. Until reaching size of output as 2 × 2 × 6, next convolutional and max pooling layers are connected to one another. There are 2 × 2 × 6 = 24 neurons in fully connected layer. They are used in clustering analysis.

3.6 Clustering Deep Features Using ERELM The features extracted from architecture of CNN are clustered using ERELM algorithm. The number of cluster is set as 2. Two categories of feature sub-regions are formed. They are suspicious and non-suspicious mass area. Satisfied results are not produced by the supervised learning, if the training data is very small. The effect can be improved by the use of semi-supervised learning. Clustering is also performed by this. The relationship in internal of unlabeled dataset can be computed by using ERELM algorithm, which is a semi-supervised learning algorithm. Deep feature matrix X is given as input to algorithm and feature clusters are produced at the output. From training set X, Laplacian operator L is constructed. Randomly generate the output matrix of hidden layer neuron.  To compute, output weights, expression min β 2 + λT r β T H T L Hβ is used, β∈R nh×n 0

if input neuron number is greater than hidden neuron number. The weight between output and hidden by β. If not this condition, the output weight  layer is represented  is computed by I0 + l H T L H v = γ H T H v. Embedded matrix is computed next and N point is classified into K categories using k-means algorithm.

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3.7 Enhanced Recurrent Extreme Learning Machine with GWO For medical diagnosis purpose, a new computational framework, GWO-ERELM, is proposed by this study. There are two phases in this framework. The irrelevant and redundant information is filtered out by GWO in first stage. In medical data, best combination of features is searched adaptively for doing the same. The population’s initial positions are generated by GA in proposed IGWO. In discrete space of searching, population’s current position is updated using a GWO. From first stage, optimum subset of features is obtained. On this subset, ERELM classification is performed which is an efficient and effective technique. There are three steps in proposed procedure. Step 1: Finalize the parameters of optimum networks like context neurons and neuron count network approximation function. The biases and weights are optimized at first time by employing recurrent extreme learning machine (RELM) with GWO learning algorithm. The accuracy of forecasting can be improved by this optimization. Step 2: Forecasting ERELM accuracy is calculated using RMSE and R2 . Proposed technique will calculate RMSE used for prediction measurement MAE and MSE as shown below. N  t=1

MSE =

RMSE =

X (t) − Xˆ (t)

2

N     N X (t) − Xˆ (t) 2

t=1

MAE =

N N  

2 X (t) − Xˆ (t)

t=1

where current iteration is represented by t, number of samples is given by N, actual value is indicated by X and predicted value is indicated by X . In ELM, randomly select biases and weights [21]. The forecasting error minimization depends on these biases and weight values, as shown by simulation. GWO metaheuristic is used to optimize the weights. Input data with the structure of network and rate of learning is given to search algorithm. Best weight and biases values are searched by GWO. The mechanism of forecasting is shown in Algorithm 1. 1. Input: Original image dataset which has N image sample and objective function 2. Output: Segmented part with desired predicted value 3. Begin

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4. Input weights wi are assigned and after optimization from GWO, receive biases b as 5. Output matrix H of a hidden layer  Where H = h(i = 1, . . . , N )  is computed. and j = (1, . . . , K ) and h i j = g w j · xi + b j 6. Output weight matrix β = H + T is computed, where Moore-Penrose generalized inverse of matrix H is given by H + 7. To hidden and input layer context neurons, updated weights are fed 8. End

3.8 Classify Benign and Malignant Masses Based on Features Fused with CNN Features Deep fusion feature-based ELM classifier for diagnosis is proposed in this subsection. CNN is used to extract deep features. From the area of mass, density, texture and morphological features are extracted. Fusion features are classified using an ELM classifier. The results of malignant and benign are obtained. Feature Modeling: The early stage of breast disease is indicated by masses of breast in clinical domain. There are two classes of masses. They are benign and malignant. The most important mass properties are represented by deep features and they are extracted using a CNN. The following characteristics are contained in malignant mass of mammography as per the experience of doctor. They are, shape will be irregular and it has blurred edges, surface will be unsmoothed and it may have hard nodules and with respect to tissues in the surroundings, and they may have various intensities. The following characteristics are contained in benign mass of mammography. They will be regular in shape and edge, surface will be smooth and nodules are not accompanied and uniform distribution of density. Fusion features can be modeled as F = [F1 , F2 , F3 , F4 ]. where F1 denotes deep features, F2 denotes morphological features, F3 represents texture features and F4 represents density features. Classification: A feed-forward neural network with single hidden layer is proposed and it, i.e., termed as ERELM. The algorithm has better performance in generalization, manual parameter setups cannot affect the performance and it has high speed in learning. The results of breast cancer malignant and benign are obtained by using ERELM classifier in this work.

4 Experiment Results and Discussion The effectiveness of fusion feature-based diagnosis of breast cancer and US-ELM algorithm and CNN-based detection methods are analyzed in this section. The dataset

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Fig. 2 Result of precision rate

Precison (%)

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80 60 40 20 0

5

10

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Number of images with 400 mammogram is used for experimentation and it has 200 benign and malignant images. To evaluate the results of the experiments accuracy, f-measure, precision and recall are used between the methods of ELM, RELM and ERELM.

4.1 Precision Rate Comparison From Fig. 2, the graph explains that the precision comparison for the number of images in specified datasets. The methods are executed such as CNN and DCNN. When number of image increased according with the precision value is increased. From this graph, it is learnt that the proposed DCNN provides higher precision than the previous methods which produce better CBMIR results.

4.2 Recall Rate Comparison From Fig. 3, the graph explains that the recall comparison for the number of images in specified datasets. The methods are executed such as ELM, RELM and ERELM. When number of images is increased corresponding recall value is also increased. From this graph, it is learnt that the proposed ERELM provides higher recall than Fig. 3 Result of recall rate

Recall (%)

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the previous methods. The reason is that the GWO produces the optimal ERELM parameters which will improve the breast cancer detection results.

4.3 F-Measure Rate Comparison From Fig. 4, the graph explains that the f-measure comparison for the number of images in specified datasets. The methods are executed such as ELM, RELM and ERELM. When the number of data is increased and the f-measure value is increased correspondingly. From this graph, it is learnt that the proposed ERELM provides higher f-measure than the previous methods. Thus, the proposed ERELM algorithm is greater to the existing algorithms and has better results of retrieval. This is due to pre-processing of image that will improve the breast cancer detection results even better than the existing methods such as ELM and RELM.

Fig. 4 Result of f-measure rate

F-measure (%)

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Number of Images Fig. 5 Result of processing time

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4.4 Accuracy Comparison From Fig. 5, the graph explains that the processing time comparison for the number of images in specified datasets. The methods are executed such as ELM, RELM and ERELM. In x-axis, the number of data is considered and in y-axis the accuracy value is considered. From this graph, it is learnt that the proposed ERELM provides lower processing time than the previous methods such as ELM and RELM. Thus, the output explains that the proposed ERELM algorithm is greater to the existing algorithm in terms of better cancer detection results with high accuracy rate.

5 Conclusion and Future Work In this work, fusion deep features are used to propose a breast CAD. From CNN, deep features extracted and they applied to mass diagnosis and detection. The USELM clustering and deep feature sub-domain of CNN-based method are used in mass detection stage. Malignant and benign breast are classified using ELM classifier in mass diagnosis stage. Fused set of features, morphological features, density features and texture features are used in classification stage. The process of selecting important features defines the accuracy of breast CAD system. Malignant and benign of breast mass are classified by a classifier, after feature extraction. The proposed work used ERELM as a classifier. Accurate results in classification of multi-dimensional feature are produced by ERELM classifier. This method can be applied to practical problems in the future and in parallel manner they can be implemented.

References 1. M.L. Giger, N. Karssemeijer, J.A. Schnabel, Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu. Rev. Biomed. Eng. 15, 327–357 (2013) 2. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015) 3. G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Bejnordi, F. Ciompi, M. Ghafoorian, et al., A survey on deep learning in medical image analysis (2017) 4. R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, R.X. Gao, Deep Learning and Its Applications to Machine Health Monitoring: A Survey (2016) 5. J.G. Lee, S. Jun, Y.W. Cho, H. Lee, G.B. Kim, J.B. Seo et al., Deep learning in medical imaging: general overview. Korean J Radiol. 4(18), 570–584 (2017) 6. M.A. Hedjazi, I. Kourbane, Y. Genc, On identifying leaves: a comparison of CNN with classical ML methods, in Signal Processing and Communications Applications Conference (SIU) 2017 25th (IEEE, 2017), pp. 1–4 7. T. Kooi, A. Gubern-Merida, J.J. Mordang, R. Mann, R. Pijnappel, K. Schuur, et al., A comparison between a deep convolutional neural network and radiologists for classifying regions of interest in mammography, in International Workshop on Digital Mammography (Springer, 2016), pp. 51–56

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8. J. Wang, H. Ding, F. Azamian, B. Zhou, C. Iribarren, S. Molloi, et al., Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans. Med. Imaging (2017) 9. R. Platania, S. Shams, S. Yang, J. Zhang, K. Lee, S.J. Park, Automated breast cancer diagnosis using deep learning and region of interest detection (BC-DROID, in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM, 2017), pp. 536–543 10. Y. Zhang, B. Zhang, F. Coenen, W. Lu, Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach. Vision Appl. 24(7), 1405–1420 (2013) 11. Y. Zhang, B. Zhang, F. Coenen, W. Lu, J. Xiao, One-class kernel subspace ensemble for medical image classification. EURASIP J. Adv. Signal Process. (1), 17 (2014) 12. A. Krizhevsky, L. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 1097–1105 (2012) 13. T. Araújo, G. Aresta, E. Castro, J. Rouco, P. Aguiar, C. Eloy, A. Campilho, Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6), e0177544 (2017) 14. I. Saratas, Prediction of breast cancer using artificial neural networks. J. Med. Syst. 36(5), 2901–2907 (2012) 15. S. Bagchi, A. Huong, Signal processing techniques and computer-aided detection systems for diagnosis of breast cancer-a review paper. Indian J. Sci. Technol. 10(3) (2017) 16. S. Sharma, M. Kharbanda, G. Kaushal, Brain tumor and breast cancer detection using medical images. Int. J. Eng. Technol. Sci. Res. 2 (2015) 17. C. Bahlmann, A. Patel, J. Johnson, J. Ni, A. Chekkoury, ParmeshwarKhurd, A. Kamen, L. Grady, E. Krupinski, A.Graham, et al., Automated detection of diagnostically relevant regions in H&E stained digital pathology slides, in SPIE Medical Imaging (International Society for Optics and Photonics, 2012), pp. 831504–831504 18. P. Gu, W.-M. Lee, M.A. Roubidoux, J. Yuan, X. Wang, P.L. Carson, Automated 3d ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 65 (2016) 19. F. Strand, K. Humphreys, A. Cheddad, S. Törnberg, E. Azavedo, J. Shepherd, P. Hall, K. Czene, Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study. Breast Cancer Res. 18(1) (2016) 20. H.D. Cheng, J. Shan, W. Ju, Y. Guo, L. Zhang, Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn. 43(1), 299–317 (2010) 21. R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics. CA Cancer J. Clin. 66(1), 7–30 (2016)

Deep Learning-Based Severe Dengue Prognosis Using Human Genome Data with Novel Feature Selection Method Aasheesh Shukla and Vishal Goyal

Abstract In recent years, patients affected by dengue fever are getting increased. Outbreaks of dengue fever can be prevented by taking measures in the initial stages. In countries with high disease incidence, it requires early diagnosis of dengue. In order to develop a model for predicting, outbreak mechanism should be clarified and appropriate precautions must be taken. Increase in temperature, sea surface temperature, rapid urbanization and increase of rainfall due to global warming is the interplay factors which influences the outbreaks. Human travel and displacement due to increase in urbanization and temperature, causes dengue virus-infected mosquitoes to be spread. High accurate classification can be achieved by deep learning methods. It is a versatile and regressive method. Small amount of tuning is required by this and highly interpretable outputs are produced. Healthy subjects and dengue patients are differentiated by the factors determined by this and they are used to visualize them also. These factors increase the stability and accuracy of the boosting process in construction of dengue disease survivability prediction model. Problems in overfitting can also be reduced. Applications like decision support systems in healthcare, tailored health communication and risk management are incorporated with the proposed method. Keywords Machine learning · Data mining · Deep learning · Dengue virus · Particle swarm optimization · Feature selection

1 Introduction As per the records of Union Health Ministry, over 80 billion people are claimed by dengue and around 40,000 people are affected by dengue in our country. Till A. Shukla (B) · V. Goyal Department of Electronics & Communication Engineering, GLA University, Mathura 281406, India e-mail: [email protected] V. Goyal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_43

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September 30, disease has claimed the lives of 83 people as stated by National Vector Borne Disease Control Programme (NVBDCP) under the Health Ministry. Last year, 325 people have been killed by mosquito-borne tropical disease. In Kerala, 35 people are claimed by dengue till 30 September and around 3,660 are affected in the state. In Maharashtra, 18 people were killed and 4,667 people are affected by this. It is very important to identify it in initial stages. This is required to avoid hospitalization of patients unnecessarily. Adaptive and innate responses are comprised of human’s immune system. The first defence line system in human body is immune system. Innate immune responses are triggered by pattern recognition receptors (PRR). In antigen presenting cells, PRP are present and it includes, Fc-receptors, toll-like receptors, lectins and complement receptors. Various forms of APC responses are activated by these receptors engagement. Genetic background defines the adaptive and innate pathways. Associations between single polymorphisms (SNPs) and dengue infection phenotype in multiple genes are computed. It includes transporter associated with antigen processing (TAP), cytotoxic T lymphocyte-associated-antigen-4 (CTLA-4), endritic cell-specific intercellular adhesion molecule 3 (ICAM-3)-grabbing nonintegrin (DCSIGN), vitamin D receptor (VDR), acute plasma glycoprotein mannose-binding lectin (MBL) and human platelet-specific antigens (HPA), FCcRIIa, cytokines (IL, IFN, TNF and others), Fc gamma receptor IIA (a pro-inflammatory regulatory Fc receptor), human leukocyte antigen (HLA) genes and vitamin D receptor and [1]. Classification and regression trees (CART), support vector machines (SVM) and linear discriminant analysis (LDA) classification algorithms are used in dengue research based on ML. None methodology is predicting dengue severity based on genomic markers. PSO-SNP is used for feature selection and deep learning for prediction is proposed in this study. This chapter is organized as follows and related works are reviewed in Sect. 2, and in Sect. 3 proposed methodology is presented. Section 4 discusses experimental results and in Sect. 5 conclusion and future work is presented.

2 Related Work From clinical data, disorder mentions are extracted by entity recognition and time expressions and other features are extracted by the authors [2]. The absence and presence of dengue are predicted by a proposed model. The manifestation of dengue symptoms and occurrence of dengue are correlated by performing frequency analysis. Multivariate Poisson regression is proposed by [3] and it is a statistics-based technique. In science, it is a well-established statistics methodology. They are used to find the relationship between linear parameters. In hidden layer, knowledge is computed by data mining techniques.

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In small area, real-time dengue risk prediction model is proposed in [4]. Early warning is given by using this model instead of traditional statistical model. Decision tree algorithm is used by [5]. In tribal area, occurrences of dengue disease are predicted by a decision tree model. Gomes et al. [6] used radial basis function (RBF) kerneland gene expression data. Guo et al. [7] applied support vector regression (SVR). Various machine learning techniques are approached by this. In a more recent work, Carvajal et al. [8] studied the incidence of dengue in Philippines using meteorological factors. They compare several machine learning techniques such as general additive modelling, seasonal autoregressive integrated moving average with exogenous variables, random forest and gradient boosting. Fathima et al. [9] use random forests to discover significant clinical factors that predict dengue infection prognosis. Khan et al. [10] used traditional statistical techniques to identify the factors that predict severe dengue. Potts et al. [11] showed the results of applying traditional statistical models to prediction of dengue severity in Thai children using early laboratory tests. Caicedo et al. [12] compared the performance of several machine learning algorithms for early dengue severity prediction in Colombian children. Lee et al. [13] used ML models in specific case of dengue–chikungunya discrimination to perform differential diagnosis among dengue and chikungunya. It uses multivariate logistic regression and decision trees. Furthermore, they employ an adult cohort and include clinical symptoms. Laoprasopwattana et al. [14] used small cohort of children in southern Thailand to conduct prospective study. Under standard logistic regression model, around 70.6% of correct predictions are made by the proposed study and 83.3% of specificity is shown by it. Paternina-Caicedo et al. [15] performed differential diagnosis using a decision tree. Children under 24 months are used to collect dataset and interesting results have been produced.

3 Proposed Methodology In proposed method, deep learning methods are used to prognosis the dengue. There are four stages in the proposed method (i) data acquisition, (ii) data pre-processing, (iii) feature selection and (iv) patient classification. Figure 1 shows the entire process of processed method.

3.1 Patient Cohort and Clinical Definitions In this study, hospitals in Brazil and Recife city are used to collect the database of patients with dengue symptoms. The patients are explained with the process

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Fig. 1 Deep learning-based dengue prognosis

Genome data

Feature extraction and normalization

Feature selection using PSO

Prognostic results

Deep learning

of proposed technique, and their consent is obtained from willing patients. Ethics committee of FIOCRU reviewed this study and approved it. Patient’s blood is processed at laboratory and obtained the results of the following test: White blood cell count, platelet count, hemogram, serum albumin, serum alanine transaminase (ALT), serum aspartate transaminase (AST) and haematocrit. Summarize the cohort information of patient. Between group of DF and SD, age bias is not considered as shown in Table 2. (1) Peripheral blood mononuclear cells (PBMC) isolation and genomic DNA extraction (features F): Centrifugation at 931 g for 30 min on a Ficoll-Paque PLUS gradient (Amersham Biosciences, Uppsala, Sweden) is used to isolate PBMC from blood. From interface, mononuclear cells are collected and in cold, they are washed with phosphate-buffered saline. Supernatant is discarded after centrifugation for 335 g for 15 min. To remove residual red blood cells, pellet was washed in ammonium-chloride-potassium (ACK) lysing buffer. In supplemented culture medium, PBMC is re-suspended and they are cryopreserved at 80 °C. From PBMC of patients, extract the DNA using Wizard DNA extraction kit by following protocol defined by the manufacturer. (2) Illumina Golden Gate Genotyping Kit (Cat# GT-901-1001) is used to genotype the selected dengue patients. Allele-specific extension method is employed by this protocol. At high multiplex level, PCR amplification reaction is conducted. High quality Bead Chips and multiplexed assay are combined in a precise scanning system. In imaging system tray, Bead Chips carriers are placed to process genome data. Bead Array Reader is used to image the Bead Chips. Two high-resolution laser imaging channels are used. These channels scan the Bead Chips simultaneously to generate to images. It has high throughput and data quality. 3.1.1

Data Pre-processing

There are genotypes of 102 patients in genome data and at 322 loci polymorphisms they are measured. Indicators are formed by encoding the data by heterozygous or homozygous recessive and homozygous dominant. One feature per SNP genotype is produced by this. Additional category of data is formed by the missing data.

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3.2 Feature Selection In order to avoid overfitting of classifier, reduction of dimensionality is needed. This is also due to the fact that of small sample size [16]. PSO algorithm is used for feature selection, which in turn reduces the dimensionality. Algorithm 1. PSO-based Feature Selection Input : D-Dataset, M-features, N-instances, SWARMSIZE- number of gene subset, pbestlocGene subset’s is Personal best position, pbestis- Fitness value associated with pbestloc, gbestisFitness value corresponding to gbestloc and gbestlocis-entire swarm’s Best position. Output : Feature subset m 1. n random subset of genes are initialized and they are assumed as particles There are m features in each subset. 2. For every random subset, velocity and position are initialized. 3. Swarm’s fitness value is computed as follows, 4. Using equation (1), fitness value is set as squared error

5. 6. 7. 8. 9. 10.

For every subset, pbestloc and pbest are initialized using initial value of fitness. Repeat if pbest value of subset is greater than its fitness then Subset’s pbestloc and pbest are updated end if Among all subsets, minimum fitness value position is computed and gbestloc, gbest are set. 11. for to SWARMSIZE-1 do 12. Using equation (2) , new velocity is computed (2) 13. Using equation (3), location is updated as, (3) 14. End for 15. Squared error is computed and gene subset’s current location is used for setting fitness value 16. Repeat until reaching maximum number of iterations. 17. Best subset of genes is outputted by gbestloc

3.3 Classification Based on Deep Learning (DL) DL is trained using 37 points in original dataset. Test set is formed using 25 samples. This study proposes pretrained deep learning architectures based on transfer learning. Prior to training phase, various parameters have to be defined in CNN algorithm [17]. Classification results are influenced by these parameters based on application. The set of parameters having high influences are computed performing prior random

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test. In full factorial experiment, these computed factors are analysed statistically to obtain best results. In algorithm, other parameters with less influence are set by default.

3.3.1

Convolutional Neural Network

Mammal brain’s deep structure is inspired by deep learning method and it is a machine learning technique [18]. Multiple hidden layers are used to characterize the deep structure. Feature’s different level of abstraction is allowed by this. It is a nun supervised single layer greedily training algorithm and layers are used to train the deep network. Various deep networks are trained by this method due to its effectiveness. Convolutional neural network is a more powerful deep network. It has various hidden layers. Subsampling and convolution are performed by this layer to extract, features of high and low level from input. There are three layers in the network. They are fully connected, pooling and convolutional layers. Following section explains every layer in detail.

3.3.2

Convolution Layer

A kernel with size a ∗a is convolved with an input image with R ∗C size in this layer. The kernel is convolved with every block of input matrix independently. This is used to generate output pixel. N features of output image are generated by convolving the kernel with input image. Filter refers to the convolutional matrix’s kernel and feature map refers to the obtained feature by convolving input data with kernel and it has a size of N. There are various convolutional layers in CNN. Feature vector is the input and output of convolutional layer. In every convolutional layer, there are n filters. Input is convolved with these filters. In the operation of convolution, number of filters equals the generated feature map’s depth. At dataset’s certain attribute, specific feature is considered as an every filter. The C (l) j denotes the lth convolution layer output. Feature maps are contained in this and it can be calculated as, Ci(l)

=

Bi(l)

ai(l−1)

+



K i,(l−1) ∗ C (l) j j

(4)

j=1

where bias matrix is represented as Bi(l) and convolution filter or kernel is represented and it is of size a ∗ a. The jth feature map in layer (l − 1) with ith feature as K i,(l−1) j map in same layer are connected by this. Feature maps are contained by this. First convolutional layer is input space in Eq. (4), that is, Ci(0) = X i .

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Feature map is generated by this kernel. The outputs are transformed nonlinearly, by the application of activation function after convolutional layer.   Yi(l) = Y Ci(l)

(5)

where output of activation function is represented as Yi(l) and input is represented as Ci(l) . Sigmoid, rectified linear units (ReLUs) and tanh are commonly used activation function. Rectified linear units (ReLUs) is used in this work and denoted as Yi(l) =  

max 0, Yi(l) . In deep learning models, this function is used commonly because of reduction in interaction and effects of nonlinear. If ReLU receives a negative input, output is converted to 0 by this and if it receives positive value, same value is returned. Due to error derivatives, faster training of activation function is achieved. In saturation region, they are very small and it vanish the weight updates. This is termed as vanishing gradient problem. Full Connection: Traditional feed-forward network is a final layer of a CNN and it has one or more hidden layers. Softmax activation function is used by the output layer: yi(l)

= f



z i(l)



, z i(l)

m i(l−1)

=



wi(l−1) yi(l−1)

(6)

i=1

where weights are represented as wi(l−1) . Complete fully connected layer is used to tune these weights to make a representation of every class. Transfer function is represented as f and it represents nonlinearity. Within the neurons of fully connected layer, nonlinearity is built. It is not built in separate layers like convolutions and pooling layers. CNN training is initiated after computing output signal. Stochastic gradient descent algorithm is used to perform training [19]. From training set, single example is picked randomly to estimate gradients. CNN parameters are computed by training.

4 Experimental Results and Discussion The application of designed convolutional neural network on genomic DNA is presented in this section. Through the experiments, network’s suitable learning parameters are computed. Based on the measures like accuracy, recall and precision, the performance is compared with SVM method, which is an existing technique.

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Fig. 2 Result of precision rate

SVM

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80 60 40 20 0

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4.1 Precision Rate Comparison From Fig. 2, the graph explains the precision comparison for the number of data in specified datasets. The methods are executed such as SVM and proposed CNN method. When number of data increased according with the precision value is increased. From this graph, it is learnt that the proposed CNN provides higher precision than the previous method of SVM which produce better classification of disease detection results. The reason is that the proposed method has PSO-based feature selection for further classification.

4.2 Recall Rate Comparison From Fig. 3, the graph explains that the recall comparison for the number of data in specified datasets. The methods are executed such as SVM and proposed CNN. When number of images is increased corresponding recall value is also increased. The graph shows that the proposed CNN method provides higher recall than the previous method of SVM. The reason is that the CNN will train the bridge images which will improve the nearest disease detection results of crack through optimal feature value. Fig. 3 Result of recall rate

Recall (%)

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Deep Learning-Based Severe Dengue Prognosis Using Human Genome … Fig. 4 Result of accuracy

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4.3 Classification Accuracy Comparison From Fig. 4, the graph explains that the processing time comparison for the number of data in specified datasets. The methods are executed such as SVM and proposed CNN. In x-axis, the number of images is considered and in y-axis the accuracy value is considered. From this graph, it is learnt that the proposed CNN provides higher accuracy than the previous method of SVM. Thus, the output explains that the proposed CNN algorithm is greater to the existing algorithm in terms of better classification results with high accuracy rate. The reason is that existing approaches has a low rate of success as well, which has a high probability to cause misdetection of disease data.

5 Conclusion Dengue disease is diagnosed by designing convolutional neural network (CNN) in this work. This prediction method has various major advantages. At any stage, this model can be applied including stage before infection. It is the tissue sample of human in broad way. The experimental results show the efficiency of the proposed method in predicting severity of dengue. Optimum loci combination of data can be selected by this model. Based on patient’s genome background, it predicts accurately the development of severe phenotype. Large volume of data can be accommodated by this technique and better performance is produced with increased number of data. So, it is adaptive as well as scalable. In genetically influenced diseases, key element of defining clinical phenotype is multivariate (multi-loci) genomic signatures which are defined by genetic context. Deep structure of CNN produced better results. From various levels, features are extracted powerfully and it has capability in generalization. Consent The consent of the data was obtained from the participants/patients was verbal.

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References 1. Carvalho et al., Host genetics and dengue fever. Infect. Gen. Evol. (2017) 2. V. Nandini, R. Sriranjitha, T.P. Yazhini, Dengue detection and prediction system using data mining with frequency analysis. Comput. Sci. Inf. Technol. (CS & IT) (2016) 3. P. Siriyasatien, A. Phumee, P. Ongruk, K. Jampachaisri, K. Kesorn, Analysis of significant factors for dengue fever incidence prediction. BMC Bioinf. (2016) 4. T.-C. Chan, T.-H. Hu, J.-S. Hwang, Daily forecast of dengue fever incidents for urban villages in a city. Int. J. Health Geograph. (2015) 5. N.K. Kameswara Rao, G.P. SaradhiVarma, M. Nagabhushana Rao, Classification rules using decision tree for dengue disease. Int. J. Res. Comput. Commun. Technol. 3(3) (2014) 6. A.L.V. Gomes, L.J.K. Wee, A.M. Khan, et al., Classification of dengue fever patients based on gene expression data using support vector machines. PLoS One 5(6), Article ID e11267 (2010) 7. P. Guo, T. Liu, Q. Zhang, et al., Developing a dengue forecast model using machine learning: a case study in China. PLoS Neglect. Trop. Dis. 11(10), Article ID e0005973 (2017) 8. T.M. Carvajal, K.M. Viacrusis, L.F.T. Hernandez, H.T. Ho, D.M. Amalin, K. Watanabe, Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in Metropolitan Manila, Philippines. BMC Infect. Dis. 18(1), 183 (2018) 9. A. ShameemFathima, D. Manimeglai, Analysis of significant factors for dengue infection prognosis using the random forest classifier. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 6(2) (2015) 10. M.I.H. Khan, et al., Factors predicting severe dengue in patients with dengue fever. Mediterr. J. Hematol. Infect. Dis. 5(1) (2013) 11. J.A. Potts et al., Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators. PLoS Negl. Trop. Dis. 4(8), e769 (2010) 12. W. Caicedo-Torres, A. Paternina, H. Pinz´on, Machine learning models for early dengue severity prediction, in M. Montes-y-G´omez, H.J. Escalante, A. Segura, J.D. Murillo (eds.), IBERAMIA 2016. LNCS (LNAI), vol. 10022 (Springer, Cham, 2016), pp. 247–258 13. V.J. Lee et al., Simple clinical and laboratory predictors of chikungunya versus dengue infections in adults. PLoS Negl. Trop. Dis. 6(9), e1786 (2012) 14. K. Laoprasopwattana, L. Kaewjungwad, R. Jarumanokul, A. Geater, Differential diagnosis of chikungunya, dengue viral infection and other acute febrile illnesses in children. Pediatr. Infect. Disease J. 31(5) (2012) 15. A. Paternina-Caicedo, et al., Features of dengue and chikungunya infections of Colombian children under 24 months of age admitted to the emergency department. J. Trop. Pediatr. (2017) 16. Keogh, Mueen, Curse of dimensionality, in Encyclopedia of Machine Learning (Springer, 2011), pp. 257–258 17. V.O. Andersson, M.A.F. Birck, R.M. Araujo, Towards predicting dengue fever rates using convolutional neural networks and street-level images, in 2018 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2018), pp. 1–8 18. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015) 19. R.G.J. Wijnhoven, P.H.N. de With, Fast training of object detection using stochastic gradient descent, in Proceedings of International Conference on Pattern Recognition (ICPR) (Tsukuba, Japan, 2010), pp. 424–427

An Improved DCNN-Based Classification and Automatic Age Estimation from Multi-factorial MRI Data Ashish Sharma and Anjani Rai

Abstract In recent years, automatic age estimation has gained popularity due to its numerous applications in forensic and medical applications. In this effort, a programmed multi-factorial age estimation technique is proposed dependent on MRI information of hand, clavicle and teeth to broaden the lifetime period run starting from 19 years, as usually utilized for age appraisal depends on hand bone, to as long as 25 years, as joined with clavicle bone and slyness teeth. Intertwining ageapplicable data starting every one of the three anatomical destinations, this work uses an improved deep complexity neural system. Besides, when worn for greater part age grouping, we demonstrate that a group got from performance our relapse-based indicator is more qualified than a group legitimately prepared with categorization misfortune, particularly when considering that cases of minors being wrongly named grown-ups need to be limited. Copying how radiologists carry out age judgment, the projected technique dependent on deep complexity neural systems accomplishes improved outcomes in anticipating ordered age. These outcomes will support scientific deontologists and different experts to assess with elevated exactness both age and dental development in kids and youth. Keywords Information fusion · Multi-factorial · Complexity neural network · Age estimation · Mainstream age categorization · Magnetic resonance imaging

1 Introduction Age opinion of livelihood persons missing legitimate distinguishing proof records right now is an exceptionally significant research field in legal and lawful prescription. Its primary request originates from ongoing movement inclinations, where it A. Sharma (B) · A. Rai Department of Computer Engineering & Applications, GLA University, Mathura 281406, India e-mail: [email protected] A. Rai e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_44

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is a legally significant inquiry to recognize grown-up shelter searchers from young people who have not yet arrived at period of larger part. Broadly utilized radiological techniques for criminological age estimation in youngsters and youths consider the corresponding natural improvement of bone and dental structures. This enables a specialist to look at progress in physical development identified with the shutting of epiphysis holes and mineralization of astuteness teeth. Notwithstanding organic variety between concepts of the equivalent ordered age (CA), hand bone is mainly appropriate anatomical place to pursue bodily development in minors, because epiphysis holes begin shutting at various occasions, with distal bone completing prior and for example the radius bone completing at a period of around 18 years. In any case, the age scope of attention for criminological mature valuation is somewhere in the range of 13 and 25 years. The advancement of the substantial development of youthful people can be utilized as an organic marker associated with maturing. Assessing biological age (BA) from bodily advancement is in this way a profoundly significant subject in both clinical and legitimate (measurable) prescription usage. In scientific medication, BA evaluation is roused with the conclusion of end ocarina logical ailments similar to quickened otherwise deferred improvement in young people, or for ideally arranging the timepurpose of paediatric orthopaedic medical procedure intercessions while bone is as yet developing. Instances of such intercessions incorporate leg-length disparity adjustment or spinal distortion redress medical procedure. In legitimate medication, when distinguishing proof records of youngsters or teenagers are missing, as might be the situation in haven looking for strategies or in criminal examinations, estimation of physical development is utilized as a guess to evaluate obscure ordered age (CA). Set up radiological techniques for evaluation of bodily development depend on the optical assessment of bone hardening in X-beam pictures. Hardening is best followed in the lengthy bone and radiologists for the most part analyse hand bones because of the huge amount of quantifiable bone that are noticeable in X-beam pictures of this anatomical district, jointly by the way that maturing development isn’t synchronous for all hand bones. All the additional explicitly, carpal and distal phalanges are the main unresolved issues hardening, while in sweep and ulna, development may be pursued up to a time of roughly 18 years. As of the degree of solidification evaluated by means of the radiologist, the evaluation of bodily development of a person is next measured by partner its development to the age of the concepts in the suggestion map book who demonstrated a similar degree of hardening. Within the rest of this original copy, we will allude to this measurement as organic age as evaluated by radiologists (BAR). A significant downside of the generally utilized BAR method is the presentation to ionizing radiation cannot be supported in lawful drug usage for looking at solid kids and teenagers with no indicative reasons. Moreover, the reliance on abstract optical correlation with reference X-beam pictures makes these techniques inclined to elevate between and intra-ratter changeability. At long last, the populace utilized in constructing the reference chartbook dates from the centre of the only remaining century, which, because of changes in wholesome propensities just as

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quicker improvement of the current populace, prompts this technique being viewed as off base and outdated. In excess of the ongoing years, explore in age assessment has shown gigantic enthusiasm for magnetic resonance imaging (MRI) as an ionizing emission at no cost choice to grow novel plans for subsequent hardening development comparing to the advancement of bone. Right off the bat, evaluating CA based on the appraisal of natural improvement is intrinsically limited because of organic variety among subjects of the similar CA [1]. This natural variety characterizes the least blunder to any strategy for criminological age assessment can make. Through no apparent accord in the writing, the organic variety is supposed to be as long as one year in the set of free tools for digital image forensics applicable era contemplated in this original copy. Also, because of optical test, built up radiological techniques for assessing organic improvement include intraand between ratter variability [2], which can be wiped out by using programmingbased routine age assessment. A tale pattern in criminological age assessment study is to reinstate X-beam-based strategies with attractive reverberation imaging (MRI), because lawful frameworks in many nations deny the utilization of ionizing radiation on solid subjects. As of late, programmed strategies for age assessment dependent on MRI information were created [3, 4], by the by means of the hand they additionally exclusively examine a solitary anatomical site. A significant disadvantage of the previously mentioned strategies suggested in favour of multi-factorial age evaluation is their utilization of ionizing radiation, which is lawfully restricted in solid subjects for non-indicative reasons. Be that as it may, because of the absence of a recognized criminological age estimation strategy without involving energy released by atoms in the form of electromagnetic wave, a few European nations have prepared a precise exclusion to this rule on account of shelter looking fraction. As of late, to beat the downside of ionizing radiation, a great deal of investigation has concentrated on utilizing compelling resonance imaging (MRI) for scientific age assessment [5]. It is right now vague if similar organizing rules developed for ionizing radiation-based techniques can likewise be used for MRI [6]. Subsequently, unique MRI-dependent process has been produced for surveying natural expansion for every one of the three anatomical destinations [7]; however, these strategies still depend on the thought of discretizing biological development into various stages and on emotional optical assessment. To empower target age assessment with no the disadvantage of intra-or between ratter fluctuation as presented by radio-rationale optical assessment, programmed age opinion from X-ray imagery of the hand has just been projected in the creative writing with various strategies. Defeating the requirement for limitation, very recently [8] demonstrated a profound knowledge method including complexity neural systems (CNNs) for age assessment, which done age relapse on entire X-beam pictures of the hand. Analysed on 200 pictures, the victor of the struggle utilized the profound beginning V3 CNN [9] with added sexual orientation data. Diversely to the enormous attention in programmed age assessment from hand X-beam pictures, up to our information no AI-dependent arrangements have yet been projected for assessing age from clavicle CTs, as for wisdom teeth OPGs an initial step.

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The gathering has recently added to the growth of mechanized age assessment techniques from hand and wrist MRI. Afterward, we improved performance of the era relapse part via preparing a profound CNN (DCNN) for age assessment in [10]. To the best of the learning, no routine image examination strategy for multi-factorial age assessment, independently of the imaging methodology, has been exhibited yet. In these work, novel strategies for multi-factorial age evaluation are investigated from MRI information of hand bones, clavicles and shrewdness teeth. Propelled by how radiologists perform organizing of various anatomically locales, our strategies consequently combine the age-important form data from singular anatomical into a solitary ordered age. The projected techniques are assessed on an MRI database of 322 subjects by performing analyses surveying CA gauges regarding relapse, just as differentiation of minority/lion’s share age, characterized as having passed the eighteenth birthday celebration, as far as characterization. The outcomes exhibit the raise in exactness and lessening in vulnerability when utilizing the multi-factorial move towards as contrasted with depending on a solitary anatomical.

2 Proposed Methodology Subsequent of the built up radiological arranging advance including diverse anatomical destinations in a multi-factorial arrangement, subsequent to editing of agepertinent structures, we carry out age evaluation from trimmed knowledge teeth, hand, and clavicle bone, whichever through utilizing RF or an IDCNN design. In the projected strategy, multi-factorial age evaluation is done with a IDCNN design foreseeing age (see Fig. 1). This nonlinear relapse prototype depends on plotting look data from hand and clavicle bones and also the astuteness teeth towards the nonstop CA objective movable. So, via removing age-significant data on behalf of various anatomical locations got over trimming after the info MRI statistics, our approach imitates the set up radiological arranging methods established aimed at allocation independently, yet deprived of the requirement aimed at defining distinct phases. In Fig. 1 IDCNN-based programmed multi-factorial age assessment outline

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a start towards finish way, we consolidate the data originating from various anatomical sites to consequently evaluate the age of a concept as of its MRI information delineating age-pertinent anatomical arrangements.

2.1 Cropping of Age-Relevant Structures Inspiration for trimming age-important structures in a different preprocessing phase is towards streamlining the issue of relapsing age since arrival data, with the end goal which the situation is additionally appropriate on behalf of information sets which are restricted in dimension. Moreover, related to the deep-examining of the first 3D pictures, which unavoidably prompts for the defeat of significant maturing evidence as of the epiphyseal hole areas, editing of the age-related constructions likewise diminishes GPU memory prerequisites in addition permits us towards a shot at an a lot higher picture goals. Diverse automated milestone limitation techniques as introduced in [11] could be utilized to precisely confine, adjust and metric measurements yield age-important anatomical constructions as of bone and dental 3D MRI information (Fig. 1). Through tracing dual anatomical tourist spots each bone, on behalf of the hand MRI information we yield a similar 13 bones which are utilized in the Tanner-Whitehouse RUS technique (TW2). In clavicle MRI information, the dual clavicle bones remain trimmed independently depends on two known milestones used for every clavicle, correspondingly. The districts typifying intelligence teeth are removed after the dental MRI information utilizing the areas of the focuses of next and subsequent molars. If there should arise an occurrence of a missing insight tooth, we gauge its in all likelihood area as per the next molars also concentrate the locale enclosing the lost tooth equally if it would be available.

2.2 Deep CNN Construction Each DCNN square comprises of three stages of dual back to back 3 × 3 × 3 intricacy coats deprived of stuffing also a maximum combining coating that parts the goals. Rectified linear units (ReLUs) remain utilized as nonlinear actuation capacities. A completely associated (fc) coating towards the finish of the characteristic mining (fb) square (fcˆfb) prompts a dimensionality diminished characteristic depiction for each edited info volume independently (Fig. 2), which fills in as a component selection on behalf of that particular anatomical arrangement. In this effort, three distinct systems investigated while to combine data as of anatomical destinations inside the CNN architecture. The principal methodology is to meld the three anatomical locations straightforwardly on the contribution through linking wholly trimmed response capacities as stations afore the particular DCNN square (Fig. 3), trailed through dual completely associated coatings fcˆi and fcˆo. In the second centre combination design, the locales are melded right after the DCNN squares (one for each trimmed volume)

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Fig. 2 Distinct bone/tooth feature extraction block used iDCNN constructions for multi-factorial age estimation

by concatenating the yields of their completely associated layers fcˆfb before the two completely associated layers fcˆi and fcˆo. At last, in the late combination engineering, the separate DCNN chunks are initially joined through completely associated coatings fcˆi for every of the three anatomical destinations independently, beforehand intertwining the locations with the latter completely associated coating fcˆo that produces the age forecast. For training, each training sample associated as sn , n ∈ {1, . . . , N }, comprising of 13 trimmed hand bone capacities s_(n, h)ˆj, j ∈ {1, …, 13}, dual clavicle areas s_(n, c)ˆl, l ∈ {1, 2} also four regions casing astuteness teeth s_(n, w,)ˆk k ∈ {1, …, 4}, any with CA as objective variable y_n for a relapse job, or through a parallel variable y_n which is 1 for a minor (m); i.e. CA is littler than 18 years, and 0 for a grown-up (an); i.e. CA is bigger or equivalent than 18 years, in a characterization job. Enhancing a relapse DCNN design ϕ with limits w is achieved by stochastic inclination plunge limiting an L_2 misfortune on the relapse objective y = (y_1, …, y_N)ˆT in Eq. (1): wˆ = arg min

N 

ϕ(sn ; w)2

(1)

n=1

To regularize the relapse issue, a benchmark mass rot regularization expression used just as fall out. For assessing if a concept is immature or mature, the aftereffect of the relapse DCNN engineering may be applied for order by attaining the evaluated age. In this effort, we look at the grouping outcomes got from the deterioration forecast with the characterization results obtained by preparing the equivalent DCNN design with a multinomial logistic order misfortune registered as wˆ = arg min

N   n=1 j∈{m,a}

−ynj log 

e(ϕ j (sn ;w)) (ϕk (sn ;w)) k∈{m,a} e

(2)

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(a) Early fusion architecture

(b) Middle fusion architecture

(c) Late fusion architecture Fig. 3 Three DCNN architectures for multi-factorial age estimation with a early, b middle and c late fusion strategies

Once more weight rot and idler are utilized for regularization. To decide the significance of every bone or tooth and every anatomical location freely for various forecast ages in our multi-factorial age assessment technique, we compute the impact of the separate DCNN obstructs on the system forecast. Aimed at every tried example and the situation anticipated age, we compute the mean initiation esteem later the completely connected coating fcˆfb at the finish of each element mining square. To

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picture the comparative significance on the anticipated age of every bone or tooth, the mean start prices are standardized to summarize to one. Furthermore, we imagine the comparative significance on the anticipated period of every anatomical location autonomously, by first figuring the mean start estimation of all component mining squares contributing to hand, clavicle and teeth locales independently, trailed by a standardization of the three determined qualities to summarize to single.

2.3 Improved Deep Convolutional Neural Network In this effort, alterations are performed in the guidance algorithm of DCNN to make it progressively effective in the wording of correctness and unpredictability. Customarily, there are two parameters (one in the convolutional coating and other which is fully associated coating) in DCNN which should be adjusted, though, in the projected methodology, the parameter in the convolutional layer alone will be balanced. Appropriate modifications are made in the completely associated layer to estimate the weight esteems with no emphasis. The structural design of the IDCNN is same as that of the DCNN. The preparing calculation of the proposed IDCNN is specified under. Stage 1: Execute steps are pursued as talked about in the guidance algorithm of DCNN. Stage 2: Fix the stochastic slope drop limiting in Eq. (1). The perfect value of crossentropy is nil. In any case, it isn’t virtually achievable. Indeed, even in traditional DCNN, the calculation is supposed to be joined when it arrives at a higher rate than nil. Henceforth, in this methodology, this worth is fixed at 0.01 which is the most ordinarily utilized an incentive in the literature. Stage 3: As relapse esteem and the objective worth are known, the yield esteem y is evaluated utilizing Eq. (1). Stage 4: With the assessed yield estimation of yield layer, the NET estimation of the yield layer can be evaluated using Eq. (2). Stage 5: Since the info and the NET worth are known, the weights of the shrouded layer are assessed utilizing Eq. (2). Step 6: The remainder of the preparation procedure continues as before as conventional DCNN. Therefore, the weight esteems are evaluated without any iteration. It has been assessed with basic numerical process. It might be noticed that this procedure is accomplished for only one cycle which decreases the computational difficulty to high degree. Presently, just the channel coefficients in the convolutional layers need modification. In the IDCNN, the complex weight modification conditions are not essential which improves the reasonable attainability of the projected approach. Here in our effort, we have explored three unique methodologies while intertwining data as of anatomical locales inside our CNN engineering. Subsequently, making sure that we follow the soul of profound CNNs that the system is able to

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separate entirely data significant aimed at an evaluation task without anyone else, Our initial combination procedure feedback areas from every single anatomical site are intertwined by connecting them before they are exhibited to the system. By means of a mean total blunder of 1.18 ± 0.95 years in relapsing age, this system stayed defeated through the two; subsequently, the interpretation invariance, a significant property of CNNs, may not be completely abused in our restricted preparing information set due to the huge varieties in the general situation of anatomical sites while being linked. Also, in other two procedures, we firstly, concentrate age-applicable highlights in a CNN square and afterward consolidate includes on two distinct levels. In the centre combination system, data from all bones and teeth are intertwined following highlights are removed. This system relates to a measurable master as shown in the pictures of the separate anatomical arrangements at the same time and rationally melding all data when assessing age in a multi-factorial way. The presentation of this technique was like the third late combination arranges design, which first consolidate’s data for every anatomical site independently, trailed by intertwining the three anatomical locales with a completely associated layer. The late combination system is enlivened by how criminological specialists are right now joining individual data from hand radiographs, insight teeth OPGs and clavicle CTs by and by when executing multi-factorial age assessment. We utilized combination organize engineering for our further assessments at last because of its astounding age relapse execution as far as mean supreme reversion mistake of 1.01 ± 0.74 years.

3 Experimental Results and Discussion The MRI information set was taken from Ludwig Boltzmann Institute for scientific forensic imaging in Graz having a major aspect to a revise examination job of MRI in estimating criminological age, learning includes male Caucasian offers being achieved in accordance with Declaration of Helsinki which is endorsed by the moral advisory group of the Medical University of Graz (EK21399 ex 09/10). Every single qualified member gave composed educated assent and from underage members composed assent of the legitimate gatekeeper was furthermore gotten. Prohibition standards are considered as a history of endocrinal, metabolic, hereditary or formative sickness. Our proposed multifactorial assessment technique on the information set 3D MRIs from N = 322 concepts with realized CA running, somewhere in the range of 13.0 and 25.0 years (mean ± std: 19.1 ± 3.3 years, 134 subjects were minors beneath 18 years at the hour of the MRI filter). Aimed at every topic, we apply as our contribution for the DCNN design the three relating MRI dimensions of the left hand, upper thorax, and the jaw, which were taken in MRI filter session. CA of topics was determined as contrast among birthday and date of the MRI examination. T1weighted 3D angle reverberation successions with fat immersion were utilized for obtaining the hand and clavicle facts (physical voxel goals of 0.45 × 0.45 × 0.9 and 0.9 × 0.9 × 0.9 mm3 , separately), while teeth were scanned using a proton thickness weighted turbo turn reverberation sequence (0.59 × 0.59 × 1.0 mm3 ). Voxel size of

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the entire input volumes was 288 × 512 × 72 for hand, 168 × 192 × 44 for clavicle and 208 × 256 × 56 for knowledge teeth, respectively. Attainment times of hand, clavicle, and astuteness teeth MR orders were about 4, 6 and 10 min, respectively, whereas it shows the possible further acceleration through undersampling. To assess the results of the experiments accuracy, f-measure, precision, recall are used between the methods of DCNN and IDCNN.

3.1 Precision Rate Comparison From Fig. 4, the graph explains that the precision comparison for the number of data in specified datasets. The methods are executed such as DCNN and IDCNN. When number of image increased according to the precision value is increased. From this graph, it is learnt that the proposed IDCNN provides higher precision than the previous methods which produce better age detection results.

Fig. 4 Result of precision rate

Fig. 5 Result of recall rate

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Fig. 6 Result of f-measure rate

3.2 Recall Rate Comparison From Fig. 5, the graph explains that the recall comparison for the number of data in specified datasets. The methods are executed such as DCNN and IDCNN. When number of data is increased, corresponding recall value is also increased. From this graph, it is learnt that the proposed IDCNN provides higher recall than the previous methods. The reason is that the IDCNN produces the weight parameters which will improve the age detection results.

3.3 F-Measure Rate Comparison From Fig. 6, the graph explains that the f-measure comparison for the number of data in specified datasets. The methods are executed such as DCNN and IDCNN. When the number of data is increased, the f-measure value is increased correspondingly. From this graph, it is learnt that the proposed ERELM provides higher f-measure than the previous methods. Thus, the proposed IDCNN algorithm is greater to the existing algorithms in terms of better retrieval results. The reason that the preprocessing of image will improve the age detection rate even better than the existing method DCNN.

3.4 Accuracy Comparison From Fig. 7, the graph explains that the processing time comparison for the number of images in specified datasets. The methods are executed such as ELM, RELM and ERELM. In x-axis, the number of data is considered, and in y-axis, the accuracy value is considered. From this graph, it is learnt that the proposed ERELM provides lower processing time than the previous methods such as ELM and RELM. Thus, the output explains that the proposed ERELM algorithm is greater to the existing algorithm in terms of better age detection results with high accuracy rate.

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Fig. 7 Result of accuracy

4 Conclusion and Future Work In this effort, a profound knowledge-dependent multifactorial age approximation process is studied from the information gathered from MRI taking a huge about 322 subjects. Forensically, it is important that the age between 13 and 25 years, which naturally intertwines data from hand bones, intelligence teeth and clavicle bones. We try to access the balance time with a methodology that tries to defend a limit of the technique which is utilized presently in lawful training, i.e., utilization of ionizing radiation along with the subjectivity due to conveying separate plans for the separate anatomical locations and the absence of agreement in the data from separate locations have to be optimized in the final age estimate. Afterward going through diverse arrange structures, we have demonstrated that the multi-factorial age assessment is conceivable via consequently intertwining age-relevant information from every individual site. In this effort, we also explored the legitimately significant inquiry of the greater part age classification, by contrasting threshold expectations from the similar technique having the outcomes committed from binary classifier, which is prepared with the IDCNN structural design. The results indicated that the relapsebased strategy is better suitable for this undertaking, not withstanding, because of the great biotic difference of topics among equivalent ordered age, distinctive care must be taken to choose the trade-off among minor age groups which are classified incorrectly as grown-ups as well the same are incorrectly grouped as minors. Likewise, the profound learning calculations additionally experience the disappearing/detonating slope issue found in counterfeit neural system with angle-based learning techniques and backpropagation, which can be settled in upcoming effort. Consent Every single qualified member gave composed educated assent.

References 1. T.J. Cole, The evidential value of developmental age imaging for assessing age of majority. Ann. Hum. Biol. 42(4), 379–388 (2015)

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2. P. Kaplowitz, S. Srinivasan, J. He, R. McCarter, M.R. Hayeri, R. Sze, Comparison of bone age readings by pediatric endocrinologists and pediatric radiologists using two bone age atlases. Pediatr. Radiol. 41(6), 690–693 (2011) 3. D. Stern, C. Payer, V. Lepetit, M. Urschler, Automated age estimation from hand MRI volumes using deep learning, in MICCAI 2016, vol. 9901, LNCS, ed. by S. Ourselin, L. Joskowicz, M.R. Sabuncu, G. Unal, W. Wells (Springer, Cham, 2016), pp. 194–202 4. D. Stern, M. Urschler, From individual hand bone age estimation to fully automated age estimation via learning-based information fusion, in 2016 IEEE 13th International Symposium on Biomedical Imaging (2016), pp. 150–154 5. E. Hillewig, J. De Tobel, O. Cuche, P. Vandemaele, M. Piette, K. Verstraete, Magnetic resonance imaging of the medial extremity of the clavicle in forensic bone age determination: a new four-minute approach. Eur. Radiol. 21(4), 757–767 (2011) 6. P. Baumann, T. Widek, H. Merkens, J. Boldt, A. Petrovic, M. Urschler, B. Kirnbauer, N. Jakse, E. Scheurer, Dental age estimation of living persons: comparison of MRI with OPG. Forensic Sci. Int. 253, 76–80 (2015) 7. S. Serinelli, V. Panebianco, M. Martino, S. Battisti, K. Rodacki, E. Marinelli, F. Zaccagna, R.C. Semelka, E. Tomei, Accuracy of MRI bone age estimation for subjects 12–19. Potential use for subjects of unknown age. Int. J. Legal Med. 129(3), 609–617 (2015) 8. C. Spampinato, S. Palazzo, D. Giordano, M. Aldinucci, R. Leonardi, Deep learning for automated bone age assessment in X-ray images. Med. Image Anal. 36, 41–51 (2017) 9. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 2818–2826 10. D. ˇStern, C. Payer, V. Lepetit, M. Urschler, Automated Age estimation from hand MRI volumes using deep learning, in S. Ourselin, L. Joskowicz, M. Sabuncu, G. Unal, W. Wells (eds.), Medical Image Computing and Computer-Assisted Intervention MICCAI 2016, volume 9901 LNCS (Springer, Cham, Athens, 2016), pp. 194–202 11. C. Lindner, P.A. Bromiley, M.C. Ionita, T.F. Cootes, Robust and accurate shape model matching using random forest regression voting. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1862– 1874 (2015)

The Application of Machine Learning Methods in Drug Consumption Prediction Peng Han

Abstract Nowadays, drug abuse is a universal phenomenon. It can bring a huge damage to the human body and cause an irreversible result. It is important to know what can lead to the abusing so that it can be prevented. In order to prevent the abusing of those drugs, it is necessary to figure out what elements make people abuse the drug and how they relative to the abusing. According to the drug consumption data from the UCI database, Big Five personality traits (NEO-FFI-R), sensation seeking, impulsivity, and demographic information are considered to be the relative elements of the abusing. However, how they affect on the abusing of drugs is not clear so they cannot predict the probability of a person whether he is going to abuse a drug. There are many traditional ways to analysis the data based on scoring, such as give every element a score and but they can only tell an inaccurate predictive value. Machine learning is very hot nowadays because of its strong learning ability, high efficiency, and high accuracy. In this paper, we build models for accurate prediction of drugabusing with the personality traits and some other information, based on logistic regression, decision tree, and random forest separately. We find out that the sensation of respondents and the country which they are from is the most important factor for drug abuse. And we can get a conclusion that drug abuse is not only depending on a person’s inner being, but also affected by the environment they lived in. Keywords Machine learning · Drug abuse · Logistic regression · Decision tree · Random forest

1 Introduction Nowadays, drug abuse has become a serious problem in almost every country around the world. Drug abuse means use a drug in a great quantity or with methods which can harm the user’s body, such as influence their fertility. It can cause a serious disease P. Han (B) College of Beijing University of Technology, NO. 100, Pingleyuan Road, Chaoyang District, Beijing, People’s Republic of China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_45

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and even cause death especially for young teenagers, and drug abuse can destroy their whole life. Many drug users’ personality was changed during the long-term influence of a drug which cause a psychological harm, so that many of them engage in criminal activities which cause great damage to the society. It is significant to prevent drug abuse, and the best way is to find the potential population, educate and psycho treat them before they have the thought of drug abuse. It just like finding the naughtiest kids in the school and educate him before he made a mistake. Now, the problem becomes how to identify the naughty kid. The traditional way to model the relationship between personality traits and drug abuse is correlation analysis. This kind of analysis can only tell if there is possible relationship between personality traits and drug abuse, but it cannot tell a predictive value and the accuracy of the prediction is limited especially when there are too many relative factors, it would cost a lot of time. However, machine learning is popular for its high accuracy and efficiency, which is suitable to solve this problem. Machine learning is a way which uses computer to finish a task effectively relying on patterns and inference instead of using explicit instructions. Machine learning algorithms can build mathematical model with a large amount of simple data. The mathematical model can make a prediction with a set of data, and the result is accurate in most time. Using machine learning to do the prediction is also efficiently because you do not have to analysis every set of data by yourself, the computer can do it faster and more accurate. Logistic model [1], decision tree [2], and random forest [3] are three classical methods in machine learning fields. Logistic model is a statistical model which uses logistic function to model a binary-dependent variable. Decision trees are very popular in machine learning [4] and operation research, and it can explicitly and directly show the decisions. It can predict the value of a target based on all the input variables. The leaves represent the labels of several potential results, and the branches represent the judgment conditions of the decision tree. Random forest model is a kind of upgrade model of decision tree model. It helps dealing with the over-fitting problem of decision tree model. Random forest model can average the prediction results of plenty of decision trees, and they are trained on different variables of the same training set so that the performance of the model can have a great improvement. We use logistic model, decision tree and random forest to build models for 3 drugs so that we can get an accurate prediction on whether a person have a tendency of drug abuse through his personality traits and some other basic information. The prediction accuracy rate can reach 75% for all three drugs. By the way, we also compare the prediction of those three models which can lead to a result which model can do the most accurate prediction. The rest of the paper is organized as follows. In Sect. 2, we would describe the data we used for the analyzation. In Sect. 3, we briefly describe those three models we used, they are logistic model, decision tree model, and random forest model. In Sect. 4, we would have a summary through all the process of training the model and how we get the result. And we also will compare these three models with all the results and have a conclusion about those models in this section.

The Application of Machine Learning Methods … Table 1 Numbers and proportions of respondents who have used and never used drugs

499

Amphet

Benzos

Coke

Have used

909 (48.2%)

885 (46.9%)

847 (44.9%)

Never used

976 (51.8%)

1000 (53.1%)

1038 (55.1%)

2 Data Description We use the data donate by Evgeny M. Mirkes to build our prediction models, which is publicly available at https://archive.ics.uci.edu/ml/datasets/Drug+consumption+% 28quantified%29. The data including records for 1885 respondents. Each record includes 12 features which include 5 personality measurements. And the 5 personality measurements include neuroticism, extraversion, openness to experience, agreeableness and conscientiousness (NEO-FFI-R), BIS-11 (impulsivity), and ImpSS (sensation seeking), level of education, age, gender, country of residence, and ethnicity. I build model for drugs (Amphet, Benzos, and Coke) with aforementioned variables, respectively. The numbers and proportions of respondents who have used and never used drugs are given in Table 1. We chose these three drugs because the proportion on who have used and never used these drugs is well balanced so that we have a better opportunity to build a more accurate model. We divide all the data into training data (70%) and testing data (30%), which contains of 1320 respondents for training data and 565 respondents for testing data.

3 Models 3.1 Logistic Regression Logistic regression is a common tool widely used in many areas. It uses a logistic function to model a binary dependent variable. Logistic regression can conduct a binary regression [5] which means the dependent variable of the model only have two possible values such as 0 and 1, pass and fail. We conduct logistic regression for the use of Amphet, Benzos, and Coke with abovementioned variables, respectively. The regression coefficients and p-valves are given in Tables 2, 3 and 4. We can see that age, gender, country, Oscore, Cscore, and SS are significant variables for Amphet according to Table 2. They play the key role in whether a person will abuse Amphet. From Table 3, we can realize that age, gender, country, Nscore, Oscore, and Cscore are significant variables for Benzos. In Table 4, we can realize that age, gender, country, Nscore, Oscore, Ascore, Cscore, and SS are significant variables for Coke. We summarize the training and testing accuracy of logistic model together with train and test AUC in Table 5. According to the data in Table 5, the accuracy of training

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Table 2 Logistic regression results for Amphet Estimate (Intercept) Age

0.071

Std. error 0.129

z value 0.548

Pr(>|z|) 0.584

0.199

0.078

2.566

0.010

Gender

−0.588

0.136

−4.338

0.000

Education

−0.030

0.070

−0.431

0.666

Country

−0.498

0.101

−4.914

0.000

Ethnicity

0.237

0.362

0.653

0.514

Nscore

0.003

0.074

0.036

0.972

Escore

−0.119

0.076

−1.565

0.118

Oscore

0.247

0.074

3.338

0.001

Ascore

−0.028

0.067

−0.409

0.682

Cscore

−0.184

0.075

−2.454

0.014

Impulsive

0.059

0.085

0.694

0.488

SS

0.439

0.092

4.784

0.000

Table 3 Logistic regression results for Benzos Estimate

Std. error

z value

Pr(>|z|)

(Intercept)

0.315

0.141

2.241

Age

0.540

0.082

6.555

0.000

−0.354

0.140

−2.534

0.011

Gender Education

0.025

0.012

0.071

0.169

0.866

−0.976

0.107

−9.088

0.000

Ethnicity

0.521

0.401

1.301

0.193

Nscore

0.469

0.077

6.067

0.000

Escore

0.077

0.077

0.997

0.319

Country

Oscore

0.217

0.075

2.884

0.004

Ascore

−0.062

0.069

−0.897

0.370

Cscore

−0.181

0.076

−2.373

0.018

Impulsive

0.025

0.087

0.289

0.772

SS

0.105

0.093

1.133

0.257

and testing are basically the same for those three drugs, with training accuracy and AUC fluctuating around 68 and 74%, and testing accuracy and AUC fluctuating around 67 and 73%. The performance of logistic regression is comparable on the training and testing data for all three drugs, which implies the model is appropriate without significant over-fitting or under-fitting.

The Application of Machine Learning Methods …

501

Table 4 Logistic regression results for Coke Estimate (Intercept) Age Gender Education

0.043

Std. error

z value

0.136

Pr(>|z|)

0.316

0.752

0.319

0.079

4.062

0.000

−0.387

0.136

−2.845

0.004

0.032

0.070

0.456

0.648

−0.552

0.102

−5.411

0.000

Ethnicity

0.495

0.389

1.270

0.204

Nscore

0.157

0.074

2.136

0.033

Escore

0.052

0.076

0.684

0.494

Oscore

0.188

0.074

2.555

0.011

Ascore

−0.176

0.067

−2.614

0.009

Cscore

Country

−0.197

0.075

−2.620

0.009

Impulsive

0.110

0.085

1.288

0.198

SS

0.338

0.091

3.711

0.000

Table 5 Training/testing accuracy/AUC of logistic model

Amphet

Benzos

Coke

Training accuracy

0.680

0.697

0.684

Testing accuracy

0.678

0.687

0.676

Training AUC

0.744

0.755

0.734

Testing AUC

0.754

0.757

0.725

3.2 Decision Tree Decision tree is a commonly used model in machine learning. It is a model which can use several input variables to predict the value of a target variable. Every interior node related to one input variable. Every possible result is contained in one of the edges in the tree. Every leaf is one possible result for the model and the chance depends on the path from root to the leaf. The way of building a decision tree is splitting the source set which is based on settled splitting rules which is based on classification features [4]. The splitting process repeats on each derived subset which make the tree expand from a root node to plenty of leaf nodes. This recursion is completed when the splitting can no longer bring a new value to the predictions. And these are the learning process of a decision tree [6]. We took the first drug as an example to show the process of model building. At first, we build a tree which is big enough to avoid under-fitting. However, in order to avoid over-fitting, we have to do the pruning for the tree [7]. So, we introduce a complexity parameter which is an important value to balance between the accuracy and the size of the decision tree. In Fig. 1, we present the relative error for different complexity parameters. In order to have more accurate model, we have to make sure

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Fig. 1 Relative error for different complexity parameters

the relative error is at its deepest point. According to Fig. 1, we chose cp = 0.0077 to do the pruning tree after the pruning is shown in Fig. 2. We also build decision trees for the other two drugs in a similar way. The training and testing accuracy together with training and testing AUC in are shown in Table 6. Generally speaking, the performance of decision tree is worse than that of logistic regression. From Table 6, we can see that the accuracy of prediction for the three drugs has some differences. That is because different data sets have different scales, so when we build models, we chose different value to do the pruning for the decision tree and that lead to an uncontrollable error which cause the difference between the prediction accuracy for the three drugs.

3.3 Random Forest Random forest is an ensemble learning method which is commonly used in classification, regression, and other tasks. It is formed by numbers of decision trees at training time and outputting the mode of classes or the mean prediction of all the individual trees. The decision trees built-in random forest are based on sub-data sets

The Application of Machine Learning Methods …

503

Fig. 2 Tree after the pruning

Table 6 Traing/testing accuracy/AUC of decision tree model

Amphet

Benzos

Coke

Training accuracy

0.689

0.683

0.702

Testing accuracy

0.650

0.657

0.634

Training AUC

0.710

0.686

0.747

Testing AUC

0.670

0.665

0.688

which are chosen randomly from the original data set. Random forest overcomes the over-fitting habit of decision tree [8] and brings the accuracy and stability of prediction to a new level. Random forest has a lot of advantages. It can produce classifiers with high accuracy for many kinds of data. It can also handle plenty of input variables without sacrifice its accuracy. In a word, random forest is a very useful model in machine learning. We take Amphet as an example to show how we build a random forest model. Firstly, we build a random forest model with 1000 decision trees. The model is one hundred percent over-fitting now so we have to simplify it to neutralize over-fitting. Out-of-bag (OOB) error is a commonly used value to evaluate the prediction error of random forest model [9]. The relationship between out-of-bag (OOB) error and the number of trees is shown in Fig. 3. In order to have a more accurate model, we have to find the bottom value of OOB error. Based on this figure, to avoid over-fitting and under-fitting we have to choose the parameter when the number of trees is 385. We use the same way to build random forest model for the other two drugs and the training and testing accuracy and AUC is given in Table 7. From the table, we can see that training accuracy and AUC of random forest are obviously higher than decision tree model and logistic model we mentioned before. The testing accuracy

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P. Han

Fig. 3 Relationship between out-of-bag (OOB) error and the number of trees

Table 7 Traing/testing accuracy/AUC of random forest model

Amphet

Benzos

Coke

Training accuracy

0.744

0.752

0.776

Testing accuracy

0.681

0.694

0.678

Training AUC

0.836

0.832

0.855

Testing AUC

0.742

0.760

0.742

and AUC of random forest model are comparable to logistic model but better than decision tree model.

4 Summary In the whole process, we build three models for three drugs in order to find a more suitable way to predict drug abuse. The accuracy and AUC of three models are given in Table 8. From the table, we can figure out that in these three models, random forest model has the best performance comprehensively. Logistic regression model is comparable to random forest model, and it also has a good prediction accuracy

The Application of Machine Learning Methods … Table 8 Traing/testing accuracy/AUC of all three models

505 Logistic

Tree

Random forest

Train accuracy

0.680

0.689

0.744

Test accuracy

0.678

0.650

0.681

Train AUC

0.744

0.710

0.836

Test AUC

0.754

0.670

0.742

Fig. 4 ROC curves of three models on training data set

which is acceptable compare to its simplicity. Decision tree model takes the last place but it is the most intuitive model and easy to understand. The ROC curves [10] of three models on training data set are set shown in Fig. 4, and the ROC curves of three models on testing data set are shown in Fig. 5. The measure of area below the curve is the AUC value of the model which is a significant value to tell which model is better [11]. From Fig. 4, we can see that on training data, random forest model is better than logistic model and logistic model is better than decision tree. From Fig. 5, we can see that on testing data, random forest model is comparable to logistic model, and they are all better than decision tree model.

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Fig. 5 ROC curves of three models on testing data set

References 1. S.H. Walker, D.B. Duncan, Estimation of the probability of an event as a function of several independent variables. Biometrika 54(1–2), 167–179 (1967) 2. L. Rokach, O. Maimon, Data mining with decision trees: theory and applications. World Scientific Pub Co Inc. ISBN 978-9812771711 (2008) 3. T.K. Ho, Random decision forests (PDF), in Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995 (1995), pp. 278–282. Archived from the original (PDF) on 17 April 2016. Retrieved 5 June 2016 4. S. Shalev-Shwartz, S. Ben-David, 18. Decision Trees. Understanding Machine Learning (Cambridge University Press, Cambridge, 2014) 5. S.H. Walker, D.B. Duncan, Estimation of the probability of an event as a function of several independent variables. Biometrika 54(1/2), 167–178 (1967). https://doi.org/10.2307/2333860. JSTOR 2333860 6. J.R. Quinlan, Induction of decision trees (PDF). Mach. Learn. 1, 81–106 (1986). https://doi. org/10.1007/BF00116251 7. Mansour, Y. Pessimistic decision tree pruning based on tree size, in Proceedings of 14th International Conference on Machine Learning (1997), pp. 195–201 8. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, 2nd ed. (Springer, 2008). ISBN 0-387-95284-5 9. G. James, D. Witten, T. Hastie, et al., An introduction to statistical learning: with applications in R, in An Introduction to Statistical Learning (2013)

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10. T. Fawcett, An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006) 11. J.A. Hanley, B.J. McNeil, A method of comparing the areas under receiver operating characteristic curves derived from the same cases

Set Representation of Itemset for Candidate Generation with Binary Search Technique Carynthia Kharkongor and Bhabesh Nath

Abstract Till date, there are many association mining algorithms which have been used in varied applications. The performance of these mining algorithms are greatly affected by the execution time and the way the itemsets are stored in the memory. Most of the time is wasted while scanning the database and searching for the frequent itemsets. The search space keeps on increasing when the number of attributes in the database is large, especially when dealing with millions of transactions. Furthermore, the representation of itemsets in the memory plays a vital role when large databases are handled. To solve this problem, this paper shows an improvement of the algorithms by representing a concise representation and reducing the searching time for the frequent itemsets. Keywords Association rule mining · Apriori algorithm · Frequent itemset · Binary search

1 Introduction Apriori algorithm was first introduced by Agarwal [2]. It is the basis of the mining algorithms. Since then, many improved versions of Apriori algorithm have been developed such as [4, 16, 22]. However, these algorithms have not been able to solve all the problems of the mining algorithms. This paper will solve the problem of memory requirement and the efficiency of Apriori algorithm by reducing the searching time of the candidate itemset [3]. The main task of mining the association mining algorithms is divided into two: • Generation of the frequent itemsets having support count greater than the minimum threshold. • After the generation of frequent itemsets, the rules are generated depending on the confidence threshold. C. Kharkongor (B) · B. Nath Tezpur University, Tezpur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_46

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For a given dataset with transaction T = {t 1 , t 2 , t 3 , …, t n }, each transaction contains a set of items called itemsets. An itemset I consists of a set of items I = {i1 , i2 , i3 , …, in } where each item can represent an element. The two important metrics used in association rule mining are: • Support: is the frequency of the items when it appears in the dataset. Support = Frequency of the item (X)/Number of transaction in the dataset (N). • Confidence: is another metric used for measuring the strength of the rule. Confidence of the rule X → Y = support(X ∪ Y )/Support(X) [1, 15]. An itemset is said to be frequent itemset if its support count is greater than the minimum threshold value [11, 15].

1.1 Apriori Algorithm Firstly, the database scans multiple times to find the support count of the items. The items at k−1th level are called large itemsets, L k −1 . The large itemsets L k −1 is self-join with itself to generate L k itemsets. Union operation is performed during the self-join where only those itemsets which share a common prefix are joined. The support count of the itemsets L k −1 is measured. These itemsets are called candidate itemsets. The next step in Apriori algorithm is the pruning of the itemsets. The itemsets are prune when their support count is less than the minimum threshold count. These itemsets are now, called frequent itemsets. Furthermore, those itemsets whose subset are not frequent are also prune. This ensures that when the itemsets are not frequent then its superset is also not frequent [2].

2 Related Works A bitmap representation approach has been adopted by Bitmap Itemset Support Counting (BISC) [19], Closed FI and Maximal FI [17]. In BISC, the data is stored as bit sequences. These bit sequences are treated as bitmaps [19]. The transaction sets are stored in the form of bits. This reduces the memory storage especially when large datasets are handled. RECAP algorithm also provides a concise representation of itemsets by grouping together the patterns sharing regularities among attributes [12]. TM algorithm compresses the transaction ID’s by mapping the ID’s in a different space. Then, the itemsets are counted by intersecting these interval lists [14]. Similarly, VIPER algorithm uses bit vectors for itemset representation [13]. Another representation called LCM is a hybrid representation that consists of array, prefix and bitmap [18]. When the dataset is sparse, the array technique works for FP-tree. Array representation is used for representing itemsets in FPmax. The transversal time for all items is saved by using array [7].

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3 Itemset Representation for Apriori Algorithm The itemsets in the memory are represented in horizontal or vertical layout. • Horizontal Layout: In this layout, the itemsets are represented using the item ID’s. In each transaction, the ID’s of the items are stored as shown in Fig. 1. • Vertical layout: The itemsets are represented as a set of columns where only the item ID’s are stored shown in Fig. 2. Depending on the type of representation, algorithms such as [12] which uses horizontal layout while [10] and [11] uses vertical layout. To represent the itemset I = {1, 5, 12, 25, 29, 31}, some of the data structures that can be used for itemsets representation are as follows: • Linked list: Suppose the itemset I needs to be represented in the memory. The total memory requirement will be 6 × (2 × Integers) regardless of the itemset size. Suppose the integer is 4 bytes, then memory requirement is 6 × (2 × 4) = 48 bytes. A representation using linked list is shown in Fig. 3. • Array: The above itemset I can be represented using array. Each item in the itemsets is stored as an element in the array. The memory requirement is 32 × Integers (32 bit). If integer is 4 bytes, then the total memory requirement is 32 × 4 = 128 bytes shown in Fig. 4. Algorithms such as [4] and [16] have used array for representing. • Bitmap: The itemsets I can be represented using bitmap by marking ‘1’ if the item is present in the itemset and ‘0’ if the item is absent from the itemset [20, 21]. The memory requirement to represent the itemset I is 32 × 1 byte = 32 bytes. Some Fig. 1 Horizontal layout for the itemsets

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Fig. 2 Vertical layout for the itemsets

Fig. 3 Linked list representation for the itemsets I = {1, 5, 12, 25, 29, 31}

Fig. 4 Array representation for the itemsets I = {1, 5, 12, 25, 29, 31}

algorithms such as [5, 9, 10, 19] have used bitmap for itemset representation as shown in Fig. 5. • Set representation: Each item can be stored by labeling ‘1’ if present or ‘0’ if absent. Using set representation, the maximum size of the attributes is the cardinality of set. Therefore, a 4 bytes long integer can be used for representing the itemset I as shown in Fig. 6 [8].

Fig. 5 Bitmap representation for the itemsets I = {1, 5, 12, 25, 29, 31}

Set Representation of Itemset for Candidate Generation …

513

Fig. 6 Set representation for the itemsets I = {1, 5, 12, 25, 29, 31}

4 Problem Definition The main challenge of Apriori algorithm is how the candidate itemsets are represented in the memory. With millions number of transactions, the process of mining becomes infeasible. If the database is of size 100, then the total number of generated candidate itemsets will be 2100 ∼ 1030 . If the integer takes 4 bytes each, then the memory consumption will be 4 × 1030 bytes for a candidate itemset size of only 100. When the database is large consisting of million of attributes, storing these itemsets in the main memory is impractical. Additional storage is required if the itemsets does not fit in the main memory. This will incur cost and I/O operations for retrieving itemsets from main memory to secondary and vice versa. Moreover, time is wasted while searching for those itemsets in the memory. This affects the overall performance of the algorithm.

5 Searching Techniques In Apriori algorithm, the itemsets are inserted in the list after the generation of candidate itemsets. In order to avoid redundant itemset in the list, the duplicate itemset needs to be removed from the list. This requires searching the whole list to find the duplicate itemset. The two types of searching that are most commonly used are linear and binary search. • Linear search: perform the searching operation sequentially. When the item is present at the first position, then the complexity will be O(1), which is the best case. When the item is present at the n/2 position where n is the total number of items present, then the number of comparisons will be n/2. The probability of finding the items is equally likely to be present. This gives the average case. However, when the item is present at the last position, the number of comparisons is n. This implies that searching has to start from the first position and then goes on checking each and every element until it reaches the last position. Hence, the complexity is O(n) which is the worst case. • Binary search: works on sorted list. It first divides the entire list into half with mid as the middle point. The first half of the list consists of items which is lesser than the middle value and the second half contains items which is greater than the middle value. The searched item is first compared with the middle value. If it is less than the middle value, then the first half of the list is again divided and

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it is then compared. If it is more than the middle value, the second half is again divided into two halves and then it is compared. This process of dividing the list into halves continues until the item is found [4, 6, 23].

6 Set Representation for Apriori Algorithm with Binary Search In this paper, the itemsets are represented using set representation for Apriori algorithm [8]. To represent the itemsets in the memory, the item is mark ‘1’ if it is present and ‘0’ if the item is absent. The operations used in set for Apriori algorithm are as follows: 1. Union operation: is done by using OR bitwise operation as shown in Fig. 7. 2. Subset operation: is check whether the itemset is a subset of another itemset. 3. Superset operation: is also similar to subset operation and checks whether a particular itemset is a superset of another itemset or not. 4. Membership operation: checks whether an item is a part of the itemset or not. 5. Intersection operation: is computed by using AND bitwise operation as shown in Fig. 8. 6. Set Difference operation: of two itemsets contain of only those items which are present in first itemset but not in the second itemset. This is shown in Fig. 9. The Apriori algorithm is implemented which involves using the above-mentioned operations that is needed for generation of the candidate itemsets. The candidate

Fig. 7 Union operation

Set Representation of Itemset for Candidate Generation …

Fig. 8 Intersection operation

Fig. 9 Set difference operation

515

516 Table 1 Complexity difference between binary search and linear search

C. Kharkongor and B. Nath Complexity

Binary search

Linear search

Best case

O(1)

O(1)

Average case

O(log n)

O(n)

Worst case

O(log n)

O(n)

itemsets are then sorted in ascending order. As seen from Table 1, the complexity of using binary search is better than the linear search in all the cases theoretically. In this paper, binary search technique is performed on these generated candidate itemsets to find the duplicate or redundant itemsets. The sorted and the unique itemsets are inserted into the candidate item list. The efficiency of the algorithm will improve if binary search is used.

7 Experimental Analysis The candidate itemsets generated in Apriori algorithm are tested using the three synthetic datasets. These datasets are of 50 attributes having size of 1000, 5000 and 20,000. With varying size of the datasets, the performance of the algorithm also changes. The candidate itemsets are represented using both the set and array representation with varying support count. The candidate itemsets are tested with support count 2.5, 5 and 10%. The set representation is compared with the array representation for candidate itemsets generation. Theoretically, using array representation each element will take 4 bytes while using set representation each element will take only 1 bit. Moreover, using linear search for worst case the time complexity is O(n) where as for binary search the complexity is log n. Both the searching techniques are used in set and array representation and the results are shown in Tables 2, 3 and 4.

8 Conclusion As seen from the results, we can say that the time requirement using set representation is less than the representation using array. The memory using set representation is also less than representation using array. Furthermore, using binary search the performance of set representation is better than array representation with linear search. Therefore, the set representation with binary search is more efficient for candidate generation. This will eventually increase the overall performance of the mining algorithms.

37881.2

24536

212.606

2.5%

5%

10%

21152

21198

21326 206

24495

37658.61 21140

21188

21253

Memory (Kbs)

83.522

15530.7

25858.4 13258

13358

13440

Memory (Kbs)

Time (ms)

Time (ms)

Time (ms)

Memory (Kbs)

Set representation using linear search

Support array representation count Array representation using binary using linear search search

80.88

15423.1

25633.7

Time (ms)

13158

13293

13382

Memory (Kbs)

Set representation using binary search

Table 2 Candidate itemset generation using array and set representation with binary and linear search for 1000 datasets respectively

Set Representation of Itemset for Candidate Generation … 517

653789

357076

943.788

2.5%

5%

10%

21592

22972

24635 909.78

355526

639126 21342

22206

24418

Memory (Kbs)

484.9

304867

597860 13892

14513

16383

Memory (Kbs)

Time (ms)

Time (ms)

Time (ms)

Memory (Kbs)

Set representation using linear search

Support array representation count Array representation using binary using linear search search

426.8

303867

591665

Time (ms)

13600

14293

16157

Memory (Kbs)

Set representation using binary search

Table 3 Candidate itemset generation using array and set representation with binary and linear search for 5000 datasets, respectively

518 C. Kharkongor and B. Nath

1247806

920218

14949.7

2.5%

5%

10%

27682

31693

40210 14639.7

901362

1190246 26382

30693

38458

Memory (Kbs)

14370.6

479508

783429 16782

21376

31721

Memory (Kbs)

Time (ms)

Time (ms)

Time (ms)

Memory (Kbs)

Set representation using linear search

Support array representation count Array representation using binary using linear search search

14178.3

477506

701665

Time (ms)

16582

20083

29703

Memory (Kbs)

Set Representation using binary search

Table 4 Candidate itemset generation using array and set representation with binary and linear search for 20,000 datasets, respectively

Set Representation of Itemset for Candidate Generation … 519

520

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References 1. M. Abdel-Basset, M. Mohamed, F. Smarandache, V. Chang, Neutrosophic association rule mining algorithm for big data analysis. Symmetry 10(4), 106 (2018) 2. R. Agrawal, T. Imieli´nski, A. Swami, Mining association rules between sets of items in large databases, in ACM Sigmod Record, vol. 22 (ACM, 1993), pp. 207–216 3. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A.I. Verkamo et al., Fast discovery of association rules. Adv. Knowl. Discov. Data Mining 12(1), 307–328 (1996) 4. M. Al-Maolegi, B. Arkok, An improved apriori algorithm for association rules (2014). arXiv: 1403.3948 5. G. Antoshenkov, Byte-aligned bitmap compression, in Proceedings DCC ’95 Data Compression Conference (IEEE, 1995), p. 476 6. F. Bodon, A fast apriori implementation. FIMI 3, 63 (2003) 7. G. Grahne, J. Zhu, Efficiently using prefix-trees in mining frequent itemsets. FIMI 90 (2003) 8. C. Kharkongor, B. Nath, Set representation for itemsets in association rule mining, in 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (IEEE, 2018), pp. 1327–1331 9. N. Koudas, Space efficient bitmap indexing, in CIKM (2000), pp. 194–201 10. A. Moffat, J. Zobel, Parameterised compression for sparse bitmaps, in Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, 1992), pp. 274–285 11. T.T. Nguyen, Mining incrementally closed item sets with constructive pattern set. Expert Syst. Appl. 100, 41–67 (2018) 12. D. Serrano, C. Antunes, Condensed representation of frequent itemsets, in Proceedings of the 18th International Database Engineering & Applications Symposium (ACM, 2014), pp. 168– 175 13. P. Shenoy, J.R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, D. Shah, Turbo-charging vertical mining of large databases, in ACM Sigmod Record, vol. 29 (ACM, 2000), pp. 22–33 14. M. Song, S. Rajasekaran, A transaction mapping algorithm for frequent itemsets mining. IEEE Trans. Knowl. Data Eng. 18(4), 472–481 (2006) 15. R. Srikant, R. Agrawal, Mining generalized association rules (1995) 16. R. Srikant, R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, in International Conference on Extending Database Technology (Springer, 1996), pp. 1–17 17. V. Umarani, et al., A bitmap approach for closed and maximal frequent itemset mining. Int. J. Adv. Res. Comput. Sci. 3(1) (2012) 18. T. Uno, M. Kiyomi, H. Arimura, Lcm ver. 3: collaboration of array, bitmap and prefix tree for frequent itemset mining, in Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations (ACM, 2005), pp. 77–86 19. K. Wu, E.J. Otoo, A. Shoshani, Optimizing bitmap indices with efficient compression. ACM Trans. Datab. Syst. (TODS) 31(1), 1–38 (2006) 20. M.C. Wu, Query optimization for selections using bitmaps, in ACM SIGMOD Record, vol. 28 (ACM, 1999), pp. 227–238 21. M.C. Wu, A.P. Buchmann, Encoded bitmap indexing for data warehouses, in Proceedings 14th International Conference on Data Engineering (IEEE, 1998), pp. 220–230 22. Y. Ye, C.C. Chiang, A parallel apriori algorithm for frequent itemsets mining, in Fourth International Conference on Software Engineering Research, Management and Applications (SERA’06) (IEEE, 2006), pp. 87–94 23. M.J. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

Robust Moving Targets Detection Based on Multiple Features Jing Jin, Jianwu Dang, Yangpin Wang, Dong Shen, and Fengwen Zhai

Abstract Moving targets detection is the most basic part of intelligent video analysis, and its detection effect is directly related to the accuracy of subsequent processing. Aim to cope with the challenges of multi-modal background in complex video environment, the paper proposes a new detection method that combines pixelbased feature and region-based feature. It proposes a new region textural statistic by fuzzy clustering to statistical texture and then fuses it with intensity of pixel. Feature vectors which consisted of rich information are used in background model. Optimal threshold segmentation method is used to obtain adaptive threshold for foreground detection. The experiments indicate that the method can achieve expected results and obviously outperform performance in scenes including multi-modal background. Keywords Moving detection · Fuzzy textural statistic · Kernel FCM · Optimalthreshold segmentation

1 Introduction In most computer vision applications, the detection of moving targets is a key task and plays an important role in the intelligent video analysis system. Its goal is to segment the moving foreground from the video scene. Accurate detection results will contribute to the next stage of tracking, classification and other high level processing. However, the research field is still full of challenges due to multi-modal background interference, illumination changes, camera shake, foreground occlusion and shadows and so on in the video scenes [1, 2]. The background modeling method is the used widely because of its high accuracy and real-time performance. The essence of background modeling is to establish a statistical expression for the scene that must be robust enough to deal with changes in the scene. The Gaussian Mixture Model [3] is a classical parametric statistical J. Jin (B) · J. Dang · Y. Wang · D. Shen · F. Zhai Lanzhou JiaoTong University, Lanzhou, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_47

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model. It uses multiple Gaussian models to fit the multi-peak state of pixel brightness variation. The algorithm has large computational complexity. The CodeBook Model [4] creates codebook for each pixel in the time domain. The codebooks of all pixels form a complete background model. The codebook model can handle multimodal scenes well while the memory consumption is large. The subspace statistical model carries the background model by robust principal component analysis [5]. This method can separate moving targets accurately, but it has to satisfy the assumption of background stillness. The self-organizing background modeling method (SOBS) [6] maps one pixel in the background model to multiple locations of the model and adopts the pixel neighborhood spatial correlation in update. Adaptive background segmentation algorithm (PBAS) [7] based on feedback mechanism introduces cybernetics by which changes the foreground judgment threshold and background model update rate adaptively. PBAS has high accuracy, but the calculation and setting of multiple adaptive thresholds increases the algorithm complexity. The Vibe [8] is a lightweight algorithm that initializes the model with a random sampling strategy in the first frame and updates the background model by quadratic random sampling. The propagation mechanism of spatial domain information makes the algorithm robust to camera jitter. Vibe algorithm has higher time efficiency. Of course, a lot of new algorithms are also being proposed. Literature [9] proposes a novel detection method for underwater moving targets by detecting their extremely low frequency emissions with inductive sensors. Minaeian proposes an effective and robust method to segment moving foreground targets from a video sequence taken by a monocular moving camera [10]. It handles camera motion better. With the popularity of deep learning, some related methods have emerged for moving targets detection. Literature [11] trains a convolutional neural network (CNN) for each video sequence to extract a specific background which improves the accuracy of the background model. It has to takes a lot of time to train. The requirements of the hardware platform also limit the application of the deep learning method. Combining of multiple features in foreground detection is an important research idea [12]. All of these methods either use single pixel features or just use local features. This paper proposes a new method that combines pixel-based feature and region-based feature. Brightness is used as a basic feature of a single pixel and combined with regional texture features for background modeling. The most important thing is that the traditional statistical texture features are fuzzy clustered which improves the discrimination and robustness of regional texture features.

2 Our Moving Targets Detection Method In this part, our detection method is given completely. In Sect. 2.1, it introduces a new fuzzy textural statistic through Kernel FCM to original statistic textural feature and describes calculation process in detail. In Sects. 2.2 and 2.3, background modeling and update mechanism is recommended respectively.

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2.1 Construction of Fuzzy Textural Statistic Pixel-based features are effective in extracting the accurate shape of the objects, but it is not effective enough and robust in dynamic backgrounds. The combination of region-based features will inherit the advantages of both. Intensity, color and gradient can be chosen as pixel-based features, region-based features such as Local Binary Pattern (LBP) have been attempted in some research. LBP owns many advantages such as fast calculation speed and gray scale invariance, but it is not effective in consistent textures. Therefore, the paper proposes a fuzzy textural statistic which applies Kernel Fuzzy C-Means (KFCM) to statistical texture. It obtains fuzzy texture vector by fuzzy clustering to Gray-Level Run-Length Matrix (GLRLM) and then calculates feature parameter. The fuzzy texture vector is more robust and more suitable for video processing by combining richer texture information. The final feature vector used in background model is consisted of region texture feature described by these feature parameters and pixel intensity information. Gray-level run-length is a representation of the grayscale correlation between pixels at different geometric locations. It is characterized by the conditional probability that successive pixels have same grayscale. As for an image I (M × N size), the element in its Gray-Level run-length Matrix R (L × N size, L is gray quantization level) is R θ (n, l). R θ (n, l) is called run-length which indicates the probability that continuous n(1 ≤ n ≤ N ) pixels have same gray level l(1 ≤ 0 ≤ L) starting from any location along the θ direction. θ is the angle between pixels in two-dimensional plane (θ = 0◦ in this paper). As for a center pixel (x, y), the GLRLM R of its local region is expressed by a vector C R in row major order. Initializing cluster centers Vi = {vxi , v yi }i=0,1,...d−1 and performing clustering by Kernel FCM on the intervals of C R , we can obtain the membership matrix M. Its element m(i, j)(i = 0, 1, . . . , d − 1; j = 0, 1, . . . , L × N − 1) indicates the membership that the jth interval of GLRLM belongs to the ith cluster center. In Kernel FCM algorithm, Eqs. (1) and (2) are used to iteratively update the cluster centers and the membership matrix until the error is less than threshold ε or maximum iteration has reached [13]. m(i, j) = d−1

1

1−K ( p j ,vi ) 1 r −1 k=0 ( 1−K ( p j ,vk ) )

0 ≤ i < d, 0 ≤ j < L × N

(1)

 L×N −1

m(i, j)r K ( p j , vi ) p j j=0 vi =  L×N −1 m(i, j)r K ( p j , vi ) j=0

0≤i 1) is a constant that  p −v 2 controls the clustering ambiguity; K ( p j , vi ) = exp(− j σ 2 i ). After obtaining the convergent membership matrix, the vector C R (L × N ) is converted into a vector F with d dimension.

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F = MC R

(3)

Every element F(i) in F could be expressed as Eq. (4): F(i) =

L×N −1

m(i, j)C R ( j) i = 0, 1, . . . , d − 1

(4)

j=0

F(i) reflects the comprehensive membership that gray-level run-length vector belong to ith cluster center. Vector F realizes effective dimensionality reduction as well as contains fused and richer texture information. The paper names it as Fuzzy Textural Vector (FTV). The membership matrix is fixed under the determined parameters. So it can be calculated online in advance in order to ensure the time efficiency of the algorithm. GLRLM uses the statistic such as the run-length factor to reflect the texture information. As for fuzzy textural vector, two statistics such as fuzzy long run-length factor R F1 and fuzzy total run-length percent R F2 are applied to textural description on the analogy. As shown in Eqs. (5) and (6), Q is the total number of run-length with value of 1. R F1 =

d−1 



v2y j F (i)

(5)

i=0

R F2 =

d−1 



F (i)/Q

(6)

i=0 

F is the normalized vector from F: F  F = d−1 i=0

F(i)

(7)

In order to measure the textural feature characterization ability of the fuzzy texture vector FTV proposed in the paper, it is compared with other statistical textural feature such as Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM). The Support Vector Machine (SVM) is used to classify texture images in the FMD dataset and the Correct Classification Percentages (CCPs) is used as the final evaluation standard for classification result. The classification results are shown in Table 1. It can be seen that the statistics of FTV achieve certain improvement on the correct classification rate with better texture discrimination ability. To verify the effectiveness of FTV in video processing, we select the pixel point A (lower left grass) located in the background with the repeated perturbation in Fig. 1a and observe its change within a certain period (total 844 frames) by different texture

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Table 1 Comparison of texture classification ability Method

GLCM

Features

Energy

Correlation

Long run-length factor

GLRLM Total run–length percent

FTV R F1

R F1

CCPs (%)

82.3

86.6

87.5

89.5

89.8

92.3

(a) A frame of the video

(b) Variation statistics of different texture measurement Fig. 1 Feature variation statistics of repeated perturbed pixels in video

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metrics. As shown in Fig. 1b, the fuzzy long run-length factor and fuzzy total runlength percent of FTV are more stable and more robust in spatial textural feature description than raw intensity feature and LBP in video environment.

2.2 Background Modeling For a pixel p(x, y), its feature vector v = [I, R F1 , R F2 ]T is consisted of raw intensity, the fuzzy long run-length factor and fuzzy total run-length percent computed in m × m p-centered region by process flow in section A. The feature vectors constitute the sample set. The first frame is used for background model initialization. The model of every pixel is established by random sample in its 3 × 3 neighborhood: M(x, y) = {v1 , v2 , . . . vn }

(8)

where, n is model size.

2.3 Foreground Detection and Model Update For ith frame in video sequence, feature vector of one pixel is v. vi is a sample in its background model. Each pixel is processed as follow: 1. Foreground detection in the current frame. Whether the current pixel is the background is judged by the similarity between the current pixel and its corresponding sample set. Euclidean distance is used for calculating distance between two feature vectors. The marker variable will be given value 1 if Euclidean distance is bigger than a threshold R:  ci =

1 distance(v, vi ) < R (1 ≤ i ≤ n) 0 otherwise

(9)

In video processing, scenes always change under the influence of environment. The fixed threshold R that cannot adapt to different frame will be inaccurate and reduce the accuracy of moving detection inevitably. Therefore, the paper adopts an adaptive threshold calculation method. Threshold-based methods are common in image segmentation. Considering the computational efficiency, the optimal threshold segmentation method with good segmentation results and simple calculation is used to calculate the adaptive threshold in our algorithm. The calculation method is displayed as follows: (1) Find the maximum and minimum gray values zmax and zmin of the frame and initialing threshold T = (zmax + zmin)/2;

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(2) The image frame is divided into foreground and background according to the threshold. The average gray value z F and z B of the two parts are obtained separately; (3) Compute new threshold T = (z F + z B)/2; (4) T is the final threshold until it does not change anymore. Otherwise the flow path goes to the second step and continues the iterative calculation. The adaptive threshold Ri of ith frame f i is calculated as follows: T Bi = Optimal_S( f i − Bave )

(10)

T Fi = Optimal_S( f i − f i−1 )

(11)

Ri = ∂ T Fi + (1 − ∂)T Bi

(12)

where Optimal_S means doing optimal threshold segmentation and Bave is made up of sample mean of each pixel in the current model. The weighted factor ∂ is proportion of foreground pixels in the previous frame f i−1 . The binarization result of pixel p(x, y) in ith frame is determined by (13), where T is an experience threshold: ⎧ n ⎨0  c ≥ T i Bi (x, y) = ⎩ i=1 1 otherwise

(13)

2. Background Model update. If a pixel in current frame is judged as background, its background model will be updated with 1/ϕ probability in order to adapt background change. A randomly selected sample in M(x, y) will be replaced by the feature vector v of current pixel. At meanwhile, neighborhood update idea proposed in Vibe is used in our process flow. We randomly select a pixel (xnb , ynb ) in 8-neighborhood region and also replace its model M(xnb , ynb ) with feature vector v of current pixel.

3 Experiments Experiments are performed on the CDNET standard library [14]. The quantitative indicators are F1 (F_measure), Re (Recall) and Pr (Precision). In experiments, five videos such as PETS2006, Waving tree, highway, fountain and canoe are selected for performance test of the algorithm. All of these scenes include background perturbance. Parameters setting of compared algorithms are identical to the original references. Figure 2 shows the experimental results of six algorithms in five different scenes.

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J. Jin et al. PET2006 116th frame

Wavingtree 249th frame

Highway 241th frame

fountain 1119th frame

canoe 996th frame

Input frame

GMM

KDE

CodeBook

ViBe

PBAS

Ours

Fig. 2 Detection results of 6 algorithms

“PET2006” is a scene where multiple pedestrians are moving in the subway station. The indoor lighting makes each moving object have projected shadow and the twinkling metal fence is also interferes the foreground detection due to the change of the light. The contour of the pedestrian in the mask generated by the GMM is incomplete. KDE algorithm generates a lot of noise for both the fence and the shadow. Comparing the masks obtained by CodeBook, ViBe and PBAS, it is obvious that our algorithm can get the better result in multi-modal part such as moving shadow and mental fence. The tree in the “Wavingtree” scene is multi-modal complex background. Although the detection results of KDE and CodeBook retain more complete foreground, but a lot of noise is left in the swaying area. Due to the introduction of multiple features and neighborhood spread method, the proposed method outperforms the other four algorithms in the detection of dynamic background. Although the PBAS algorithm can effectively remove the multi-modal background, but its time consumption is higher than the proposed algorithm because that multiple thresholds are adjusted. The challenge in the “Highway” scene is the tree on the left and the shadow of the moving vehicles. Compared with other algorithms, the proposed algorithm has better

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effect on multi-modal background and foreground integrity. As for the shadows of moving cars in the scene, the effects of these six algorithms are not ideal. “fountain” is a very complicated scene because of the fountain that keeps spraying. In 1119th frame, a white car is driving from right to left. Our method removes the interference of multi-modal fountain in the greatest extent and retains the most complete foreground area of sports car. The main reason is that the proposed method introduces robust and stable fuzzy texture features and adaptive threshold in background modeling. The “canoe” scene is similar. The combination of region-based feature and pixel-based feature and flexible model update mechanism contribute to obtain better detection result in river area that keeps shaking. The adaptive threshold computed by optimal segmentation is useful for getting complete canoe. Comparing our method with other algorithms by quantitative analysis, the average performance of the six algorithms in the five scenes is shown in the Table 2. It is obviously that the paper that combines robust fuzzy texture statistic with color information and adopts flexible background modeling and updating mechanism is efficient to achieve better comprehensive performance in this kind of scenes. The comparison of the average time efficiency for the algorithms is shown in the Fig. 3. All of these algorithms are able to achieve real-time performance. Although process time of our method is more than light-weight algorithm Vibe because of calculation in Kernel FCM and distances between feature vectors, detection performance is increased by 10.6%. Table 2 Compare of average performance GMM

KDE

CodeBook

ViBe

PBAS

Ours

Re

0.68

0.58

0.76

0.78

0.79

0.83

Pr

0.64

0.49

0.61

0.73

0.78

0.84

F1

0.66

0.53

0.68

0.75

0.78

0.83

Fig. 3 Time efficiency of 6 algorithms (frame/s)

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4 Conclusions Aim to enhance detection precision of moving foreground extraction in video processing, the paper proposes a robust method for complicated multi-modal scene. It obtains fuzzy textural vector FTV through kernel FCM to traditional statistic textural feature. Then fuzzy textural statistics is combined with intensity to construct feature vectors. Random sample and neighborhood spread mechanisms are used in modeling and update stages. The judgment threshold of samples distance is calculated adaptively. Experiments have proved the efficiency of the method in multi-modal scenes comparing to other outstanding algorithm. Acknowledgements This work was supported by National Natural Science Foundation of China with grant No. 61562057 and Gansu Provincial Technology Plan with grant No. 17JR5RA097.

References 1. A. Siham, A. Abdellah, S. My Abdelouahed, Shadow detection and removal for traffic sequences, in Proceeding of International Conference on Electrical and Information Technologies (Guangzhou, 2016, April) pp. 168–173 2. T. Bouwmans, Traditional and recent approaches in background modeling for foreground detection: an overview. Computer Science Review 11, 31–66 (2014) 3. C. Stauffer, W. Grimson, Adaptive background mixture models for real-time tracking. in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Fort, 1999, July) pp. 2246–2252 4. K. Kim, T.H. Chalidabhongse, D. Harwood et al., Real-time foreground-background segmentation using codebook model. Real-Time Imaging 3, 172–185 (2005) 5. J. Jing, D. Jianwu, W. Yangping et al., Application of adaptive low-rank and sparse decomposition in moving objections detection. J. Front. Comput. Sci. Technol. 12, 1744–1751 (2016) 6. L. Maddalena, A. Petrosino, The SOBS algorithm: what are the limits? in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (Rhode Island, 2012, June) pp. 21–26 7. M. Hofmann, P. Tiefenbacher, G. Rigoll, Background segmentation with feedback: the pixelbased adaptive segmenter, in Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Washington, 2012, July) pp. 38–43 8. O. Barnich, V.D. Mac, Vibe: a universal background subtraction algorithm for video sequences. Image Processing 6, 1709–1724 (2011) 9. J. Wang, B. Li, L. Chen et al., A novel detection method for underwater moving targets by measuring their elf emissions with inductive sensors. Sensors 8, 1734 (2017) 10. S. Minaeian, L. Jian, Y.J. Son, Effective and efficient detection of moving targets from a UAV’s camera. IEEE Transactions on Intelligent Transportation Systems 99, 1–10 (2018) 11. M. Braham, M. Van Droogenbroeck, Deep background subtraction with scene-specific convolutional neural networks, in Proceeding of International Conference on Systems, Signals and Image Processing (Bratislava, May 2016), pp. 1–4 12. J. Zhai, Z. Xin, C. Wang, A moving target detection algorithm based on combination of GMM and LBP texture pattern, in Guidance, Navigation and Control Conference (2017), pp. 139–145

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13. D.Q. Zhang, S.C. Chen, Kernel-based fuzzy and possibilistic—means clustering, in Proceeding of ICANN (2003), pp. 122–125 14. N. Goyette,P.M. Jodoin, F. Porikli et al., Change detection net: a new change detection benchmark dataset, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Rhode Island, 2012, July) pp. 16–21

Digital Rock Image Enhancement via a Deep Learning Approach Yunfeng Bai and Vladimir Berezovsky

Abstract Digital Rock Images have been widely used for rock core analysis in petroleum industry. And it has been noticed that the resolution of Digital Rock Images are not fine enough for complex real-world problems. We propose Deep Neural Networks to increase resolution and quality for Digital Rock Images. The results demonstrate that our proposed method indeed can produce Digital Rock Images of higher resolution. And the proposed method for two-dimensional images has potential value to extend to 3D situation which means a lot for three-dimensional Digital Rock reconstruction. Keywords Deep neural networks · Digital rocks images · Image enhancement · Image resolution

1 Introduction Deep Neural Networks have been widely used to process 2D image in recent years and become more and more popular as time goes by. There are plenty of impressive applications of Deep Neural Networks in the fields of image recognition, image enhancement, image clustering, image cutting, object detection, face recognition, style migration and other fields [1–5]. Deep Neural Networks have brought in revolutionary achievements for image processing. In this paper, we are inspired by some successful image processing applications of neural networks to apply Deep Neural Networks to increase resolution and quality for Digital Rock Images. Rock core images play an important role in analyzing rock core in petroleum industry. Petroleum engineers are interested in obtaining a series of numerical descriptors or features which statistically describe porous materials [6]. If these Y. Bai (B) · V. Berezovsky Northern (Arctic), Federal University Severnaya Dvina Emb, 17, Arkhangelsk, Russia e-mail: [email protected] V. Berezovsky e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_48

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features are valid, they may eventually be used for the prediction of the physical properties of porous media including porosity, specific permeability and formation factor which are important components in production studies [6]. Traditionally, rocks picture are studied by experts and they are interested in Digital Rock Images of better resolution. The better the resolution of the pictures, the more accurate numerical descriptors or features they can get. That is why we propose a machine learning method to supply Digital Rock Images of better resolution for them to overcome complex real-world problems. We also plan to make these enhanced pictures to be a dataset for another part of our project and apply the similar way to 3D Digital Rock reconstruction. The rest of this paper includes 5 parts: Sect. 2 introduces several impressive works related under this topic. Section 3 introduces machine learning algorithm implementation framework which is named Tensorflow. Section 4 introduces some detail of our proposed method. Section 5 introduces experiment results and makes discussion. Section 6 makes some conclusion.

2 Related Works In 2017, a method was proposed by Pratik Shah and Mahta Moghaddam to improve the spatial resolution of microwave images [7]. They eased the issue based on the strategy to provide additional information through learning [7]. They incorporated learning using machine learning algorithm. The model includes two stages which are convolutional neural networks and a non-linear inversion approach [7]. The results showed that their method could produce image of higher resolution [7]. In 2019, a deep recurrent fusion network (DRFN) was proposed by Yang et al. [8]. Their proposed method was utilized transposed convolution instead of bicubic interpolation in image processing [8]. They adopted learning method which has a larger receptive field and reconstructs images more accurately [8]. Extensive benchmark datasets evaluations show that even using fewer parameters, their approach achieves better performance than most of deep learning approaches in terms of visual effects and accuracy [8]. In 2019, Shen et al. proposed a multi-level residual up-projection activation network (MRUAN). Their model includes residual up-projection group, upscale module and residual activation block [9]. Specifically, residual up-projection group mines hierarchical low resolution feature information and high resolution residual information with the help of recursive method [9]. Subsequently, the upscale module adopts multi-level LR feature information as input to obtain HR features. Extensive benchmark datasets evaluations show that their MRUAN performs favorable against state-of-the-art methods [9].

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3 Tensorflow Tensorflow is machine learning algorithm implementation framework and deployment system designed to study ultra-large-scale deep neural networks for the Google Brain project launched in 2011 [10]. Tensorflow can be easily deployed on a variety of systems. In recent years, the performance of Tensorflow has been tested by some researchers and Tensorflow become one of the most popular deep learning libraries [11–13]. Meanwhile, Tensorflow supports Python which is very popular in academia. All of these situations make researchers easily and quickly to develop their deep neural network models and test their new ideas without worrying about the underlying algorithm.

4 Proposed Methods After comparing the methods and applications mentioned in the related works, we chose to apply SRCNN method to this project. At the same time, the SRCNN method has been used to enhance medical images [7], radar images [14], and underwater images [15] and is demonstrated the effectiveness in these fields. That is why we are confidence in our choice about the approach to produce Digital Rock Images of higher resolution. The working process of SRCNN is of two steps [7]. First, low-resolution images are interpolated to the desired scale with interpolation method [7]. Second, the interpolated images are mapped to the higher resolution image by Convolutional Neural Network whose filters’ weights are obtained by learning plenty of images [7]. In our project, filters’ weights of Convolutional Neural Network are obtained by learning plenty of Digital Rock images. For training the network, we used images from our cooperative laboratory. First, we cut these 400*500 pixel images into small pictures of 72*72 pixels. Second, we randomly selected 80% of the slices to prepare for training data and the remaining 20% slices to prepare for testing data. Third, we generated these low resolution images of 36*36 pixels by downsampling and blurring the features. Finally, we upscaled the images to 72*72 pixels with interpolation to fulfill the first step of SRCNN and then trained the Deep Neural Networks.

5 Experiment Result and Discussion The network was trained with general-purpose GPU. The GPU is GeForce GTX 1050 Ti. It has 768 NVIDIA CUDA Cores and 4G GDDR5 memories which are of 7 Gbps Speed. The GPU Architecture is Pascal.

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The Deep Neural Networks were developed with Tensorflow. Upscaled pictures in the training data and original images in the training data were used as to train the Deep Neural Networks. Upscaled pictures were used as input and their output to compare with corresponding original images. Then adjusting the weight of the deep neural network by comparing the differences between the two groups until the training was completed. The filters’ weights of Convolutional Neural Network are randomly initialized by the function of Tensorflow before the step of training. After this, stochastic gradient descent is applied to minimize the mismatch. The model has been trained for several times. The iterations for training distributed within 200–600 then the networks were tested on the images from the testing data which has never been used to train Deep Neural Networks.

6 Conclusion The test demonstrated the feasibility of SRCNN to produce Digital Rock Images of higher resolution, and the potential practical value in rock image analysis in petroleum industry. One of our future works is scale this GPU implementation into several GPUs with a bigger dataset. And we also plan to extend this method to 3D Digital Rock reconstruction. Statement Compliance with Ethical Standards. Funding This study was funded by the Russian Foundation for Basic Research (grant number No. 16-29-15116) and China Scholarship Council. Conflict of Interest The authors declare that they have no conflict of interest.

References 1. P. Neary, Automatic hyperparameter tuning in deep convolutional neural networks using asynchronous reinforcement learning. in 2018 IEEE International Conference on Cognitive Computing (ICCC). (IEEE, 2018). http://doi.ieeecomputersociety.org/10.1109/ICCC.2018. 00017 2. S. Park, S. Yu, M. Kim, K. Park, J. Paik, Dual autoencoder network for retinex-based low-light image enhancement. in IEEE Access. vol 6, (2018) pp. 22084–22093. https://doi.org/10.1109/ access.2018.2812809 3. Y. Li, T. Pu, J. Cheng, A biologically inspired neural network for image enhancement. in 2010 International Symposium on Intelligent Signal Processing and Communication Systems. (IEEE, 2010). https://doi.org/10.1109/ISPACS.2010.5704686 4. Y. Zhao, Y. Zan, X. Wang, G. Li, Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation. in 2010 Chinese Control and Decision Conference. (IEEE, 2010). https://doi.org/10.1109/CCDC.2010.5498558

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5. T. Kinattukara, B. Verma, Clustering based neural network approach for classification of road images. in 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR). (IEEE, 2013). https://doi.org/10.1109/socpar.2013.7054121 6. R.M. Haralick, K. Shanmugam, Computer classification of reservoir sandstones. IEEE Transactions on Geoscience Electronics 11(4), 171–177 (1973). https://doi.org/10.1109/TGE.1973. 294312 7. P. Shah, M. Moghaddam, Super resolution for microwave imaging: A deep learning approach. in 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. https://doi.org/10.1109/apusncursinrsm.2017.8072467 8. X. Yang, H. Mei, J. Zhang, K. Xu, B. Yin, Q. Zhang, X. Wei, DRFN: deep recurrent fusion network for single-image super-resolution with large factors. IEEE Trans. Multimedia 21(2), 2019. https://doi.org/10.1109/tmm.2018.2863602 9. Y. Shen, L. Zhang, Z. Wang, X. Hao, Y. –L. Hou, Multi-level residual up-projection activation network for image super-resolution. in 2019 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2019.8803331 10. Y. Bai, V. Berezovsky, Digital rock image clustering based on their feature extracted via convolutional autoencoders. in Proceedings of the International Conference on Digital Image & Signal Processing. ISBN (978-1-912532-05-6) 11. D.C. Ciresan, U. Meier, J. Masci, L.M.Gambardella, J. Schmidhuber, Flexible high performance convolutional neural networks for image classification. in Twenty-Second International Joint Conference on Artificial Intelligence (2011, June) pp. 1237–1242 12. C. Feng, The basement of CNN fully connected layer. Deep learning in an easy way—–learn core algorithms and visual practice (Publishing House of Electronics Industry, 2017) pp. 50 13. W. Huang, Y. Tang, Tensorflow combat. (Publishing House of Electronics Industry, 2017) pp. 1–2. ISBN 978-7-121-30912-0 14. Y. Dai, T. Jin, Y. Song, H. Du, SRCNN-based enhanced imaging for low frequency radar. in 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama). https://doi.org/10. 23919/piers.2018.8597817 15. Y. Li, C. Ma, T. Zhang, J. Li, Z. Ge, Y. Li, S. Serikawa, Underwater image high definition display using the multilayer perceptron and color feature-based SRCNN. Access vol 7, IEEE. https://doi.org/10.1109/access.2019.2925209

Enhancing PSO for Dealing with Large Data Dimensionality by Cooperative Coevolutionary with Dynamic Species-Structure Strategy Kittipong Boonlong and Karoon Suksonghong

Abstract It is widely recognized that the performance of PSO in dealing with multiobjective optimization problem is deteriorated with an increasing of problem dimensionality. The notions of cooperative coevolutionary (CC) allow PSO to decompose the large-scale problem into the multiple subcomponents. However, the combination of CC and PSO tends to perform worse if interrelation among variables is exhibited. This paper proposes the dynamic species-structure strategy for improving search ability of CC and incorporates it with PSO algorithm. The resulting algorithm, denoted as “DCCPSO”, is tested with the standard test problems, widely known as “DTLZ”, with 3–6 optimized objectives. In the large-scale problem setting, the experimented results reveal that our proposed decomposition strategy helps enhancing performance of PSO and overcoming problems pertaining to interrelation among variables issues. Keywords Cooperative coevolutionary algorithm · Particle swarm optimization · Non-separable problem · Problem decomposition · Dynamic species

1 Introduction The main obstacle of utilizing the particle swarm optimization (PSO) in multiobjective (MO) optimization framework is identifying the global best solutions of large-scale problem domain with various optimized objective. The cooperative coevolutionary algorithm (CCA) proposed by Potter and De Jong [1, 2] pioneers the applicable method for decomposing and handling the large-scale problem simultaneously. This technique is motivated by the notion of divide-and-conquer. According

K. Boonlong Faculty of Engineering, Burapha University, Chonburi, Thailand K. Suksonghong (B) Faculty of Management and Tourism, Burapha University, Chonburi, Thailand e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_49

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to the Potter and De Jong’s framework, a solution consisting of N variables is decomposed into n subcomponents which are subsequently assigned to N corresponding species. Evolution of these species is performed more or less independently of one another. Meanwhile, the objective function can be evaluated after a complete solution is formed. Since any given species represents only a subcomponent of a complete solution, a full solution can be constructed by merging the individuals of considered species with those of the remaining species, the so-called “collaborator”. However, the problem decomposition strategy and the structure of the problem at hand are crucial in determining the performance of CCA [3]. Many researches effectively applied the concept of cooperative coevolution to the particular optimization problems [4–7]. Observing from the literature, although CCA performs well for large-scale optimization problem, its performance is rapidly dropped once the number of optimized objectives increases with the presence of interdependency among variables, i.e. non-separable problems [8]. To address the potential drawback on interdependency among variables, Bergh and Engelbrecht [8] incorporating CCA with PSO, decomposed an N variables problem into K species where each of them consists of s variables, e.g. N = K × s. As a result, each species represents s variables instead of one as the case of original CCA. However, the member of s variables in each species has never been adjusted during algorithms courses. Subsequently, the splitting-in-half decomposition scheme was proposed by Shi et al. [3]. In this scheme, solution chromosome is equally separated into two parts and evolutionary processes of these parts are performed individually. The main objective of these two strategies is to enlarge the number of variables assigned into species, i.e. species-structure in order to increase the chance of having interacting variables placed in the similar species. However, CCA adopted these two static decomposition schemes still perform poorly when dealing with large-scale problem. Besides, these schemes require a user to have a priori knowledge about the structure of the optimized problem. Alternatively, the species-structure that can be altered as algorithms run progresses and triggered by pre-specified rules could make a good sense for alleviating above problems [9, 10]. Yang et al. [9] articulated that once the frequency of alteration of grouping size increases, the probability of placing interacting variables within the same group also rises up. In [9], a new dynamic decomposition strategy whose variables assignment method is based on probability was proposed. Their proposed decomposition method enables species-structure to be adjusted throughout the optimization run. The proposed method was integrated to CCDE and tested with a set of non-separable problems with up to 1000 variables. Zhang et al. [10] proposed a dynamic cooperative coevolutionary framework in order to solve non-separable large scale global optimization problems. This framework appropriately incorporates two critical attributes of the variables, i.e., interaction relationship and contribution, in solving non-separable problems. To exploit the prominent features of CCA, this paper proposes a new dynamic decomposition strategy and integrates to PSO algorithm with extensive modifications for dealing with the large data dimensionality where interdependency among variables exists. This paper is organized as follows. The proposed dynamic species

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structure and its procedures are discussed in Sect. 2. Section 3 explains the standard test problem employed together with experimental setting. The simulation results are presented in Sect. 4, while our conclusions are stated in Sect. 5.

2 Cooperative Coevolutionary PSO with Dynamic Species Structure Strategy Particle swarm optimization (PSO), a derivative free method, is originally introduced by Kennedy and Eberhart [11]. PSO mimic the behavior of a bird flock, it has a set of candidate solutions which is called swam similar to population in genetic algorithm (GA). Each candidate solution is represented by a particle which also has its corresponding personnel best solution. In PSO each particle would be changed according to its personnel best solution and the global best solution, which is the best solution in swam. PSO was developed to be used in multi-objective optimization. The multiobjective particle swarm optimization (MOPSO) was originally developed in by embedding the Pareto domination [12]. By Pareto domination, non-dominated solutions are represented as the best solutions where the number the non-dominated solutions are usually more than one. The repository, a set containing the non-dominated solutions, is represented for the global best solutions in multi-objective optimization. In MOPSO each particle will be changed by its personnel best solution and one is randomly picked from the repository. To incorporate our proposed strategy into MOPSO, a predetermined set of speciesstructure (SS) is identified. For simplicity, a geometric series having first term equals to 1 and common ratio equals to 2 is employed for determining SS. A number of species (NS) is computed by dividing number of problem variables (N) by a predetermined SS, or N S = N /SS. At initial stage, algorithm randomly selects a speciesstructure from SS. Then variables are assigned chronologically from the first species to the last species. In the other words, variables will be randomly selected and placed into the first species, and then repeating this process until number of assigned variables meets the selected SS. Then, a variable assignment process shifts to the second species and so forth. It is worth noting that, if there is a fraction from computation of N /SS, the last species will not be fully utilized compared to other previous species. Our setting rule is that, if the empty space of species-structure is less than 50% of selected value of SS, this considered species will be used in the next process, although it is not completely filled up. In contrast, if the empty space is greater than 50%, all variables placed in this species will be moved and combined into the previous species. The underlying idea of the proposed method is to allow algorithm to experiment the diverse species-structure varying from smallest to largest structure. Besides, the species-structure is predetermined using a simple method compared to other dynamic methods that require high-level information from a user. In this scheme, to guarantee that all species-structures will be used during the course of algorithm run, the value of species-structure is selected from a set of SS without replacement.

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In the dynamic process, changing the species-structure depends on the comparison result of fitness functions between solutions obtained from the current and the previous iterations. If current solutions are fitter than previous ones, the employed species-structure will be repeatedly utilized in the next iterations; else, the new species-structure will be adopted. We used the convergence detection as a trigger criterion for adjusting of species-structure. Given Ai and Ai−1 are non-dominated solution sets of the current iteration and previous iteration, respectively. The condition for solutions convergence is proposed as: C(Ai , Ai−1 ) ≤ C(Ai−1 , Ai )

(1)

where C(Ai , Ai−1 ) is coverage ratio of solution set Ai over solution set Ai−1 while C(Ai−1 , Ai ) is the reverse value of C(Ai , Ai−1 ). The solution coverage, C, being used to assess two sets of solutions, can be stated as: C(Ai , Ai−1 ) =

|{ai−1 ∈ Ai−1 ; ∃ai ∈ Ai : ai ≺ ai−1 }| |Ai−1 |

(2)

where ai ≺ ai−1 indicates that solution ai covers or dominates solution ai−1 where C(Ai , Ai−1 ) ∈ [0, 1]. If C(Ai , Ai−1 ) = 1, all solutions in set Ai−1 are covered by those in set Ai . Meanwhile, C(Ai , Ai−1 ) = 0 indicates that none of solutions in set Ai−1 are dominated by those of set Ai . Therefore, C(Ai , Ai−1 ) > C(Ai−1 , Ai ) means that solutions obtained from the current iteration is fitter than those of previous iteration. As a result, C(Ai , Ai−1 ) ≤ C(Ai−1 , Ai ) triggers the condition of solutions convergence and thus activates the dynamic mechanism to modify the species-structure. According to cooperative coevolutionary concept, in order to evaluate an interested species, the fitness of a solution will be calculated. Since one species contains only a part of a full solution, it must be combined with other species, the so-called “collaborator”, to form a complete solution. In the literature, it is acknowledged that CCA performance also depends on the adopted technique for selecting the collaborators. In this study, we utilize a selection of collaborator that equips with the elitism strategy. Figure 1 illustrates the process of the adopted collaborator selection technique. According to Fig. 1, we consider an optimization problem with four variables. The species-structure is randomly selected and the resulting value is one. At the end of iteration, PSO attains a set of non-dominated solutions and stores them in repository. Suppose that species 1 that is filled up with variable x1 is evaluated, CCA selects collaborators from the current archive. To form the complete solutions, CCA merges collaborators with evaluated species with random order. This scheme helps protecting solutions to be trapped in the local optima because random placing collaborator promotes CCA to explore the new search space. The procedure of the proposed dynamic species-structure cooperative coevolutionary PSO, denoted as “DCCPSO”, is illustrated in Fig. 2.

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Fig. 1 Collaborator selection technique

3 Experimental Setting The scalable test problems DTLZ problems [13] whose number of optimized objective can be adjusted are used as the experimental cases. To test our main objectives, in problem setting, we consider 3–6 optimized objectives and 1024 decision variables. In DTLZ1, the true Pareto-optimal solutions have the objective function values laying on the linear hyper-plane. The search space contains many local Pareto optimal fronts that could make an optimization algorithm to be trapped before reaching the global Pareto optimal front. Among the DTLZ problems, DTLZ2 is the simplest one having sphered true Pareto optimal front. This problem can also be used to investigate an algorithm’s ability in dealing with a large number of objectives problem. DTLZ3 has many local optimal regions exhibited within the objective space which places difficulty to the optimization algorithm. Similarly, DTLZ3 has sphere true Pareto optimal front as DTLZ2. The test problem DTLZ4 is proposed for investigating the performance of the optimization algorithm in achieving the well-distributed set of solutions. Once again, DTLZ4 has sphere true Pareto optimal front as DTLZ2 and DTLZ3. For DTLZ5,vthe true Pareto optimal front is presented as a curve. The Interaction between decision variables had been introduced into this problem. Similarly, DTLZ6 has a curve true Pareto optimal front as DTLZ5. In addition, for DTLZ6, there is low density near the true Pareto optimal front in objective space causing difficulty to the optimization algorithm to reach the front. Lastly, DTLZ7 has a disconnected set of Pareto-optimal regions. This test problem has 2M −1 disconnected Paretooptimal regions in the search space where M is the number of optimized objectives. The parameter setting for DCCPSO is summarized and reported in Table 1.

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Enhancing PSO for Dealing with Large Data Dimensionality … Table 1 Parameter settings

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Parameter

Setting and values

Test problems

DTLZ problems with 3–6 objectives

Chromosome coding

Real-value chromosome with 1024 decision variables.

Population size

100

Repository size

100

Archive size for DCCPSO

100

Number of generations used for termination condition

1000

4 Simulation Results This study employed the average differences of objective vectors of to the true Pareto front (M 1 ) [14] and the hypervolume (HV) [15] as performance measures. To compute M 1 , the difference of objective vector of a solution i in repository to the true Pareto front (d i ) is the Euclidean distance between the objective vectors of solution to the nearest objective vector of a solution on the true Pareto front. M 1 is equal to the average of di of all solution in repository. The other criterion, hyper volume (HV) can be used to measure not only the difference of the solutions and the true Pareto front but also the variety of solutions. High value of HV reflects good performance of an algorithm. The HV can be referred to area, volume, and hypervolume, for two, three, and four-or-more objectives, respectively, between a pre-defined reference point and the solution to be evaluated. Tables 2, 3, 4, 5 exhibit results of M 1 with different test prob-lems, while the results of HV are reported in Tables 6, 7, 8, 9. Overall, according to results reported in Tables 2, 3, 4, 5, 6, 7, 8, 9, the proposed strategy is successful in boosting up the performance of DCCPSO since it outperforms the standard MOPSO, regardless of performance measure as well as test problem em-ployed. Considering results presented in Tables 2 and 6, based on DTLZ1 Table 2 Results of s of DTLZ problems with 3 objectives Test problems

MOPSO Mean

DCCPSO Std. dev.

Mean

Std. dev.

DTLZ1

43,337.16

657.43

19,581.10

DTLZ2

66.31

5.04

4.30

833.74 1.15

DTLZ3

72,007.67

9727.25

28,342.65

2603.05

DTLZ4

104.14

14.51

33.03

2.13

DTLZ5

110.12

16.86

8.79

2.32

DTLZ6

803.91

17.17

381.41

26.93

DTLZ7

5.60

1.78

0.91

0.32

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Table 3 Results of M 1 of DTLZ problems with 4 objectives Test problems

MOPSO Mean

DCCPSO Std. dev.

Mean

Std. dev.

DTLZ1

43,715.34

748.53

22,453.74

DTLZ2

121.65

3.60

10.65

867.75 2.41

DTLZ3

86,942.95

2603.41

34,141.94

4473.11

DTLZ4

113.52

7.11

40.99

7.12

DTLZ5

119.64

8.85

43.06

7.26

DTLZ6

821.06

15.32

587.78

44.11

DTLZ7

15.52

2.25

3.18

0.79

Table 4 Resuts for M 1 of DTLZ problems with 5 objectives Test problems

MOPSO

DTLZ1

43,769.82

Mean

DCCPSO Std. dev. 715.71

Mean 23,172.22

Std. dev. 772.87

DTLZ2

122.75

4.32

19.84

5.69

DTLZ3

87,854.46

3910.63

34,259.48

3829.82

DTLZ4

116.59

12.00

46.06

6.26

DTLZ5

122.79

4.88

48.40

8.07

DTLZ6

819.71

9.19

510.70

52.21

DTLZ7

22.14

3.97

4.68

0.88

Table 5 Results for M 1 for DTLZ problems with 6 objectives Test problems

MOPSO

DCCPSO

Mean

Std. dev.

Mean

DTLZ1

44,043.65

1063.36

22,809.98

Std. dev. 869.98

DTLZ2

124.28

4.22

16.75

6.01

DTLZ3

87,370.17

2814.89

35,106.89

4238.34

DTLZ4

121.01

8.00

53.51

10.25

DTLZ5

121.95

3.95

49.14

10.46

DTLZ6

821.24

17.43

516.18

56.84

DTLZ7

27.64

3.65

7.61

1.82

and DTLZ3, DCCPSO has superior ability for searching many local optimal points in both linear hyper-plane and sphere Pareto front. Besides, DCCPSO performs well in the large number of optimized objectives environment since its performance measures of DTLZ2 is better than that of MOPSO. Similarly, DCCPSO is able to

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Table 6 Results of HV of DTLZ problems with 3 objectives Test problems

MOPSO

DCCPSO

Mean

Std. dev.

Mean

Std. dev.

DTLZ1

0.7555

0.0237

0.9780

0.0198

DTLZ2

0.5229

0.0351

0.9998

0.0112

DTLZ3

0.5091

0.0499

0.9695

0.0316

DTLZ4

0.6239

0.0284

0.9868

0.0395

DTLZ5

0.6701

0.0457

0.9992

0.0065

DTLZ6

0.4199

0.0169

0.9392

0.0304

DTLZ7

0.0854

0.0098

0.5051

0.0798

Table 7 Results of HV of DTLZ problems with 4 objectives Test problems

MOPSO

DCCPSO

Mean

Std. dev.

Mean

Std. dev.

DTLZ1

0.8860

0.2015

0.9826

0.0508

DTLZ2

0.5283

0.0787

0.9841

0.0938

DTLZ3

0.5362

0.0722

0.9274

0.0927

DTLZ4

0.7493

0.1210

0.9406

0.0940

DTLZ5

0.5897

0.0887

0.9675

0.0969

DTLZ6

0.5204

0.0713

0.7637

0.0761

DTLZ7

0.0858

0.0105

0.5305

0.0808

Table 8 Results of HV of DTLZ problems with 5 objectives Test problems

MOPSO

DCCPSO

Mean

Std. dev.

Mean

Std. dev.

DTLZ1

0.8862

0.1080

0.9937

0.0508

DTLZ2

0.6278

0.0752

0.9921

0.0592

DTLZ3

0.5578

0.0598

0.9282

0.0928

DTLZ4

0.7258

0.0959

0.9351

0.0936

DTLZ5

0.5954

0.0885

0.9344

0.0840

DTLZ6

0.5499

0.0716

0.8188

0.0588

DTLZ7

0.0674

0.0093

0.5305

0.0808

achieve well-distributed solution in the sphere Pareto front as tested by DTLZ4 and, one again, outperforms standard MOPSO. As mentioned earlier, one of our main goals of the proposed strategy is to enable PSO to effectively deal with non-separable problem. Results from DTLZ5 and DTLZ6, which introduce degree of interdependency among variables, reveal that

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Table 9 Results of HV of DTLZ problems with 6 objectives Test problems

MOPSO

DCCPSO

Mean

Std. dev.

Mean

Std. dev.

DTLZ1

0.9049

0.1841

0.9859

0.0947

DTLZ2

0.5824

0.0650

0.9913

0.0777

DTLZ3

0.5827

0.0707

0.9125

0.0912

DTLZ4

0.7973

0.1284

0.9135

0.0583

DTLZ5

0.5806

0.0663

0.9117

0.0933

DTLZ6

0.5391

0.0577

0.7580

0.0727

DTLZ7

0.0529

0.0130

0.4221

0.1317

our proposed dynamic solution decomposition strategy is capable to effectively deal with variable coupling problem. The DCCPSO performs much better than standard MOPSO in this aspect. Lastly, DCCPSO also outperforms MOPSO in the aspect of searching for disconnected true Pareto front as reported in the results of DTLZ7. In this paper, the proposed DCCPSO is tested within the large problem dimensionality environment by setting large number of decision variables, i.e. 1024 variables, regardless of test problem. The proposed dynamic problem decomposition strategy reveals its superior capability in this sense, regardless of difficulties in searching true Pareto front. It can be observed that, by fixing number of 1024 decision variables, once optimized objective increases, DCCPSO’s performances are slightly worse. However, its performances are superior compared to those of standard MOPSO.

5 Conclusion This study contributes to the literature in the sense that the dynamic species-structure is proposed to improve search ability of CCA by preventing CCA from getting trapped in the local optimal and by overcoming issues related to interrelation among variables. The propose strategy allows species-structure to be adjusted throughout the course of optimization. This promotes probability of interrelated variables will be placed in the similar species. Besides, with the proposed convergence detection method, our strategy encourages species-structure that performs well to be continuously used in the subsequently generation. The experimented results reveal that the proposed DCCPSO outperforms the standard MOPSO, regardless of performance measure, test cases, as well as the number of optimized objective. Results from solving DTLZ1 and DTLZ3 show that DCCPSO is capable to obtain the true Pareto front with different front shape. Its superiority in dealing with non-separable problems is clearly presented when solving DTLZ4 and it is even more pronounced in the case of solving DTLZ5 whose interrelation problem is stronger than that of DTLZ4.

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References 1. M.A. Potter, K.A. De Jong, A cooperative coevolutionary approach to function optimization, in Parallel Problem Solving from Nature — PPSN III: International Conference on Evolutionary Computation The Third Conference on Parallel Problem Solving from Nature Jerusalem, Israel, October 9–14, 1994 Proceedings, ed. by Y. Davidor, H.-P. Schwefel, R. Männer (Springer, Berlin Heidelberg, Berlin, Heidelberg, 1994), pp. 249–257 2. M.A. Potter, K.A. de Jong, Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000) 3. Y.-j. Shi, H.-f. Teng, Z.-q. Li, Cooperative co-evolutionary differential evolution for function optimization, in Advances in Natural Computation: First International Conference, ICNC 2005, Changsha, China, August 27–29, 2005, Proceedings Part II, ed. by L. Wang, K. Chen, Y.S. Ong (Springer Berlin Heidelberg, Berlin, Heidelberg, 2005) pp. 1080–1088 4. K. Boonlong, Vibration-based damage detection in beams by cooperative coevolutionary genetic algorithm. Adv Mech Eng. 6(Article ID 624949): 13 (2014) 5. J.C.M. Diniz, F. Da Ros, E.P. da Silva, R.T. Jones, D. Zibar, Optimization of DP-M-QAM transmitter using cooperative coevolutionary genetic algorithm. J. Lightwave Technol. 36(12), 2450–2462 (2018) 6. Z. Ren, Y. Liang, A. Zhang, Y. Yang, Z. Feng, L. Wang, Boosting cooperative coevolution for large scale optimization with a fine-grained computation resource allocation strategy. IEEE Trans. Cybernetics. 49(2), 1–14 (2019) 7. A. Pahlavanhoseini, M.S. Sepasian, Scenario-based planning of fast charging stations considering network reconfiguration using cooperative coevolutionary approach. J. Energy Storage. 23, 544–557 (2019) 8. F.v.d. Bergh, A. P. Engelbrecht, A Cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8, 225–239 (2004) 9. Z. Yang, K. Tang, X. Yao, Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178, 2985–2999 (2008) 10. X.Y. Zhang, Y.J. Gong, Y. Lin, J. Zhang, S. Kwong, J. Zhang, Dynamic cooperative coevolution for large scale optimization. IEEE Evol. Comput. 14 (in-press) 11. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol 4. (1995), pp. 1942–1948 12. C.A.C. Coello, G.T. Pulido, M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Tran. Evol. Comput. 8, 256–279 (2004) 13. K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable test problems for evolutionary multiobjective optimization, in EMO, AIKP, ed. by A. Abraham, L. Jain, R. Goldberg (Springer, Berlin Heidelberg 2005), pp. 105–145 14. E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8, 173–195 (2000) 15. E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Evol. Comput. 3, 257–271 (1999)

A New Encoded Scheme GA for Solving Portfolio Optimization Problems in the Big Data Environment Karoon Suksonghong and Kittipong Boonlong

Abstract Working with a big data environment, it is inevitably dealing with the large search space which requires lots of computation resources. This paper proposes a new solution encoding method that helps to enhance the genetic algorithm (GA) performances. The proposed mixed integer-real value chromosome is specifically designed for solving large-scale optimization problems whose optimal solution consists of only a few selected variables. In the experiment setting, a bi-criterion portfolio optimization problem is transformed and solved within the single-objective optimization framework. Besides, the proposed encoding method also allows GA to handle the practical cardinality constraint in an efficient manner. Further, our new encoding scheme does not require any additional development on the evolutionary operator. Decision-maker is free to adopt any standard exiting crossover and mutation operator that is already well established. The simulation results reveal that the proposed helps improving GA performance in both exploitation and exploration tasks. Keywords Chromosome encoding · Cardinality constraint · Portfolio optimization · Integer value coding · Genetic algorithm

1 Introduction In the field of finance, portfolio optimization is considered one of the most recognized multiple criteria decision making (MCDM) problem which aims to solve for the optimal results in the complex scenarios including various practical constraints, completing and conflicting objectives and criteria. According to portfolio selection theory [1], an investor makes decisions on capital allocation to available investment assets in order to simultaneously maximize expected return and minimized K. Suksonghong (B) Faculty of Management and Tourism, Burapha University, Mueang Chonburi 20130, Thailand e-mail: [email protected] K. Boonlong (B) Faculty of Engineering, Burapha University, Mueang Chonburi 20130, Thailand e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_50

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risk, measured by the variance of returns, with respect to several constraints. From MCDM perspective, the optimal portfolio for an investor can be obtained by firstly optimizing this bi-criterion optimization problem subject to a set of constraints, and then selecting a single optimal solution, based upon the other higher level of information, from the obtained set of non-dominated solutions. Alternatively, this problem can be transformed into a single-objective counterpart by optimizing the “Sharpe ratio” [2] which is a measure of expected return per unit of risk. This approach allows investors to make the optimal choice without considering other higher levels of information, such as risk preference and risk tolerance, by selecting the best scalar vector among all possible solutions. Theoretically, increasing number of invested assets within a portfolio help reducing portfolio risk since a firm’s specific risk will be diversified away. In practice, however, the majority of investors tend to lessen the number of invested assets within their portfolio in order to balance between diversification benefit and monitoring and transaction costs [3]. This practical aspect introduces the so-called “cardinality constraint” to the portfolio optimization problem. Solving the cardinality constraint portfolio optimization problem, hereafter “CCPOP”, in the big data environment raises challenges to evolutionary-based algorithms, especially in the computation resources utilization perspective [4]. Considering a situation of optimizing CCPOP with thousands of available investment choices and only a few assets will be selected into portfolio, computation resources will be utilized unnecessarily to explore large search space due to a large number of possible solutions. This paper proposes a new solution encoding method for the chromosome representation process of the genetic algorithm (GA). The proposed scheme helps to enhance GA performance since it helps reducing search space as well as handling cardinality constraints at the same time. In our approach, a solution vector is encoded with the combination between integer and real number which helps reducing search space better than other approaches [5, 6]. In addition, our mixed integer-real chromosome tends to balance well between exploiting and exploring tasks of GA. Further, several modified processes during the chromosome representation stage allow user to employ the standard crossover and mutation methods rather than requiring newly developed operators for the new encoding scheme. This paper is organized as follows. The proposed encoding method together with several prerequisite solution representation processes is discussed in Sect. 2. Section 3 explains the formulation of a portfolio optimization problem with cardinality constraint. The simulation results are presented in Sect. 4, while our conclusions are stated in Sect. 5.

2 The Proposed Encoding Method Solving a portfolio optimization problem with cardinality constraint enforces GA to perform in the unnecessary large search space because the optimal solution contains only small selected variables. Using the conventional chromosome encoding method, lots of computation resources are needed for performing search, repairing solutions

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to handle constraints, and evolving solutions to obtain a better solution. Within the big data context, therefore, it discourages GA to achieve the optima since not only solution convergence is slow, but also poorly balance between exploiting and exploring tasks. The main goal of the proposed encoding method is to manage search space and handle constraint, instantaneously. In our method, the mixed integer-real number is used for encoding to the fixedsize chromosome that allows limiting the number of selected investment assets. As a result, search space becomes smaller which allows GA to perform better. For a numerical example, considering a situation where an investor is constructing the optimal portfolio among assets listed in the US NASDAQ index consisting of 2196 investment choices and limiting her investment allocation to 5 assets, number of possible solution for the conventional chromosome encoding method is 10002196 or 106588 solutions, i.e., each chromosome is encoded with a real number with 3 decimal digits. Meanwhile, with cardinality constraint of 5 assets, our proposed method initializes a solution chromosome with 10 species whereby the first 5 species are encoded with integer number identifying asset index to be invested, and the latter 5 species are encoded with a real number representing investment proportion. As a result, searching space is reduced to 2196!/(2196 − 10)!] × 100010 or 5.081031 . Figure 1 illustrates mixed-integer-real encoding chromosome with 10 species for handling 5-asset cardinality constraint. In addition to lessen search space, exploitation and exploration capability of GA is enhanced by incorporating a few prerequisite processes before performing evolutionary operators. First, to preserve asset index that is selected repeatedly by the majority of solutions, the integer value species and its corresponding real value species will be rearranged by placing the repeatedly chosen asset index at the very first place of chromosome. Then, the rest of species containing dissimilar asset index, together with their corresponding investment proportion, will be randomly placed into solution chromosome. The first process ensures that a good asset index will not be evolved which eventually promotes the speed of solution convergence. Meanwhile, the second process stimulates GA to explore new areas of search space. In fact, our proposed encoding scheme has restrictions in the sense that the integer number cannot be repeated within a single solution chromosome. If there is a happened case, the embedded repairing mechanism will be activated by replacing the repeated asset index by a newly random asset index together with its corresponding real

Fig. 1 Mixed integer and real encoding

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number value. Subsequently, the standard GA operators, namely, fitness evaluation, crossover, and mutation, can be performed. Regarding evolutionary operators, we employ the standard methods widely used in the literature with slightly modifications. For the crossover process, a solution chromosome is divided into two parts, including, integer and real value species which will be evolved separately. The one-point crossover is employed on the integer value part representing the asset index, whereas, the simulated binary crossover (SBX) [7] is applied to the real value part. Figure 2 demonstrates an example of prerequisite processes explained earlier with the case of mixed-integer and real encoding with cardinality constraint of five invested assets. It can be observed that the crossover process is also graphically explained in Fig. 2 where the one-point crossover is performed at species 4 of the integer value onwards and SBX is applied to the entire real value species. For the mutation method, the integer and real value species are also partitioned for performing mutation separately. In this paper, standard technique as the simulatedbinary crossover and variable-wise polynomial mutation are adopted. Figure 3 demonstrates mutation procedures for similar case explained above. Additionally, the situation where the embedded repairing mechanism will be activated is incorporated. It is clear from Fig. 3 that integer value species show the identical asset index after the mutation process adopted, i.e., asset index 1395 is selected twice. As explained earlier, the embedded repairing mechanism replaces this species by the newly random asset index, i.e., asset index 1486, then integer and real value species can be combined in order to subsequently performing fitness evaluation.

3 Problem Formulation Considering the portfolio selection process where N investment assets are available and decision-maker determines the proportion of investment that will be allocated to any available investment assets. Based upon our previous work [8], the standard portfolio optimization problem can be formulated by assigning x, representing a portfolio solution, as a N × 1 vector of investment allocation ratio to N available assets. Let vector R with size N × 1 represents the expected returns of each of N investment choices. Matrix  is a N × N representing the variance-covariance matrix. According to Markowitz’s mean-variance portfolio theory, investors prefer a portfolio that offers a high level of expected returns and exhibits a low level of risk. Suppose that the expected return and variance of a portfolio are denoted by Rp (x) and V p (x), respectively. Two objectives of the portfolio optimization problem can be expressed as follows: Maximize R p (x) = xT R =

N  i=1

x i Ri

(1)

A New Encoded Scheme GA for Solving Portfolio …

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1913

1395

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Fig. 2 Prerequisite processes of chromosome representation and example of crossover

Minimize V p (x) = xT x =

N 

xi x j σi, j

(2)

i=1

where xT is the transpose of vector x and xi is the proportion of investment allocated to security i. Ri is the expected return of asset i and σi, j is the covariance between asset i and j. The cardinality constraint portfolio optimization problem (CCPOP) can

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Normalize 0.160

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Summation is equal to one

1913

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Combine 1983 0.161

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Fig. 3 Mutation operation on the mixed integer-real encoded chromosome

be transformed into a single objective model as follows: Prob.1 Subject to

  min F(x) = −Sharpe Ratio p (x) x

N 

xi = 1

i=1

xi ≥ 0 N  qi = K max

i=1

where Sharpe Ratio p (x) is the maximum criterion that represents the expected return per a unit of risk, i.e., R p (x)/V p (x). The first constraint is applied to make sure that all investment is fully allocated to available assets. The second constraint implies that the short selling is not allowed. For the third constraint, K max is the maximum number of assets can be held in portfolio. qi ≥ 0 for xi ≥ 0 and qi = 0 for xi ≥ 0. Thus, by optimizing F(x) in Prob. 1, the optimal solutions of CCPOP can be obtained within a single of algorithm run.

4 Simulation Results The test problems are quoted from http://w3.uniroma1.it/Tardella/datasets.html which is publicly available. These data sets contain the historical weekly data of stocks listed in the EuroStoxx50 (Europe), FTSE100 (UK), MIBTEL (Italy), S&P500 (USA), and NASDAQ (USA) capital market indices during March 2003 and March

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2007. The data set of the US S&P500 index and NASDAQ index is used for testing our proposed method. There are 476 and 2196 available investment assets listed in S&P500 and NASDAQ index, respectively. Cardinality constraint limiting the number of invested assets is set as 4, 5, 6, 8, and 10. For the algorithm setting aspect, population size is 100, while the number of elite individuals is 2. The fitness scaling [ref] is also used in this paper with the scaling factor of 2. Stochastic universal sampling selection (SUS) is used for the selection of parent individuals. Simulatedbinary crossover and Variable-wise polynomial mutation are used for real numbers in both real encoding and mixed integer and real encoding. One-point crossover and Bit-flipped mutation are employed on the integer value chromosome. All parameter settings of GA are summarized and reported in Table 1. As mentioned earlier, the main goal of the proposed encoding scheme is not only to lessen the use of unnecessary computation resources by reducing search space but also balancing between exploitation and exploration tasks of GA. Table 2 highlights the superiority in the first aspect by revealing that, regardless of type of time spent, the computation time of GA with the proposed encoding method is very much shorter than that with the standard encoding method. In fact, to conserve space, we report only computation time of GA with standard encoding for solving POP without cardinality constraint which supposed to be faster than that for solving CCPOP. Nevertheless, it is still much slower than the proposed encoded GA in solving CCPOP. Similarly, Table 1 Parameter settings of experimented GA Parameter

Setting and values

Test problems

US S&P500 index, and US NASDAQ index

Chromosome coding

Real-value chromosome with N decision variables for real encoding, mixed integer and real chromosome with K = 4, 5, 6, 8, 10

Population size

100

Number of elite individuals

2

Scaling factor

2.0

Selection method

Stochastic universal sampling selection

Crossover method – Crossover probability – Real encoding – Integer encoding

1.0 Simulated-binary crossover (ηc = 15) [7] One-point crossover

Mutation method – Mutation probability – Real encoding – Integer encoding

0.025 Variable-wise polynomial mutation (ηm = 20) [7] Bit-flipped mutation

Number of generations used for termination condition

1000

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Table 2 Algorithm run time for the US NASDAQ problem Encoding scheme Standard real-encoding Proposed mixed integer-real encoding

K =4

Algorithm time (s)

Objective calculation time (s)

Total time (s)

417.85

6035.38

6453.23

5.96

1.29

7.25

K =5

6.51

1.45

7.96

K =6

6.57

1.52

8.09

K =8

7.37

1.79

9.16

K = 10

8.06

2.06

10.12

Figs. 4 and 5 reveal that by lessen search space; solutions of the proposed encoded GA converged much faster than those of standard GA. Considering the optimal solution, finance theory suggests that the higher the number of assets held in the portfolio, the lower the risk of investment. In other words, a portfolio consisting of a large number of investment assets tends to have small risks, then exhibiting a high value of Sharpe ratio. Tables 3 and 4 report the objective value obtained from the standard GA and proposed encoded GA with different problem scales. As explained above, according to finance theory, the objective value (Sharpe ratio) of unconstrained POP should be greater than that of constraint one if solutions are obtained from a similar method. Our results reported in Table 3 reveal that objective value obtained from standard GA for solving unconstrained POP is lower than the objective value obtained from our proposed encoded GA, although it is employed for solving constrained POP. This superiority is more pronounced when dealing with

Fig. 4 Solution convergence for the US S&P 500 problem

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Fig. 5 Solution convergence for the US NASDAQ problem Table 3 Computation results of the US S&P problem Encoding scheme

Standard real-encoding Proposed mixed integer-real encoding

Objective value

Number of nonzero assets

Average

SD

Average

SD

24.8757

0.7365

473.70

4.57

K =4

26.9520

0.5653

4.00

0.00

K =5

30.4843

0.2196

5.00

0.00

K =6

32.2547

0.0946

6.00

0.00

K =8

35.2157

0.0935

8.00

0.00

K = 10

36.8051

0.6258

10.00

0.00

Table 4 Computation results of the US NASDAQ problem Encoding scheme

Standard real-encoding Proposed mixed integer-real encoding

Objective value

Number of nonzero assets

Average

SD

Average

SD

19.7013

1.7249

1338.13

99.91

K =4

37.3658

0.3015

4.00

0.00

K =5

43.6956

0.5735

5.00

0.00

K =6

47.9927

0.6883

6.00

0.00

K =8

52.1254

0.5891

8.00

0.00

K = 10

54.6933

0.9475

10.00

0.00

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a big data environment. Table 4 exhibits that our proposed encoded GA outperforms remarkably regardless of cardinality constraint values.

5 Conclusion In this paper, the mixed integer-real chromosome representation is proposed for GA encoding processes. The main objective of the proposed method is to lessen search space and to balance between exploitation and exploration task of GA. As a result, GA adopting our new encoding scheme is anticipated to exhibit superior performance in the aspect of computation resources spent as well as the fitness of the solution. Optimizing portfolio in the big data environment, our proposed encoded GA shows its capability through the faster computation run time and faster solution convergence compared to the GA with conventional encoding technique. In addition, the proposed encoded GA ensures its ability in balancing between exploiting and exploring tasks by reporting better fitness solutions compared to those of classical GA. Conflict of Interest The authors declare that they have no conflict of interest.

References 1. H. Markowitz, Portfolio selection. J. Finance 7, 77–91 (1952) 2. W.F. Sharpe, Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964) 3. K. Suksonghong, K. Boonlong, K.-L. Goh, Multi-objective genetic algorithms for solving portfolio optimization problems in the electricity market. Int. J. Elec. Power 58, 150–159 (2014) 4. K. Suksonghong, K. Boonlong, Particle swarm optimization with winning score assignment for multi-objective portfolio optimization, in Simulated evolution and learning: 11th international conference, SEAL 2017, Shenzhen, China, November 10–13, 2017, proceedings, ed. by Y. Shi, K.C. Tan, M. Zhang, K. Tang, X. Li, Q. Zhang, Y. Tan, M. Middendorf, Y. Jin (Springer International Publishing, Cham, 2017), pp. 1003–1015 5. K. Liagkouras, K. Metaxiotis, A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem. Ann. Oper. Res. 267, 281–319 (2018) 6. F. Streichert, H. Ulmer, A. Zell, Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem, in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753) (IEEE, 2004), pp. 932–939 7. K. Deb, Multi-objective optimization using evolutionary algorithms (John Wiley & Sons Inc., New York, 2001) 8. K. Suksonghong, K. Boonlong, Multi-objective cooperative coevolutionary algorithm with dynamic species-size strategy, in Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science, vol. 10784, eds by K. Sim, P. Kaufmann (Springer, Cham, 2018)

Multistage Search for Performance Enhancement of Ant Colony Optimization in Randomly Generated Road Profile Identification Using a Quarter Vehicle Vibration Responses Kittikon Chantarattanakamol and Kittipong Boonlong Abstract In general, roughness of road affects vehicle performance by contributing vibration to the vehicle. In road maintenance, road profile can be used to detect damaged region of the road. Therefore, it is important to identify the road profile. Actually, the road profile can be evaluated by vibration response of the vehicle. In fact, the detection of road profile can be formulated as an optimization problem. Ant colony optimization (ACO) is used in optimization of the formulated optimization problem. In order to enhance performance of ACO in the road profile identification, multistage search (MS) is proposed. In the MS, decision variables to be optimized are divided into a number of parts. Each part is evolved as ACO process separately from other parts of the variables. There four classes of randomly generated road are used in test cases of the investigation. From the simulation runs, the MS can enhance performance of ACO. Keywords Optimization · Ant colony optimization · Road profile identification

1 Introduction Vibration caused by road profile affects the performance of a vehicle. In fact, the road profile could be evaluated by vibration signal measured at significant positions on the vehicle [1, 2]. The road profile identification could be formulated as an optimization problem in which corresponding objective function is the numerical difference between vehicle vibration responses due to actual road profile and that due to predicted road profile. The optimization methods are then required. It is difficult to obtain functional derivative of the formulated objective. The derivative-free methods are suitable for optimization in the road profile identification such as [1, 3]. K. Chantarattanakamol · K. Boonlong (B) Faculty of Engineering, Burapha University, Mueang Chonburi 20130, Thailand e-mail: [email protected] K. Chantarattanakamol e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_51

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Ant colony optimization (ACO) [4] is a derivative-free method that mimics the behavior of ants in finding the shortest path from their nest to a location of food. ACO was originally proposed for combinatorial optimization such as [5–7]. Thereafter, the real-encoded ACO for continuous domain was proposed by Socha and Dorigo [8]. Consequently, many researches had been successfully applied real-coded ACO in optimization problems with continuous domain such as [9–11]. In the road profile identification, decision variables are represented as the height of road profile at sampled locations. There are a number of decision variables to be optimized. This paper proposes multistage search for ACO in road profile identification using vibration response of a quarter vehicle model. In multistage search, a full solution is divided into a number of solution parts. A part of a full solution is evolved by GA generation in each solution search stage. The multistage search is quite different from cooperative coevolution employed in genetic algorithm (CCGA) [12–15] in which a solution is also divided into a number parts, the so-called species. In CCGA, each species is evolved simultaneously with other species. In the contrast, each solution part in multistage search is evolved separately to other parts. In multistage search, the first part is optimized until its termination condition is satisfied, the second part is started to be optimized, and so on.

2 Road Profile Identification Using a Quarter Vehicle Vibration Responses A quarter vehicle model, two-degree of freedom system, is shown in Fig. 1. In the vehicle model, there are two independent coordinates of the system—vertical displacement of seat mass (zc ) and vertical displacement of sprung mass (zs ). The vehicle is moved directly with constant speed of 20 m/s and excited by the road profile of which length is 100 m. In the model, sprung mass (ms ), tire mass (mt ), equivalent spring constant of suspension (k s ), equivalent damping constant of suspension (cs ), equivalent spring constant of tire (k t ) are 250 kg, 53 kg, 10 kN/m, 200 kN/m, 1000 Ns/m, respectively. The vehicle model is moved onto the randomly generated road profile. The vibration sensor, which is assumed to be a acceleration sensor, is measured at the sprung mass so that the vibration signal is the acceleration of the sprung mass. The road profile identification is based on the fact that different road profile contributes dissimilar vibration response. By using the vibration signal, the road profile identification can be formulated as an optimization problem of which an objective function is numerical difference between vibration signals obtained from an actual road profile and that obtained from a predicted road profile. The decision variables to the problem represent sampled height of the predicted road profile.

Multistage Search for Performance Enhancement of Ant Colony …

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Vibration signal

zs

ms

20 m/s cs

ks

zt

mt

kt zr

x

Fig. 1 Road profile identification using a quarter vehicle vibration responses

3 Ant Colony Optimization with Multistage Search In ant colony optimization (ACO), there are two sets of solutions—population and archive—are used in which sizes of population and archive are firstly defined. Each variable of a solution in archive is assigned pheromone, an indicator of the preference of the variable in the solution. The pheromone is directly evaluated from objective function of solutions in population. In generation of solutions in population, each variable of the generated solution is created by using the variable from an individual selected from archive. The selection of the individual from archive is based on probabilities evaluated from the assigned pheromone. In order to enhance performance of ACO for the optimization in the road profile identification, multistage search (MS) is proposed. The MS is applied into ACO by dividing decision variables into a number of parts each of which is evolved as ACO process. The evolution of each part in MS is quite different from that in cooperative coevolutionary (CC) concept. Each part in MS is evolved until it is finished once the termination condition is satisfied. Objective function of an individual of any part is calculated by using only the corresponding decision variables and optimized decision variables from the previous parts. For instance, Fig. 2 shows an example of road profile identification using a quarter vehicle vibration responses of eight sampled height road profile. The road profile identification can be then formulated as an optimization problem with eight decision variables. Figure 3 shows the comparison of optimization without and with multistage

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zs

ms

zr zr8

To be optimized

ks

zr7

cs

zr6

zt

mt

zr1

zr2

zr3

zr4

1

2

3

4

zr5

kt zr (a) a Quarter vehicle vibration model

5

7

6

i

8

(b) Eight sampled height road profile

Fig. 2 Road profile identification using a quarter vehicle vibration responses of eight sampled height road profile zr

zr zr8

z*r1, z*r2

zr

Optimization

Op

z zr1 zr2 zr3 r4

zr8

z*r1, z*r2,z*r3,z*r4,z*r5,z*r6,z*r7,z*r8

zr5

1 2

zr6

zr1

z zr2 zr3 r4

3 4

3

4

zr6

z zr1 zr2 zr3 r4

6

6

8

i

zr8

z*r1, z*r2,z*r3,z*r4,z*r5,z*r6 Optimization

5

7

7

8

(a) Optimization without multi stage search

1 2

3 4

zr5

5

zr6

6

7

8

i

zr

z zr1 zr2 zr3 r4

1 2

5

zr7

Optimization

zr

n

tio

iza

tim

zr7

zr5

zr8

z*r1, z*r2,z*r3,z*r4

zr7

zr5

3 4

5

zr6 z zr1 zr2 zr3 r4

6

7

zr8

zr7

i 1 2

z*r1, z*r2,z*r3,z*r4,z*r5,z*r6,z*r7,z*r8 Optimization

zr7

8

i

1 2

3 4

zr5

5

zr6

6

7

8

i

(b) Optimization with multi stage search

Fig. 3 Comparison of optimization without and with multistage search

search for the example in Fig. 2. Without MS, all decision variables are optimized simultaneously as shown in Fig. 3a. On the other hand, in MS, a solution is divided into four parts each of which represents two decision variables as illustrated in Fig. 3b. Staring with part 1, zr1 and zr2 are optimized so that z*r1 and z*r2 are optimized values of height of road profile at sampled time i = 1 and 2. Part 2 is consequently implemented to obtain z*r3 and z*r2 , the optimized values at i = 3 and 4, and so on. The detail description of MS in ACO is shown in Fig. 4. Figure 4 displays ACO with MS in an optimization problem with eight decision variables. Similar to Fig. 3, decision variables, x 1 , x 2 , …, x 8 to be optimized are

Multistage Search for Performance Enhancement of Ant Colony …

0.2084 0.9376 0.4464 0.5855 0.7629 0.4652 0.6138 0.3525 0.1473 0.8443

0.3330 0.8306 0.4606 0.8438 0.7337 0.0440 0.6145 0.1744 0.7651 0.8627

Initial Population 0.0310 0.8596 0.7109 0.4897 0.8324 0.8409 0.6895 0.2928 0.6223 0.7755 0.4459 0.9547 0.3012 0.6695 0.8291 0.1851 0.1763 0.7013 0.0072 0.8348

0.5080 0.9561 0.3985 0.7323 0.4778 0.2467 0.0276 0.8699 0.3287 0.1044

0.5354 0.0119 0.8245 0.3888 0.7495 0.7663 0.2382 0.5900 0.7271 0.8192

565

0.2501 0.9544 0.0211 0.3969 0.5902 0.9254 0.6834 0.4551 0.2100 0.4966

0.4645 0.8075 0.1344 0.1266 0.6287 0.1390 0.8621 0.0857 0.4989 0.3032

Divided into 4 parts

Part 1

0.2084 0.9376 0.4464 0.5855 0.7629 0.4652 0.6138 0.3525 0.1473 0.8443

0.3330 0.8306 0.4606 0.8438 0.7337 0.0440 0.6145 0.1744 0.7651 0.8627

Part 2

0.5080 0.9561 0.3985 0.7323 0.4778 0.2467 0.0276 0.8699 0.3287 0.1044

0.0310 0.7109 0.8324 0.6895 0.6223 0.4459 0.3012 0.8291 0.1763 0.0072

Part 3

0.8596 0.4897 0.8409 0.2928 0.7755 0.9547 0.6695 0.1851 0.7013 0.8348

Part 4

0.5354 0.0119 0.8245 0.3888 0.7495 0.7663 0.2382 0.5900 0.7271 0.8192

0.2501 0.9544 0.0211 0.3969 0.5902 0.9254 0.6834 0.4551 0.2100 0.4966

0.4645 0.8075 0.1344 0.1266 0.6287 0.1390 0.8621 0.0857 0.4989 0.3032

ACO Process

ACO Process

ACO Process

ACO Process

Part 1 finished

Part 2 finished

Part 3 finished

Part 4 finished

Best

0.2711 0.4459 0.6287 0.7165 0.1015 0.4687 0.1833 0.1507 0.2312 0.2079

0.0626 0.7681 0.0665 0.9320 0.8793 0.0025 0.4194 0.7613 0.6938 0.7487

Best

0.7031 0.7112 0.6142 0.3740 0.9101 0.5281 0.9440 0.2676 0.3343 0.3014

0.3947 0.8563 0.5045 0.7470 0.0985 0.4238 0.2166 0.1372 0.3031 0.3313

Best

0.4732 0.6001 0.0356 0.0974 0.1968 0.8755 0.6161 0.6043 0.5566 0.5245

Best

0.0464 0.0512 0.9115 0.9841 0.6925 0.9112 0.2248 0.7677 0.8683 0.8961

0.8837 0.7418 0.7110 0.7689 0.4778 0.1377 0.3083 0.6561 0.6621 0.4082

0.9319 0.6283 0.1513 0.0169 0.2848 0.1131 0.8768 0.7107 0.7086 0.6158

Combine

0.2711

0.0626

0.7031

0.3947

0.4732

0.0464

0.8837

0.9319

Output of ACO with Multistage Search Fig. 4 ACO with multistage search in an optimization problem with eight decision variables

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divided into four parts each of which represents two decision variables. In Fig. 4, after initial population is randomly generated, the population is divided into four parts. Part 1 having x 1 and x 2 is performed ACO process until this part is finished, the optimized x 1 and x 2 , which are 0.2711 and 0.0626 from the best solution as shown in the figure, are subsequently obtained. The next part 2 is then implemented, and the optimized x 1 and x 2 are also used in objective calculation of individuals in this part. After part 2 is finished, after that part 3 is executed until part 4 is completely performed. The output of ACO with multistage search is the full solution obtained from the combination of optimized corresponding variables from all parts as shown in the figure. The MS is particularly proposed to reduce solution search space. As shown in the figure, if normal ACO is used in the problem with eight decision variables in which each variable is encoded with four decimal digits number between 0.0000 and 1.0000, the number of possible solution is approximately equal to 10,0008 = 1032 . With the proposed MS, the number of possible solutions is exponentially reduced to be only about 5 × 10,0002 = 5 × 108 .

4 Test Problems The road profile identification using vibration response of the quarter vehicle in Fig. 1 is used as test problems. There are four test cases due to different classes A, B, C, and D of random road profile [16]. Class A of the road profile has the lowest roughness, while class D of the road profile has the largest roughness. The random road profiles of all road classes are shown in Fig. 5. Its length is 100 m and is randomly generated

Fig. 5 Four classes of randomly generated road profile

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at sampled distance (d) of 0.2 m so that 500 decision variables to be optimized. In the numerical calculation of objective function, sampling time is equal to d/v = 0.01 s. As previously described, the objective function is numerical difference between vibration signal, acceleration of the sprung mass (Fig. 1), of actual road profile and that of a predicted road profile.

5 Simulation Results Parameter settings of ant colony optimization (ACO) for simulation runs are displayed in Table 1. The number of all decision variables to be optimized is 500. The numbers of the variables in each solution search stage (NVE) used in ACO with multistage search (MS) are 250, 100, 50, 25, 10, and 2. Population and archive have equal sizes of 100. Number of generations in each solution search stage is equal to 20 × number of decision variables so that 106 generated solutions in each run. Objective values of optimized solutions are displayed in Table 2. From Table 2, objective values are tenderly worse from class A to D due to the increase of road roughness. The objective values of optimized solutions obtained Table 1 ACO parameter settings for simulation runs Parameters

Settings and values

Chromosome coding

Real-value chromosome with 500 decision variables

Number of decision variables in each solution search stage (NVE)

Normal ACO: 500 ACO with MS: 250, 100, 50, 25, 10, 2

Population and archive sizes

100 for both sizes

Number of generations in each solution search stage

20 × number of decision variables

Number of repeated runs

10

Table 2 Objective values of optimized solutions NVE

Class A

Class B

Class C

Class D

Avg

SD

Avg

SD

Avg

SD

Avg

Avg

500 (normal ACO)

856.55

22.62

869.99

22.40

970.80

21.13

1534.30

48.47

250

804.14

24.06

794.23

33.26

882.87

35.68

1428.94

24.46

100

672.33

15.49

689.19

19.98

770.80

26.57

1250.20

28.63

50

570.66

20.09

600.60

28.66

647.93

23.95

1043.03

43.73

25

443.31

21.31

477.87

7.84

500.17

23.69

820.96

48.09

10

267.98

3.79

268.78

9.82

299.08

22.70

503.00

39.76

2

33.18

3.87

36.39

3.23

33.75

4.15

63.49

8.32

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Fig. 6 Comparison of actual road profile and road profiles from optimized solutions from normal ACO and ACO with MS with NVE = 2 of road profile class A

from ACO with the MS are better than those obtained from normal ACO for all classes of road profile. With the use of MS, as previously described, the MS could reduce the number of possible solutions. In fact, the number of possible solutions is exponentially reduced with the decrease of NVE so that the ACO with the smallest NVE contributes the best solutions for each class of randomly generated roads as displayed in the table. Figure 6 shows the comparison of actual road profile and road profiles from ones of optimized solutions from normal ACO and ACO with MS with NVE = 2 of the selected class of road profile, class A. From the figure, the road profile obtained from the ACO with MS is obviously better than that obtained from the normal ACO and is quite close to the actual road profile. Figure 7 shows the comparison of vibration signal, which is the acceleration of sprung mass used in objective function evaluation, obtained from road profile in Fig. 6. In Fig. 7, the vibration signal due to the road profile obtained from the ACO with MS is much closer to vibration response due to actual road profile than that the vibration signal from the road profile by the normal ACO.

6 Conclusions To enhance performance of ant colony optimization (ACO) in the identification of randomly generated road profile using vibration responses of a quarter vehicle, the multistage search (MS) had been proposed. There were four classes—A, B, C, and D—of the randomly generated roads to be considered. The identification of

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Fig. 7 Comparison of vibration signal obtained from road profile in Fig. 6

road profile was formulated as a optimization problem of which the corresponding objective is the numerical difference between vibration responses of a measured points from predicted road profile and that from actual road profile. The simulation results revealed that the MS can enhance performance of ACO in the road profile identification regardless of classes of road profiles. In addition, the MS with the smaller number of decision variables in each solution search stage (NVE) contributed better optimized solutions than the MS with the larger NVE since the number of possible solutions from the MS with smaller NVE is less than the MS with larger NVE.

References 1. Z. Zhang, C. Sun, R. Bridgelall, M. Sun, Road profile reconstruction using connected vehicle responses and wavelet analysis. J. Terramechanics. 80, 21–30 (2018) 2. Y. Qin, Z. Wang, C. Xiang, E. Hashemi, A. Khajepour, Y. Huang, Speed independent road classification strategy based on vehicle response: theory and experimental validation. Mech. Syst. Signal. Pr. 117, 653–666 (2019) 3. B. Zhao, T. Nagayama, K. Xue, Road profile estimation, and its numerical and experimental validation, by smartphone measurement of the dynamic responses of an ordinary vehicle. J. Sound Vib. 457, 92–117 (2019) 4. M. Dorigo, T. Stützle, Ant Colony Optimization (MIT Press, Cambridge, MA, 2004) 5. J.E. Bell, P.R. McMullen, Ant colony optimization techniques for the vehicle routing problem. Adv. Eng. Inform. 18(1), 41–48 (2004) 6. N.C. Demirel, M.D. Toksarı, Optimization of the quadratic assignment problem using an ant colony algorithm. Appl. Math. Comput. 183(1), 427–435 (2006)

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7. Y. Hani, L. Amodeo, F. Yalaoui, H. Chen, Ant colony optimization for solving an industrial layout problem. Eur. J. Oper. Res. 83(2), 633–642 (2007) 8. K. Socha, M. Dorigo, Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008) 9. A. ElSaid, F.E. Jamiy, J. Higgins, B. Wild, T. Desell, Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration. Appl. Soft Comput. 73, 969–991 (2018) 10. M.G.H. Omran, S. Al-Sharhan, Improved continuous ant colony optimization algorithms for real-world engineering optimization problems. Eng. Appl. Artif. Intel. 85, 818–829 (2019) 11. C.C. Chen, L.P. Shen, Improve the accuracy of recurrent fuzzy system design using an efficient continuous ant colony optimization. Int. J. Fuzzy Syst. 20(3), 817–834 (2018) 12. M.A. Potter, K.A. De Jong, A cooperative coevolutionary approach to function optimization. Lect. Notes Comput. Sci. 866, 249–257 (1994) 13. K. Boonlong, Vibration-based damage detection in beams by cooperative coevolutionary genetic algorithm. Adv. Mech. Eng. 6, 624949, 13 (2014) 14. J.C.M. Diniz, F. Da Ros, E.P. da Silva, R.T. Jones, D. Zibar, Optimization of DP-M-QAM transmitter using cooperative coevolutionary genetic algorithm. J. Lightwave Technol. 36(12), 2450–2462 (2018) 15. A. Pahlavanhoseini, M.S. Sepasian, Scenario-based planning of fast charging stations considering network reconfiguration using cooperative coevolutionary approach. J. Energy Storage. 23, 544–557 (2019) 16. M. Agostinacchio, D. Ciampa, S. Olita, The vibrations induced by surface irregularities in road pavements—a Matlab approach. Eur. Transp. Res. Rev. 6(3), 267–275 (2014)

Classification and Visualization of Poverty Status: Analyzing the Need for Poverty Assistance Using SVM Maricel P. Naviamos and Jasmin D. Niguidula

Abstract A household can fall into poverty for many reasons. This research study focuses on determining significant attributes that can determine poor household units by means of different non-income indicators such as household assets, housing conditions, available facilities like electricity, water supply, and sanitation. The study also includes the magnitude of poverty assistance received by the identified poor household units. The researchers used supervised learning such as Support Vector Machine (SVM) to classify households into two classes: the poor and non-poor based on training data. The training data consists of measurable quantitative attributes and properly correlated using Pearson Coefficient Correlation. To test the accuracy of the algorithm in evaluating the model, an 80% training and 20% testing data is set. And Table 2, it shows the accuracy that resulted to 71.93% which means that the model used is approximately 80% accurate. Keywords Poverty visualization · Poverty classification · Social protection program and services · Poverty assistance · Support vector machine

1 Introduction The Philippines is a nation arranged in Southeast Asia contained 7000 islands. Poverty has turned out to be the most significant difficulties confronting the country and its nationals. Filipinos are experiencing serious difficulties getting by in such troublesome conditions, and increasingly more are falling into extreme poverty. The country is wealthy in natural assets and biodiversity because of its closeness to the equator; notwithstanding, it is inclined to seismic tremors and tempests, making it the third most calamity inclined country in the world. M. P. Naviamos (B) · J. D. Niguidula Technological Institute of the Philippines, Manila, Philippines e-mail: [email protected] J. D. Niguidula e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_52

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The Rural Poverty Portal (RPP) reports that half of the poor in the Philippines live in the country’s rural areas. The poorest of the poor are the indigenous, landless workers, fishermen, little agriculturists or farmers, mountain society, and women. Deforestation, exhausted fisheries, and unproductive farmland are serious issues for these groups of people. Absence of education and the absence of instructive open doors are additionally basic issues as well. Poverty statistics are the most fundamental bits of data for surveying the poverty status of a nation and for arranging an anti-poverty strategy [1]. The National Household Targeting System for Poverty Reduction (NHTS-PR) additionally called Listahanan of the Department of Social Welfare and Development (DSWD) is a framework for distinguishing poor households. The framework ensures the generation and foundation of a financial database of poor household units. This framework makes open to National Government Agencies (NGAs) and other Social Protection accomplices a database of poor household units as a reason in perceiving potential beneficiaries of the Social Protection Programs and Services (SPPS) [2]. The framework comprises a lot of uniform and target criteria to recognize poor individuals. A target model to choose those who need assistance the most was seen by the DSWD as a fundamental tool to improve the delivery of SPPS. The structure consolidated three successive advances, for example, geographic focusing, household unit appraisal, and approval to convey best outcomes. The execution was done in stages over a cycle of a three-year period time [3]. In this research, data mining has been utilized to analyze large data sets and transforming it into several sources to detect patterns. The study has a few interesting data that can be utilized to prepare and test the model for accuracy. The data is tested using a Support Vector Machine (SVM) algorithm, this can distinguish critical instances that decide the limits of the algorithm. In this connection, the study expects to utilize the SVM algorithm in structuring a model that can decide significant attributes in distinguishing poor and non-poor household units in selected five provinces in the Philippines.

2 Methodology The sample raw data collected and utilized in this research depends on the field interviews conducted from both rural and urban areas of the five selected provinces for a three-period time starting from the year 2014 to 2016 using a Household Assessment Form (HAF). The HAF is divided into three sections: the household identification, socio-economic information, and household roster; where household identification is consists of household address (municipality, barangay, district/province), length of stay, number of nuclear family in household and number of bedrooms/sleeping rooms while socio-economic includes the information of the household regarding the type of construction materials used in their roofs, outer walls, type of building where the household resides, tenure status of the property occupied, toilet, water, and electrical facility, assets (radio, tv, washing machine, microwave, refrigerator,

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video player, air conditioning, living room, dining set, vehicle, telephone/mobile, and computer), kind of inability (hearing, visual, speech, orthopedic, various incapacities, mental and different sorts of handicap) if applicable, displacement experienced and SPPS received. lastly, the household roster information which is used only for name matching and verification of poor household unit, ensuring that there are no duplicates of names or any kind and to guarantee that only identified poor household units will receive any type of SPPS from government and non-government agencies.

2.1 Data Pre-processing The raw data might be consisting of uproarious information, unimportant traits, and additionally missing data. Thus, data should be pre-prepared before applying any kind of data mining calculation which is done through different advances [4]. In the research study, the data cleaning process is done which includes identifying and reexamining errors in the data, filling in missing data, and removing duplicates. The next step is to make sure that the data set of poor and non-poor households will have the same number of populations, a random selection of the household sample is done in all five selected provinces to prevent any bias results. There are exactly a total of 221,324 identified poor households and 80% of it was used as a training set and 20% is used as a testing set. In the preparation stage, the calculation moves toward the estimations of both indicator characteristics and the objective quality for all cases of the preparation set, and it uses that data to build a classification model. This model speaks a classification of knowledge which is fundamental, a connection between indicator trait values and classes that license the forecast of the class of a model given its indicator characteristic qualities. In the testing stage, essentially after a prediction is made the algorithm allowed us to see the real class of the characterized group model. One of the critical goals of a characterization algorithm is to upgrade the predictive accuracy acquired by the grouping model when arranging models in the test set is concealed during training.

2.2 Feature Selection Various unimportant traits may be accessible in data to be mined. In this way, it should be removed. Likewise, many mining algorithms do not perform well with an enormous number of features or characteristics. Subsequently, the researchers applied a feature determination strategy before any kind of mining algorithm is applied. The crucial reason why feature determination is utilized in this research is to keep away from overfitting and improve model execution and to give snappier and more financially savvy models [4]. The Pearson Correlation Coefficient is a test that measures the quantifiable relationship, or relationship, between two consistent characteristics. It is known as the

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best system of evaluating the connection between the traits of interest since it relies upon the procedure for covariance. It gives data about the degree of affiliation, or connection, similarly as the course of the relationship. It utilizes the accompanying as referenced to distinguish the significance or impact of each score [5]. Great: If the worth is close ±1, by then it said to be an ideal relationship: as one variable expands, the other variable will in general additionally increment (if positive) or diminishing (if negative). High degree: If the coefficient esteem lies between ±0.50 and ±1, at that point it is said to be a solid relationship. Moderate degree: If the worth lies between ±0.30 and ±0.49, at that point it is said to be a medium connection. Low degree: When the worth lies beneath +0.29, at that point it is said to be a little connection. No relationship: When the worth is zero.

2.3 Classification In data mining algorithm it can pursue three distinctive learning approaches and the researchers used a supervised learning approach in the research study since all labels are known. The characterization undertaking can be seen as a directed method where every event has a place with a class, which is shown by the estimation of the objective trait or basically the class property. The objective characteristic can take on all-out qualities, and each is relating to a class. Each model involves two segments, an indicator property estimations, and objective quality values. The indicator credits should be significant for foreseeing the class of an event. In the classification task the course of action of models being mined is confined into two on a very basic level inconsequential and exhaustive sets called the training set and the test set in the preparation stage.

2.4 Support Vector Machine The purpose behind the use of supervised learning, such as SVM is to classify objects into at least two classes dependent on training data. The training data comprised of measurable attributes that include the household assets, the received government, and non-government social protection program and the physical type of house materials used. For each training data, the predetermined class will determine whether a household is a poor or non-poor individual. The researchers utilized the SVM algorithm on account of its capacity to arrange complex issues and locate the ideal non-direct choice limit. To solve the objective of the research the steps used are the following:

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(1) set initial values of the model parameter, (2) Set the value of the objective function, (3) Compute the constraint equation for each row of the training data, and lastly, (4) use Python programming to view the results [6]. Once the training data is calibrated and trained, proper evaluation is done to check whether the training is successful or not. The following procedure is done to evaluate the SVM model: (1) computing the margin score for each training data, (2) classifying the margin score for each training data, (3) creating a confusion table, and lastly, (4) compute the percentage of correctly predicted or the error based on the prediction [6].

3 Results and Discussion 3.1 Data Table 1 shows the five selected provinces that includes province A to province E. These provinces were properly assessed by a group of field assessment officers to identify the magnitude of poor household units in each province and making sure only qualified household units that belong to the poorest families can receive SPPS or poverty assistance from any government and non-government agencies. To properly compute for the percentage of poor households in each province the formula used is found below: The Computational Formula: Magnitude of Poor HH (% ) = Identified Poor HH /Assessed HH As a result, Table 1 shows that Province C and D get the highest magnitude of identified poor households. And although, Province C in Fig. 1 shows as the second most populated province in the region with 173,288 households compared to Province E which has 59,039 households, the said province still results in a higher percentage of poor household units with 35.2%. Figure 1 also shows that areas like Province C Table 1 The number of assessed households units Province

Assessed households

Identified non-poor households

Identified poor households

Magnitude of poor households (%)

Province A

51,010

36,959

14,051

27.5

Province B

91,707

52,525

39,182

42.7

Province C

173,288

117,925

55,363

31.9

Province D

209,518

117,567

91,951

43.9

Province E

59,039

38,262

20,777

35.2

584,562

363,238

221,324

37.9

Total

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Fig. 1 Identified poor and non-poor households

and D with an obvious high percentage of identified poor households should receive more poverty assistance to defeat the circumstance and advance the improvement of poor zones around the nation [7]. In the Household Assessment Form (HAF) of the National Household Targeting System for Poverty Reduction (NHTS-PR) the poverty status of a household can be identified based on the type of house the household resides, these include the following: (1) the type of house the household reside, (2) the number of available sleeping rooms, (3) the roof materials and the (4) the type of materials used for outer walls. Figure 2 shows that Province D showed the highest magnitude of household that uses strong materials like tile, concrete, brick, stone, wood, and plywood as a material for outer walls. Province D also had the highest magnitude of a roof

Fig. 2 Type of house a household resides

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Fig. 3 Type of available household facilities

material that uses light materials like cogon, nipa, and anahaw. And a house type duplex consisting of zero to nine sleeping rooms. Thus, the figure also shows that the Province A had the lowest magnitude of households that used a strong type or light type materials for roof and outer walls. And somehow reside in a cave, boat, under the bridge, etc. There are only three types of facilities being assessed by the NHTS-PR these are the following: (1) Toilet, (2) Electricity, and (3) Water Supply. Figure 3 shows that Province D had the highest magnitude of households with no electricity and whose main water source uses either shared, faucet, and community water systems. It also shows that the province of Palawan had the highest result of the household with a closed pit toilet. A closed pit toilet has a stage with a gap in it and a lid to spread the opening when it is not being used. The platform can be made of wood, cement, or logs covered with earth. Concrete stage keeps water out and last in numerous years [8]. The NHTS-PR is also assessing the household-owned items such as radio, television, video, stereo, refrigerator, clothes washer, cooling, phone, microwave, sala set, eating area, vehicle, or jeep, and motorcycle. And among all items, within the five provinces, phone gets the highest magnitude of owned items of a household. Probably because the phone nowadays is used not only for communication but also in many forms such as video, camera, entertainment, news updates, etc. Items such as stereo, refrigerator, clothes washer, cooling, microwave, sala, eating area, and vehicle or jeep are items that show a lower value of owned items in a household. The number of identified poor household units from Figs. 1, 2, 3, and 4 can be utilized as a reference why the province of Palawan among all other provinces has the highest magnitude of received SPPS of any government and nongovernment agencies which is clearly shown in Fig. 5. The following are the lists of all Social Protection Program and Services: (1) Pantawid Pamilyang Pilipino

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Fig. 4 Type of items does the household-owned

Fig. 5 Social protection program and services received

Program (4Ps), (2) KC-NCDDP, (3) Sustainable Livelihood Program (SLP), (4) Social Pension Program, (5) Supplementary Feeding Program (SFP), (6) Educational Assistance under the Assistance to Individuals in Crisis Situations (AICS) and Non-Government Agencies initiated programs: (1) Scholarship (CHED), (2) Skills Training (TESDA), (3) Universal Health Care Program (DOH), (4) WASH Program (DOH), (5) Nutrition Programs—National Nutrition Council (NNC), (6) Rural Electrification—DOE/NEA, (7) Resettlement/Socialized Housing -NHA, (8) Microcredit/Microfinance—DTI/Government Financial Institutions, (9) Assistance to Pantawid Farmers Beneficiaries, and lastly, (10) Other SWD programs (for Local Government Units, Non-Government Organizations, and Civil Society Organizations). After identifying all necessary attributes on both the Identification Section, Socioeconomic Section, and Roster Section of the HAF, a random selection is done and

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balancing the number of poor and non-poor households to prevent any bias results. A total of 221,324 poor and 221,324 non-poor households are used to properly train and test the model. Then, split the total number of poor and non-poor households into 80% training sets and 20% testing sets. As a result, a total of 354,118 households are used as training sets and 88,530 testing sets. Python is used to plot the data into two target classes-the poor and non-poor households. The application of SVM with linear kernels is applied in a linearly separable data to properly see the difference between the two classes’ position in relation to each other.

3.2 Training Performance Once the model is built, the next important task is to make sure that the model is evaluated, this will delineate how good the predictions are, Tables 2 and 3 show the results of the precision, recall, F1-score, accuracy, and as well as the confusion matrix of the model. The following formula is used to check the performance metric: Precision = TP/(TP + FP) Recall = TP/(TP + FN) F1 - Score = 2 ∗ (Recall ∗ Precision)/(Recall + Precision) Accuracy = (TP + TN)/(TP + FP + FN + TN) In Table 2, precision means the proportion of accurately anticipated positive perceptions to the all-out anticipated positive perceptions. This implies among all Table 2 Precision, recall, and F1-score for poor and non-poor using SVM Classification algorithm

SVM linear Kernel

Target class Poor

Non-poor

Precision

Recall

F-score

Precision

Recall

F-score

Accuracy

79.47%

59.30%

67.92%

67.43%

84.61%

75.05%

71.93%

Table 3 Confusion Matrix for Poor and Non-Poor Households

Predicted class Class = yes (poor)

Class = no (non-poor)

Class = yes (poor)

26,311

18,051

Class = no (non-poor)

6794

37,374

Actual class

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households that are labeled as poor households how many were identified as poor households. High precision identifies with the low false-positive rates. As a result, there is 79.47% precision which means that the model used is good enough to identify poor household units. The recall is the extent of effectively anticipated positive perceptions of all perceptions in the actual class. This means that of all the households, how many were properly identified as poor. As a result, there is 59.30% which is still above 50%. The F1-score is weighted normal of precision and recall. Consequently, if the amount of false positives and false negatives are through and through various the F1-score is a lot of dependable than the accuracy and for this situation, the score is 67.92%. Lastly, accuracy is the most characteristic performance measure and it is only the extent of accurately anticipated perception to the total perception. The result of the accuracy can be used as a great measure of the performance of the model. And in Table 2, it shows the accuracy that resulted to 71.93% which means that the model used is approximately 80% accurate. In Table 3, the confusion matrix is helpful to check whether there is misclassification. As a result, there are 26,311 accurately anticipated positive qualities which infer that the estimation of the actual class demonstrates this is a poor household unit and the predictive class is likewise something very similar. Thus, there are 37,374 accurately anticipated negative qualities which infer that the estimation of the actual class is non-poor household unit and predictive class tells something very similar. The false positives and false negatives, these qualities happen when the actual class contradicts with the predictive class.

4 Conclusion A household can fall into poverty for many reasons. The current findings in this research study suggest that a household unit can be measured based on the type of house, facilities that include water supply, electricity, and toilet, and as well as the types of items owned. The result can also be used to further understand why there is a great need for poverty assistance in areas with extremely low-income. Helping poor households is enough reason to expand the funding of the government and nongovernment agency programs and providing the basic needs of poor households can also help the entire country by advancing economic recovery and employment goals. This can also reduce food insecurity, hunger, and poor health. Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interest. Ethical Approval This chapter does not contain any studies with human participants or animals performed by any of the authors. Informed Consent Informed consent was obtained from all individual participants included in the study.

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References 1. T. Fujii, Dynamic poverty decomposition analysis: an application to the Philippines. World Dev. 69–84 (2017) 2. Listahanan/National Household Targeting System for Poverty Reduction (NHTS-PR), 12 Feb 2018 [Online]. Available https://www.dap.edu.ph/coe-psp/innov_initiatives/listahanan-nat ional-household-targeting-system-for-poverty-reduction-nhts-pr/. Accessed 26 Aug 2019 3. L. Fernandez, Design and Implementation Features of the National Household Targeting System in the Philippines (2012) 4. J.A. Sunita Beniwal, Classification and feature selection techniques in data mining. Int. J. Eng. Res. Technol. 1(6) (2012) 5. X.L.L.W. Yashuang Mu, A Pearson’s correlation coefficient based decision tree and its parallel implementation. Inf. Sci. (2017) 6. K. Teknomo, Support Vector Machine Tutorial (2018) 7. Y.Z.Y.L. Yuanzhi Guo, Targeted poverty alleviation and its practices in rural China: a case study of Fuping county, Hebei Province. J. Rural Stud. (2019) 8. J. Conant, Sanitation and Cleanliness for a Healthy Environment

Comparative Analysis of Prediction Algorithms for Heart Diseases Ishita Karun

Abstract Cardiovascular diseases (CVDs) are the leading source of demises universally: More individuals perish yearly from heart disease than due to any other reason. An estimated 17.9 million humans died from CVDs in 2016, constituting 31% of all global deaths. [1] Such high rates of death due to heart diseases have to cease. This idea can be accelerated by the prediction of risk of CVDs. If a person can be medicated much earlier, before they have any symptoms that can be far more beneficial in averting sickness. The paper strives to communicate this issue of heart diseases employing various prediction models and optimizing them for better outcomes. The accuracy of each algorithm guides to a relative enquiry of these prediction models, forming a solid base for further research, finer prognosis and detection of diabetes. Keywords Machine learning · Heart disease prediction · Classification · Disease prediction · Receiver operating characteristics curve · Comparative analysis · Predictive modeling

1 Introduction Dysfunctions of the heart and blood vessels are known as cardiovascular diseases. These constitute coronary heart disease (CHD), rheumatic heart disease, cerebrovascular disease and other conditions. Eighty percentile of CVD deaths are attributable to heart attacks and strokes. [1] Heart attacks and strokes are predominantly generated by a clot that forbids blood from streaming to the heart or brain. Typically, the conventional rationale behind this is the accumulation of fatty dump in the interior linings of the blood vessels that provides for the heart and brain. Blood clots and bleeding from a blood vessel in the brain can also trigger a stroke. Use of tobacco, physical inactivity, unhealthy diet and obesity, excessive alcohol consumption, high blood pressure, diabetes and abnormally high concentration of fats in the blood are few explanations for the occurrence of CVDs. While an upsurge has been observed I. Karun (B) Department of Mathematics, Christ (Deemed to be University), Bengaluru, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_53

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in the rate of heart diseases around the globe due to unhealthy approach toward life, machine learning can be attested as an extremely beneficent tool in prediction of occurrence of heart diseases. This will enable an individual to live a malady free life. Thus, in this research paper, attempts have been made to use various prediction algorithms to shrink the risk of heart diseases around the world after selecting the most important and relevant information. The paper is well structured into distinct segments. Section 2 elucidates the literature review carried out over the past research. Section 3 deals with the methodology used to choose the best model for speculating heart diseases using various prediction algorithms. Section 4 explains the execution of these algorithms in Python and Sect. 5 has the effects derived. Finally, in Sect. 6, the work is encapsulated.

2 Literature Review In [2], the aim of the research was to examine the connection between blood pressure (Joint National Committee) and the cholesterol categories (National Program for Cholesterol Education) with coronary heart disease (CHD) threat, integrate them into coronary prediction algorithms and compare the discriminatory characteristics of this strategy with other non-categorical prediction features. A straightforward coronary malady forecast calculation was created utilizing categorical factors, which permits doctors to foresee multivariate CHD chance in patients without unmistakable CHD. The Framingham Heart Study in [3] generated predictive features for sex-specific coronary heart disease (CHD) to assess the chance of developing CHD event in a middle-class white populace. Concerns existed with respect to whether these capacities could be generalized to other populations as well. The Framingham features worked well in populations after careful assessment, taking into consideration distinct prevalence of risk variables and fundamental rates of development of CHD. Palaniappan and Awang [4] emphasize on advanced data mining techniques for effective decision making. The study has established a prototype Intelligent Heart Disease Prediction System (IHDPS) using data mining approaches, namely Naive Bayes, neural network and decision trees. Every one method empowers critical information and has its one of a kind quality in realizing the goals of the characterized mining objectives. Using medical profiles such as blood pressure, sex, age and blood sugar IHDPS can anticipate the probability of people acquiring a heart ailment. In this paper [5], C-reactive protein is an inflammatory marker that is thought to have importance in coronary event forecast. The inference drawn from the study is that C-reactive protein is a comparatively mild anticipator of coronary heart disease and proposals with respect to it utilize in foreseeing the likelihood of coronary heart illness may ought to be reviewed. In [6], a modern approach based on coactive neuro-fuzzy inference system (CANFIS) was displayed for forecast of heart illness. The suggested CANFIS model mixed the adaptive capacities of the neural network and the qualitative view of the fuzzy logic that is then incorporated with the genetic algorithm to determine the disease’s existence. The model’s performance was assessed in regards

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with training performance and categorization accuracy, and the findings revealed that the suggested CANFIS model has an excellent ability to predict heart disease. To compare efficiency of diverse lipid measures for CHD forecast utilizing bias and calibration characteristics and renaming of peril categories and to evaluate incremental usefulness of apolipoproteins over conventional lipids for CHD forecast, a study has been conducted by Ingelsson et al. [7]. Here, in [8], conventional CHD hazard forecast systems require upgradation as the larger part of the CHD occasions happens within the “low” and “intermediate” risk bunches. On an ultrasound check, CIMT and existence of plaque are correlated with CHD and thus may possibly assist enhance the forecast of CHD danger. Adding plaque and CIMT to traditional dangerous factors improves CHD risk prediction in the Atherosclerosis Risk in Communities Study. The destinations of this enquiry in [9] were to create a coronary heart disease (CHD) chance representation amidst the Korean Heart Study (KHS) population and note the similarity or dissimilarity between it with the Framingham CHD hazard result. This research offers the primary proof that the peril function of Framingham over evaluates the risk of CHD in the Korean population where the rate of CHD is small. The Korean CHD chance demonstrate is a well-intended alteration and can be utilized to anticipate one’s hazard of CHD and give a valuable direct to recognize the bunches at tall chance for CHD amidst Koreans. Pattekari and Parveen [10] talk about developing an intelligent system using data mining modeling technique called Naive Bayes to bring to light and draw out the camouflaged proficiency related with diseases (cancer, diabetes and heart attack) from an ancient heart disease database. It can respond to complex disease diagnosis inquiries and hence help healthcare professionals design smart clinical choices that conventional frameworks are unable to create. It also helps to decrease the cost of therapy by offering efficient treatments. In paper [11], the authors seek to calculate the 10-year possibility in diabetic individuals of coronary heart disease (CHD) and how well fundamental and novel risk variables foretell the threat of CHD followed by the conclusion that while all diabetic grown-ups are at tall hazard for CHD, their disparity in CHD chance can be anticipated modestly well by essential hazard variables. No single novel threat marker significantly increases the entire risk assessment of CHD, but a battery of novel markers does. Also, their model could furnish rough calculation of the danger of CHD in individuals with type 2 diabetes for the primary prevention of this disorder. Wilson and Peter approach a new angle in [12] by claiming that the previous studies and surveys concentrated on conventional risk variables for cardiovascular disease and that scarce data on the role of overweight and obesity was accessible. In [13], the focus is on the HIV-infected sufferers with fat redistribution and the prediction of CHD in them. The authors talk about although how the risk of CHD is increased in fat redistribution patients infected with HIV, the pattern of fat allocation and sex is potentially significant elements in deciding the prospect in the inhabitants. Here, in [14], the goal was to develop and remotely approve a CHD genomic risk scores, in regards to lifetime CHD chance and respective to conventional clinical hazard scores. This was done because hereditary qualities play a critical part in coronary heart illness but the clinical usefulness of genomic risk scores relative to clinical hazard scores, like the Framingham Hazard Score (FRS), is speculative. The authors

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in [15] perform contrast analysis on plasmaIL-6, fibrin D-dimer and coagulation factors VII and XIIa. Activated swelling and blood changing to a solid or semi-solid state are thought to boost and are linked to the danger of coronary thrombosis. They deduced that while D-dimer harmonized with IL-6 and C-reactive protein, only Ddimer exhibited a major autonomous coronary risk association. In the research paper [16], the authors introduce a method that uses significant risk factors to predict heart disease. The most effective data mining instruments, genetic algorithms and neural networks are included in this method. The framework was executed in MATLAB and forecasts the threat of heart infection with an exactness of 89%.

3 Methodology Prediction of heart diseases has been carried out using the methodology given in Fig. 1. To begin with, the dataset is thoroughly understood. There are multiple terminologies from the medical domain that need a clear understanding to start with. Once the terminologies are understood, proper analysis of data is carried out by finding the mean, median, standard deviation, count, percentile scores, minimum and maximum to facilitate description of data. Heat maps are a creative way of using colors to visualize the correlation between data. Histogram analysis also helps in segregating the data into required buckets. This is followed by data preprocessing which is a useful step in removing unwanted values or redundant data. This stage also handles missing data and outliers. The data is then brought to a common scale to further reduce data redundancy. Once the data is ready, the attributes contributing the most toward the Fig. 1 Methodology used for prediction of heart diseases

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prediction are chosen. Not every attribute has a significant contribution toward the prediction of heart diseases and hence lowers the accuracy, if not handled beforehand. There are multiple ways of carrying this out. With this, the data preprocessing stage comes to an end. The data is not ready to predict using different prediction algorithms. SVC, KNN and random forest classifiers are used here for the dataset. Precision, recall, accuracy and receiver operating characteristic plot are used to find the performance of each model. Finally, a comparative analysis is done to realize the finest model used to predict heart diseases.

4 Experimentation The heart disease UCI is a huge dataset with 76 attributes. Here, we are using only a subset of it containing 14 attributes. The subset dataset is thoroughly studied by finding out the mean, standard deviation, twenty-fifth and seventy-fifth percentile, minimum and maximum count for each and every attribute. These attributes are then categorized into different buckets using histogram analysis to find out the range for every attribute and also for outlier detection. Further, a heat map is used to analyze the correlation between each attribute, how they are impacted by each other and how they could impact the prediction in general. With a good understanding of the data, the dataset is checked for missing values. Since the dataset is devoid of missing values, data redundancy and outlier’s removal were targeted. Then, the dataset was split into training (80%) and testing (20%) sets for cross-validation. Cross-validation is used to model the figures and conduct testing on the test set figures to analyze how the outcomes of prediction on the training set generalize for the testing set as well. SVC, KNN and random forest classifiers are chosen for modeling the data and prediction due to the ease with which they can be understood for further work and also for their robustness. Precision, recall and accuracy for each model are calculated. Received operating characteristic plot is generated for each model to plot the true positives against the false positives. Finally, a comparative analysis is made based on accuracy of the models to find the best-suited model for heart disease prediction.

5 Results and Discussions Here, Table 1 shows the values obtained for calculating the precision, recall, ROC_AUC (received operating characteristics area under the curve) and accuracy. Figures 2, 3 and 4 represent the received operating characteristic curve for every one model and also show the region under the curve. ROC_AUC is used for diagnostic test evaluation which basically measures how well a parameter can identify the difference between diseased and normal. Lastly, Fig. 5 is an accuracy plot for the three classification models that clearly shows that random forest classifier outperformed the rest.

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Table 1 Results of classification models used Model

Precision

Recall

ROC_AUC

Accuracy

SVC

0.796

0.771

0.867

0.769

KNN

0.638

1

0.904

0.796

Random forest classifier

0.809

0.844

0.899

0.829

Fig. 2 SVC receiver operating characteristic plot

Comparative Analysis of Prediction Algorithms for Heart Diseases

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Fig. 3 KNN receiver operating characteristic plot

6 Conclusion In this paper, the aim has been to predict the occurrence of heart disease using machine learning algorithms. Combating different heart diseases has become a major concern given the alarming number of people who are currently suffering from it. With improved prediction techniques, finding solutions to problems and predicting them early on has become an easier task. The work carried on demonstrates how random forest classifier could be a helpful model in predicting the chances of suffering from a heart disease. There are so many other models that could be used for future work to find out better and optimized solutions. The work is based on a subset of the entire database. Further work could be carried out on larger subsets to enhance the prediction results.

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Fig. 4 Random forest classifier receiver operating characteristic plot

Fig. 5 Accuracy plot of algorithms

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References 1. Cardiovascular Diseases (CVDs). World Health Organization, 17 May 2017, https://www.who. int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 2. P.W.F. Wilson et al., Prediction of coronary heart disease using risk factor categories.”. Circulation 97(18), 1837–1847 (1998) 3. R.B. D’Agostino et al., Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. Jama 286(2), 180–187 (2001) 4. S. Palaniappan, R. Awang, Intelligent heart disease prediction system using data mining techniques, in 2008 IEEE/ACS International Conference on Computer Systems and Applications (IEEE, 2008) 5. J. Danesh et al., C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease.”. New Engl. J. Med. 350(14), 1387–1397 (2004) 6. L. Parthiban, R. Subramanian, Intelligent heart disease prediction system using CANFIS and genetic algorithm. Int. J. Biol. Biomed. Med. Sci. 3(3) (2008) 7. E. Ingelsson et al., Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. Jama 298(7), 776–785 (2007) 8. V. Nambi et al., Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk in Communities) study. J. Am. Coll. Cardiol. 55(15), 1600–1607 (2010) 9. S.H. Jee et al., A coronary heart disease prediction model: the Korean heart study. BMJ Open 4(5), e005025 (2014) 10. S.A. Pattekari, A. Parveen, Prediction system for heart disease using Naïve Bayes. Int. J. Adv. Comput. Math. Sci. 3(3), 290–294 (2012) 11. A.R. Folsom et al., Prediction of coronary heart disease in middle-aged adults with diabetes. Diab. Care 26(10), 2777–2784 (2003) 12. P.W.F. Wilson et al., Prediction of first events of coronary heart disease and stroke with consideration of adiposity. Circulation 118(2), 124 (2008) 13. C. Hadigan et al., Prediction of coronary heart disease risk in HIV-infected patients with fat redistribution. Clin. Infect. Dis. 36(7), 909–916 (2003) 14. Gad Abraham et al., Genomic prediction of coronary heart disease. Eur. Heart J. 37(43), 3267–3278 (2016) 15. G.D.O. Lowe et al., Interleukin-6, fibrin D-dimer, and coagulation factors VII and XIIa in prediction of coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 24(8), 1529–1534 (2004) 16. S.U. Amin, K. Agarwal, R. Beg, Genetic neural network based data mining in prediction of heart disease using risk factors, in 2013 IEEE Conference on Information & Communication Technologies (IEEE, 2013)

Sarcasm Detection Approaches Survey Anirudh Kamath, Rahul Guhekar, Mihir Makwana, and Sudhir N. Dhage

Abstract Sarcasm is a special way of expressing opinion most commonly on social media websites like Twitter and product review platforms like Amazon, Flipkart, Snapdeal, etc., in which the actual meaning and the implied meanings differ. Generally, sarcasm is aimed at insulting someone or something in an indirect way or expressing irony. Detecting sarcasm is crucial for many natural language processing (NLP) applications like opinion analysis and sentiment analysis which in turn play an important role in analysing product reviews and comments. There are a lot of challenges proposed when it comes to detecting sarcasm and it is not as simple and straightforward as sentiment analysis. In this survey, we focus on the various approaches that have been used to detect sarcasm primarily on social media websites and categorise them according to the technique used for extracting features. We have also elucidated the challenges encountered while detecting sarcasm and have considered a particular use case for detecting sarcasm in Hindi-English mixed tweets. An approach has been suggested to detect the same. Keywords Natural language processing (NLP) · Long short-term memory network (LSTM) · Convolutional neural network (CNN) · Recurrent neural network (RNN) · Content and user embedding CNN (CUE-CNN) · Nonlinear subspace embedding (NLSE)

A. Kamath (B) · R. Guhekar · M. Makwana · S. N. Dhage Computer Engineering Department, Bharatiya Vidya Bhavan’s Sardar Patel Institute of Technology, Andheri (West), Mumbai 400058, India e-mail: [email protected] R. Guhekar e-mail: [email protected] M. Makwana e-mail: [email protected] S. N. Dhage e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 S. K. Bhatia et al. (eds.), Advances in Computer, Communication and Computational Sciences, Advances in Intelligent Systems and Computing 1158, https://doi.org/10.1007/978-981-15-4409-5_54

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1 Introduction Sarcasm is a form of expression in which the implied and actual meanings of the sentence differ. It is used popularly on social media websites like Twitter, Facebook, online discussion forums like Reddit and other discussion forums and product reviews for instance Amazon and Flipkart product reviews. This form of expression is chosen generally to emphasise certain aspects; for instance, sarcastic reviews might be made about a product which did not really live up to its hype or something which fell very short of user expectations. The question which needs to be answered is “Why is sarcasm detection gaining importance and turning a lot of heads?” The answer is—traditional sentiment analysis fails to perform accurately when it encounters sarcastic tweets. This leads to a lot of discrepancies because opinion mining which uses sentiment analysis leads to incorrect results. This is because the implied and actual meaning of the sentence differs. In order to deal with all these situations, sarcastic comments need to be detected and processed separately for opinion mining and sentiment analysis. Conversation with digital assistants becomes more engaging once they start understanding the irony and sarcasm in human speech. We will be focusing on sarcasm detection only from text which is more challenging as the non-verbal modes of communication like gestures and body language are not readily available to us. Therefore, our task is to try and extract