Proceedings of International Conference on Innovations in Information and Communication Technologies: ICI2CT 2020 (Algorithms for Intelligent Systems) 9811608725, 9789811608728

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

Lalit Garg Hemant Sharma S. B. Goyal Amarpreet Singh   Editors

Proceedings of International Conference on Innovations in Information and Communication Technologies ICI2CT 2020

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

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

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

Lalit Garg · Hemant Sharma · S. B. Goyal · Amarpreet Singh Editors

Proceedings of International Conference on Innovations in Information and Communication Technologies ICI2CT 2020

Editors Lalit Garg University of Malta Msida, Malta S. B. Goyal City University Petaling Jaya, Malaysia

Hemant Sharma Department of Energy Argonne National Laboratory Lemont, IL, USA Amarpreet Singh Amritsar College of Engineering and Technology Amritsar, India

ISSN 2524-7565 ISSN 2524-7573 (electronic) Algorithms for Intelligent Systems ISBN 978-981-16-0872-8 ISBN 978-981-16-0873-5 (eBook) https://doi.org/10.1007/978-981-16-0873-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Committee

Conference General Chair Deepak Garg, Professor and Head CSE, Bennett University, India

Conference Program Chair Lalit Garg, Professor, University of Malta, Malta, Europe

Technical Program Committee Sheroz Khan, Department of ECE, International Islamic University Malaysia S. B. Goyal, City University, Malaysia Mohammad Israr, Sur University College, Sur, Sultanate of Oman Osama Mohammed Elmardi Suleiman Khaya, Nile Valley University, Sudan Raad Farhood Chisab, Technical Institute—Kut in Middle Technical University Jeremiah Chukwuneke, Nnamdi Azikiwe University, Awka, Nigeria Kailash Kumar Maheshwari, Saudi Electronic University, Riyadh, Saudi Arabia Chidi E. Akunne, Nnamdi Azikiwe University, Awka, Anambra State António Espírito Santo, Universidade Da Beira Interior P. Ganesh Kumar, K. L. N. College of Engineering and Linyi Top Network Co. Ltd., China Amit Kumar Saraf, Jagannath University, Jaipur Anuj Kumar Gupta, Chandigarh Group of Colleges Renuka Mohanraj, Maharishi International University, Fairfield, IA, USA Arjan Singh, Punjabi University, Patiala, Punjab Balasubramani R., NMAM Institute of Technology, Nitte Balkrishan, YCoE, Punjabi University Guru Kashi Campus, Talwandi Sabo v

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Committee

Ch. Chengaiah, Sri Venkateswara University, Tirupati, Andhra Pradesh Chandresh Kumar Chhatlani, JRN Rajasthan Vidyapeeth Charu Saxena, Chandigarh University D. Sivabalaselvamani, Kongu Engineering College Daisy Joseph, Bhabha Atomic Research Centre, Trombay, Mumbai-400085 Dattatraya V. Kodavade, DKTE Society’s Textile and Engineering Institute, Ichalkaranji Dattatreya P. Mankame, SKSVACET, Lakshmeshwar D. N. Sujatha, VTU/BMS College of Engineering, Bangalore H. L. Sharma, Government College Jukhala, Bilaspur Kamlesh Lakhwani, Lovely Professional University Latika Kharb, JIMS, Delhi Mahesh Kumar Porwal, Sree Chaitanya College of Engineering, Hyderabad Manoj Challa, CMR Institute of Technology, Bangalore R. Sujatha, Vellore Institute of Technology Rajiv Pandey, Amity University, Lucknow Rashmi Soni, Dayananda Sagar Institutions, Bangalore S. N. Panda, Chitkara University Punjab Sanjay C. Patil, Thakur College of Engineering and Technology Sanjeev Kumar, NIMS University, Jaipur, Rajasthan Sankit Ramkrishna Kassa, SNDT Women’s University Santosh Kumar Singh, JECRC Foundation Jaipur, Rajasthan Technical University Sarvesh Tanwar, Amity University, Noida Shankar Chatterjee, Former Prof. and Head (CPME), NIRDPR, Hyderabad Shuchita Upadhyaya, Kurukshetra University Shyamal Dutta, (Formerly) University of Burdwan, West Bengal Soumendra Darbar, Jadavpur University Sunil Kumar Gupta, BCET Gurdaspur Vaibhav Bhatnagar, S. S. Jain Subodh P.G. Autonomous College Williamjeet Singh, Punjabi University, Patiala Yash Paul, Guru Jambheshwar University of Science and Technology, Hisar, Haryana Yogesh Kumar Sharma, Shri J. J. T. University, Churela, Jhunjhunu, Rajasthan M. Nirmala, Kumaraguru College of Technology, Coimbatore Charu Saxena, Chandigarh University Baljit Singh Khehra, BBSB Engineering College, Fatehgarh Sahib, Punjab, India Akash Saxena, CIITM, Jaipur Shirish Joshi, Symbiosis International Deemed University Arjan Singh, Punjabi University, Patiala Neeraj Mangla, M. M. D. U., Mullana Dinesh Kumar, Sri Sai College of Engineering and Technology, Badhani, Pathankot Monika Mathur, SKITM, Jaipur S. Mathivilasini, Ethiraj College For Women, Chennai Sandhya Save, UoM/Thakur College of Engineering and Technology Girish P., Toc H Institute of Science and Technology

Committee

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Arjun Choudhary, Sardar Patel University of Police, Security and Criminal Justice, Jodhpur Deepak Kumar Mohapatra, Trident Academy of Technology Harsh Khatter, ABES Engineering College, Ghaziabad Hemant Sahu, Geetanjali Institute of Technical Studies, Udaipur K. G. Revathi, DMI College of Engineering Lalji Prasad, Sage University/SIRT, Indore Mamoon Rashid, Lovely Professional University Naiyer Mumtaz, Jharkhand University of Technology/Cambridge Institute of Technology, Tatisilwai, Ranchi, Jharkhand, India Prateek Agrawal, LPU, Punjab Puja Das, Techno India University S. Raviraja, Royal Research Foundation Saravanan K., Anna University Regional Campus, Tirunelveli Shashikant Sharma, B. K. Birla Institute of Engineering and Technology, Pilani Subramanyam Kunisetti, R. V. R and J. C. College of Engineering, Andhra Pradesh, India Suresh M., Kongu Engineering College, Perundurai, Affiliated to Anna University, Chennai Vijay Kumar, Manav Rachna International Institute of Research and Studies, Faridabad, India VishuMadaan, School of Computer Science Engineering, Lovely Professional University, Punjab G. Jayalakshmi, V. R. Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India Atul Patel, CHARUSAT, Changa, Gujarat Koppula Vijay Kumar, CMR College of Engineering and Technology Satyadhyam Chickerur, KLE Technological University R. B. Jadeja, Marwari University, Rajkot G. V. Padma Raju, SRKR Engineering College Wilscy M., Saintgits College of Engineering B. G. Prasad, BMS College of Engineering M. V. Padmavathi, Bhilai Institute of Technology, Durg Amit Verma, Chandigarh Engineering College, Chandigarh Sunil Surve, FCRCE, Mumbai Sabu M. Thampi, IIITM, Trivandrum Ratan Rana, Poornima University, Jaipur Debika Bhattacharya, IEM, Lucknow V. K. Jain, Modi University, Rajasthan Shom Prasad Dass, NSIT, Orissa S. N. Londhe, VIIT, Pune B. Rajathilagam, Amrita University, Coimbatore Latika, Ansal University, Gurugram G. Vishnu Murthy, Anurag Group of Institutions D. Suresh, B. V. Raju Institute of Technology

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Committee

Avani R. Vasant, Babaria Institute of Technology M. G. Sumithra, Bannari Amman Institute of Technology M. Vijay Karthik, CMR Engineering College Raj Kumar Patra, CMR Technical Campus Yashodhara V. Haribhakta, College of Engineering Pune Roshani Raut, D. Y. Patil School of Engineering and Technology, Lohegaon, Pune Milind Shah, Fr. Conceicao Rodrigues Institute of Technology R. Y. Sable, G. H. Raisoni Institute of Engineering and Technology Vishnu Sharma, Galgotias College of Engineering and Technology, Uttar Pradesh K. Thammi Reddy, Gandhi Institute of Technology and Management (GITAM), Visakhapatnam, Andhra Pradesh M. Mary Shanthi Rani, Gandhigram Rural Institute (Deemed To Be University) R. Priya Vaijayanthi, GMR Institute of Technology K. Subba Rao, Godavari Institute of Engineering and Technology B. Sujatha, Godavari Institute of Engineering and Technology Santosh S. Saraf, Gogte Institute of Technology, Belagavi, Karnataka Pushpender Sarao, Hyderabad Institute of Technology and Management Navanath Saharia, Indian Institute of Information Technology Manipur G. L. Prajapati, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore S. J. Saritha, Jawaharlal Nehru Technological University, Anantapur A. Kalyana Saravanan, Kongu Engineering College Vivek Richhariya, Lakshmi Narain College of Technology, Bhopal M. A. Maluk Mohamed, M. A. M. College of Engineering and Technology R. P. Ram Kumar, Malla Reddy Engineering College (Autonomous) Tessy Mathew, Mar Baselios College of Engineering and Technology Anila M., MLR Institute of Technology Rohit Raina, Model Institute of Engineering and Technology S. N. Tirumala Rao, Narasaraopeta Engineering College V. Gomathi, National Engineering College, Kovilpatti Rajat Subhra Goswami, National Institute of Technology, Arunachal Pradesh S. Rao Chintalapudi, Pragati Engineering College D. G. Harkut, Prof. Ram Meghe College of Engineering and Management D. Bujji Babu, Qis College of Engineering and Technology G. Kishor Kumar, Rajeev Gandhi Memorial College of Engineering and Technology R. Balaji Ganesh, Ramco Institute of Technology B. Raveendra Babu, RVR and JC College of Engineering, Guntur, Andhra Pradesh Vijay Dhir, SBBS University G. Prabhakar, Scient Institute of Technology Shailendra Aswale, Shree Rayeshwar Institute of Engineering and Information Technology Kamal Mehta, Shri Shankaracharya College of Engineering, Bhilai S. Ravi Chandra, Shri Vishnu Engineering College For Women P. Kavitha Rani, Siddharth Institute of Engineering and Technology M. Naresh Babu, Sree Vidyanikethan Engineering College, Tirupathi

Committee

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S. Jyothi, Sri Padmavati Mahila Visvavidyalayam (Women’s University) R. Anuradha, Sri Ramakrishna Engineering College K. Shirin Bhanu, Sri Vasavi Engineering College V. Janardhan Babu, Sri Venkatesa Perumal College of Engineering and Technology, Puttur D. Nagaraju, Sri Venkateswara College of Engineering and Technology Deepthi Jordana, Srinivasa Ramanujan Institute of Technology D. Haritha, SRK Institute of Technology Jatindra Kumar Dash, SRM University, Amaravati, Andhra Pradesh A. Swarnalatha, St. Joseph’s College of Engineering A. Kiran Mayee, Talla Padmavathi College of Engineering Aditya Maheshwari, Techno India NJR Udaipur Shachi Natu, Thadomal Shahani Engineering College Sudipta Roy, Triguna Sen School of Technology, Assam University E. Sreenviasa Reddy, University College of Engineering and Technology, Acharya Nagarjuna University L. Sumalatha, University College of Engineering, Kakinada A. Rajanikanth, Vardhaman College of Engineering K. Giri Babu, Vasireddy Venkatadri Institute of Technology Manne Suneetha, Velagapudi Ramakrishna Siddhartha Engineering College D. Murali, Vemu Institute of Technology Shalu Chopra, VES Institute of Technology Ramani Bai V., Vidya Academy of Science and Technology D. Aruna Kumari, Vidya Jyothi Institute of Technology E. Lakshmi Lydia, Vignan’s Institute of Information Technology D. Srinivasarao, VNR Vignana Jyothi Institute of Engineering and Technology T. Samraj Lawrence, Francis Xavier Engineering College Patil V.P., Smt. Indira Gandhi College of Engineering Manjunath V. Joshi, Dhirubhai Ambani Institute of Information and Communication Technology Balamurgan, Galgotias University P. Kumar, Rajalakshmi Engineering College Mita Parikh, Sarvajanik College Engineering Vaibhav Gandhi, B. H. Gardi College of Engineering and Technology Krishna K. Warhade, MIT World Peace University Nishu Gupta, Vaagdevi College of Engineering (Autonomous), Warangal, India Premchand Bhagwan Ambhore, Government College of Engineering, Amravati, India Muhammad Naufal Bin Mansor, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia

Preface

The International Conference on Innovations in Information and Communication Technologies was organized with the objective of bringing together researchers, developers and practitioners from academia and industry working in Information and Communication Technologies. This conference consisted of keynote lectures, tutorials, workshops and oral presentations on all aspects of Information and Communication Technologies. It was organized specifically to help the industry to derive benefits from the advances of next-generation information and communication technology. Researchers invited to speak presented the latest developments and technical solutions in the areas of computing, advances in communication and networks, advanced algorithms, image and multimedia processing, databases and machine learning. This conference promotes fundamental and applied research which can help in enhancing the quality of life. This conference served as an ideal platform for people to share views and experiences in futuristic research techniques in various related areas. With regards Msida, Malta Lemont, USA Petaling Jaya, Malaysia Amristar, India

Lalit Garg Hemant Sharma S. B. Goyal Amarpreet Singh

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Contents

Machine Learning in Spark for Attack Traffic Classification in IoT Devices Using Protocol Usage Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaojian Wang, Sikha Bagui, and Subhash Bagui Mental Immersion in Virtual Reality Avatar (MIVRA)—Social Communication Rehabilitation Assistive Tool for Autism Children . . . . . Tamil Selvi, Sai Naveena Sri, Bhavani Devi, Manju Thomas, Rajkumar, and Sathiya Prakash Ramdoss

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13

Efficiency Estimation of an Integrated Dual-Output Z Source Converter Using Coupled Inductor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kumar Raja Mothukuri and Mukti Barai

21

Location-Based Sentiment Analysis of the Revocation of Article 370 Using Various Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . Abhineeth Mishra, Arti Arya, and H. R. Devanand

45

Enhanced Algorithm for Logical Topology-Based Fault Link Recovery in Crossbar Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Umarani and S. PavaiMadheswari

57

Automatic Extraction of Spatio-Temporal Gait Features for Age Group Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timilehin B. Aderinola, Tee Connie, Thian Song Ong, and Kah Ong Michael Goh

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Designing a Virtual Mother-In-Law (or) Designing a Virtual Kitchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Lakshmi Narasimhan

79

SDN-Enabled ABE-Based Secure Communication for Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Pavithra and D. Rekha

87

Dual Palm Print-Based Human Recognition Using Fusion . . . . . . . . . . . . . 101 Milind Rane and Umesh Bhadade xiii

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Contents

Star Sensor Algorithm Using the Overlapping Grid Method for Attitude Determination First . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Sanjana Rao, S. Sathyanarayanan, C. M. Tejaswini, and Benjamin Rohit CDID: Cherry Disease Identification Using Deep Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Alarsh Tiwari, Swapnil Panwala, Akshita Mehta, Naman Bansal, Mohit Agarwal, Rahul Mishra, and Suneet Gupta Evaluation of Source to Target and Target to Source Word Alignment for English to Hindi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Arun R. Babhulgaonkar and Shefali P. Sonavane Energy-Aware Disk Storage System for Cloud Data Centers . . . . . . . . . . . 145 Sumedha Arora and Anju Bala

About the Editors

Dr. Lalit Garg is Lecturer in Computer Information Systems at the University of Malta, Malta. He is also Honorary Lecturer at the University of Liverpool, UK. He has also worked as a researcher at the Nanyang Technological University, Singapore, and the University of Ulster, UK. He received his first degree in Electronics and Communication Engineering from the Barkatullah University, Bhopal, India, in 1999, and his postgraduate in Information Technology from the ABV-Indian Institute of Information Technology and Management (IIITM), Gwalior, India, in 2001. He received his Ph.D. degree from the University of Ulster, Coleraine, the UK, in 2010. His research interests are machine learning, data mining, stochastic modelling, operational research, and their applications, especially in the healthcare domain. He has published over 120 technical papers in refereed high-impact journals, conferences and books. Hemant Sharma did his B.Tech. from Punjab Engineering College and M.S. from Delft University of Technology, Delft, The Netherlands. He was the recipient of the prestigious Huygens Award and graduated cum laude. He graduated cum laude in his Ph.D. from Delft University of Technology in 2012. Since 2013 he has been working in Argonne National Laboratory on developing scientific software for x-ray science. He has more than 28 papers in refereed journals and has an H-index of 10. His expertise involves massively parallel computing, GPU computing, on-demand cloud computing and high-throughput image processing.

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About the Editors

Dr. S. B. Goyal has an extensive experience in Teaching and Education, Learning and Development, Administration and Research and Development offering over 16+ years of career success in building and enhancing quality of research and education in higher education and to be worthy of international accreditation. Now, he is Dean and Professor in City University, Malaysia, and has given his services in many renowned universities like JRE Institute, Singapore, Jaypee University, Galgotia and many more. He has completed many research projects and published more than 30 research papers in international conferences and journals. Dr. Amarpreet Singh is Head of Computer Science Department and Associate Professor in ACET, Amritsar, India. He is doctorate in the field of Computer Science and Engineering and holds different executive positions with progressively increasing levels of responsibility. He has substantial R&D management experience and taught academic and industrial courses to UG as well as PG students in the field of technology. He has administrative experience for NBA Accreditation process of AICTE, NAAC of UGC and Autonomous Status. He has published more than 60 papers in international conferences and journals.

Machine Learning in Spark for Attack Traffic Classification in IoT Devices Using Protocol Usage Statistics Xiaojian Wang, Sikha Bagui , and Subhash Bagui

1 Introduction Emerging applications and falling device costs have led to an enormous increase in the number of connected devices and hence to the growth of Internet of Things (IoT) [1]. IoT devices are projected to reach 75.44 billion worldwide by 2025 [2]. This rapid growth of IoT devices has also brought increased security vulnerabilities. IoT devices can and also do suffer from traditional cyberattacks like DoS attacks, manin-the-middle-attacks, and botnet malware attacks. For example, the Mirai software can scan IoT devices quickly and efficiently and infect devices with weak passwords. And the infected devices can also become botnet robots allowing hackers to control them. Since the Internet or wireless sensor networks are the backbone of IoT devices, this makes them extremely vulnerable, and hence, the greatest challenge is to be able to secure IoT devices in real time. So, the focus of intrusion detection systems (IDS) to protect IoT systems has to be in real time. In this study, we use the Kitsune dataset [3]. This original data stream is used to dynamically generate features and identify malicious traffic in real time. Hence, in this work, we extract real-time network traffic information to detect malicious traffic. Only protocol usage statistics, generated from pcap files from the original data streams, is used to detect malicious traffic in real-time using the Big Data framework. For intrusion detection, machine learning has been used heavily to exploit characteristics of different attacks and help classify different attacks [4, 5]. In this paper, we X. Wang · S. Bagui (B) Department of Computer Science, University of West Florida, Pensacola, FL, USA e-mail: [email protected] S. Bagui Department of Mathematics and Statistics, University of West Florida, Pensacola, FL, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Garg et al. (eds.), Proceedings of International Conference on Innovations in Information and Communication Technologies, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-0873-5_1

1

2

X. Wang et al.

use machine learning in the context of Big Data. With the predicted increase in IoT systems [1, 3], it makes it more logical to use the Big Data framework to analyze the enormous amounts of network traffic data that would be generated. In this work, we use Apache Spark, an open source cluster computing framework that takes advantage of distributed processing. Spark uses in-memory processing and hence is faster than Hadoop’s MapReduce framework, which requires read/write access during the intermediary steps of algorithm processing. We use three different machine learning classifiers in Spark: decision tree (DT), random forest (RF), and logistic regression (LR) to classify the attack traffic of different types of IoT devices from the Kitsune dataset [3]. Kitsune is a plug and play network intrusion detection system (NIDS) which can learn to detect attacks on the local network without supervision, and in an efficient online manner [3]. We compare the performance of these three classifiers in Spark in terms of accuracy, attack detection rate (ADR), false alarm rate (FAR), and runtime. The rest of the paper is organized as follows. Section 2 presents the related works; Sect. 3 describes the data and structure of the datasets; Sect. 4 presents the feature extraction methodology; Sect. 5 very briefly presents the three algorithms compared in this work; Sect. 6 presents the results and discussion, and Sect. 7 presents the conclusion and future work.

2 Related Works Several works have looked at building intrusion detection systems for IoT systems. Shahid et al. [6] designed and implemented an IDS for IoT systems, SVELTE. Their primary target was routing attacks such as spoofed or altered information, sinkhole, and selective-forwarding. Pongle and Chavan [7] proposed a method that uses the location information of nodes and neighbors to identify the wormhole attack and receive signal strength to identify attacker nodes. Other works focused on real-time information processing. Damopoulos et al. [8] proposed a real-time information capturing and integration architecture of the Internet of Manufacturing Things (IoMT). Gunupudi et al. [9] proposed a real-time method which was used to distinguish important network IDS alarms from frequently occurring false positives and low important events. To detect DoS attacks and attack protocols for 6LoWPAN and CoAP communication, [10] proposed an IDS framework for detecting and preventing attacks in the Internet-integrated environment in the CoAP communication environment. Zhang et al. [11] proposed a lightweight intrusion detection model based on node consumption analysis in 6LowPAN. Midi et al. [12] developed Kalis, an adaptive, comprehensive, knowledge-driven expert IDS, which could monitor various protocols without changing existing IoT software.

Machine Learning in Spark for Attack Traffic Classification …

3

Jun and Chi [13] proposed an architecture that used Bayesian event prediction modeling using historical event data generated by the IoT cloud to calculate the probability of future events. Machine learning techniques have also been proposed to improve detection performance by many [4, 5, 8, 13, 14]. Gunupudi et al. [9] designed a method to design fuzzy membership functions to solve dimensionality and anomaly mining, reducing computational complexity and improving the computational accuracy of the classifier algorithm. In this work, we use only protocol usage statistics for classification, not addressed by previous works.

3 Data Description 3.1 Datasets • • • • • • • • • •

Nine network capture datasets were used [3]: ARP MitM: ARP man-in-the-middle attack between a camera and DVR SSDP Flood: SSDP flooding attack against the DVR server OS Scan: NMAP OS scan of the subnet Active Wiretap: A bridged Raspberry Pi placed between all cameras and the DVR server SYN Flooding: A SYN DoS attack against a camera Fuzzing: A fuzzing attack against DVR’s Web server’s CGI Video Injection: A MitM video content injection attack into a camera’s live stream SSL Renegotiation: A DoS attack against an SSL enabled camera Mirai: The initial infection and propagation of the Mirai malware (on a different [IoT] network). Table 1 presents the data sizes of each of the attacks.

Table 1 Dataset sizes

Data

Size

SSDP flood

10.9G

OS scan

4.6G

Active wiretap

6.1G

ARP MitM

6.7G

SYN flooding

7.4G

Fuzzing

6.0G

Video injection

6.6G

SSL renegotiation

5.9G

Mirai

1.27G (764,136 packets)

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X. Wang et al.

3.2 Data Structures The following data structures were used: • pcap.pcapng: This is a raw pcap capture of the original N packets. The packets have been truncated to 200 bytes for privacy reasons. • dataset.csv: An N-by-M matrix of M-sized feature vectors, each describing the packet and the context of that packet’s channel. • _labels.csv: An N-by-1 vector of 0–1 values which indicates whether each packet in _pcap.pcapng (and _dataset.csv) is malicious (‘1’) or not (‘0’). _dataset.csv structure. When a packet arrives, a behavioral snapshot of the hosts and protocols which communicated the given packet is extracted. A total of 23 features are extracted from a single time window. The same set of features are extracted from five time windows: 100, 500 ms, 1.5, 10 s, and 1 min into the past, thus totaling 115 features. Hence, the snapshots consist of 115 traffic statistics capturing a small temporal window into: (1) the packet’s sender; and (2) the traffic between the packet’s sender and receiver [3]. The following statistics summarizes all of the traffic extracted [3]: • • • •

originating from packet’s source MAC and IP address (denoted SrcMAC-IP). originating from packet’s source IP (denoted SrcIP). sent between packet’s source and destination IPs (denoted channel). sent between packet’s source and destination TCP/UDP Socket (denoted Socket).

4 Feature Extraction The larger pcap files from the dataset were split into a number of smaller one second pcap files using Wireshark. Multiple protocols, including ARP, HTTP, UDP, TCP, were parsed, and different features were extracted from these pcap files, such as source port, destination port, protocol used, and protocol usage statistics. For the protocol usage statistics, statistical information of the different protocol usage, such as IP, TCP, ARP, UDP, was captured. Four statistical features were extracted, as given in Table 2: (i) the frequency of protocols used by different packets; (ii) the frequency of packet lengths in different ranges—the first element represents the number of packets with a length between 0 and 300, the second element represents the number of packets with a length between 301 and 600, and so on; (iii) the number of incoming and outgoing packets per unit of time; and (iv) the frequency of the protocols and data packets communicated per unit of time. These four features were used in classification.

Machine Learning in Spark for Attack Traffic Classification …

5

Table 2 Detail of statistical features Feature name

Explanation

Example of data

data_dict_value

The frequency of protocols used by different packets

=

pcap_len_dict_value

Frequency of packet lengths in different ranges

=

data_flow_dict

The number of incoming and outgoing packets per unit of time

=

proto_flow_dict

Frequency of protocols and data packets communicated per unit of time

=

5 Classifiers Used Three classifiers were used in this paper: decision trees, random forest, and logistic regression. Decision Trees are built using only attributes best able to differentiate the concepts to be learned. These attributes are determined by information gain or the gain ratio. The attribute with the highest information gain is at the root of the tree. The attribute with the next highest information gain will be at the next node level, and so on. Each non-leaf node represents a test on a feature attribute, and each branch represents the output of the feature attribute on a certain range. Each leaf node stores a category. Big Data decision trees are parallelized in different ways to enable large-scale learning. Data parallelism partitions the data, so that different tree nodes are built on different processors [15]. Random Forest is a class of ensemble methods designed for decision tree classifiers. RF combines predictions made by multiple decision trees, where each tree is generated based on the values of an independent set of random vectors. Spark parallelizes the random forest algorithm. Logistic Regression is appropriate when the outcome variable is binary, i.e., Y = 0 or 1. Instead of modeling the expected value of the response directly as a linear function of explanatory variables, a logistic transformation is used. Instead of modeling the response variable Y as a multiple regression function, logit (log of the odds) is modeled as a multiple linear regression function. This model classifies an object to population-1 if the estimated odds are greater than one or equivalently if the logit is greater than zero. Spark also parallelizes this algorithm.

6 Classifiers Used For the results, we look at measures that capture the number of malicious packets which were classified correctly and malicious packets that were accidentally missed with few and no false alarms. It is important that there be a minimal number of false

6

X. Wang et al.

alarms. So, in addition to classification accuracy, we look at the attack detection rate (ADR) and false alarm rate (FAR) for all nine attacks for all three classifiers. Classification accuracy is defined here as the true positive (TP) plus true negative (TN) divided by the total number of instances: Accuracy = (TP + TN)/Total no. of instances. Attack detection rate (ADR) is the true positive divided by the true positive plus false negative: ADR = (TP)/(TP + FN). False alarm rate (FAR) is the false positive (FP) plus false negative (FN) divided by the total number of instances: FAR = (FP + FN)/Total no. of instances. Table 3 presents the true positive (TP), true negative (TN), false positive (FP), false negative (FN), classification accuracy, ADR, FAR, F-1 score, and run time (in seconds) for each attach for each of the classifiers, DT, RF, and LR. Run time is the classification time for each packet. Table 4 presents the averages of accuracy, ADR, FAR, and runtime. From Table 4, we can see that DT had the highest average accuracy, highest ADR, and lowest FAR. RF performed the second best, but LR performed the best in terms of run time. Figure 1 presents the accuracy of the attacks on the different classifiers. Though active and ARP were both close to 90% classification accuracy on the average, ARP’s accuracy was a little higher, but overall active and ARP had the lowest average accuracy on all three classifiers. One reason for this could be that the characteristics of active traffic and ARP traffic may be very similar in terms of protocol usage statistics, making them difficult to classify using the four protocol usage statistics. More features might be needed to get a better classification accuracy for these two attacks. From Fig. 2, we can see that the ADR was also lower, on the average, for active and ARP. LR did not have a high ADR for three attacks, active, fuzzing, and SSDP. RF did not perform well with ARP in terms of ADR. From Fig. 3, we observe that, on the average, the FAR was the highest for active and ARP. Overall, LR did not perform well in terms of the FAR. LR had high FARs for all attacks but Mirai and Syn. Infact, the FARs for Mirai were zero percent for all three classifiers, and Syn was very close to zero percent for all three classifiers. This means that the four protocol usage statistical features were doing a very good job of not raising any false alarms for Mirai and Syn. There was a 100% or very close to 100% ADR for these two attacks. The ADR, which is also the true positive rate (TPR), was also compared with the false negative rate (FNR), as shown in Fig. 4. False negative rate is the false negative divided by the false positive plus true negative: FNR = FN/(FP + TN). The TPR is much higher than the FPR, and the FPRs are very low, which makes these four protocol usage statistical attributes a good selection for these classifiers. We also look at the TPR when the FPR (FP/(FP + TN)) is zero. This is shown in Fig. 5. Figure 6 presents the FNR when the FPR is zero, and Fig. 7 presents the TPR when the FPR is less than 0.1%. Figure 8 presents the time (in seconds) it took to classify each package. Since a Big Data classification framework was used, the classification time was pretty low. LR performed the best in terms of packet classification time, and DT had the highest classification time.

Machine Learning in Spark for Attack Traffic Classification …

7

Table 3 Accuracy, ADR, FAR, F-1 score, and run time (in seconds) for the nine attacks using the different classifiers Attack

Algo TP TN

Active

DT

14

390 10 36

89.78

28.00

10.22

37.84

0.0131

RF

39

363 37 19

89.33

78.00

12.23

61.90

0.0072

ARP

LR

2

DT

40

398

FP FN Accuracy ADR (%) FAR (%) F-1 score Run time (%) (%)

2 48

88.89

4.00

11.11

7.41

371 29 10

91.33

80.00

8.67

67.23

0.0054

400

0.0046

RF

2

89.33

4.00

10.67

7.69

LR

47

359 41 3

90.22

94.00

9.78

68.12

6.13E−05

Fuzzing DT

50

399

1 0

99.78

100.00

0.22

99.01

0.0062

RF

50

400

0 0

100.00

100.00

0.00

100.00

0.0057

LR

2

400

0 48

89.33

4.00

10.67

7.69

DT

49

400

0 1

99.78

98.00

0.22

98.99

0.0076

RF

50

400

0 0

100.00

100.00

0.00

100.00

0.0053

LR

50

400

0 0

100.00

100.00

0.00

100.00

7.34E−05

DT

50

400

0 0

100.00

100.00

0.00

100.00

0.0046

RF

50

400

0 0

100.00

100.00

0.00

100.00

0.0043

LR

50

360 40 0

91.11

100.00

8.89

71.43

6.21E−05

DT

50

398

2 0

99.56

100.00

0.44

98.04

0.0038

RF

50

336 64 0

85.78

100.00

14.22

60.98

0.0036

LR

9

400

0 41

90.89

18.00

9.11

30.51

6.72E−05

DT

48

400

0 2

99.56

96.00

0.44

97.96

0.0035

400

0 48

89.33

4.00

10.67

7.69

0.0031

91.56

100.00

8.44

72.46

100.00

100.00

0.00

100.00

Mirai

OS

SSDP

SSL

0 48

6.75E−05

8.26E−05

RF

2

LR

50

362 38 0

DT

50

400

0 0

RF

49

400

0 1

99.78

98.00

0.22

98.99

LR

50

400

0 0

100.00

100.00

0.00

100.00

INJECT DT

48

398

2 2

99.11

96.00

0.89

96.00

0.0036

RF

50

394

6 0

98.67

100.00

1.33

94.34

0.003

LR

50

384 16 0

96.44

100.00

3.56

86.21

6.21E−05

SYN

6.55E−05 0.0036 0.0031 6.18E−05

Table 4 Averages for the different classifiers Classifier

Accuracy (%)

ADR (%)

FAR (%)

Time (s)

DT

97.66

88.67

2.35

0.005711111

RF

94.69

76.00

5.48

0.004433333

LR

93.16

68.89

6.84

6.70454E−05

8

X. Wang et al.

Accuracy 1.1 1 0.9 0.8 0.7 DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR Acve

ARP

Fuzzing

Mirai

OS

SSDP

SSL

SYN

INJECT

Fig. 1 Accuracy of the different attacks on the different classifiers 120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

Aack Detecon Rate

DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR Acve

ARP

Fuzzing

Mirai

OS

SSDP

SSL

SYN

INJECT

ADR 28 78 4. 80 4. 94 10 10 4. 98 10 10 10 10 10 10 10 18 96 4. 10 10 98 10 96 10 10

Fig. 2 Attack detection rate of the different attacks on the different classifiers

False Alarm Rate 15.00% 10.00% 5.00% 0.00%

DTRFLRDTRFLRDTRFLRDTRFLRDTRFLRDTRFLRDTRFLRDTRFLRDTRFLR

Acve ARP Fuzzing Mirai OS SSDP SSL SYN INJECT FAR 101211 8. 10 9. 0. 0. 10 0. 0. 0. 0. 0. 8. 0. 14 9. 0. 10 8. 0. 0. 0. 0. 1. 3.

Fig. 3 False alarm rate of the different attacks on the different classifiers

TPR vs FPR

150.00% 100.00% 50.00% 0.00%

DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR DT RF LR Acve

ARP

Fuzzing Mirai TPR

OS

SSDP

SSL

SYN

INJECT

FPR

Fig. 4 True positive rate versus false positive rates for the different attacks on the different classifiers

ACTIVE

ARP

Fig. 8 Time (in seconds) to run the different classifiers

FUZZING MIRAI

OS

SSDP

0.015 0.01 0.005 0

DT me

SSL

RF me

SYN

Fig. 7 True positive rate when false positive rate is less than 0.1%

Time

LR me 4.00%

100.00%

100.00%

0.00% 0.00%

DT

2.00% 0.00%

RF

1.50%

RF

100.00%

SSL

96.00%

DT

0.50%

0.00% 0.00%

96.00%

SSL

0.00%

98.00%

DT

0.00%

0.00%

RF

0.00%

LR

100.00%

OS

100.00%

DT

9.50%

RF 4.00% 0.00%

82.00%

LR

96.00%

DT 0.00%

OS

0.00% 4.00% 0.00%

LR

100.00%

RF 0.00% 0.00%

RF

16.00% 18.00% 0.00%

MIRAI

100.00%

100.00%

0.00% 0.00%

DT

0.50%

100.00%

100.00%

MIRAI

10.00%

0.00%

0.00% 0.00%

LR

0.00%

100.00%

100.00%

0.00% 0.00%

DT

0.00%

LR

RF

0.00%

FUZZING

98.00%

2.00% 0.00%

0.00%

96.00%

DT

0.00%

100.00%

RF

100.00%

DT

0.25%

0.00% 0.00% LR

0.00% 4.00% 0.00%

94.00%

96.00% RF

10.25%

0.00% 0.00% 0.25%

RF

80.00%

78.00%

RF

7.25% 4.00% 0.00%

9.25% 4.00% 0.50%

2.50%28.00%

0.00%

0.00%

0.00%

4.00% 0.00%

0.00%

18.00% 0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

4.00% 0.00%

0.00%

4.00% 0.00%

100.00%

98.00%

100.00%

96.00%

100.00%

100.00%

100.00%

100.00%

98.00%

100.00%

Machine Learning in Spark for Attack Traffic Classification … 9

TPR WHEN FPR=0

SYN

LR

Fig. 5 True positive rate when false positive rate is zero

FNR WHEN FPR=0

SYN

RF LR

Fig. 6 False negative rate when false positive rate is zero

TPR WHEN FPR 0

(1)

where Lm(t), Lk (t) are magnetizing inductance and leakage inductance. The m . coupling coefficient K = L mL+L k As D2 is forward biased, VD2 (t) = 0

Fig. 2 Shoot-through state

Efficiency Estimation of an Integrated Dual-Output Z …

VD0 (t) = Vin (t) − VLm (t) − Vc0 (t) < 0

25

(2)

So, D0 is reverse biased. The inductor voltages are VLm (t) = K ∗ Vin (t) VLk (t) = (1 − K ) ∗ Vin (t)

(3)

The voltage across inductor windings is Vn2 (t) = Vin (t) Vn3 (t) = VC1 (t) − K ∗ Vin (t)

(4)

During charging, the winding (3) is inversely coupled with windings (1) and (2), thereby n 1 i m (t) = n 1 i n1 (t) + n 2 i n2 ( t) − n3 i n3 (t) i m (t) = i n1 (t) + i n2 ( t) − ni n3 (t) 2.

(5)

Active state

During the active state, the current through the inductors decreases linearly, and the voltage induced reverse biases both the diodes D1 and D2 . Energy stored in the inductor and the capacitor C 1 will be transferred to the DC load and AC load simultaneously. The voltage Vac will be negative or positive depending on the switches turned on. The input current will be same as the current that flows through the inductors and the capacitor C 1 (Fig. 3). Applying KVL, voltage equation becomes VLm (t) + VLk (t) + Vn2 (t) + Vn3 (t) = Vin (t) + Vc1 (t) − Vc0 (t)(n + 2)VLm (t) + VLk (t) = Vin (t) + Vc1 (t) − Vc0 (t) VLm (t) =

1 (2 + n) +

(1−k) k(2+n)

{Vin (t) + Vc1 (t) − Vc0 (t)}

(6)

The capacitor currents are i c1 (t) = −i n3 (t) i c0 (t) = i n1 (t) − V c0 /Rdc − i ac (t)

(7)

During discharging, the winding (3) are directly coupled with windings (1) and (2), thereby

26

K. R. Mothukuri and M. Barai

Fig. 3 Active state

n 1 i m (t) = n 1 i n1 (t) + n 2 i n2 ( t) + n 3 i n3 (t) i m (t) = i n1 (t) + i n2 ( t) + n i n3 (t) i m (t) = (n + 2) ∗ i n1 (t) 3.

(8)

Zero state

In this mode, the AC load is disconnected from the source voltage and the inductors and C 1 along with the supply voltage power the DC load. Both the diodes D1 and D2 are reverse biased in this mode. This is shown in Fig. 4. The MCL-ZSI uses the shoot-through state to increase the energy stored in the coupled inductors, and this is used to boost the DC link voltage which in turn is used by the inverter to produce an AC voltage just like a conventional inverter. Shoot-through state exists before the zero state, as in ST state Vin (t) = VLm (t) − Vin (t) + VLm (t) + VD2 (t) + Vc0 (t) = 0 VD2 (t) = −Vc0 (t) + Vin (t) − VLm (t) VD2 (t) = − Vc0 (t)

(9)

So, D1 and D2 are reverse biased From Eq. 6, in the non-shoot-through state (both active and zero states), VLm (t) = Vn2 (t) = Vn3 (t) will have negative value and < V in (t). The steady-state analysis of the converter is analyzed by assuming the converter devices to be ideal. The three winding mutually coupled inductor is modeled with L m

Efficiency Estimation of an Integrated Dual-Output Z …

27

Fig. 4 Zero state

and L k as the magnetizing and leakage inductances, respectively, and an ideal transformer with primary turns n1 and two secondary turns n2 and n3 . For the simplicity of analysis, n1 is taken equal to n2 and n3 . The voltage across capacitor is Vc1 = (1 + nk) ∗ Vin The voltage boost of MCL-ZSI is given by 2 + nk − (1−k)(3+2n) Vdc 2+n = Vin 1− D

(10)

where D is the shoot-through duty ratio. When k = 1, the above equation simplifies to Vdc 2+n = Vin 1− D

(11)

where B is called the boost factor of the converter. The modulation index for a single-phase inverter is defined as where V ac is the peak output ac voltage. Vac =M Vdc Then, the total AC gain is

28

K. R. Mothukuri and M. Barai

Vac 2+n =G=M∗ Vin 1− D

(12)

The control strategy involved in this converter is explained in the next section.

3 Control Strategy Different control methods are proposed for improving gain, modulation index range, voltage stress, etc., of a Z source inverter. Simple boost control (SBC), maximum boost control (MBC), maximum constant boost control (MCBC), and third harmonic injection control are some of them [9]. These strategies can be used for MCL-ZSI also. But for decoupled control of AC and DC voltages, SBC is used in MCL-ZSI.

3.1 Simple Boost Control In SBC of a single-phase inverter, a sine wave (va ), an inverted sine wave (−va ), two constant and equal but opposite signals (V s and −V s ) are compared with a triangular wave (vtri ), which is known as the carrier wave, to produce the switching signals for the inverter switches. The peak of the sine wave (V a ) must be less than or equal to V s in order to ensure M is less than or equal to 1 − D. The frequency of the modulating sine wave determines the frequency of the output of the inverter. S 1 is turned on when va > vtri , S 4 is turned on when va < vtri , S 3 is turned on when −va > vtri , and S 2 is turned on when −va < vtri . The control strategy involved in generation of switching signals is shown in Fig. 5. Triangular wave is compared with the sine waves to generate active states and zero state, and then, shoot through periods are evenly allocated in the zero states. Fig. 5 Control strategy for producing switching signals

Efficiency Estimation of an Integrated Dual-Output Z …

29

Fig. 6 Simple boost control PWM switching

Similar to zero state, the output of the inverter during shoot-through state is also zero. The shoot through duty ratio is given by D =1−

Vs Vm

(13)

Both V m and V s can be varied to change the duty ratio. Figure 6 shows the switching scheme according to the simple boost control strategy. For increasing the output DC voltage without affecting the AC voltage, first, the duty ratio is increased which will shift the operating point to a new point where both the DC gain and AC gain are higher. In order to bring back the AC gain to its initial value, M is reduced. Thus, the DC output can be varied independently. It should be noted that M + D should never exceed 1. The modulation index M can be changed to vary the AC gain without affecting the DC gain such that the constraint M + D ≤ 1 is always satisfied. For obtaining the transfer function of converter and designing compensator, dynamic modeling of converter is performed in next section.

30

K. R. Mothukuri and M. Barai

4 Dynamic Modeling of Converter Small-signal analysis is required in order to analyze the transient behavior and to design the closed-loop control. In small-signal analysis, the diodes, switches, and passive components present are assumed ideal. To determine the variation in DC link capacitor voltage with the perturbation in duty cycle, the small-signal analysis is done.

4.1 State Space Average Modeling For small-signal analysis, the state space averaging technique is used below. Considering d(t) as shoot-through state and 1 − d(t) as non-shoot-through state period. ∧ ∧ , . . . x2 = i n2 , . . . x3 = Vc1∧ , . . . x4 = Vc0∧ Defining state variables as x1 = i n1 ⎤ x1 ⎢ x2 ⎥ ⎥ [x] = ⎢ ⎣ x3 ⎦ x4 ⎡

(14)

After introducing the perturbations, the state space representation can be written as dx = Ax + B0 Vin∧ + Bd dt Vdc∧ = C x + D0 Vin∧ + Dd ∧

(15)

The below P, Q, and U are variables. (1 − k) k(2 + n) k(2 + n)2 Q =1+ (1 − k) L k + L m (2 + n)2 U= L3 P = (2 + n) +

(16)

The equations obtained after substituting the relations (15) and variables (16) are  ∧ Vin ((1 + D(Q(1 − K ) − 1)) + Vc1∧ − Vc0∧ (1 − D) di n1 = dt Lk Q

Efficiency Estimation of an Integrated Dual-Output Z …

+

31

d∧ [Vin (Q(1 − K ) − 1))] − Vc1 + Vc0 Lk Q

∧ d∧ [[−Vin (K U + 1)] − Vc1 (1 − U ) + Vc0 ) di n3 = dt L 3U Vin∧ ([1 − D(K U + 1)] + (Vc1∧ [1 − D(1 − U )] − Vc0∧ (1 − D) + L 3U

dVc1∧ [2D − 1] ∧ = i n3 ∗ dt C1 −Vc0∧ dVc0∧ i ∧ [1 − D] − In1 ∗ d = + n1 dt C0 ∗ Rdc C0

(17)

On substituting the Eqs. (17) in the steady-state average model, we get the below matrices. ⎡ ⎤ d−1 00 1−d Lk Q Lk Q ⎢ 00 1−d(1−U ) d−1 ⎥ ⎢ L 3U L 3U ⎥ A=⎢ ⎥ 0 2d−1 00 ⎣ ⎦ C1 (1−D) −1 00 Rdc C0 C0 ⎡ ⎤ [Vin (Q(1 − K ) − 1) − Vc1 + Vc0 ]/(L k Q) ⎢ [−Vin (K U + 1) − Vc1 (1 − U ) + Vc0 ]/(L 3 U ) ⎥ ⎥ B=⎢ ⎣ ⎦ 0 −In1 /C0 C = [0001] D = [0]

(18)

To determine the transfer function of plant in terms of output to duty cycle, Vin∧ = 0. Figure 7 shows the system is unstable with at frequency of 273 Hz (phase cross-over frequency), it has negative gain margin (GM) of 52 dB. At 5.66 kHz (gain cross-over frequency), the system has negative phase margin of −13°. The DC link voltage to voltage transfer function is given as Vdc∧ (s) d∧ (s)   −8000 s − 1.53e05 s 2 − 17.68s + 5.27e06  G p (s) =  2 s + 15.3s + 1.89e06 s 2 + 2.88s + 9.13e06 G p (s) =

To stabilize the system, a compensator is designed at f c . So, we are choosing the cut-off frequency f c < f /10, f c = 478 Hz. At cut-off frequency, GM = −79 dB and PM = 173°.

32

K. R. Mothukuri and M. Barai

Fig. 7 Open-loop bode plot without compensator

Voltage mode control is considered in design of the closed-loop control, where G c (s), G p (s) are compensator and plant transfer functions, respectively. The transfer function of type III compensator is given below.  2 7.65e−03 1 + 4.0e−03 s G c (s) =  2 s 1 + 53.7e−06 s The overall transfer function is defined as follows   −8000 s − 1.53e05 s 2 − 17.68s + 5.27e06  G(s) =  2 s + 15.3s + 1.89e06 s 2 + 2.88s + 9.13e06 2  7.65e−03 1 + 4.0e−03 s ∗  2 s 1 + 53.7e−06 s Figure 8 shows the system is stable with positive gain margin at frequency of 364 Hz at phase cross-over frequency. At 0.5 Hz, the system has positive phase margin at gain cross-over frequency.

Efficiency Estimation of an Integrated Dual-Output Z …

33

Fig. 8 Open-loop bode plot with compensator

5 Design and Implementation 5.1 Design Specifications Based on inductor ripple current, capacitor ripple voltage, maximum voltage across diode and switches, the design of components is explained as follows. Inductor Design: The inductor ripple current is 10% of average inductor current [I L(avg) = 1.87 A], I L(avg) =

Idc,max + Iac,max 0.66 + 0.707 Io = = = 1.87 A 1− D 1− D 0.75 i L = 0.18 A

L1 = L2 = L3 =

Vin ∗ D 36 ∗ 0.25 = = 100 μH i L ∗ f s 0.18 ∗ 5 ∗ 103

Capacitor Design: The capacitor ripple voltage is 1% of output voltage [Vo(avg) = 144 V], so Vo = 1.5 V C1 =

D ∗ I0 0.25 ∗ 1.36 = = 45.3 μH Vc ∗ f s 1.5 ∗ 5 ∗ 103

34

K. R. Mothukuri and M. Barai

C0 =

I0 1.36 = = 181.3 µF Vc ∗ f s 1.5 ∗ 5 ∗ 103

Output capacitor C0 is selected for rating as 450 V, 220 µF, and C 1 is selected for rating as 250 V, 50 µF. Calculation of switch voltages and currents: I S1(max) = Iac(max) I S1(max) = 0.707 A I S1(max) = I S2(max) VS1(max) = VS2(max) = Vdc(max) 3 ∗ 36 (2 + n) ∗ Vinmax = 144 V VS1(max) = VS2(max) = = 1 − dmin 0.75 Based on the maximum value of voltages and currents obtained for the switches, KGF40N60KDA power IGBT is used which are having high reverse voltage capability and high forward current. Calculation of diode voltages and currents:. Vd1(max) = Vdo(max) =

2 ∗ Vinmax 2 ∗ 36 = 96 V = 1 − dmin 0.75

Based on the maximum value of voltages and currents obtained for the diodes, MUR1560 ultrafast diodes are used which has low value of reverse recovery time. Calculation of Input current: Assuming the conductor to be lossless and equalizing the input and output power gives expression as Pin = Pdc + Pac = Vdc ∗ Idc + (Vac,rms ∗ Iac,rms *cosα) Iin = =

Vdc ∗ Idc + (Vac,rms ∗ Iac,rms *cosα) Vin

144 ∗ 0.65 + (73.4 ∗ 0.48 ∗ 1) = 129.39/36 = 3.6 A 36

Efficiency Estimation of an Integrated Dual-Output Z …

35

Calculation of Efficiency: Assuming inductor, capacitor to be lossless, the switching losses (Psw ) and conduction losses (Pcon ) in switches and diodes are calculated as follows with ON state saturation voltage of switches VDS(on) = 1.1 V, ON state saturation voltage of switches rDS(on) = 0.15 , forward voltage drop of diodes VFD = 0.8 V, ON state resistance of diodes rD = 0.1 . The total power losses are switching and conduction losses in switches and diodes, calculated as follows. a.

Switching losses in IGBTs:

The switching loss of the controlled switches (S1 − S4 ) during shoot-through state interval is PSW_ST = 4 ∗

Vs ∗ IS ∗ (E ON + E OFF ) ∗ 2 f si Vref ∗ Iref

where E ON , E OFF are switching on and off energies of switches at reference voltage Vref and reference current Iref calculated from manufacturer’s datasheet. f si is the switching frequency of H-bridge switches. Vs =

2+n ∗ Vin 1− D

Is = IL1 + IL2 + IL3 The switching loss of the controlled switches (S1 − S4 ) during active state interval is PSW_active = 4 ∗

Vinv ∗ Iinv ∗ (E ON + E OFF ) ∗ f si Vref ∗ Iref

Vinv = Iinv =

2+n ∗ Vin 1− D

1− D ∗ Iin (1 − D)∗(2 + n)

Therefore, the total calculated switching losses in controlled switches (S1 − S4 ) of MCL-ZSI is 1.51 W. b.

Switching losses in diodes:

The switching loss of the diodes (D0 , D1 , D2 ) during shoot-through state and active state interval is PSW = 2 ∗ f si ∗ Q rr ∗ VD

36

K. R. Mothukuri and M. Barai

where Q rr , reverse recovery charge is calculated from manufacturer’s datasheet. ∗ Vin VD1 = VD2 = (1−D)∗(2+n) 1−D VD0 =

2+n ∗ D ∗ Vin 1− D

Therefore, the total calculated switching losses in controlled switches (S1 − S4 ) of MCL-ZSI is 1.44 W. c.

Conduction losses in IGBTs:

The conduction losses in the controlled switches (S1 − S4 ) during shoot-through state interval and active state interval are 2 ∗ rDS(on) + IS_avg ∗ VDS(on) Pcond = IS_rms

where IS_rms , IS_avg are switch RMS and average currents. The calculated conduction losses in controlled switches (S1 − S4 ) of MCL-ZSI are 4.91 W. d.

Conduction losses in Diodes:

The conduction loss in diodes (D0 , D1 , D2 ) during shoot-through state interval and active state interval is 2 ∗ rD + VFD ∗ ID_avg Pcond = ID_rms

The calculated conduction losses in diodes (D0 , D1 , D2 ) of MCL-ZSI are 3.64 W. The total losses calculated in all switches and diodes are 11.5 watts. The efficiency calculated from experimental results is η =1− =1−

Ploss ∗ 100 Pin

11.5 ∗ 100 = 91.48%. 135

Therefore, 91.48% is efficiency obtained theoretically. The design specifications of components are tabulated in Table 1.

5.2 LTSPICE Simulation Results The coupled inductor Z source converter with above-tabulated component values is carried out LTSPICE simulation, and results are obtained. The output DC voltage is 138 V with ripple voltage of 1 V, output DC current average values 0.63 A with ripple current 0.01 A. The DC power output Pdc = 86.94 W is shown in Fig. 9.

Efficiency Estimation of an Integrated Dual-Output Z … Table 1 Parameters of the prototype

37

Parameters

Symbol

Value

Input voltage

V in

36 V

Output voltage

V dc

144 V

Output AC voltage V rms

V rms

73.32 V

Total power output

Pdc + Pac

135 W

Inductance of windings

L1

100 µH

Coupled inductor (turns ratio)

n1 : n2 : n3

1:1:1

Capacitance

C1

50 µF

Capacitance

C0

220 µF

DC load resistance

Rdc

220 

AC load resistance

Rac

150 

Shoot through duty ratio

D

0.25

Modulation index

M

0.72

Frequency of carrier wave

fs

5 kHz

Filter inductance

Lf

3 mH

Filter capacitance

Cf

2 µF

Fig. 9 Output DC voltage and DC current in LTSPICE

The output AC voltage and AC current obtained from LTSPICE results are shown in Fig. 10 with Vac,rms = 72.8 V, Iac,rms = 0.48 A . Calculating AC power output Pac = 34.94 W. The input current and inductor current are obtained in Fig. 11 with Iin,avg = 3.7 A, Iin,max = 14.2 A and IL1,avg = 1.93 A, IL1,max = 5.9 A. The efficiency calculated from LTSPICE results is η=

Pdc + Pac ∗ 100 = 90.37% Pin

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Fig. 10 Output AC voltage and AC current in LTSPICE

Fig. 11 Input current and inductor current in LTSPICE

5.3 Hardware Results For the validation of theoretical analysis, a 135 W prototype laboratory experimental setup of an integrated dual output mutually coupled Z source converter is implemented with parameters tabulated in Table 1. The gate driving signals obtained from the experimental setup for four switches are shown in Fig. 12a, b representing the active, shoot through and zero states. The steady-state experimental results of output DC voltage, output DC current, inductor current, and voltage across diode obtained are shown in Figs. 13, 14, 15, and 16, respectively. Calculating DC power output with Vdc = 132 V, Idc = 0.6 A, we get Pdc = 79.2 W.

Efficiency Estimation of an Integrated Dual-Output Z …

39

Fig. 12 a, b Switching pulse waveforms with voltage (10 V/div), time base (20 µs/div)

Fig. 13 Output voltage with respect to input voltage with voltage (50 V/div), time base (20 µs/div)

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K. R. Mothukuri and M. Barai

Fig. 14 Output DC current with current (1A/div), time base (20 µs/div)

Fig. 15 Inductor current with current (2A/div), time base (20 µs/div)

Fig. 16 Output voltage across diode with voltage (50 V/div), time base (5 µs/div)

Efficiency Estimation of an Integrated Dual-Output Z …

41

The steady-state experimental results of output AC voltage and current are obtained in Figs. 17 and 18, respectively, with Vac,rms = 66.47 V, Iac,rms = 0.44 A . Calculating AC power output Pac = 29.45 W. The efficiency calculated from experimental results is η=

Pdc + Pac ∗ 100 = 80.48% Pin

Fig. 17 Output AC voltage with voltage (50 V/div), time base (5 ms/div)

Fig. 18 Output AC current with current (500 mA/div), time base (5 ms/div)

42 Table 2 Efficiency comparison

K. R. Mothukuri and M. Barai Converter topology

Efficiency (%)

Switched inductor ZSI

75.65

Hybid LZSI

78.19

Boost derived hybrid converter

79.84

MCL-ZSI

90.37

5.4 Performance Comparison On comparison of discussed converter with switched inductor ZSI, hybrid LZSI, boost derived hybrid converter, and MCL-ZSC, the efficiency is tabulated for LTSPICE simulation results in Table 2 for 135 W of power.

6 Conclusion Study, design, and implementation of an integrated dual-output Z source converter with coupled inductor are validated experimentally. The LTSPICE simulation results give efficiency of 90.37%, whereas the hardware results shown efficiency obtained is 80.48% because of leakage inductance phenomenon. The gain variation is achieved by varying turn ratio that makes the coupled inductor Z source converter a viable choice for hybrid electric vehicles.

References 1. Peng, F.Z.: Z-source inverter. IEEE Trans. Ind. Appl. 39(2), 504–510 (2003) 2. Vakacharla, V.R., Chauhan, A.K., Reza, M.M., Singh, S.K.: Boost derived hybrid converter: problem analysis and solution. In: 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Trivandrum, pp. 1–5 (2016) 3. Shen, M., Joseph, A., Wang, J., Peng, F.Z., Adams, D.J.: Comparison of traditional inverters and Z source inverter for fuel cell vehicles. IEEE Trans. Power Electron. 22(4), 1453–1463 (2007) 4. Bachurin, P.A., Bessonov, I.O.: Quasi-Z-source inverter for four-wire power supply systems. In: 2014 15th International Conference of Young Specialists on Mi-cro/Nanotechnologies and Electron Devices (EDM), Novosibirsk, pp. 447–449 (2014) 5. Zhu, M., Yu, K., Luo, F.L.: Switched inductor Z-source inverter. IEEE Trans. Power Elecron. 25(8), 2150–2158 (2010) 6. Pan, L.: L-Z-source inverter. IEEE Trans. Power Electron. 29(12), 6534–6543 (2014) 7. Chauhan, A.K., Teja, M.S., Jain, M., Singh, S.K.: A cross regulated closed loop control for hybrid L-Z source inverter. In: IEEE Transactions on Industry Applications (2018) 8. Deepankar, Chauhan, A.K., Singh, S.K.: Integrated dual output L-Z source inverter for hybrid electric vehicle. In: IEEE Transactions on Transportation Electrification, vol. 4, no. 3, pp. 732– 743 (2018)

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9. Peng, F.Z., Shen, M., Qian, Z.: Maximum boost control of the Z-source inverter. IEEE Trans. Power Electron. 20(4), 833–838 (2005) 10. Anderson, J., Peng, F.Z.: Four quasi Z-source inverters. In: IEEE Power Electronics Specialists Conference, pp.2743–2749 (2008) 11. Nguyen, M.-K., Lim, Y.-C., Cho, G.-B.: Switched –inductor quasi Z –source inverter. IEEE Trans. Power Electron. 26(11), 3183–3191 (2011) 12. Liu, H., Ji, Y., Wheeler, P.: Coupled inductor L-source inverter. IEEE J. Emerg. Sel. Top. Power Electron. (2017) 13. Ye, H., Yang, Y., Emadi, A.: Traction inverters in hybrid electric vehicles. In: 2012 IEEE Transportation Electrification Conference and Expo (ITEC) 14. Ray, O., Mishra, S.: Boost derived hybrid converter with simultaneous DC and AC outputs. IEEE Trans. Ind. Appl. 50(2), 1082–1093 (2014)

Location-Based Sentiment Analysis of the Revocation of Article 370 Using Various Recurrent Neural Networks Abhineeth Mishra, Arti Arya, and H. R. Devanand

1 Introduction The government of India revoked the Article 370 on August 5, 2019, through a Presidential Order. Article 370 was a temporary provision, introduced in 1947 which granted special status to the state of Jammu and Kashmir, where the residents of Jammu and Kashmir were governed by a separate set of laws. Therefore, the scraping of this article unified the laws between the residents of this state and the rest of India. With over 7000 tweets posted per second as shown in [1], Twitter has become one of the most popular social media platforms, which makes it an ideal platform for analyzing public sentiment [2]. The revocation of Article 370 by Indian Government provoked a lot of people to question or support the revocation. The views range from welcoming to hostile, raising the question whether the views also have a relation with places such as Jammu, Kashmir, Pakistan occupied Kashmir, Pakistan, and other neighboring countries of India. Many people reacting to this article accounted for making the topic go trending on Twitter. The highlights of this paper are • The given deep learning models determine the sentiments, which when aggregated by locations offer insights to the public sentiment concerning the Article 370. • The performance of different deep learning models for the sentiment classification task is evaluated and compared. • This paper discovers that a user’s location influences their opinion toward this move.

A. Mishra (B) · A. Arya · H. R. Devanand Computer Science, PES Institute of Technology—Bangalore South Campus, Bangalore, India A. Arya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 L. Garg et al. (eds.), Proceedings of International Conference on Innovations in Information and Communication Technologies, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-0873-5_4

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The paper is divided into following sections. Section 2 gives the gist of the related work done in the field of sentiment analysis based on the location. Later, in Sect. 3, the paper discusses the experimental setup containing three models. Then, in Sect. 4, the paper discusses the results obtained. In the last Sect. 5, the paper draws conclusions and proposes directions for future work.

2 Literature Survey Sentiment analysis has been studied upon at various levels of granularity, from document-level [3] to sentence-level granularity [4]. Power et al. [3] have shown document-level classification on focused topics, with popularity and rarity as two different metrics to extract features. Santos et al. [5] also do document-level classification by the use of word embeddings, instead of the simple bag of words methods to achieve state-of-the-art accuracy. The paper by Jagtap et al. [4] surveys several sentiment analysis techniques such as maximum entropy, support vector machines, naive Bayes, and so on. It also surveys difficulties associated with sentiment analysis, for the three-way classification problem. Pak and Paroubek [2] use the above-mentioned techniques to analyze the sentiment of short texts gathered as tweets from Twitter, and this technique uses n gram models to evaluate sentiments. The n gram approach aims to predict the probability of a word given all the words by using the conditional probability of n previous words. [2] surveys and compares several different approaches to sentiment analysis, such as the comparison of bi-gram and tri-gram models. Kharde et al. [6] also compare various machine learning approaches toward Twitter sentiment analysis, such as support vector machines, naive Bayes, and maximum entropy at both the document and sentence levels. In contrast, this paper uses Word2Vec [7], a skip gram technique, which assigns a numerical representation to words in a vector space, so that the words similar in their meanings are closer to each other when evaluated by a distance metric such as cosine distance. This representation of words is known as a word embedding. Furthermore, along with machine learning approaches, several papers have studied multiple deep learning approaches with respect to sentiment analysis. Kalaivani et al. [8] discuss different deep learning methods such as convolutional and recurrent neural networks along with deep neural networks. This paper uses a combination of both convolutional and recurrent neural networks to solve the problem of sentiment analysis, with an intuition that a combination of these networks will show a greater accuracy. This has been shown on three datasets by Wang in [9], where the performance of the CNN-LSTM model along with word embeddings shows a slightly higher accuracy when compared to the accuracies of CNN, GRU, and LSTM models separately. Lai et al. [10] also show increased accuracy when a recurrent convolutional neural network model is used, by demonstrating it on four datasets. Lai et al. [10] showed that the CNN-LSTM model achieved the highest accuracy in comparison with logistic regression, SVM, LDA, and several other models, where various approaches like bag of words and bi-gram models were used.

Location-Based Sentiment Analysis of the Revocation of Article …

47

In addition to papers concerning sentiment analysis of short texts, papers such as [11] analyze Twitter sentiment on a certain topic. Ruhrberg [11] analyzed countryspecific sentiment toward ISIS, a terrorist organization based in Iraq. The paper by Powers et al. [3] analyzes the sentiment at a document level, where it assigns a label to each tweet by using TextBlob, a library which uses multiple lexicons with nine feature sets or 41 features which are used for supervised learning. Garg et al. [12] also analyze the public sentiment toward the Uri terror attack on September 16, 2016, using an ensemble of naive Bayes and support vector machine models. This paper uses techniques such as recurrent neural networks used in [9] and [10] and applies them to a problem similar in nature to the ones in [11] and [12]. Singh et al. [13] analyze the general public sentiment toward the demonetization of 500- and 1000rupee banknotes by the Indian government. They introduce a new metric to calculate the sentiment, known as the net score.

3 Background In recent years, deep learning architectures have taken a forefront for analyzing textual data. Long short-term memory (LSTM) is a class of recurrent neural networks. Unlike artificial neural networks, LSTMs have the ability to process sequential data due to their feedback connections. Feedback connections allow LSTMs to take multiple data points as their input. A common LSTM cell is shown in Fig. 1. An LSTM cell consists of an input gate, forget gate, cell state, and an output gate also represented as i gate, f gate, g gate, and o gate, respectively. An LSTM cell calculates its output based on its hidden state, previous cell state ct − 1, and its input vector xt, which enables it to have some form of memory. All of these cells are shown in Fig. 1. The following equations show the operations each gate performs. The following equations show the operations each gate performs.

Fig. 1 LSTM cell

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i t = σ (wi [h t − 1, xt ] + bi )

(1)

f t = σ (w f [h t − 1, xt ] + b f )

(2)

ot = σ (wo [h t − 1, xt ] + bo )

(3)

where it represents the input gate, ft represents the forget gate, ot represents the output gate, σ represents the sigmoid function, wx represents the respective gate neurons, ht − 1 represents the output the previous LSTM block (at timestamp − t 1), xt is the input at the current time stamp, and bx are the biases for the respective gates. The LSTM architecture as introduced by Hochreiter and Schmidhuber [14] is chosen because it solves the vanishing and exploding gradient problems that are posed by vanilla RNN architectures. Bidirectional LSTMs are a form of bidirectional RNNs. As the name suggests, bidirectional LSTMs connect the hidden layers of opposite directions to the same output. Figure 2 shows the two RNN architectures, where part b shows the bidirectional LSTM, characterized by information flow over both directions. This information flow indicates that the bidirectional RNN would not only learn from words appearing before the current text, it would also learn from words appearing after it. For example, assume that we train an LSTM and a bidirectional LSTM to predict the next word. Let us say that the sentence is “The boy went to the school at 8:00, and came back from school at 2:00.” Now let us say that the input to both the networks is “The boy went to the at 8:00, and came back from school at 2:00,” where the task is to predict the sixth word. An LSTMs input would be “The boy went to the” which would mostly lead it to predict the wrong answer, whereas the input to the bidirectional LSTM would include the words before and after the blank, which would mostly lead it to predict the correct answer, which is school.

Fig. 2 a Unidirectional RNN and b bidirectional RNN

Location-Based Sentiment Analysis of the Revocation of Article …

49

In the CNN-LSTM architecture, the output vector obtained from the convolutional and max pooling layers is fed to an LSTM layer. This architecture is generally used for problems with spatial inputs like images or videos, and this paper experiments with its performance on the sentiment classification task. This paper does three-way text classification using three deep learning approaches, where a label of positive, negative, or neutral is assigned to a particular piece of text. This paper uses Word2Vec [7] a pre-trained, single layer neural network with words represented as word embeddings, to help identify the word context, by converting each word into its own vector representation.

4 Experimental Setup 4.1 System Specifications Python is used to implement the models along with Scikit learn and Keras for preprocessing and model implementation, respectively. NLTK a popular natural language processing library is used for tasks like stemming and stop word removal. The models are deployed on the kaggle cloud environment, known as a kaggle kernel, which uses the Intel(R) Xeon (R) CPU, 13 GB of RAM, and a Nvidia K80 GPU, running on Debian Linux 4.9.0. Two datasets, namely the Sentiment140 dataset and the Amazon Reviews dataset are used to compare the model accuracies. Sentiment140 contains 1.6 million tweets, labeled as positive, negative, or neutral. Sentiment140 is used to train the models for sentiment classification of the extracted tweets. The Amazon Reviews dataset contains a few million text reviews along with star ratings for the respective reviews. These ratings along with their respective review text help in classifying the review sentiment. This dataset serves as an additional dataset to verify the performance of the three models used. The following sections provide an outline of the workflow pertaining to the proposed method.

4.2 Data Collection Twitter API is used as the primary tool to fetch tweets along with multiple attributes associated with the tweets, such as their language and geographical location. Tweets containing the hashtag #Article370 were taken into consideration, which accounted for around 180,000 tweets, out of which 96,814 tweets had geographical location associated with them.

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4.3 Data Preprocessing The tweets extracted from Twitter contained tweets in various languages. All nonEnglish tweets were removed. Retweets are duplicate tweets, and all retweets were removed. Leading and trailing spaces, URLs, non-alphabetic characters (such as emojis), and stop words were removed. Some tweets contain incomplete locations, such as only its city, state, or country name. For these cases, the corresponding names are filled, with the condition that the given location name is valid. Tweets with invalid locations were excluded. Finally, after these steps, the words are converted into their respective vector representations using Word2Vec. These representations are called word vectors or word embeddings. Word2Vec captures the context of the given word, where words closer to each other in an n-dimensional space would be more likely to appear in the same context.

4.4 Training The Sentiment140 dataset [15] is used for training. It is split into train and test sets using the 80:20 split rule, the training set contains 1,280,000 instances, and the test set contains 320,000 instances. Three supervised models, an LSTM, a bidirectional LSTM, and a CNN-LSTM models, are trained on Sentiment140 and their accuracies are compared. LSTM: The simple LSTM model as shown in Fig. 3 consists of one LSTM layer after the embedding layer, followed by a dense layer, which also known as the fully connected layer. This model achieved an accuracy of 78.05% on the testing set. Bidirectional LSTM: The architecture for this model is identical to the simple LSTM model, except for the fact that this model uses a bidirectional LSTM layer instead of the simple LSTM layer. Fig. 4 shows the corresponding architecture. It yields an accuracy of 79% CNN-LSTM: The CNN-LSTM architecture employs a convolutional layer before the LSTM layer. This architecture performed marginally better than the other two, with the intuition being that the convolution layer can capture spatial features and

Fig. 3 LSTM model architecture

Location-Based Sentiment Analysis of the Revocation of Article …

51

Fig. 4 Bidirectional LSTM model architecture

Fig. 5 CNN-LSTM model architecture

the recurrent layer can capture the temporal features in the text. This model yields an accuracy of 80.2%. Figure 5 shows this architecture.

5 Results and Discussion Using the model with the highest test-set accuracy, the CNN-LSTM model, the sentiment for each tweet is classified. The model accuracies are given in Table 2. The CNN-LSTM architecture performs better for the Sentiment140 dataset, and it also performs significantly better than other approaches in the Amazon Reviews dataset. The better performance of the CNN-LSTM model can be attributed to the CNN being able to capture multi-word expressions and LSTM being able to capture longterm dependencies. Combination of CNN layer with LSTM has been advantageous in terms of picking the strengths of CNN and LSTM both together thereby giving better accuracy than LSTM and Bi-LSTM. The overall sentiment scattered across the complete dataset remains largely neutral, and the number of positive tweets is significantly higher than the number of negative tweets, as given in Table 1. This is consistent with the observations in [16], which shows that for political topics, the average sentiment is highly neutral, followed by positive and negative sentiments in that order, respectively (Table 2). A metric proposed in [13] known as the net score (NS) is used to approximate the sentiment based on the location. It is defined as follows

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Table 1 Distribution of tweets Model

Number of tweets

Positive number of tweets

Neutral number of negative tweets

LSTM

31,745

46,250

18,819

Bidirectional LSTM

31,447

45,865

19,502

CNN-LSTM

32,061

45,763

18,990

Table 2 Dataset accuracies

Table 3 Tweet polarity and class

Model

Dataset Sentiment140 (%)

Amazon Reviews (%)

LSTM

78

82

Bidirectional LSTM

79

83

CNN-LSTM

80.2

88

Label

Polarity

Highly positive (H P )

≥0.85s

Positive (P)

≥0.70 and