Machine Intelligence Techniques for Data Analysis and Signal Processing

1. Front Matter

   Pages i-xiv

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2. On Diverse and Serendipitous Item Recommendation: A Reinforced Similarity and Multi-objective Optimization-Based Composite Recommendation Framework

   • Rahul Shrivastava, Dilip Singh Sisodia, Naresh Kumar Nagwani

   Pages 1-13

3. Comparative Analysis of Node-Dependent and Node-Independent Graph Matrices for Brain Connectivity Network

   • Mangesh Ramaji Kose, Mitul Kumar Ahirwal, Mithilesh Atulkar

   Pages 15-27

4. Facial Expression Recognition from Low Resolution Facial Segments Using Pre-trained Networks

   • Dhananjay Theckedath, R. R. Sedamkar

   Pages 29-41

5. Design and Analysis of Quad Element UWB MIMO Antenna with Mutual Coupling Reduction Techniques

   • B. Ananda Venkatesan, K. Kalimuthu, Palaniswany Sandeep Kumar

   Pages 43-55

6. Enhancing Agricultural Outcome with Multiple Crop Recommendations Using Sequential Forward Feature Selection

   • Souvik Mondal, Bibek Upadhyaya, Kamal Nayan, Vinay Singh, Jyoti Singh Kirar

   Pages 57-70

7. Kernel-Level Pruning for CNN

   • Pragnesh Thaker, Biju R. Mohan

   Pages 71-78

8. Linear Regression Model for Predicting Virtual Machine Consolidation Within the Cloud Data Centers (LrmP_VMC)

   • M. Tejaswini, T. Hari Sumanth, K. Jairam Naik

   Pages 79-91

9. Detection of Fraudulent Credit Card Transactions Using Advanced LightGBM Approach

   • Kotireddy Yazna Sai, Repalle Venkata Bhavana, N. Sudha

   Pages 93-103

10. Approach of Different Classification Algorithms to Compare in N-gram Feature Between Bangla Good and Bad Text Discourses

    • Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Saima Khan, Imran Mahmud, Khalid Been Badruzzaman Biplob

    Pages 105-116

11. Effective Heart Disease Prediction Using Hybrid Ensemble Learning Model

    • Deepthi Karuturi, Keerthi Pendyala, N. Sudha

    Pages 117-125

12. A SAR ATR Using a New Convolutional Neural Network Framework

    • Bibek Kumar, Ranjeet Kumar Ranjan, Arshad Husain

    Pages 127-134

13. A Distributed Data Fusion Approach to Mitigate Node Redundancies in an IoT Network

    • Pallavi Joshi, Ajay Singh Raghuvanshi

    Pages 135-150

14. ColCompNeT: Deep Learning-Based Colorization-Based Coding Network

    • Dipti Mishra, Satish Kumar Singh

    Pages 151-160

15. Printed Elliptical Cut Vivaldi 1 × 5 Linear Array Antenna for X and KU Band Applications

    • Renuka Chowdary Bezawada, Bhagya Lakshmi Munagoti

    Pages 161-172

16. Texture Classification Using ResNet and EfficientNet

    • Vinat Goyal, Sanjeev Sharma, Bharat Garg

    Pages 173-185

17. Performance Analysis of Parametric and Non-parametric Classifier Models for Predicting the Liver Disease

    • Vijayalakshmi G. V. Mahesh, M. Mohan Kumar, Alex Noel Joseph Raj

    Pages 187-199

18. angularParameter: A Novel Optimization Technique for Deep Learning Models

    • Shubhankar Bhakta, Utpal Nandi, Chiranjit Changdar, Moirangthem Marjit Singh

    Pages 201-212

19. Efficient Prediction of Annual Yield from Stocks Using Hybrid Deep Learning

    • Ashish Papanai

    Pages 213-226

20. Multitask Deep Learning Model for Diagnosis and Prognosis of the COVID-19 Using Threshold-Based Segmentation with U-NET and SegNet Classifiers

    • S. NagaMallik Raj, S. Neeraja, N. Thirupathi Rao, Debnath Bhattacharyya

    Pages 227-236

This book comprises the proceedings of the 4th International Conference on Machine Intelligence and Signal Processing (MISP2022). The contents of this book focus on research advancements in machine intelligence, signal processing, and applications. The book covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. It also includes the progress in signal processing to process the normal and abnormal categories of real-world signals such as signals generated from IoT devices, smart systems, speech, and videos and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), electromyogram (EMG), etc. This book proves a valuable resource for those in academia and industry.

• Machine Intelligence
• Signal Processing
• Brain Computing Interface
• Computational Intelligence
• Pattern Classification
• Ensemble Models
• Deep Learning
• Human Computer Interaction
• Biomedical Imaging
• Diagnosis of Mental Disorder
• MISP 2022
• MISP Proceedings

• Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, India

  Dilip Singh Sisodia

• Department of Information and Communication Technology, University of Malta, Msida, Malta

  Lalit Garg

• Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

  Ram Bilas Pachori

• Department of Mathematics, Indian Institute of Technology Indore, Indore, India

  M. Tanveer

Dilip Singh Sisodia is an Associate Professor and Head at the Department of Computer Science Engineering of National Institute of Technology Raipur. Dr. Sisodia contributed over 100 high-impact articles in reputed journals, conference proceedings, and edited volumes. He also edited Scopus indexed research book volumes published by Springer Nature and IGI Global. Dr. Sisodia was included in the World Ranking of Top 2% Scientists/researchers in 2022 by a study of Scientists from Stanford University USA and Published by Elsevier B.V. So far he has supervised four Ph.D. thesis, eight M.Tech. dissertations and more than 50 B.Tech. projects. He is a Senior Member of IEEE and ACM. He received a Ph.D. degree in computer science and engineering from the National Institute of Technology Raipur, India. He earned his M.Tech. and B.E. degrees, respectively, in information technology (with specialization in artificial intelligence) and computer science and engineering from the Rajiv Gandhi Technological University, Bhopal, India. His research interests include the applications of machine learning/soft computing techniques, artificial intelligence, biomedical signal, and image processing.

Lalit Garg is a Senior Lecturer in the Department of Computer Information Systems at the University of Malta, Malta, and an Honorary Lecturer at the University of Liverpool, UK. He has been a Researcher at the Nanyang Technological University, Singapore, and Ulster University, UK, has supervised 200+ Master’s dissertations, two DBA, and two Ph.D. theses, and published 120+ high-impact publications in refereed journals/conferences/books, five edited books, and 20 patents. His research interests are business intelligence, machine learning, data science, deep learning, cloud computing, mobile computing, the Internet of Things (IoT), information systems, management science, and their applications mainly in healthcare and medical domains.

Ram Bilas Pachori received a B.E. degree with honors in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India, in 2001, the M.Tech. and Ph.D. degrees in Electrical Engineering from Indian Institute of Technology (IIT) Kanpur, Kanpur, India, in 2003 and 2008, respectively. He was Postdoctoral Fellowship Holder at Charles Delaunay Institute, the University of Technology of Troyes, Troyes, France, from 2007 to 2008. He is a Professor in the Department of Electrical Engineering at IIT Indore, India. His research interests are in the areas of signal and image processing, biomedical signal processing, non-stationary signal processing, speech signal processing, brain-computer interfacing, machine learning, and artificial intelligence in health care. He has supervised 14 Ph.D., 20 M.Tech., and 41 B.Tech. students for their theses and projects. He has over 235 publications which include journal papers, conference papers, books, and chapters. He has worked on various research projects with funding support from SERB, DST, DBT, and CSIR.

M. Tanveer is an Associate Professor and Ramanujan Fellow at the Discipline of Mathematics of the Indian Institute of Technology Indore. Prior to that, he worked as Postdoctoral Research Fellow at the Rolls-Royce@NTU Corporate Lab of the Nanyang Technological University, Singapore. He received a Ph.D. degree in Computer Science from the Jawaharlal Nehru University, New Delhi, India. His research interests include support vector machines, optimization, machine learning, deep learning, and applications to Alzheimer’s disease and dementia. He has published over 80 research papers in journals of international repute.