Modeling and Optimization of Signals Using Machine Learning Techniques

Chandra Singh is an assistant professor in the Department of Electronics and Communication Engineering at Sahyadri College of Engineering and Management, Mangalore, India, and is pursuing a PhD from VTU Belagavi, India. He has four patents, he has published over 25 papers in scientific journals, and he is the editor of seven books. Rathishchandra R. Gatti, PhD, is an associate professor at Jawaharlal Nehru University, Delhi, India. With over 20 years of industrial, research, and teaching experience under his belt, he also has four patents, has published over 40 papers in scientific journals, and is the editor of seven research books and one journal. K.V.S.S.S.S.SAIRAM, PhD, is a professor and Head of the Electronics and Communication Engineering Department at the NMAM Institute of Technology, Nitte, India. He has 25 years of experience in teaching and research and has published over 50 papers in international journals and conferences. He is also a reviewer for several journals. Manjunatha Badiger, PhD, is an assistant professor at Sahyadri College of Engineering and Management, Adyar, Mangalore, Karnataka, India. He has over 12 years of experience in academics, research, and administration. He earned his PhD in machine learning in 2024 at Visvesvaraya Technological University. Naveen Kumar S., MTech, is an assistant professor at the Sahyadri College of Engineering and Management. Previously he was an assistant professor at JSS Academy of Technical Education, Noida, India. He obtained his MTech in automotive electronics from Sri Jayachamarajendra College of Engineering, Mysore, India. Varun Saxena, PhD, received his PhD in electromagnetic ion traps from IIT Delhi, New Delhi, in 2018. He is currently an assistant professor at the School of Engineering, Jawaharlal Nehru University, New Delhi.

• Preface xix1 Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm 1Adithya Kumar and Shivakumar B.R.1.1 Introduction 21.2 Image Classification 51.3 Unsupervised Classification 71.4 Supervised Classification 81.5 Overview of Fuzzy Sets 91.6 Methodology 111.7 Results and Discussion 161.8 Conclusion 212 Role of AI in Mortality Prediction in Intensive Care Unit Patients 23Prabhudutta Ray, Sachin Sharma, Raj Rawal and Dharmesh Shah2.1 Introduction 242.2 Background 242.3 Objectives 252.4 Machine Learning and Mortality Prediction 262.5 Discussions 342.6 Conclusion 342.7 Future Work 352.8 Acknowledgments 352.9 Funding 352.10 Competing Interest 353 A Survey on Malware Detection Using Machine Learning 41Devika S. P., Pooja M. R. and Arpitha M. S.3.1 Background 413.2 Introduction 423.3 Literature Survey 443.4 Discussion 533.5 Conclusion 534 EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey 55Bhoomika Patel H. C., Ravikumar V. and Pavan Kumar S. P.4.1 Related Work 574.2 Equations 624.3 Classification 644.4 Data Set 654.5 Information Obtained by EEG Signals 694.6 Discussion 704.7 Conclusion 725 Machine Learning Methods in Radio Frequency and Microwave Domain 75Shanthi P. and Adish K.5.1 Introduction 765.2 Background on Machine Learning 775.3 ML in RF Circuit Modeling and Synthesis 865.4 Conclusion 936 A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola-Jones Algorithm 97Vaibhav C. Gandhi, Dwij Kishor Siyal, Shivam Pankajkumar Patel and Arya Vipesh Shah6.1 Introduction 986.2 Review of Literature 996.3 Report on Present Investigation 1016.4 Algorithms 1026.5 Viola-Jones Algorithm 1046.6 Diagram 1056.7 Results and Discussion 1076.8 Limitations and Future Scope 1116.9 Summary and Conclusion 1117 Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques 115E. Fantin Irudaya Raj and M. Balaji7.1 Introduction 1167.2 Methodology for the Identification of PQ Events 1177.3 Power Quality Problems Arising in the Modern Power System 1187.4 Digital Signal Processing-Based Feature Extraction of PQ Events 1247.5 Feature Selection and Optimization 1297.6 Machine Learning-Based Classification of PQ Disturbances 1317.7 Summary and Conclusion 1418 Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection 145Shwetha N., Gangadhar N., Mahesh B. Neelagar, Sangeetha N. and Virupaxi Dalal8.1 Introduction 1468.2 Literature Survey 1478.3 Proposed Methodology 1498.4 Artificial Neural Network 1528.5 Software Implementation Requirements 1638.6 Conclusion 1709 The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic 173Biswa Ranjan Senapati, Sipra Swain and Pabitra Mohan Khilar9.1 Introduction 1749.2 Discussions on the Coronavirus 1759.3 Bad Impacts of the Coronavirus 1809.4 Benefits Due to the Impact of COVID-19 1869.5 Role of Technology to Combat the Global Pandemic COVID-19 1909.6 The Role of Artificial Intelligence, Machine Learning, and Deep Learning in COVID-19 1989.7 Related Studies 2039.8 Conclusion 20310 A Review on Smart Bin Management Systems 209Bhoomika Patel H. C., Soundarya B. C. and Pooja M. R.10.1 Introduction 20910.1.1 Internet of Things (IoT) 21010.2 Related Work 21110.3 Challenges, Solution, and Issues 21310.4 Advantages 21611 Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts 219K. Vidhyalakshmi and S. Thanga Ramya11.1 Regression 22011.2 Classification 22211.3 Clustering 22711.4 Clustering (k-means) 22711.5 Reduction of Dimensionality 23011.6 The Ensemble Method 23311.7 Transfer of Learning 24011.8 Learning Through Reinforcement 24111.9 Processing of Natural Languages 24211.10 Word Embeddings 24211.11 Conclusion 24312 Recognition Attendance System Ensuring COVID-19 Security 245Praveen Kumar M., Ramya Poojary, Saksha S. Bhandary and Sushmitha M. Kulal12.1 Introduction 24612.2 Literature Survey 24612.3 Software Requirements 24812.4 Hardware Requirements 24912.5 Methodology 25212.6 Building the Database 25312.7 Pi Camera for Extracting Face Features 25512.8 Real-Time Testing on Raspberry Pi 25612.9 Contactless Body Temperature Monitoring 25612.10 Raspberry-Pi Setting Up an SMTP Email 25812.11 Uploading to the Database 25912.12 Updating the Website 26012.13 Report Generation 26012.14 Result 26212.15 Discussion 26712.16 Conclusion 26713 Real-Time Industrial Noise Cancellation for the Extraction of Human Voice 271Vinayprasad M. S., Chandrashekar Murthy B. N. and Yashwanth S. D.13.1 Introduction 27213.2 Literature Survey 27313.3 Methodology 27513.4 Experimental Results 27813.5 Conclusion 28014 Machine Learning-Based Water Monitoring System Using IoT 283T. Kesavan, E. Kaliappan, K. Nagendran and M. Murugesan14.1 Introduction 28314.2 Smart Water Monitoring System 28414.3 Sensors and Hardware 28614.4 PowerBI Reports 28814.5 Conclusion 29115 Design and Modelling of an Automated Driving Inspector Powered by Arduino and Raspberry Pi 295Raghunandan K. R., Dilip Kumar K., Krishnaraj Rao N.S. Krishnaprasad Rao and Bhavya K.15.1 Introduction 29615.2 Literature Survey 29615.3 Results 30615.4 Conclusion 30916 Kalman Filter-Based Seizure Prediction Using Concatenated Serial-Parallel Block Technique 313Purnima P. S. and Suresh M.16.1 Introduction 31416.2 Prior Work 31416.3 Proposed Method 31616.4 Serial-Parallel Block Concatenation Approach 31816.5 Algorithm 31916.6 Kalman Filter 32016.7 Results and Discussion 32116.8 Conclusion 32317 Current Advancements in Steganography: A Review 327Mallika Garg, Jagpal Singh Ubhi and Ashwani Kumar Aggarwal17.1 Introduction 32817.2 Evaluation Parameters 32917.3 Types of Steganography 33017.4 Traditional Steganographic Techniques 33217.5 CNN-Based Steganographic Techniques 33617.6 GAN-Based Steganographic Techniques 33817.7 Steganalysis 34017.8 Applications 34117.9 Dataset Used for Steganography 34117.10 Conclusion 34418 Human Emotion Recognition Intelligence System Using Machine Learning 349Bhakthi P. Alva, Krishma Bopanna N., Prajwal S., Varun A. Naik and Lahari Vaidya18.1 Introduction 35018.2 Literature Review 35018.3 Problem Statement 35218.4 Methodology 35318.5 Results 35518.6 Applications 35518.7 Conclusion 35718.8 Future Work 35719 Computing in Cognitive Science Using Ensemble Learning 361Om Prakash Singh19.1 Introduction 36219.2 Recognition of Human Activities 36319.3 Methodology 36619.4 Applying the Boosting-Based Ensemble Learning 36919.5 Human Activity Features Computability 37319.6 Conclusion 378References 378About the Editors 383Index 385