Overview

Explore the power of machine learning to revolutionize signal processing and optimization with cutting-edge techniques and practical insights in this outstanding new volume from Scrivener Publishing. Modeling and Optimization of Signals using Machine Learning Techniques is designed for researchers from academia, industries, and R&D organizations worldwide who are passionate about advancing machine learning methods, signal processing theory, data mining, artificial intelligence, and optimization. This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia. Through comprehensive coverage of cutting-edge techniques, this book equips readers with the tools to automate signal processing and analysis, ultimately enhancing the retrieval of valuable information from extensive data storage systems. By providing both theoretical insights and practical guidance, the book serves as a comprehensive resource for researchers, engineers, and practitioners aiming to harness the power of machine learning in signal processing. Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.

Full Product Details

Author:   Chandra Singh (Sahyadri College of Engineering and Management, India) ,  Rathishchandra R. Gatti (Jawaharlal Nehru University, India) ,  K.V.S.S.S.S. Sairam (NMAM Institute of Technology, India) ,  Manjunatha Badiger (Sahyadri College of Engineering and Management, India)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
ISBN:  

9781119847687

ISBN 10:   1119847680
Pages:   416
Publication Date:   03 September 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Preface 1. Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm< Adithya Kumar and Shivakumar B.R. 1.1 Introduction 1.2 Image Classification 1.3 Unsupervised Classification 1.4 Supervised Classification 1.5 Overview of Fuzzy Sets 1.6 Methodology 1.7 Results and Discussion 1.8 Conclusion References 2. Role of AI in Mortality Prediction in Intensive Care Unit Patients Prabhudutta Ray, Sachin Sharma, Raj Rawal and Dharmesh Shah 2.1 Introduction 2.2 Background 2.3 Objectives 2.4 Machine Learning and Mortality Prediction 2.5 Discussions 2.6 Conclusion 2.7 Future Work 2.8 Acknowledgments 2.9 Funding 2.10 Competing Interest References 3. A Survey on Malware Detection Using Machine Learning Devika S. P., Pooja M. R. and Arpitha M. S. 3.1 Background 3.2 Introduction 3.3 Literature Survey 3.4 Discussion 3.5 Conclusion References 4. EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey Bhoomika Patel H. C., Ravikumar V. and Pavan Kumar S. P. Introduction 4.1 Related Work 4.2 Equations 4.3 Classification 4.4 Data Set 4.5 Information Obtained by EEG Signals 4.6 Discussion 4.7 Conclusion References 5. Machine Learning Methods in Radio Frequency and Microwave Domain Shanthi P. and Adish K. 5.1 Introduction 5.2 Background on Machine Learning 5.3 ML in RF Circuit Modeling and Synthesis 5.4 Conclusion References 6. A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola–Jones Algorithm Vaibhav C. Gandhi, Dwij Kishor Siyal, Shivam Pankajkumar Patel and Arya Vipesh Shah 6.1 Introduction 6.2 Review of Literature 6.3 Report on Present Investigation 6.4 Algorithms 6.5 Viola–Jones Algorithm on 6.6 Diagram 6.7 Results and Discussion 6.8 Limitations and Future Scope 6.9 Summary and Conclusion References 7. Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques E. Fantin Irudaya Raj and M. Balaji 7.1 Introduction 7.2 Methodology for the Identification of PQ Events 7.3 Power Quality Problems Arising in the Modern Power System 7.4 Digital Signal Processing-Based Feature Extraction of PQ Events 7.5 Feature Selection and Optimization 7.6 Machine Learning-Based Classification of PQ Disturbances 7.7 Summary and Conclusion References 8. Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection Shwetha N., Gangadhar N., Mahesh B. Neelagar, Sangeetha N. and Virupaxi Dalal 8.1 Introduction 8.2 Literature Survey 8.3 Proposed Methodology 8.4 Artificial Neural Network 8.5 Software Implementation Requirements 8.6 Conclusion References 9. The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic Biswa Ranjan Senapati, Sipra Swain and Pabitra Mohan Khilar 9.1 Introduction 9.2 Discussions on the Coronavirus 9.3 Bad Impacts of the Coronavirus 9.4 Benefits Due to the Impact of COVID-19 9.5 Role of Technology to Combat the Global Pandemic COVID-19 9.6 The Role of Artificial Intelligence, Machine Learning, and Deep Learning in COVID-19 9.7 Related Studies 9.8 Conclusion References 10. A Review on Smart Bin Management Systems Bhoomika Patel H. C., Soundarya B. C. and Pooja M. R. 10.1 Introduction 10.2 Related Work 10.3 Challenges, Solution, and Issues 10.4 Advantages Conclusion References 11. Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts K. Vidhyalakshmi and S. Thanga Ramya 11.1 Regression 11.2 Classification 11.3 Clustering 11.4 Clustering (k-means) 11.5 Reduction of Dimensionality 11.6 The Ensemble Method 11.7 Transfer of Learning 11.8 Learning Through Reinforcement 11.9 Processing of Natural Languages 11.10 Word Embeddings 11.11 Conclusion References 12. Recognition Attendance System Ensuring COVID-19 Security Praveen Kumar M., Ramya Poojary, Saksha S. Bhandary and Sushmitha M. Kulal 12.1 Introduction 12.2 Literature Survey 12.3 Software Requirements 12.4 Hardware Requirements 12.5 Methodology 12.6 Building the Database 12.7 Pi Camera for Extracting Face Features 12.8 Real-Time Testing on Raspberry Pi 12.9 Contactless Body Temperature Monitoring 12.10 Raspberry-Pi Setting Up an SMTP Email 12.11 Uploading to the Database 12.12 Updating the Website 12.13 Report Generation 12.14 Result 12.15 Discussion 12.16 Conclusion References 13. Real-Time Industrial Noise Cancellation for the Extraction of Human Voice Vinayprasad M. S., Chandrashekar Murthy B. N. and Yashwanth S. D. 13.1 Introduction 13.2 Literature Survey 13.3 Methodology 13.4 Experimental Results 13.5 Conclusion References 14. Machine Learning-Based Water Monitoring System Using IoT T. Kesavan, E. Kaliappan, K. Nagendran and M. Murugesan 14.1 Introduction 14.2 Smart Water Monitoring System 14.3 Sensors and Hardware 14.4 PowerBI Reports 14.5 Conclusion References 15. Design and Modelling of an Automated Driving Inspector Powered by Arduino and Raspberry Pi Raghunandan K. R., Dilip Kumar K., Krishnaraj Rao N.S. Krishnaprasad Rao and Bhavya K. 15.1 Introduction 15.2 Literature Survey 15.3 Results 15.4 Conclusion References 16. Kalman Filter-Based Seizure Prediction Using Concatenated Serial-Parallel Block Technique Purnima P. S. and Suresh M. 16.1 Introduction 16.2 Prior Work 16.3 Proposed Method 16.4 Serial-Parallel Block Concatenation Approach 16.5 Algorithm 16.6 Kalman Filter 16.7 Results and Discussion 16.8 Conclusion References 17. Current Advancements in Steganography: A Review Mallika Garg, Jagpal Singh Ubhi and Ashwani Kumar Aggarwal 17.1 Introduction 17.2 Evaluation Parameters 17.3 Types of Steganography 17.4 Traditional Steganographic Techniques 17.5 CNN-Based Steganographic Techniques 17.6 GAN-Based Steganographic Techniques 17.7 Steganalysis 17.8 Applications 17.9 Dataset Used for Steganography 17.10 Conclusion References 18. Human Emotion Recognizing Intelligence System Using Machine Learning Bhakthi P. Alva, Krishma Bopanna N., Prajwal S., Varun A. Naik and Lahari Vaidya 18.1 Introduction 18.2 Literature Review 18.3 Problem Statement 18.4 Methodology 18.5 Results 18.6 Applications 18.7 Conclusion 18.8 Future Work References 19. Computing in Cognitive Science Using Ensemble Learning Om Prakash Singh 19.1 Introduction 19.2 Recognition of Human Activities 19.3 Methodology 19.4 Applying the Boosting-Based Ensemble Learning 19.5 Human Activity Features Computability 19.6 Conclusion References About the Editors Index

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Author Information

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.

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