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Modeling and Optimization of Signals Using Machine Learning Techniques. Edition No. 1

  • Book

  • 416 Pages
  • September 2024
  • John Wiley and Sons Ltd
  • ID: 5983997
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.

Table of Contents

Preface xix

1 Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm 1
Adithya Kumar and Shivakumar B.R.

1.1 Introduction 2

1.2 Image Classification 5

1.3 Unsupervised Classification 7

1.4 Supervised Classification 8

1.5 Overview of Fuzzy Sets 9

1.6 Methodology 11

1.7 Results and Discussion 16

1.8 Conclusion 21

2 Role of AI in Mortality Prediction in Intensive Care Unit Patients 23
Prabhudutta Ray, Sachin Sharma, Raj Rawal and Dharmesh Shah

2.1 Introduction 24

2.2 Background 24

2.3 Objectives 25

2.4 Machine Learning and Mortality Prediction 26

2.5 Discussions 34

2.6 Conclusion 34

2.7 Future Work 35

2.8 Acknowledgments 35

2.9 Funding 35

2.10 Competing Interest 35

3 A Survey on Malware Detection Using Machine Learning 41
Devika S. P., Pooja M. R. and Arpitha M. S.

3.1 Background 41

3.2 Introduction 42

3.3 Literature Survey 44

3.4 Discussion 53

3.5 Conclusion 53

4 EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey 55
Bhoomika Patel H. C., Ravikumar V. and Pavan Kumar S. P.

4.1 Related Work 57

4.2 Equations 62

4.3 Classification 64

4.4 Data Set 65

4.5 Information Obtained by EEG Signals 69

4.6 Discussion 70

4.7 Conclusion 72

5 Machine Learning Methods in Radio Frequency and Microwave Domain 75
Shanthi P. and Adish K.

5.1 Introduction 76

5.2 Background on Machine Learning 77

5.3 ML in RF Circuit Modeling and Synthesis 86

5.4 Conclusion 93

6 A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola-Jones Algorithm 97
Vaibhav C. Gandhi, Dwij Kishor Siyal, Shivam Pankajkumar Patel and Arya Vipesh Shah

6.1 Introduction 98

6.2 Review of Literature 99

6.3 Report on Present Investigation 101

6.4 Algorithms 102

6.5 Viola-Jones Algorithm 104

6.6 Diagram 105

6.7 Results and Discussion 107

6.8 Limitations and Future Scope 111

6.9 Summary and Conclusion 111

7 Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques 115
E. Fantin Irudaya Raj and M. Balaji

7.1 Introduction 116

7.2 Methodology for the Identification of PQ Events 117

7.3 Power Quality Problems Arising in the Modern Power System 118

7.4 Digital Signal Processing-Based Feature Extraction of PQ Events 124

7.5 Feature Selection and Optimization 129

7.6 Machine Learning-Based Classification of PQ Disturbances 131

7.7 Summary and Conclusion 141

8 Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection 145
Shwetha N., Gangadhar N., Mahesh B. Neelagar, Sangeetha N. and Virupaxi Dalal

8.1 Introduction 146

8.2 Literature Survey 147

8.3 Proposed Methodology 149

8.4 Artificial Neural Network 152

8.5 Software Implementation Requirements 163

8.6 Conclusion 170

9 The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic 173
Biswa Ranjan Senapati, Sipra Swain and Pabitra Mohan Khilar

9.1 Introduction 174

9.2 Discussions on the Coronavirus 175

9.3 Bad Impacts of the Coronavirus 180

9.4 Benefits Due to the Impact of COVID-19 186

9.5 Role of Technology to Combat the Global Pandemic COVID-19 190

9.6 The Role of Artificial Intelligence, Machine Learning, and Deep Learning in COVID-19 198

9.7 Related Studies 203

9.8 Conclusion 203

10 A Review on Smart Bin Management Systems 209
Bhoomika Patel H. C., Soundarya B. C. and Pooja M. R.

10.1 Introduction 209

10.1.1 Internet of Things (IoT) 210

10.2 Related Work 211

10.3 Challenges, Solution, and Issues 213

10.4 Advantages 216

11 Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts 219
K. Vidhyalakshmi and S. Thanga Ramya

11.1 Regression 220

11.2 Classification 222

11.3 Clustering 227

11.4 Clustering (k-means) 227

11.5 Reduction of Dimensionality 230

11.6 The Ensemble Method 233

11.7 Transfer of Learning 240

11.8 Learning Through Reinforcement 241

11.9 Processing of Natural Languages 242

11.10 Word Embeddings 242

11.11 Conclusion 243

12 Recognition Attendance System Ensuring COVID-19 Security 245
Praveen Kumar M., Ramya Poojary, Saksha S. Bhandary and Sushmitha M. Kulal

12.1 Introduction 246

12.2 Literature Survey 246

12.3 Software Requirements 248

12.4 Hardware Requirements 249

12.5 Methodology 252

12.6 Building the Database 253

12.7 Pi Camera for Extracting Face Features 255

12.8 Real-Time Testing on Raspberry Pi 256

12.9 Contactless Body Temperature Monitoring 256

12.10 Raspberry-Pi Setting Up an SMTP Email 258

12.11 Uploading to the Database 259

12.12 Updating the Website 260

12.13 Report Generation 260

12.14 Result 262

12.15 Discussion 267

12.16 Conclusion 267

13 Real-Time Industrial Noise Cancellation for the Extraction of Human Voice 271
Vinayprasad M. S., Chandrashekar Murthy B. N. and Yashwanth S. D.

13.1 Introduction 272

13.2 Literature Survey 273

13.3 Methodology 275

13.4 Experimental Results 278

13.5 Conclusion 280

14 Machine Learning-Based Water Monitoring System Using IoT 283
T. Kesavan, E. Kaliappan, K. Nagendran and M. Murugesan

14.1 Introduction 283

14.2 Smart Water Monitoring System 284

14.3 Sensors and Hardware 286

14.4 PowerBI Reports 288

14.5 Conclusion 291

15 Design and Modelling of an Automated Driving Inspector Powered by Arduino and Raspberry Pi 295
Raghunandan K. R., Dilip Kumar K., Krishnaraj Rao N.S. Krishnaprasad Rao and Bhavya K.

15.1 Introduction 296

15.2 Literature Survey 296

15.3 Results 306

15.4 Conclusion 309

16 Kalman Filter-Based Seizure Prediction Using Concatenated Serial-Parallel Block Technique 313
Purnima P. S. and Suresh M.

16.1 Introduction 314

16.2 Prior Work 314

16.3 Proposed Method 316

16.4 Serial-Parallel Block Concatenation Approach 318

16.5 Algorithm 319

16.6 Kalman Filter 320

16.7 Results and Discussion 321

16.8 Conclusion 323

17 Current Advancements in Steganography: A Review 327
Mallika Garg, Jagpal Singh Ubhi and Ashwani Kumar Aggarwal

17.1 Introduction 328

17.2 Evaluation Parameters 329

17.3 Types of Steganography 330

17.4 Traditional Steganographic Techniques 332

17.5 CNN-Based Steganographic Techniques 336

17.6 GAN-Based Steganographic Techniques 338

17.7 Steganalysis 340

17.8 Applications 341

17.9 Dataset Used for Steganography 341

17.10 Conclusion 344

18 Human Emotion Recognition Intelligence System Using Machine Learning 349
Bhakthi P. Alva, Krishma Bopanna N., Prajwal S., Varun A. Naik and Lahari Vaidya

18.1 Introduction 350

18.2 Literature Review 350

18.3 Problem Statement 352

18.4 Methodology 353

18.5 Results 355

18.6 Applications 355

18.7 Conclusion 357

18.8 Future Work 357

19 Computing in Cognitive Science Using Ensemble Learning 361
Om Prakash Singh

19.1 Introduction 362

19.2 Recognition of Human Activities 363

19.3 Methodology 366

19.4 Applying the Boosting-Based Ensemble Learning 369

19.5 Human Activity Features Computability 373

19.6 Conclusion 378

References 378

About the Editors 383

Index 385

Authors

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. Naveen Kumar S. Sahyadri College of Engineering and Management, India. Varun Saxena IIT Delhi, India.