{"product_id":"modeling-and-optimization-of-signals-using-machine-learning-techniques-hardback-9781119847687","title":"Modeling and Optimization of Signals Using Machine Learning Techniques (Hardback) 9781119847687","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eModeling and Optimization of Signals Using Machine Learning Techniques\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eChandra Singh (Edited by), Singh (Author), Rathishchandra R. Gatti (Edited by), K.V.S.S.S.S. Sairam (Edited by), Manjunatha Badiger (Edited by), Naveen Kumar S. (Edited by), Varun Saxena (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119847687, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 3 September 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e22.9 x 15.2 x 2.6 cm, 0.862 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eExplore 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eModeling and Optimization of Signals using Machine Learning Techniques\u003c\/i\u003e is designed for researchers from academia, industries, and R\u0026amp;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.  \u003c\/p\u003e\n\u003cp\u003eThrough 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. \u003c\/p\u003e\n\u003cp\u003eWhether 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAdithya Kumar and Shivakumar B.R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Image Classification 5\u003c\/p\u003e \u003cp\u003e1.3 Unsupervised Classification 7\u003c\/p\u003e \u003cp\u003e1.4 Supervised Classification 8\u003c\/p\u003e \u003cp\u003e1.5 Overview of Fuzzy Sets 9\u003c\/p\u003e \u003cp\u003e1.6 Methodology 11\u003c\/p\u003e \u003cp\u003e1.7 Results and Discussion 16\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Role of AI in Mortality Prediction in Intensive Care Unit Patients 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrabhudutta Ray, Sachin Sharma, Raj Rawal and Dharmesh Shah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003c\/p\u003e \u003cp\u003e2.2 Background 24\u003c\/p\u003e \u003cp\u003e2.3 Objectives 25\u003c\/p\u003e \u003cp\u003e2.4 Machine Learning and Mortality Prediction 26\u003c\/p\u003e \u003cp\u003e2.5 Discussions 34\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 34\u003c\/p\u003e \u003cp\u003e2.7 Future Work 35\u003c\/p\u003e \u003cp\u003e2.8 Acknowledgments 35\u003c\/p\u003e \u003cp\u003e2.9 Funding 35\u003c\/p\u003e \u003cp\u003e2.10 Competing Interest 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Survey on Malware Detection Using Machine Learning 41\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDevika S. P., Pooja M. R. and Arpitha M. S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Background 41\u003c\/p\u003e \u003cp\u003e3.2 Introduction 42\u003c\/p\u003e \u003cp\u003e3.3 Literature Survey 44\u003c\/p\u003e \u003cp\u003e3.4 Discussion 53\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey 55\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBhoomika Patel H. C., Ravikumar V. and Pavan Kumar S. P.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Related Work 57\u003c\/p\u003e \u003cp\u003e4.2 Equations 62\u003c\/p\u003e \u003cp\u003e4.3 Classification 64\u003c\/p\u003e \u003cp\u003e4.4 Data Set 65\u003c\/p\u003e \u003cp\u003e4.5 Information Obtained by EEG Signals 69\u003c\/p\u003e \u003cp\u003e4.6 Discussion 70\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Machine Learning Methods in Radio Frequency and Microwave Domain 75\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShanthi P. and Adish K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 76\u003c\/p\u003e \u003cp\u003e5.2 Background on Machine Learning 77\u003c\/p\u003e \u003cp\u003e5.3 ML in RF Circuit Modeling and Synthesis 86\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola-Jones Algorithm 97\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVaibhav C. Gandhi, Dwij Kishor Siyal, Shivam Pankajkumar Patel and Arya Vipesh Shah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 98\u003c\/p\u003e \u003cp\u003e6.2 Review of Literature 99\u003c\/p\u003e \u003cp\u003e6.3 Report on Present Investigation 101\u003c\/p\u003e \u003cp\u003e6.4 Algorithms 102\u003c\/p\u003e \u003cp\u003e6.5 Viola-Jones Algorithm 104\u003c\/p\u003e \u003cp\u003e6.6 Diagram 105\u003c\/p\u003e \u003cp\u003e6.7 Results and Discussion 107\u003c\/p\u003e \u003cp\u003e6.8 Limitations and Future Scope 111\u003c\/p\u003e \u003cp\u003e6.9 Summary and Conclusion 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques 115\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eE. Fantin Irudaya Raj and M. Balaji\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 116\u003c\/p\u003e \u003cp\u003e7.2 Methodology for the Identification of PQ Events 117\u003c\/p\u003e \u003cp\u003e7.3 Power Quality Problems Arising in the Modern Power System 118\u003c\/p\u003e \u003cp\u003e7.4 Digital Signal Processing-Based Feature Extraction of PQ Events 124\u003c\/p\u003e \u003cp\u003e7.5 Feature Selection and Optimization 129\u003c\/p\u003e \u003cp\u003e7.6 Machine Learning-Based Classification of PQ Disturbances 131\u003c\/p\u003e \u003cp\u003e7.7 Summary and Conclusion 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection 145\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShwetha N., Gangadhar N., Mahesh B. Neelagar, Sangeetha N. and Virupaxi Dalal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 146\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 147\u003c\/p\u003e \u003cp\u003e8.3 Proposed Methodology 149\u003c\/p\u003e \u003cp\u003e8.4 Artificial Neural Network 152\u003c\/p\u003e \u003cp\u003e8.5 Software Implementation Requirements 163\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic 173\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBiswa Ranjan Senapati, Sipra Swain and Pabitra Mohan Khilar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 174\u003c\/p\u003e \u003cp\u003e9.2 Discussions on the Coronavirus 175\u003c\/p\u003e \u003cp\u003e9.3 Bad Impacts of the Coronavirus 180\u003c\/p\u003e \u003cp\u003e9.4 Benefits Due to the Impact of COVID-19 186\u003c\/p\u003e \u003cp\u003e9.5 Role of Technology to Combat the Global Pandemic COVID-19 190\u003c\/p\u003e \u003cp\u003e9.6 The Role of Artificial Intelligence, Machine Learning, and Deep Learning in COVID-19 198\u003c\/p\u003e \u003cp\u003e9.7 Related Studies 203\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Review on Smart Bin Management Systems 209\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBhoomika Patel H. C., Soundarya B. C. and Pooja M. R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 209\u003c\/p\u003e \u003cp\u003e10.1.1 Internet of Things (IoT) 210\u003c\/p\u003e \u003cp\u003e10.2 Related Work 211\u003c\/p\u003e \u003cp\u003e10.3 Challenges, Solution, and Issues 213\u003c\/p\u003e \u003cp\u003e10.4 Advantages 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts 219\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Vidhyalakshmi and S. Thanga Ramya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Regression 220\u003c\/p\u003e \u003cp\u003e11.2 Classification 222\u003c\/p\u003e \u003cp\u003e11.3 Clustering 227\u003c\/p\u003e \u003cp\u003e11.4 Clustering (k-means) 227\u003c\/p\u003e \u003cp\u003e11.5 Reduction of Dimensionality 230\u003c\/p\u003e \u003cp\u003e11.6 The Ensemble Method 233\u003c\/p\u003e \u003cp\u003e11.7 Transfer of Learning 240\u003c\/p\u003e \u003cp\u003e11.8 Learning Through Reinforcement 241\u003c\/p\u003e \u003cp\u003e11.9 Processing of Natural Languages 242\u003c\/p\u003e \u003cp\u003e11.10 Word Embeddings 242\u003c\/p\u003e \u003cp\u003e11.11 Conclusion 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Recognition Attendance System Ensuring COVID-19 Security 245\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePraveen Kumar M., Ramya Poojary, Saksha S. Bhandary and Sushmitha M. Kulal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 246\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 246\u003c\/p\u003e \u003cp\u003e12.3 Software Requirements 248\u003c\/p\u003e \u003cp\u003e12.4 Hardware Requirements 249\u003c\/p\u003e \u003cp\u003e12.5 Methodology 252\u003c\/p\u003e \u003cp\u003e12.6 Building the Database 253\u003c\/p\u003e \u003cp\u003e12.7 Pi Camera for Extracting Face Features 255\u003c\/p\u003e \u003cp\u003e12.8 Real-Time Testing on Raspberry Pi 256\u003c\/p\u003e \u003cp\u003e12.9 Contactless Body Temperature Monitoring 256\u003c\/p\u003e \u003cp\u003e12.10 Raspberry-Pi Setting Up an SMTP Email 258\u003c\/p\u003e \u003cp\u003e12.11 Uploading to the Database 259\u003c\/p\u003e \u003cp\u003e12.12 Updating the Website 260\u003c\/p\u003e \u003cp\u003e12.13 Report Generation 260\u003c\/p\u003e \u003cp\u003e12.14 Result 262\u003c\/p\u003e \u003cp\u003e12.15 Discussion 267\u003c\/p\u003e \u003cp\u003e12.16 Conclusion 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Real-Time Industrial Noise Cancellation for the Extraction of Human Voice 271\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVinayprasad M. S., Chandrashekar Murthy B. N. and Yashwanth S. D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 272\u003c\/p\u003e \u003cp\u003e13.2 Literature Survey 273\u003c\/p\u003e \u003cp\u003e13.3 Methodology 275\u003c\/p\u003e \u003cp\u003e13.4 Experimental Results 278\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Machine Learning-Based Water Monitoring System Using IoT 283\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eT. Kesavan, E. Kaliappan, K. Nagendran and M. Murugesan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 283\u003c\/p\u003e \u003cp\u003e14.2 Smart Water Monitoring System 284\u003c\/p\u003e \u003cp\u003e14.3 Sensors and Hardware 286\u003c\/p\u003e \u003cp\u003e14.4 PowerBI Reports 288\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 291\u003c\/p\u003e \u003cp\u003e15 Design and Modelling of an Automated Driving Inspector Powered by Arduino and Raspberry Pi 295\u003cbr\u003e\u003ci\u003eRaghunandan K. R., Dilip Kumar K., Krishnaraj Rao N.S. Krishnaprasad Rao and Bhavya K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 296\u003c\/p\u003e \u003cp\u003e15.2 Literature Survey 296\u003c\/p\u003e \u003cp\u003e15.3 Results 306\u003c\/p\u003e \u003cp\u003e15.4 Conclusion 309\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Kalman Filter-Based Seizure Prediction Using Concatenated Serial-Parallel Block Technique 313\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePurnima P. S. and Suresh M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 314\u003c\/p\u003e \u003cp\u003e16.2 Prior Work 314\u003c\/p\u003e \u003cp\u003e16.3 Proposed Method 316\u003c\/p\u003e \u003cp\u003e16.4 Serial-Parallel Block Concatenation Approach 318\u003c\/p\u003e \u003cp\u003e16.5 Algorithm 319\u003c\/p\u003e \u003cp\u003e16.6 Kalman Filter 320\u003c\/p\u003e \u003cp\u003e16.7 Results and Discussion 321\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Current Advancements in Steganography: A Review 327\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMallika Garg, Jagpal Singh Ubhi and Ashwani Kumar Aggarwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 328\u003c\/p\u003e \u003cp\u003e17.2 Evaluation Parameters 329\u003c\/p\u003e \u003cp\u003e17.3 Types of Steganography 330\u003c\/p\u003e \u003cp\u003e17.4 Traditional Steganographic Techniques 332\u003c\/p\u003e \u003cp\u003e17.5 CNN-Based Steganographic Techniques 336\u003c\/p\u003e \u003cp\u003e17.6 GAN-Based Steganographic Techniques 338\u003c\/p\u003e \u003cp\u003e17.7 Steganalysis 340\u003c\/p\u003e \u003cp\u003e17.8 Applications 341\u003c\/p\u003e \u003cp\u003e17.9 Dataset Used for Steganography 341\u003c\/p\u003e \u003cp\u003e17.10 Conclusion 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Human Emotion Recognition Intelligence System Using Machine Learning 349\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBhakthi P. Alva, Krishma Bopanna N., Prajwal S., Varun A. Naik and Lahari Vaidya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 350\u003c\/p\u003e \u003cp\u003e18.2 Literature Review 350\u003c\/p\u003e \u003cp\u003e18.3 Problem Statement 352\u003c\/p\u003e \u003cp\u003e18.4 Methodology 353\u003c\/p\u003e \u003cp\u003e18.5 Results 355\u003c\/p\u003e \u003cp\u003e18.6 Applications 355\u003c\/p\u003e \u003cp\u003e18.7 Conclusion 357\u003c\/p\u003e \u003cp\u003e18.8 Future Work 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Computing in Cognitive Science Using Ensemble Learning 361\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eOm Prakash Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 362\u003c\/p\u003e \u003cp\u003e19.2 Recognition of Human Activities 363\u003c\/p\u003e \u003cp\u003e19.3 Methodology 366\u003c\/p\u003e \u003cp\u003e19.4 Applying the Boosting-Based Ensemble Learning 369\u003c\/p\u003e \u003cp\u003e19.5 Human Activity Features Computability 373\u003c\/p\u003e \u003cp\u003e19.6 Conclusion 378\u003c\/p\u003e \u003cp\u003eReferences 378\u003c\/p\u003e \u003cp\u003eAbout the Editors 383\u003c\/p\u003e \u003cp\u003eIndex 385\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430977040664,"sku":"9781119847687","price":139.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119847687.jpg?v=1784767189","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/modeling-and-optimization-of-signals-using-machine-learning-techniques-hardback-9781119847687","provider":"Freshly Printed Books","version":"1.0","type":"link"}