Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML).
ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML.
The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML.
FEATURES
Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readersOriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams
This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.
Machine Learning in Signal Processing: Applications, Challenges and Road Ahead offers a comprehensive approach towards research orientation for familiarising ‘signal processing (SP)’ concepts to machine learning (ML).
1. Introduction to Signal Processing and Machine Learning
Kavitha Somaraj
2. Learning Theory (Supervised/Unsupervised) for Signal Processing
Ruby Jain, Bhuvan Jain, and Manimala Puri
3. Supervised and Unsupervised Learning Theory for Signal Processing
Sowmya K. B.
4. Applications of Signal Processing
Anuj Kumar Singh and Ankit Garg
5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and
Signal Processing
V. Ajantha Devi and Mohd Naved
6. BrainComputer Interfacing
Paras Nath Singh
7. Adaptive Filters and Neural Net
Sowmya K. B., Chandana G., and Anjana Mahaveer Daigond
8. Adaptive Decision Feedback Equalizer Based on Wavelet Neural Network
Saikat Majumder
9. Intelligent Video Surveillance Systems Using Deep Learning Methods
Anjanadevi Bondalapati and Manjaiah D. H.
10. Stationary Signal, Autocorrelation, and Linear and Discriminant Analysis
Bandana Mahapatra and Kumar Sanjay Bhorekar
11. Intelligent System for Fault Detection in Rotating Electromechanical
Machines.
Pascal Dore, Saad Chakkor, and Ahmed El Oualkadi
12. Wavelet Transformation and Machine Learning Techniques for Digital Signal
Analysis in IoT Systems
Rajalakshmi Krishnamurthi and Dhanalekshmi Gopinathan
Dr. Sudeep Tanwar (M15, SM21) is currently working as a Professor of the Computer Science and Engineering Department at the Institute of Technology, Nirma University, India. Dr Tanwar was a visiting Professor at Jan Wyzykowski University in Polkowice, Poland and the University of Pitesti in Pitesti, Romania. Dr Tanwars research interests include Blockchain Technology, Wireless Sensor Networks, Fog Computing, Smart Grid, and IoT. He has authored 02 books and edited 13 books, more than 200 technical papers, including top journals and top conferences, such as IEEE TNSE, TVT, TII, WCM, Networks, ICC, GLOBECOM, and INFOCOM. He is a Senior Member of IEEE, CSI, IAENG, ISTE, CSTA, and the member of the Technical Committee on Tactile Internet of IEEE Communication Society. He is leading the ST research lab where group members are working on the latest cutting-edge technologies.
Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Duy Tan University, Da Nang, Vietnam. A Certified Professional with 75+ Professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published 100+ Research Papers in various National International Journals (Scopus/SCI/SCIE/SSCI Indexed) with High Impact Factor. Member of more than 50+ Associations as Senior and Life Member. He is acting as Editor-in-Chief of IGI-Global, USA Journal titled International Journal of Smart Vehicles and Smart Transportation (IJSVST).
Dr. Rudra Rameshwar (Ph.D. IIT Roorkee, M.Tech. IIT Roorkee, D.B.E. EDII Ahmedabad, B.Tech. (Elect. Engg.) DEI Agra, B.Sc. DEI Agra) is full-time management faculty working in LMTSOM, Thapar Institute of Engineering & Technology (Deemed-to-be-University) Patiala (Punjab State), India. He is associated with core MBA specializations working in the area of Operations, Energy & Sustainability, and Analytics. Additionally, he is working in the area of Industry 4.0, Education 4.0, Business Analytics, HR Analytics, CSR, Service Operations Management, Sustainable Development, Warehouse Management, Sustainable Business Strategies, Industrial Marketing, Technology & Innovation, Research Methodology, Data Analytics, International Management, Business Statistics, Research Design and Statistical Tools Techniques, - Data Analysis, Interpretation SPSS/EViews/Minitab Training, Meta-Analysis, Advanced Regression Analysis, Qualitative & Quantitative Research, Academic Publishing and Integrity. He is a Life member of Thomason Alumni Association (IIT Roorkee), Indian Science Congress Association (ISCA) Kolkata, Confederation of Indian Industry (CII) Chandigarh.
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