Modeling and Optimization of Signals Using Machine Learning Techniques

Modeling and Optimization of Signals Using Machine Learning Techniques
Author :
Publisher : John Wiley & Sons
Total Pages : 421
Release :
ISBN-10 : 9781119847694
ISBN-13 : 1119847699
Rating : 4/5 (94 Downloads)

Book Synopsis Modeling and Optimization of Signals Using Machine Learning Techniques by : Chandra Singh

Download or read book Modeling and Optimization of Signals Using Machine Learning Techniques written by Chandra Singh and published by John Wiley & Sons. This book was released on 2024-08-23 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Modeling and Optimization of Signals Using Machine Learning Techniques Related Books

Modeling and Optimization of Signals Using Machine Learning Techniques
Language: en
Pages: 421
Authors: Chandra Singh
Categories: Computers
Type: BOOK - Published: 2024-08-23 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Explore the power of machine learning to revolutionize signal processing and optimization with cutting-edge techniques and practical insights in this outstandin
Machine Learning in Signal Processing
Language: en
Pages: 488
Authors: Sudeep Tanwar
Categories: Technology & Engineering
Type: BOOK - Published: 2021-12-10 - Publisher: CRC Press

DOWNLOAD EBOOK

Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizin
Machine Learning and Optimization for Engineering Design
Language: en
Pages: 175
Authors: Apoorva S. Shastri
Categories: Computers
Type: BOOK - Published: 2024-01-27 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of
Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
Language: en
Pages: 348
Authors: Nilanjan Dey
Categories: Science
Type: BOOK - Published: 2018-11-30 - Publisher: Academic Press

DOWNLOAD EBOOK

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical s
Advanced Machine Intelligence and Signal Processing
Language: en
Pages: 859
Authors: Deepak Gupta
Categories: Technology & Engineering
Type: BOOK - Published: 2022-06-25 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analyti