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Modeling and Optimization of Signals Using Machine Learning Techniques
Hoofdkenmerken
Auteur: Chandra Singh, Rathishchandra R. Gatti, K.V.S.S.S.S. Sairam, Manjunatha Badiger, Naveen Kumar S., Va
Titel: Modeling and Optimization of Signals Using Machine Learning Techniques
Uitgever: Wiley Global Research (STMS)
ISBN: 9781119847694
ISBN boekversie: 9781119847687
Editie: 1
Prijs: € 233.79
Verschijningsdatum: 23-08-2024
Inhoudelijke kenmerken
Categorie: Information Technology
Taal: English
Imprint: Wiley-Scrivener
Technische kenmerken
Verschijningsvorm: E-book
 

Inhoudsopgave:

\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 \u003cp\u003e\u003ci\u003eModeling and Optimization of Signals using Machine Learning Techniques\u003c/i\u003e is designed for researchers from academia, industries, and R\u0026D 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. \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. \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.
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