edited by Suman Swarnkar, J. P. Patra, Tien Anh Tran, Bharat Bhushan, and Santosh Biswas.
EDITION STATEMENT
Edition Statement
First edition.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boca Raton, FL
Name of Publisher, Distributor, etc.
CRC Press
Date of Publication, Distribution, etc.
2023.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xix, 176 pages
Other Physical Details
ill. (mostly color), tables.
SERIES
Series Title
Innovations in multimedia, virtual reality and augmentation
GENERAL NOTES
Text of Note
Editors: Suman Swarnkar, J. P. Patra, Tien Anh Tran, Bharat Bhushan, and Santosh Biswas.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
SUMMARY OR ABSTRACT
Text of Note
"This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometric, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields. Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections: In this book on multimedia data engineering and machine learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various machine learning algorithms, neural network algorithms, evolutionary techniques, fuzzy logic techniques, and deep learning techniques through projects, so that reader can easily understand, not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need of developing a foundation of machine learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from machine learning ecosystems to demonstrate the necessary machine learning skills to become a successful practitioner. The primary users for the book include undergraduate, and postgraduate students, researchers, academicians, specialists, and practitioners in Computer Science and Engineering"--