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 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.
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