Mohanty Sachi Nandan - Machine Learning Approach for Cloud Data Analytics in IoT

Scrivener Publishing
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Publishers at Scrivener

Martin Scrivener ()
Phillip Carmical ()

Edited by

Sachi Nandan Mohanty

Jyotir Moy Chatterjee

Monika Mangla

Suneeta Satpathy

Sirisha Potluri

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-78580-4

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

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10 9 8 7 6 5 4 3 2 1

Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. According to statistics, billions of connected IoT devices will be producing enormous amounts of real-time data in the coming days. In order to expedite decision-making involved in the complex computation and processing of collected data, these devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in the creation of the first artificial intelligence services. The abundant research occurring all around the world has resulted in a wide range of advancements being reported on computing platforms. This book elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. The practices, technologies and innovations of business intelligence employed to make expeditious decisions are encouraged as a part of this area of research.

This book focuses on various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization and data analytics. The featured technology presented herein optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational cost. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also essential sections of this book.

explores the anthropomorphic gamifying elements, mostly on how it can be implemented in a blockchain-enabled transitional healthcare system in a more lucrative manner.

Sachi Nandan Mohanty, India
Jyotir Moy Chatterjee, Nepal
Monika Mangla, India
SuneetaSatpathy, India
Sirisha Potluri, India
May 2021

The editors would like to pass on our good wishes and express our appreciation to all the authors who contributed chapters to this book. We would also like to thank the subject matter experts who found time to review the chapters and deliver their comments in a timely manner. Special thanks also go to those who took the time to give advice and make suggestions that helped refine our thoughts and approaches accordingly to produce richer contributions. We are particularly grateful to Scrivener Publishing for their amazing crew who supported us with their encouragement, engagement, support, cooperation and contributions in publishing this book.

M. Deepika

Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies (Formerly Vels University), Chennai, Tamil Nadu, India

Department of Computer Applications, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies (Formerly Vels University), Chennai, Tamil Nadu, India

Abstract

Artificial intelligence (AI) is a technical mix, and machine learning (ML) is one of the most important techniques in highly personalized marketing. AI ML presupposes that the system is re-assessed and the data is reassessed without human intervention. It is all about shifting. Just as AI means, for every possible action/reaction, that a human programmer does not have to code, AI machine programming can evaluate and test data to replicate every customer product with the speed and capacity that no one can attain. The technology we have been using has been around for a long time, but the influence of machines, cloud-based services, and the applicability of AI on our position as marketers have changed in recent years. Different information and data orientation contribute to a variety of technical improvements. This chapter focuses on the use of large amounts of information that enables a computer to carry out a non-definitive analysis based on project understanding. It also focuses on data collection and helps to ensure that data analysis is prepared. It also defines such data analytics processes for prediction and analysis using ML algorithms. Questions related to ML data mining are also clearly explained.