Big Data, IoT, and Machine Learning (eBook)

Tools and Applications
Artikelnummer: 978-1-00-009828-0
Einband: PDF
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools.

This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them.

Features

  • Addresses the complete data science technologies workflow
  • Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning
  • Covers data processing and security solutions in IoT and Big Data applications
  • Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems
  • Presents security issues and data migration techniques of NoSQL databases

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools.

This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them.

Features

  • Addresses the complete data science technologies workflow
  • Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning
  • Covers data processing and security solutions in IoT and Big Data applications
  • Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems
  • Presents security issues and data migration techniques of NoSQL databases
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VerlagTaylor & Francis Ebooks
EinbandPDF
Erscheinungsjahr2020
Seitenangabe337 S.
AusgabekennzeichenEnglisch
Abbildungen75 schwarz-weiße Abbildungen, 21 schwarz-weiße Tabellen
Auflage20001 A. 1. Auflage
PlattformPDF
AutorAgrawal, Rashmi (Hrsg.) / Paprzycki, Marcin (Hrsg.) / Gupta, Neha (Hrsg.)

Über den Autor Rashmi (Hrsg.) Agrawal

Rashmi Agrawal, PhD, is a professor and the Head of the Department of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, India with 20 years of experience in teaching and research. She is a lifetime member of the Computer Society of India, a senior member of the Institute of Electrical and Electronics Engineers, and a chapter chair and professional member of the Association for Computing Machinery. Alongside her affiliations, she is a series editor, has authored and co-authored over 80 research papers in peer-reviewed national and international journals and conferences, and has four patents to her credit as well as a copyright. Additionally, she has contributed as a keynote speaker at IEEE international conferences, an expert lecturer at professional development events, and a session chair for various international conferences. Pramod Singh Rathore, PhD, is an assistant professor in the computer science and engineering department at the Aryabhatta Engineering College and Research Centre, Ajmer, Rajasthan, India and is also visiting faculty at the Government University, MDS Ajmer. He has over eight years of teaching experience and more than 45 publications in peer-reviewed journals, books, and conferences. He has also co-authored and edited numerous books with a variety of global publishers, such as the imprint, Wiley-Scrivener. Ganesh Gopal Devarajan, PhD, is a professor in the Department of Computer Science and Engineering, SRM Institute of Science and Technology, India with more than 17 years of research and teaching experience in computer science and engineering. He has edited many special issues in reputed journals and is a member of the Institute of Electrical and Electronics Engineers, Association for Computing Machinery, and Computer Society of India. His research interests include Internet of Things (IoT), wireless communication, vehicular communication, and big data. Rajiva Ranjan Divivedi is an assistant professor in the Computer Science and Engineering Department at SRM Institute of Science and Technology, Delhi, India with over six years of teaching and research experience. He holds a Master's Degree in Computer Science and Engineering and has qualified under both the National Testing Agency's National Eligibility Test and the Graduate Aptitude Test in Engineering. His research interests include machine learning, data analytics, and Internet of Things.

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