Editors:
Includes chapters from leading global experts on recent theoretical and applied advances in the use of machine learning in data analytics
Presents recent research in pattern recognition and data analytics
Is intended for both experts/researchers in the fields of pattern recognition, machine learning and data analytics as well as for readers working in the general field of computer science who wish to learn more about these emerging disciplines and their applications
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 149 )
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Table of contents (13 chapters)
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Front Matter
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Data Analytics in the Medical, Biological and Signal Sciences
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Front Matter
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Data Analytics in Social Studies and Social Interactions
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Front Matter
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Data Analytics in Traffic, Computer and Power Networks
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Front Matter
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Data Analytics for Digital Forensics
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Front Matter
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Theoretical Advances and Tools for Data Analytics
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Front Matter
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About this book
This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities.
The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences.Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics.
This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.
Keywords
- Pattern Recognition
- Machine Learning
- Computational Intelligence
- Data Analytics
- Data Science
- Software Personalization
Reviews
“It contains interesting work on machine learning in the medical domain. … it is an interesting collection of machine learning applications across multiple domains. It may be of interest to readers working in one of the discussed areas.” (K. Waldhör, Computing Reviews, January, 2019)
Editors and Affiliations
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University of Piraeus , Piraeus, Greece
George A. Tsihrintzis, Dionisios N. Sotiropoulos
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Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, Australia
Lakhmi C. Jain
Bibliographic Information
Book Title: Machine Learning Paradigms
Book Subtitle: Advances in Data Analytics
Editors: George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-94030-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2019
Hardcover ISBN: 978-3-319-94029-8Published: 12 July 2018
Softcover ISBN: 978-3-030-06777-9Published: 13 December 2018
eBook ISBN: 978-3-319-94030-4Published: 03 July 2018
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVI, 370
Number of Illustrations: 21 b/w illustrations, 110 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Big Data/Analytics, Pattern Recognition, Data Mining and Knowledge Discovery