Machine Learning Paradigms : Advances in Data Analytics / edited by George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain.

By:

Tsihrintzis, George A

Contributor(s):

Material type: Text

TextSeries: Intelligent Systems Reference Library ; 149 Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019Description: 1 online resource (XVI, 370 pages 131 illustrations, 110 illustrations in color.)Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319940304

Subject(s):

Additional physical formats: Print version:: Machine learning paradigms.; Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23 TSI-M 2019 790486

Contents:

Data Analytics in the Medical, Biological and Signal Sciences -- Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics -- Classification Methods in Image Analysis with a Special Focus on Medical Analytics -- Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field -- Machine Learning Methods for the Protein Fold Recognition Problem.

Summary: 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.

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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode	Item holds
Reference	Faculty of CS & IT Library Book Cart	Book	006.3 TSI-M 2019 790486 (Browse shelf(Opens below))	1	Not For Loan (Restricted Access)		790486
Books	Faculty of CS & IT Library Book Cart	Book	006.3 TSI-M 2019 790489 (Browse shelf(Opens below))	2	Available		790489

Total holds: 0

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.

Description based on publisher-supplied MARC data.