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Deep learning and edge computing sol...

Suresh, A.

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Deep learning and edge computing solutions for high performance computing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning and edge computing solutions for high performance computing/ edited by A. Suresh, Sara Paiva.
other author:	Suresh, A.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xii, 279 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Deep learning methods for applications -- High performance Computing systems for applications in Healthcare -- Hyperspectral data analysis and intelligent systems -- Microarray data analysis -- Sequence analysis -- Genomics based analytics -- Disease network analysis -- Techniques for big data Analytics and health information technology -- Deep Learning and Cross-Media Methods for Big Data Representation -- Mobile edge computing for Large-scale multimodal data acquisition techniques -- Personal Big data driven approaches to collect and analyze large volumes of information from emerging technologies -- Mobile edge computing techniques for healthcare applications -- Swarm intelligence big data computing for healthcare applications -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Medical informatics. -
Online resource:	https://doi.org/10.1007/978-3-030-60265-9
ISBN:	9783030602659

Deep learning and edge computing solutions for high performance computing
Deep learning and edge computing solutions for high performance computing[electronic resource] /edited by A. Suresh, Sara Paiva. - Cham :Springer International Publishing :2021. - xii, 279 p. :ill., digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8595. - EAI/Springer innovations in communication and computing..

Introduction -- Deep learning methods for applications -- High performance Computing systems for applications in Healthcare -- Hyperspectral data analysis and intelligent systems -- Microarray data analysis -- Sequence analysis -- Genomics based analytics -- Disease network analysis -- Techniques for big data Analytics and health information technology -- Deep Learning and Cross-Media Methods for Big Data Representation -- Mobile edge computing for Large-scale multimodal data acquisition techniques -- Personal Big data driven approaches to collect and analyze large volumes of information from emerging technologies -- Mobile edge computing techniques for healthcare applications -- Swarm intelligence big data computing for healthcare applications -- Conclusion.

This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology. Identifies deep learning techniques in mobile edge data analytics and computing environments suitable for applications in healthcare; Introduces big data analytics to the sources available and possible challenges and techniques associated with bioinformatics and the healthcare domain; Features advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data.

ISBN: 9783030602659

Standard No.: 10.1007/978-3-030-60265-9doiSubjects--Topical Terms:

661258
Medical informatics.

LC Class. No.: R859.7.A78 / D447 2021

Dewey Class. No.: 610.28563

Deep learning and edge computing solutions for high performance computing
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505 0 $a Introduction -- Deep learning methods for applications -- High performance Computing systems for applications in Healthcare -- Hyperspectral data analysis and intelligent systems -- Microarray data analysis -- Sequence analysis -- Genomics based analytics -- Disease network analysis -- Techniques for big data Analytics and health information technology -- Deep Learning and Cross-Media Methods for Big Data Representation -- Mobile edge computing for Large-scale multimodal data acquisition techniques -- Personal Big data driven approaches to collect and analyze large volumes of information from emerging technologies -- Mobile edge computing techniques for healthcare applications -- Swarm intelligence big data computing for healthcare applications -- Conclusion.
520 $a This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology. Identifies deep learning techniques in mobile edge data analytics and computing environments suitable for applications in healthcare; Introduces big data analytics to the sources available and possible challenges and techniques associated with bioinformatics and the healthcare domain; Features advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data.
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700 1 $a Suresh, A. $3 3489843
700 1 $a Paiva, Sara. $3 3383094
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