Description: Deep Learning and Edge Computing Solutions for High Performance Computing

Deep Learning and Edge Computing Solutions for High Performance Computing [E-Book] / edited by A. Suresh, Sara Paiva.

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 pers...

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Personal Name(s):	Paiva, Sara, editor
	Suresh, A., editor
Edition:	1st edition 2021.
Imprint:	Cham : Springer, 2021
Physical Description:	XII, 279 pages 117 illustrations (online resource)
Note:	englisch
ISBN:	9783030602659
DOI:	10.1007/978-3-030-60265-9
Series Title:	EAI/Springer Innovations in Communication and Computing
Subject (LOC):	Medical informatics. Signal processing. Telecommunication.

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.