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Deep Learning and Edge Computing Solutions for High Performance Computing
Title
Deep Learning and Edge Computing Solutions for High Performance Computing [electronic resource] / edited by A. Suresh, Sara Paiva.
ISBN
9783030602659
Edition
1st ed. 2021.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2021.
Physical Description
1 online resource (XII, 279 p.) 117 illus.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
February 05, 2021
Series
EAI/Springer Innovations in Communication and Computing,
Contents
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.
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