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020 _a9783030602659
_9978-3-030-60265-9
024 7 _a10.1007/978-3-030-60265-9
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
245 1 0 _aDeep Learning and Edge Computing Solutions for High Performance Computing
_h[electronic resource] /
_cedited by A. Suresh, Sara Paiva.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXII, 279 p. 117 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aEAI/Springer Innovations in Communication and Computing,
_x2522-8609
505 0 _aIntroduction -- 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 _aThis 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.
650 0 _aTelecommunication.
_910437
650 0 _aSignal processing.
_94052
650 0 _aMedical informatics.
_94729
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aHealth Informatics.
_931799
700 1 _aSuresh, A.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_948608
700 1 _aPaiva, Sara.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_948609
710 2 _aSpringerLink (Online service)
_948610
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030602642
776 0 8 _iPrinted edition:
_z9783030602666
776 0 8 _iPrinted edition:
_z9783030602673
830 0 _aEAI/Springer Innovations in Communication and Computing,
_x2522-8609
_948611
856 4 0 _uhttps://doi.org/10.1007/978-3-030-60265-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78254
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