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008			210127s2021 gw a o |||| 0|eng d
020			_a9783030602659
024	7		_a10.1007/978-3-030-60265-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aR858 _b2021 EB
245	0	0	_aDeep learning and edge computing solutions for high performance computing _cedited by A. Suresh, Sara Paiva
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XII, 279 páginas) _b117 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEAI/Springer Innovations in Communication and Computing _x2522-8595
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
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	3		_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.
988			_aSpringer_Engineering_2021
650		7	_2embne _aInformática médica _9421154
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aSuresh, A. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPaiva, Sara _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-60265-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eb _zSI