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008			230201s2023 si | o |||| 0|eng d
020			_a9789811957239
024	7		_a10.1007/978-981-19-5723-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.73 _b2023 EB
245	0	0	_aDeep Learning Technologies for the Sustainable Development Goals : _bIssues and Solutions in the Post-COVID Era _cedited by Virender Kadyan, T. P. Singh, Chidiebere Ugwu
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aAdvanced Technologies and Societal Change _x2191-6861
505	0		_aHow Deep Learning can help in Regulating the Subscription Economy to Ensure Sustainable Consumption and Production Patterns (12th goal of SDGs) -- Deep Technologies using Big Data in: Energy and Waste Management -- QoS aware service provisioning and resource distribution in 4G/5G heterogeneous networks -- Leveraging Fog Computing for Healthcare -- Intelligent self-tuning control design for wastewater treatment plant based on PID and Model Predictive methods -- Impact of Deep learning models for technology sustainability in tourism using Big data analytics -- Study of UAV Management Using Cloud Based Systems -- Contemporary Role of Blockchain in Industry 4.0 -- SDGs Laid Down by UN 2030 Document -- Healthcare 4P: Systematic Review of Applications of Decentralized Trust using Blockchain Technology -- Implementation of An IOT Based Water And Disaster Management System By Using Hybrid Classification Approach -- Knowledge Representation to Expound Deep -- Learning Black Box -- Ann : Concept And Application In Brain Tumor Segmentation -- Automation Of Brain Tumor Segmentation Using Deep Learning -- Transportation Management using IoT Deep Learning to Predict various Traffic States.
520			_aThis book provides insights into deep learning techniques that impact the implementation strategies toward achieving the Sustainable Development Goals (SDGs) laid down by the United Nations for its 2030 agenda, elaborating on the promises, limits, and the new challenges. It also covers the challenges, hurdles, and opportunities in various applications of deep learning for the SDGs. A comprehensive survey on the major applications and research, based on deep learning techniques focused on SDGs through speech and image processing, IoT, security, AR-VR, formal methods, and blockchain, is a feature of this book. In particular, there is a need to extend research into deep learning and its broader application to many sectors and to assess its impact on achieving the SDGs. The chapters in this book help in finding the use of deep learning across all sections of SDGs. The rapid development of deep learning needs to be supported by the organizational insight and oversight necessary for AI-based technologies in general; hence, this book presents and discusses the implications of how deep learning enables the delivery agenda for sustainable development.
988			_aSpringer_Computer_2023
650		7	_2embne _9166090 _aAprendizaje automático
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-5723-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eIG _zSI