04943nam 22007575 450 99654684530331620230201032054.0981-19-5723-110.1007/978-981-19-5723-9(MiAaPQ)EBC7188519(Au-PeEL)EBL7188519(CKB)26076457200041(DE-He213)978-981-19-5723-9(PPN)268206430(EXLCZ)992607645720004120230201d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning Technologies for the Sustainable Development Goals[electronic resource] Issues and Solutions in the Post-COVID Era /edited by Virender Kadyan, T. P. Singh, Chidiebere Ugwu1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (254 pages)Advanced Technologies and Societal Change,2191-6861Print version: Kadyan, Virender Deep Learning Technologies for the Sustainable Development Goals Singapore : Springer,c2023 9789811957222 Includes bibliographical references and index.How 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.This 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.Advanced Technologies and Societal Change,2191-6861Computational intelligenceBlockchains (Databases)SustainabilityQuantitative researchArtificial intelligenceInternet of thingsComputational IntelligenceBlockchainSustainabilityData Analysis and Big DataArtificial IntelligenceInternet of ThingsComputational intelligence.Blockchains (Databases).Sustainability.Quantitative research.Artificial intelligence.Internet of things.Computational Intelligence.Blockchain.Sustainability.Data Analysis and Big Data.Artificial Intelligence.Internet of Things.006.31Singh T. P.Ugwu ChidiebereKadyan VirenderMiAaPQMiAaPQMiAaPQBOOK996546845303316Deep Learning Technologies for the Sustainable Development Goals3303430UNISA