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Deep learning technologies for the sustainable development goalsissues and solutions in the post-COVID era /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning technologies for the sustainable development goalsedited by Virender Kadyan, T. P. Singh, Chidiebere Ugwu.
Reminder of title:	issues and solutions in the post-COVID era /
other author:	Kadyan, Virender.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xii, 246 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning)
Online resource:	https://doi.org/10.1007/978-981-19-5723-9
ISBN:	9789811957239$q(electronic bk.)

Deep learning technologies for the sustainable development goalsissues and solutions in the post-COVID era /
Deep learning technologies for the sustainable development goalsissues and solutions in the post-COVID era /[electronic resource] :edited by Virender Kadyan, T. P. Singh, Chidiebere Ugwu. - Singapore :Springer Nature Singapore :2023. - xii, 246 p. :ill. (some col.), digital ;24 cm. - Advanced technologies and societal change,2191-6861. - Advanced technologies and societal change..

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.

ISBN: 9789811957239$q(electronic bk.)

Standard No.: 10.1007/978-981-19-5723-9doiSubjects--Corporate Names:

829718
Sustainable Development Goals.
Subjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning technologies for the sustainable development goalsissues and solutions in the post-COVID era /
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505 0 $a 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.
520 $a 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.
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