Deep Learning Technologies for the Sustainable Development Goals

Issues and Solutions in the Post-COVID Era
1. Auflage 2023
ISBN: 978-981-19-5725-3
Verlag: Springer

Buch, Englisch, 246 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 400 g

Reihe: Advanced Technologies and Societal Change

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Kadyan, Virender

Singh, T. P.

Ugwu, Chidiebere

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Geowissenschaften Umweltwissenschaften Nachhaltigkeit
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

Part 1: Fundamentals of Deep Learning Technologies

Part 2: SDGs laid down by UN 2030 document

Part 3: Deep technologies using IOT in:  

·         Transportation management,

·         Energy and waste management,

·         Water and disaster management,

·         Woman and child safety.

Part 4: Deep technologies using Big data in: 

·         Transportation management,

·         Energy and waste management,

·         Water and disaster management,

·         Woman and child safety.

Part 5: Fundamental concepts, current issues, and application for:

·         Data Analytics, Image, Speech, formal methods

·         Cloud/Edge/Fog tools and technologies for better life, health, education etc.

Part 6: Development of deep Learning models for:

·         Technology introduction and sustainability in tourism,

·         Hospitality and events using big data analysis and metrics.

Part 7: Factor affecting in designing relationships among:

·         Business,

·         Policy,

·         Stakeholders,

·         Society, and the environment using case studies of image, speech synthesis and spoken language generation.

·         Impact of Deep Learning approaches in cross-disciplinary approaches to the SDGs.

Part 8: Study of the impact of the recent technologies in post covid era:

·         Bloackchain based societal issues for managing knowledge and innovation.

·         Blockchain  for compliance through evaluation and governance.

·         Security implementation for smart city, building and alert system generation.

·       QoS issues in proving better communication network using 4G, and 5G technologies.

·         Exemplary or proposed teaching, training or entrepreneurship initiatives.

·         Speech, voice, and hearing disorders.

Über Autor(innen)

Dr. Virender Kadyan is currently working as Associate Professor in Informatics Cluster , School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India. He has completed his Ph.D. and M.Tech. from Chitkara University, Punjab, in 2018 and Kurukshetra University, Kurukshetra, in 2013.  His research interest includes automatic speech recognition and speaker identification & verification.

Dr. T P Singh is currently positioned as Professor and Head, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, Uttarakhand, India. He holds a Doctorate in Computer Science from Jamia Millia Islamia University, New Delhi. He carries 26 years of rich experience with him. To his credit there are more than 35 research publications and 6 edited books. Dr. Singh is a senior member of IEEE, member of various professional bodies including ACM, IEI, ISTE, IAENG, etc., and also on the editorial/reviewer panel of different journals. He is Fellow of IETA-India. His research interests are pattern recognition, machine learning and bulding intelligent hybrid systems.

Prof. Chidiebere Ugwu is currently Director, Information & Communication Technology Centre(ICTC), University of Port Harcourt, Rivers State, Nigeria. He has successfully supervised many Ph.D., M.Sc, B.Sc projects and is currently supervising some. He is a Fellow, Nigeria Computer Society (FNCS); Member, Nigeria Professional Registration Council of Nigeria (MCPN); Member, Institute of Electrical and Electronics Engineers (MIEEE); Member, International Research and Development Institute (MIRDI); and Fellow, Institute of Corporate Administration of Nigeria (FICAN).