Smart Technologies for Sustainable Development Goals

Auteur

A. Jose Anand is a Professor at the Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, India. He has presented several papers in prominent conferences, has published several papers in reputed journals, and published books in professional & engineering subjects. He is a member of the Institution of Engineers, Computer Society of India, Institution of Engineering and Technology, Indian Society for Technical Education, etc. His current research interests include Wireless Sensor Networks, Embedded Systems, IoT, Machine Learning and Image Processing, etc.

Saravanan Krishnan is an Associate Professor, Department of Computer Science & Engineering in College of Engineering, Guindy, Anna University, Chennai, India. He is also Deputy Director for Centre for University-Industry Collaboration (CUIC), Anna University, Chennai. He has a master's in Software Engineering and PhD in Computer Science and Engineering. His research interests include Cloud Computing, Blockchain, Software Engineering, IoT, and Smart cities. He published several papers conferences, book chapters reputed journals. He has edited 15 books with major publishers. He is a Fellow of the Institution of Engineers and senior member of the Association of Computing Machinery, Indian Society for Technical Education, etc.

Texte du rabat

Sustainable Development Goals (SDGs) give the UN a road-map for development with Agenda 2030 as target. It was built on the previously established Millennium Development Goals (MDG).

Contenu

Preface. Introduction to Technological Assistance for Poverty Reduction. Food Security and Safety to End Poverty. ICT Alleviates Poverty Focusing on Healthcare, Agriculture and Entrepreneurship. Role of ICT in Harnessing Poverty Reduction: An Empirical Study using an ARDL Approach. Developing Social Protection Systems to End Poverty in Indonesia. Smart Media Technology in No Poverty Reduction. Sustainable Livestock Production and Poverty Alleviation. Modern Farming Techniques to Sustainable Livelihoods. Artificial Intelligence for Hunger Monitoring and Mitigation. Machine Learning Methods to Predict and Classify Poverty. Machine Learning Technologies for Agricultural Prediction to Enhance Economic Growth. Empowering Poverty Prediction with Sustainable AI Techniques. Precision Poverty Evaluation: Leveraging Generalized Linear Models and XG Boost Algorithms. Transforming Data into Action: Leveraging Big Data Analytics to End Poverty. Eradication of Poverty using Smart Governance: Case Study of European Union Cities. Index.