Название: Machine Learning for Sustainable Development
Автор: Kamal Kant Hiran, Deepak Khazanchi, Ajay Kumar Vyas
Издательство: De Gruyter
Год: 2021
Страниц: 216
Язык: английский
Формат: pdf (true), epub
Размер: 120.6 MB

Machine learning (ML) is a part of computerized reasoning which comprises algorithms and artificial neural networks and displays qualities firmly connected with human insight. The book focuses on the applications of ML for sustainable development. This book provides an understanding of sustainable development and how we can forecast it using ML approaches.

The ML models for sustainable development include weather forecasting, management of clean water, food security, life on land, product design and life cycle, sustainable development in tourism, policymaking process and e-governance renewable energy with experimental and analytical results. The book has compressive studies regarding the energy demand prediction, agriculture, weather forecasting and medical applications using models of ML that have profoundly contributed. It also covered ML approaches for green Internet of things(IoT) and environmental IoT for sustainable development.

The book provides a straightforward approach to ML-based model sustainable solutions in various sectors of business and society. It is a framework for business opinion leaders and professionals, as well as an orientation for stakeholders of academia. This volume intends to deliberate some of the latest research findings of the applications of ML. The volume comprises 11 well-versed chapters on the subject.
Chapter 1 describes Internet of nano-things (IoNT) with artificial intelligence (AI) and ML applications. It describes the current state of IoNT research and its implications and proposes how AI tools can be leveraged in IoNT applications, and future research possibilities of IoNT in healthcare, medicine, smart buildings and home automation, utility, environment monitoring and agriculture.

Chapter 2 presents a 360-degree approach to an education model using ML, and also a conceptual framework has been proposed on how ML can be adopted in different stages of higher education to enhance the quality of education delivery and prepare job-ready graduates that show the opportunities and challenges in transforming higher education through ML...