Machine Learning for Sustainable Development /
ed. by Kamal Kant Hiran, Deepak Khazanchi, Ajay Kumar Vyas, Sanjeevikumar Padmanaban.
- 1 online resource (XIII, 201 p.)
- De Gruyter Frontiers in Computational Intelligence , 9 2512-8868 ; .
Frontmatter -- Preface -- Contents -- About editors -- List of contributors -- Chapter 1. A framework for applying artificial intelligence (AI) with Internet of nanothings (IoNT) -- Chapter 2 Opportunities and challenges in transforming higher education through machine learning -- Chapter 3 Efficient renewable energy integration: a pertinent problem and advanced time series data analytics solution -- Chapter 4 A comprehensive review on the application of machine learning techniques for analyzing the smart meter data -- Chapter 5 Application of machine learning algorithms for facial expression analysis -- Chapter 6 Prediction of quality analysis for crop based on machine learning model -- Chapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach -- Chapter 8 Machine learning for sustainable agriculture -- Chapter 9 Application of machine learning in SLAM algorithms -- Chapter 10 Machine learning for weather forecasting -- Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study -- Index
restricted access http://purl.org/coar/access_right/c_16ec
The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.
Mode of access: Internet via World Wide Web.
In English.
9783110702514
10.1515/9783110702514 doi
Internet der Dinge.
Künstliche Intelligenz.
Maschinelles Lernen.
Nachhaltige Entwicklung.
Artificial Intelligence. Machine Learning. Sustainable Development.
Frontmatter -- Preface -- Contents -- About editors -- List of contributors -- Chapter 1. A framework for applying artificial intelligence (AI) with Internet of nanothings (IoNT) -- Chapter 2 Opportunities and challenges in transforming higher education through machine learning -- Chapter 3 Efficient renewable energy integration: a pertinent problem and advanced time series data analytics solution -- Chapter 4 A comprehensive review on the application of machine learning techniques for analyzing the smart meter data -- Chapter 5 Application of machine learning algorithms for facial expression analysis -- Chapter 6 Prediction of quality analysis for crop based on machine learning model -- Chapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach -- Chapter 8 Machine learning for sustainable agriculture -- Chapter 9 Application of machine learning in SLAM algorithms -- Chapter 10 Machine learning for weather forecasting -- Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study -- Index
restricted access http://purl.org/coar/access_right/c_16ec
The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.
Mode of access: Internet via World Wide Web.
In English.
9783110702514
10.1515/9783110702514 doi
Internet der Dinge.
Künstliche Intelligenz.
Maschinelles Lernen.
Nachhaltige Entwicklung.
Artificial Intelligence. Machine Learning. Sustainable Development.