Machine learning for sustainable development / edited by Kamal Kant Hiran [and three others].

Format:

Book

Contributor:

Hiran, Kamal Kant, 1982- editor.

Series:

De Gruyter Frontiers in Computational Intelligence

De Gruyter Frontiers in Computational Intelligence ; 9

Language:

English

Subjects (All):

Sustainable development.

Machine learning.

Physical Description:

1 online resource (XIII, 201 p.)

Place of Publication:

Berlin ; Boston : De Gruyter, [2021]

Language Note:

In English.

Summary:

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.

Contents:

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

Notes:

Description based on print version record.

Includes index.

ISBN:

1-5231-5446-2

3-11-070251-7

OCLC:

1262308295