Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence - Softcover

Softcover

ISBN 10: 0323997147 ISBN 13: 9780323997140

Publisher: Elsevier, 2022

Synopsis

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world.

This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action.

• Includes case studies on the application of AI and machine learning for monitoring climate change effects and management
• Features applications of software and algorithms for modeling and forecasting climate change
• Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability

About this title

About the Authors

Ashutosh Kumar Dubey is an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Himachal Pradesh, India. He is also a Postdoctoral Fellow of the Ingenium Research Group Lab, Universidad
de Castilla-La Mancha, Ciudad Real, Spain.

Dr Abhishek Kumar is Assistant Director and Professor in the Department of Computer Science & Engineering at Chandigarh University, Punjab. He holds a PhD in Computer Science from the University of Madras and completed postdoctoral research at the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Spain. He brings extensive expertise in data science and AI-driven analytical modelling. He has published impactful research in reputed journals such as Expert Systems with Applications, Archives of Computational Methods in Engineering, and Scientific Reports, and has published books such as Computer Vision and Machine Intelligence for Renewable Energy Systems (Elsevier) and Quantum Protocols in Blockchain Security (Springer). His research areas are artificial intelligence, renewable energy, machine learning, and image processing.

Sushil Kumar Narang is Dean and an Associate Professor in the Department of Computer Science & Engineering at Chitkara University, Rajpura, Punjab (India) since 2019. From 2006-2019, He was head of IT department at SAS Institute of IT & Research, Mohali, Punjab (India). From 1996-2006, He was Assistant Professor at Department of Computer Science & Applications, MLN College, Yamuna agar, Haryana (India).He has completed his Ph.D. at Panjab University, Chandigarh (India). His Research on “Feature Extraction and Neural Network Classifiers for Optical Character Recognition for Good quality hand written GurmukhiandDevnagariCharacters” focused on various image processing, machine as well as deep learning algorithms. His research interests lie in the area of programming languages, ranging from theory to design to implementation, Image Processing, Data Analytics and Machine Learning. He has collaborated actively with researchers in several other disciplines of computer science; particularly Machine Learning on real world use cases.He is a certified Deep Learning Engineer from Edureka. ​He possesses expertise in Object-Oriented Analysis & Design and Development using Java and Python programming using OpenCV in Image Processing and Neural Network construction. ​He has strong knowledge of C++ and Java with experience in component architecture of product interface. With Solid training and management skills, He has demonstrated proficiency in leading and mentoring individuals to maximize levels of productivity, while forming cohesive team environments.

Moonis Ali Khan received his doctoral degree (Ph.D.) in Applied Chemistry from Aligarh Muslim University, Aligarh, India, in 2009. From 2009 to 2011, he worked as a Post-Doctoral Researcher at Yonsei University, South Korea and Universiti Putra Malaysia, Malaysia. In 2011, he joined the Chemistry Department at the King Saud University (KSU), Saudi Arabia as an Assistant Professor. Currently, he is working as an Associate Professor at KSU. He is an interfacial chemist and his research is focused on the synthesis and development of novel materials for environmental remediation applications. To date, he has guided two doctoral students for their respective degrees. He has published more than hundred (research and review) articles and has two U.S. patents to his credit.

Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.

From the Back Cover

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer aided Artificial Intelligence and machine learning technologies as related to impacts of climate change and potential to prevent/remediate the effects. Different types of algorithms, mathematical relations, and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact and chances of improving and saving lives and creating a healthier world.

These techniques are advancing and are being used in every field of science. Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e. climate action.

Edition details

• Publisher: Elsevier
• Publication date: 2022
• Language: English
• ISBN 10: 0323997147
• ISBN 13: 9780323997140
• Binding: Paperback
• Edition number: 1
• Number of pages: 498
• Editor: Dubey Ashutosh Kumar, Kumar PhD Abhishek, Kumar Narang Sushil, Ali Khan Moonis, Srivastav Arun Lal