Artificial Intelligence and Data Science in Environmental Sensing

by Mohsen Asadnia (Editor), Amir Razmjou (Editor), Amin Beheshti (Editor)

Overview

Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.

Product Details

ISBN-13:	9780323905084
Publisher:	Elsevier Science
Publication date:	02/14/2022
Series:	Cognitive Data Science in Sustainable Computing
Pages:	324
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Mohsen Asadnia is a Professor and group lader in Mechatronics-biomechanics and at Macquarie University, Australia. He received his PhD degree in Mechanical Engineering from Nanyang Technological University, Singapore. Prior to joining Macquarie University, Mohsen had several teaching and research roles with the University of Western Australia, Massachusetts Institute of Technology and Nanyang Technological University. His research interest lies in environmental/ biomedical sensors, Artificial Intelligence, and bio-inspired sensing.

Amir Razmjou is an Associate Professor at Edith Cowan University and the Leader of the Mineral Recovery Research Centre (MRRC).

Associate Professor Amir Razmjou (PhD from the University of New South Wales (UNSW), Sydney, Australia, 2012) is an experienced academic and industry professional with over 20 years of expertise in desalination, water treatment, membrane technology, and mineral processing. As a Board Director of the Membrane Society of Australasia (MSA) and Founder of the Mineral Recovery Research Centre (MRRC) at Edith Cowan

University (ECU), Western Australia, Associate Professor Razmjou has made significant contributions to the fields of mining and resource extraction, particularly in lithium processing.

He has published over 200 peer-reviewed articles and secured research funding

exceeding $9.2 million AUD. Dr. Razmjou has received awards such as the 2024 WA FHRI

Fund Innovation Fellow, the 2023 MSA Industry Innovation Award, and the 2021 UTS Chancellor Research Fellow. He has supervised more than 40 master’s and Ph.D. candidates and serves in editorial roles for journals such as Desalination, DWT, and JWPE. At MRRC, he has established a DLE line, including various processes such as membranes, ion exchange, and adsorption at laboratory and pilot scales. His research also includes developing and implementing advanced technologies for DLE’s pretreatment and posttreatment to enhance the Li/TDS ratio and purify the final product to battery-grade

quality"

Amin Beheshti is a Full Professor of Data Science and the Director of AI-enabled Processes (AIP) Research Centre, School of Computing, Macquarie University. Amin is also the head of the Data Analytics Research Lab and Adjunct Academic in Computer Science at UNSW Sydney. Amin completed his Ph.D. and Postdoc in Computer Science and Engineering at UNSW Sydney and holds a Master and Bachelor in Computer Science both with First Class Honours. He is the leading author of several authored books in data, social, and process analytics, co-authored with other high-profile researchers.

Table of Contents

1. Smart sensing technologies for wastewater treatment plants
2. Recent advancement in antennas for environmental sensing
3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport
4. Language of Response Surface Methodology (RSM) as an experimental strategy for electrochemical wastewater treatment process optimization
5. Artificial intelligence and sustainability: Solutions to social and environmental challenges
6. Application of multi attribute decision making tools for site analysis of offshore wind turbines
7. Recent Advances of Image Processing Techniques in Agriculture
8. Applications of Swarm Intelligence in Environmental Sensing
9. Machine learning applications for developing sustainable construction materials
10. The AI-assisted removal process of contaminants in the aquatic environment
11. Recent progress in biosensors and data processing systems for wastewater monitoring and surveillance
12. Machine learning in surface plasmon resonance for environmental monitoring

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From the Publisher

Presents high-level information on various sensing systems and data processing platforms for environmental monitoring