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Artificial Intelligence and Data Science in Environmental Sensing

  • 1st Edition - February 9, 2022
  • Latest edition
  • Editors: Mohsen Asadnia, Amir Razmjou, Amin Beheshti
  • Language: English

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 artifi… Read more

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Description

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.

Key features

  • Presents tools, connections and proactive solutions to take sustainability programs to the next level
  • Offers a practical guide for making students proficient in modern electronic data analysis and graphics
  • Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery

Readership

Researchers, practitioners, graduate students and undergraduate students in the field of environmental science, AI, electrical, chemical engineering; environment scientists who want to learn data science techniques. Undergraduate students in the field of electrical and chemical engineering, data scientists who want to work in environmental data areas

Table of contents

1. Smart sensing technologies for wastewater treatment plants
Reza Maleki, Ahmad Miri Jahromi, Ebrahim Ghasemy, Mohammad Khedri

2. Recent advancement in antennas for environmental sensing
Ali Lalbakhsh

3. Intelligent geo-sensing for moving toward smart, resilient, low emission, and less carbon transport
Omid Ghaffarpasand, Reza Maleki, Ahmad Miri Jahromi, Elika Karbassiyazdi and Rhiannon Blake

4. Language of Response Surface Methodology (RSM) as an experimental strategy for electrochemical wastewater treatment process optimization
A Yagmur Goren, Yaşar Kemal Recepoğlu and Alireza Khataee

5. Artificial intelligence and sustainability: solutions to social and environmental challenges
Payam Rahnamayiezekavat, Firouzeh Taghikhah, Dr Eila Erfani, Ivan Bakhshayeshi, Sara Tayari, Alexandros Karatopouzis and Bavly Hanna

6. Application of multi attribute decision making tools for site analysis of offshore wind turbines
Mohamad Yazdi

7. Recent Advances of Image Processing Techniques in Agriculture
Mohsen Asadnia and Amin Beheshti

8. Applications of Swarm Intelligence in Environmental Sensing
Shadi Abpeikar, Matthew A. Garratt, Kathryn Kasmarik, Vu Tran, Sreenatha Anavatti and Md Mohiuddin Khan

9. Machine learning applications for developing sustainable construction materials
Asghar Habibnejad Korayem

10. The AI-assisted removal process of contaminants in the aquatic environment
Milad Rabbani Esfahani

11. Recent progress in biosensors and data processing systems for wastewater monitoring and surveillance
Rouzbeh Abbassi

12. Machine learning in surface plasmon resonance for environmental monitoringMasoud Mohseni-Dargah, Zahra Falahati, Bahareh Dabirmanesh, Parisa Nasrollahi and Khosro Khajeh

Product details

  • Edition: 1
  • Latest edition
  • Published: February 14, 2022
  • Language: English

About the editors

MA

Mohsen Asadnia

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.
Affiliations and expertise
Professor, Mechatronics-Biomechanics and an ARC DECRA Fellow, Macquarie University, Australia

AR

Amir Razmjou

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"

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

AB

Amin Beheshti

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.
Affiliations and expertise
Director of AI-enabled Processes (AIP) Research Centre and the head of the Data Analytics Research Lab, Department of Computing, Macquarie University, Sydney, Australia

View book on ScienceDirect

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