Autorentext
Dr. Kamil Reza Khondakar is a distinguished researcher with over a decade of experience in interdisciplinary science, combining physics, biomedical engineering, materials science, and artificial intelligence. He completed four years of doctoral training at the University of Queensland, Australia after receiving IPRSS fellowship from Government of Australia. He worked as research fellow for four years at the National Physical Laboratory, India, supported by a prestigious Government of India grant. His research focuses on lab-on-chip technology, microfluidics, biosensors, and point-of-care diagnostics, contributing to over 50 peer-reviewed publications in high-impact journals such as Accounts of Chemical Research, ACS Nano, Advances in Colloid and Interface Science, BMEMat and Biosensors and Bioelectronics, Small, etc. Dr. Kamil has delivered invited lectures in the USA and Australia on lab-on-a-chip technology for cancer detection. He has edited three Elsevier books and serves as an editor for journals: Frontiers in Nanotechnology and Biosensors. As a peer reviewer for top journals and a mentor to young researchers, he plays a vital role in advancing scientific innovation and publishing worldwide. Dr. Hirak Mazumdar is a distinguished Biomedical Researcher and Algorithm Developer specializing in Computer Vision, Image Processing, and advanced Machine Learning Algorithms. He earned his PhD from Sogang University, South Korea, in collaboration with Samsung Mechatronics R&D. During his PhD, he developed comprehensive algorithms for fault detection in semiconductor sensors, enhancing defect prediction and manufacturing yield. His significant contributions include sensor correlation analysis and machine learning applications to improve wafer fabrication processes.

Following his doctorate, Dr. Mazumdar was a Post-Doctoral Fellow at Cheongju National University, South Korea, where he worked on a Ministry of Semiconductor Design-funded project developing machine learning software for wafer testing. He then joined the University of Houston, Texas, as a Post-Doctoral Scientist on a Department of Energy project focused on groundwater contamination detection using machine learning.

Dr. Mazumdar's research spans industrial big data analysis, intelligent medical sensor devices, sensor manufacturing systems, and quality assurance through complex machine learning algorithms. He has industry experience with Samsung and Hyundai, specializing in machine learning and data fault analysis. His interests also include nanotechnology, nano biosensor simulation, computational biology, and biomedical image analysis, particularly in analyzing cell cultures.

In his post-PhD work, Dr. Mazumdar has expanded into healthcare technology and sustainability, developing secure machine learning frameworks for early diagnosis, personalized health management, remote monitoring, and digital health platforms such as telemedicine and virtual clinics. His work aims to improve patient outcomes through predictive analytics and AI-driven disease prevention.

Dr. Ajeet Kaushik is Associate Professor at the NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, USA. He is the recipient of various reputed awards for his service in the area of nano-biotechnology for health care. He has edited seven books, written more than 100 international research peer reviewed publications, and has three patents in the area of nanomedicine and smart biosensors for personalized health care. In the course of his research, Dr. Kaushik has been engaged in the design and development of various electro-active nanostructures for electrochemical biosensor and nanomedicine for health care. His research interests include nanobiotechnology, analytical systems, design and develop nanostructures, nano-carries for drug delivery, nano-therapeutics for CNS diseases, on-demand site-specific release of therapeutic agents, exploring personalized nanomedicines, biosensors, point-of-care sensing devices, and related areas of health care monitoring.

Klappentext

Sustainable Smart Water System: A Comprehensive Approach is an essential guide for students and researchers dedicated to sustainable water management. This book thoroughly examines potential threats to water quality from industrial activities, agricultural practices, and climate change, while also addressing the vital regulations and policies for smart water systems. With a deep dive into innovative technologies, particularly advanced sensors, readers will uncover cutting-edge advancements in water treatment, including membrane filtration, advanced oxidation processes, and green chemistry methods. The insightful chapters illustrate the transformative impact of artificial intelligence and big data analytics on water quality monitoring, prediction, and decision-making, enriched with compelling case studies. Moreover, the book explores the revolutionary use of Internet of Things (IoT) devices for real-time water quality monitoring and control, shedding light on the challenges and future research directions in this dynamic field.

Inhalt

1. Introduction to Sustainable Smart Water Systems
   
   2. Sustainability Risk Assessment and Management in Water Quality
   3. Sources of Water Pollution and Its Impact on Ecosystems
   4. Smart Sensors for Water Testing and Quality Assessment
   5. Nano-separation Techniques for Sustainable Water Treatment
   6. Sustainable Membrane Technology for Water Treatment
   7. IoT-based Monitoring and Control for Sustainable Water Quality
   8. Artificial intelligence and big data applications for drinking water quality monitoring
   9. AI-supported approaches for sustainable water management in Urban areas.
   10. Sustainable Agriculture through Smart Practices of Water using AI and IOT
   11. Water Quality Regulations and Policies for Sustainable Management
   12. Challenges and Future Directions in Sustainable Water Ecosystems