About the Book
Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future.
Table of Contents:
1. Introduction to innovative Technologies for Waste-to-Energy Conversion using automation and machine learning
2. Basics of Machine learning
3. Basics of Automation
4. Waste classification into plastics, industrial, domestic, and agriculture waste
5. Plastics recycling and the automation role in the recycling process
6. Artificial intelligence for reutilizing the Plastics
7. Handling Metals waste to Salvage with Automation
8. Machine learning: A better means for Metal waste to reprocess
9. Automation in the recycling of Industrial waste
10. Machine learning in Industrial Waste to value added
11. Waste to Value Added: Role of automation in Organic waste
12. Impact of Artificial Intelligence for the recycling of Organic waste
13. Future of Agriculture: Automated vertical farming
14. Agriculture: The Next Machine-Learning Frontier
15. Recycling robots to tackle electrical waste
16. Machine learning for sustainable development in electronics
17. Automated Sorting of Recyclable Domestic waste
18. Machine learning for recycling domestic waste
19. Future aspects of Machine learning/automation for the waste management
About the Author :
Dr. Kishor Kumar Sadasivuni is an Assistant Professor at the Center for Advanced Materials, Qatar University, with expertise in polymer composite materials and high-performance polymer nanocomposites for industrial applications. Since 2009, he has promoted interdisciplinary collaborations in nanocomposites and industrial technologies. His research includes sensor technology, piezoelectrics, energy storage, and flexible electronics. He’s a very prolific author and his work has contributed to advancements in materials science and electronics, which have earned him the recognition of being ranked among the top researchers in the AD Scientific Index 2022.
His prolific academic output comprises more than 400 journal articles, 20 book chapters, and 10 edited books, with his publications amassing 18,763 citations and achieving an h-index of 76. Dr. Nebojsa Bacanin received his Ph.D. degrees from Faculty of Mathematics, University of Belgrade in 2015 (study program Computer Science, average grade 10,00). He was the vice-dean of the Graduate School of Computer Science and Faculity of Informatics and Computing in Belgrade, Serbia. He currently works as a Full Professor and as a Vice-Rector for Scientific Research at Singidunum University. He is involved in scientific research in the field of computer science and his specialty includes artificial intelligence, machine learning, deep learning, stochastic optimization algorithms, swarm intelligence, soft-computing, optimization and modeling, image processing, computer vision and cloud and distributed computing. He actively works in the domain of novel and prospective research field, hybrid methods between machine learning and metaheuristics, where metaheuristics are applied for addressing non-deterministic polynomial hard (NP-hard) challenges from machine learning domain such as hyper-parameters optimization (tuning), training and feature selection. Besides improving machine learning/deep learning models for tackling various practical tasks for classification and regression, his research also involves optimized deep learning models for univariate and multivariate time-series forecasting. Moreover, he is an expert from the area of metaheuristics, and he has been actively doing research in enhancing swarm intelligence, as well as other types of metaheuristics, by incorporating minor changes (e.g., modification in exploitation/exploration expressions, parameters’ adjustments, etc.) and/or major modifications by performing hybridization with other methods (e.g., low-level and high-level hybrid metaheuristics methods). He has been applying his methods to wide variety of practical research areas, e.g., cloud computing scheduling, wireless sensor networks (WSNs) localization, coverage and energy consumption, X-ray images classification, stock price forecasting, portfolio optimization, as well as many others.
Jaehwan Kim serves as an Inha Fellow Professor at the Department of Mechanical Engineering at Inha University, Korea. He is also designated as Director of CRC for NanoCellulose Future Composites. Dr. Kim is a Fellow of The Korean Academy of Science and Technology, the National Academy of Engineering of Korea, and the Institute of Physics. He is an Associate Editor of Smart Materials and Structure as well as Smart Nanosystems in Engineering and Medicine and Editor of International Journal of Precision Manufacturing and Engineering, Helyon and Actuators. He has been the Director of Creative Research Center for EAPap Actuator funded by the National Research Foundation of Korea (NRF). Recently, he started the Creative Research Center for Nanocellulose Future Composites, sponsored by NRF. His research interests are smart materials, structures and devices, biomaterial-based smart materials, cellulose, electroactive polymers, power harvesting, biomimetic actuators, biosensors, tactile sensors, and flexible electronics. He has published more than 320 journal papers, presented 360 international conference papers, and filed more than 40 patents. Dr. Neha Vashisht is working as Researcher at as a researcher at Smart Nano Solution group, Centre for Advanced materials (CAM), Qatar University. She received her PhD in 2017 from Department of Basic & Applied Sciences, Guru Gobind Singh Indraprastha University of Science and Technology, New Delhi, India. During her Ph.D, she worked on Synthesis and Characterization of Plasmonic noble metal-Tin oxide hybrid nanocomposites for sensing applications. She has many research publications in leading scientific research journals with good impact factors. Her area of interest are synthesis and characterization of new smart hybrid materials, nanocomposites, their modifications, and optics. She has actively participated in and presented her research work at several international conferences and seminars.
