Autorentext

Dr. Keyurkumar Patel, who earned his Ph.D. in Robotics and AI Engineering from IITRAM (2020), currently serves as an assistant professor at Rashtriya Raksha University's School of IT, Artificial Intelligence, and Cyber Security, an institute of national importance under India's Ministry of Home Affairs. With previous experience at Karnavati University, the Institute of Advanced Research, and a public sector unit under the Ministry of Heavy Industries, he has published in prestigious journals, including IEEE Transactions on Industrial Informatics. His research spans IoT security, robotics, AI, deep learning, quantum machine learning, computer vision, and medical imaging, and he recently developed a Centre of Excellence Lab for Emerging Technologies with SAP India's support.

Dr. Ankit D. Oza is currently working as a research professor at Parul University, Vadodara, Gujarat. He received his Ph.D. in Industrial Engineering from Pandit Deendayal Petroleum University (PDPU), Gandhinagar, India. His major research areas include non traditional and hybrid micrömachining processes and their applications. Additionally, he is working on AI and IoT based manufacturing processes. He has published more than 70 articles and over 30 conference papers. He holds more than 45 national and international patents and has also delivered expert talks at various prestigious universities and colleges. He is also a reviewer for a number of prestigious journals.

Dr. Ankit Sharma holds the position of head of the Publications Division of the Chitkara University Research and Innovation Network (CURIN), Chitkara University, Punjab, India. He received his Ph.D. in Mechanical Engineering from the Thapar Institute of Engineering and Technology, India. He has more than 10 years of experience in academics, research, consulting, training, and industry. He has authored numerous national and international publications in SCI, Scopus, and Web of Science indexed journals and has filed/published over 30 international patents. He is also the book series editor of Innovations in Smart Manufacturing for Long Term Development and Growth for CRC Press, Taylor & Francis Group. He has delivered several invited seminars and keynote talks on international platforms (USA, China, India) and has been awarded best research paper awards.

Dr. Archana Mantri holds a Ph.D. in Electronics and Communication Engineering with 30 years of experience across research, academics, and administration. Her expertise spans project management, problem based learning, curriculum development, and pedagogical innovation, with interests in education technology, cognitive sciences, and augmented reality. A prolific scholar with numerous publications in reputable journals, she has received prestigious awards, including "Excellence in Education Leadership," and holds several patents in educational technology. Dr. Mantri has led international research projects and guided Ph.D. and M.E. scholars. He serves as an advisor to IUCEE, a senior member of IEEE, and a member of the Global Engineering Deans Council, continuing to drive innovation in education through her research leadership.

Dr. Hreetabh Kishore received his Ph.D. degree in Mechanical Engineering from Indian Institute of Technology Ropar, Rupnagar, Punjab, India. Presently, he is working in the Department of Manufacturing and Engineering Technology, Tennessee Tech University, Cookeville, Tennessee, USA. Dr. Kishore is an enthusiastic teacher who embraces student focused approaches in his teaching, providing real time learning to his students. He has attended two international conferences funded by DST, India Travel Grant. He is working on AI and IoT based manufacturing processes and other related areas. He has published several publications in the peer reviewed journals, and few are in under review, more than 21 international conference papers has been presented and published worldwide. He has also contributed to intellectual properties as filling the Patent in the advanced engineering domain.

Klappentext

As industries move toward intelligent, adaptive, and efficient manufacturing processes, the integration of (artificial intelligence) AI, (Internet of Things) IoT, signal processing, and computer vision is crucial. This book serves as a comprehensive guide for professionals, researchers, and academics seeking to leverage the power of cutting edge technologies in the field of smart manufacturing.

Smart Manufacturing with AIoT: Signal Processing, Computer Vision, and Data Analytics is a comprehensive guide that focuses on practical implementation, bridging the gap between theoretical concepts and real world applications. Each chapter of this book explores a different aspect of the synergy between AI, IoT, signal processing, and computer vision, starting with foundational concepts and progressing toward advanced applications. From enhancing quality control processes to implementing proactive maintenance strategies and enabling dynamic threat assessment, this book covers a wide range of topics that define the contemporary landscape of smart manufacturing.

This book explores the intricate details of AIoT, signal processing, and computer vision, providing valuable insights for both beginners and experienced professionals in the field. Readers will gain actionable knowledge that empowers them to implement transformative solutions in their smart manufacturing environments.

Inhalt

Chapter 1: Deep Learning-Based Improved Gait Recognition Using 3D Skeletal Data for Smart Surveillance Systems, Chapter 2: IoT-Based Automated Fog Detection System Using kNN and Real-Time Meteorological Data Analytics, Chapter 3: Intelligent Waste Classification Framework Based on Machine Learning and Deep Learning for Smart City Application, Chapter 4: AI-Powered Data Analytics for Optimizing Tea Tourism: An Investigation on Determinants and Destination Information, Chapter 5: Substation Level Short-Term Load Forecasting Using Graph-Based Signal Processing and Machine Learning Algorithms, Chapter 6: AI-Based Histopathological Image Analysis for Breast Cancer Diagnosis Using Deep and Machine Learning Algorithms, Chapter 7: AI-Optimized Active Cell Balancing Technique Using Single Inductor for Intelligent Battery Management in IoT-Enabled Energy Systems, Chapter 8: Integrated Simulation and Hardware-in-the-Loop Analysis of ISO15118-Based Smart EV Charging Communication Systems, Chapter 9: AI-Driven Analysis of Air Gap Eccentricity Effects in PMSM for Condition Monitoring in Smart Industrial Systems, Chapter 10: Deep Convolutional Neural Network-Based Smart Diagnostic System for Enhanced Breast Cancer Detection