This book presents a thorough examination of computational intelligence and its significant influence on solving intricate engineering challenges. It covers fundamental theories as well as advanced applications, illustrating how techniques in computational intelligence are transforming engineering practices. The book is organized into distinct sections, each concentrating on specific engineering domains in which computational intelligence assumes a critical role.
* Presents a methodical coverage of computational intelligence principles and techniques, illustrated by examples from various engineering domains
* Emphasises a problem-solving approach, demonstrating how Computational Intelligence can be used to tackle complex problems
* Offers use cases and real-world examples to facilitate targeted learning and understanding
* Includes discussion on ethical considerations, advocating for the responsible use of technology and consideration of societal impacts
This reference book is for engineers, researchers, and scholars interested in computational intelligence techniques in engineering.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Academic
Illustrationen
56 s/w Tabellen, 72 s/w Zeichnungen, 4 s/w Photographien bzw. Rasterbilder, 76 s/w Abbildungen
56 Tables, black and white; 72 Line drawings, black and white; 4 Halftones, black and white; 76 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Gewicht
ISBN-13
978-1-032-87342-8 (9781032873428)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
T. Joshva Devadas is a computer technocrat with Doctor of Philosophy in Computer Science and distinctions in Master of Technology and Master of Science degrees in Computer Science, and with more than two decades of experience in teaching and guiding young computer scientists. He has contributed to projects using current, cutting edge technologies and programming languages and also guided young students in choosing and experimenting current computing trends for the past two and half decades of academic work. He works towards creating state of art computing facilities for visually and physically challenged individuals to navigate and use computers independently.
Naveenkumar Jayakumar, PhD, is an Associate Professor at VIT, Vellore, specializing in storage systems, cybersecurity, and intelligent computing. His research is centered on secure cyber-physical systems, edge computing, and applied machine learning, evidenced by multiple Scopus-indexed publications, patents, and funded projects. With over a decade in academia, he has guided postgraduate research in areas such as NLP, deep learning, and software engineering, driving innovations from campus automation to advanced cloud platforms.
N. Narayanan Prasanth, PhD, is a Professor at the School of Computer Science and Engineering, Vellore Institute of Technology, Vellore India. His research interest includes network switch scheduling algorithms, switching architectures, parallel and distributed computing, high performance computing and IoT. He has published more than 30 papers in the reputed conferences and journals. He is a life member of ISTE and member of CSTE. He has also served as a technical committee member and session chair of several conferences and journals.
P. Anandhakumar, PhD, is a Professor in the Department of Information Technology at Anna University, Chennai. He has completed his doctorate in the year 2006 from Anna University. He has produced 20 PhD s in the field of Image Processing, Cloud Computing, Multimedia technology and Machine Learning. His ongoing research lies in the field of Digital Twin Technology, Machine Learning and Artificial Intelligence. He has published more than 150 papers indexed in SCI, SCOPUS, WOS etc. He has more than 27 years teaching experience in the field of Computer Science and Information Technology.
Shrikant Jadhav joined SJSU as an Assistant Professor of Electrical Engineering in the Fall of 2021. Before joining SJSU, he worked as an Assistant Professor for a year at Fort Lewis College, Durango, CO. He moved to the United States to pursue a Ph.D. in 2013. He received his Ph.D. in Electrical Engineering from the North Carolina A & T State University, Greensboro 2018. His research includes developing methodologies for accelerating computationally intensive applications like image processing, bioinformatics, machine learning, etc. He was hired as a postdoc from 2018 to 2020 to work on the "A Mobile High-Performance Computing System for Deployment in the Field" project supported by the DoD grant. During his Ph.D. and Postdoc period, he mentored several high school, undergraduate, and graduate students. He has also served as a technical committee member and session chair of several conferences and journals.
Section A: Computational Intelligence: Healthcare and Biomedical Applications. Chapter 1. Artificial Intelligence in Healthcare 2. An Extensive Survey of Gene-Centric Approach to Parkinson's Disease Identification 3. Enhancing Autism Prediction using a Hybrid Approach of Neural Networks and Evolutionary Algorithms 4. Advances in Computational and in Silico Tools for Anticancer Drug Discovery and Development Section B: Computational Intelligence: Image and Signal Processing Applications 5. A Comparative Study on Hyperspectral Image Enhancement using Traditional and Deep Learning Methods 6. Emotion Recognition for Images and Audios by Cross-Model with Deep Learning 7. Deepfake Image Detection: A Computational Intelligence Perspective Section C: Computational Intelligence: Environmental and Societal Applications 8. Analysis And Prediction of Soil Fertility using Machine Learning Techniques in North Wollo Zone, Amhara Region In Ethiopia 9. Revolutionizing Student Placement Trends: The Role of Machine Learning Algorithms Section D: Computational Intelligence : Algorithms, Methodologies and Ethics 10. Enhanced Informer Homed Routing Protocol for Homogeneous Wireless Sensor Network 11. AI Controlled Automatic Speed Detector in Vehicle Communication System 12. Predicting Faulty Products with Supervised Classification Models 13. Swarm Intelligence in Smart Manufacturing
