Computational intelligence has emerged as a pivotal field at the intersection of computer science and artificial intelligence. This book explores the latest developments in computational systems and its diverse applications. The focuses on both theoretical foundations and practical implementations of computational algorithms in developing intelligent systems, providing readers with a roadmap for understanding and effectively implementing data-driven innovations. Notably, the book explores how visual computing, Artificial Intelligence and Machine Learning could potentially be used to provide real-time decision support to both researchers and practitioners. In conclusion, it aims to be a definitive resource for those seeking a deep understanding of the rapidly evolving field of computational intelligence. The book's structure, content, and focus on practical applications position it as a valuable asset for both researchers and practitioners in the Artificial Intelligence and Machine Learning communities.
The book is designed for researchers, academics, and professionals working in the fields of computer science, artificial intelligence, and machine learning. It is also suitable for graduate students seeking a thorough understanding of computational intelligence concepts and
their real-world applications.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic and Postgraduate
Illustrationen
9 s/w Photographien bzw. Rasterbilder, 5 Farbfotos bzw. farbige Rasterbilder, 63 s/w Zeichnungen, 5 farbige Zeichnungen, 25 s/w Tabellen, 72 s/w Abbildungen, 10 farbige Abbildungen
25 Tables, black and white; 5 Line drawings, color; 63 Line drawings, black and white; 5 Halftones, color; 9 Halftones, black and white; 10 Illustrations, color; 72 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
Gewicht
ISBN-13
978-1-032-84202-8 (9781032842028)
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
Gautam Kumar is associated with the Department of Computer Science and Engineering as an Assistant Professor at the National Institute of Technology Delhi, India. His research includes image processing and computer vision, machine learning and deep learning, Robotics and healthcare applications.
Aditya Gupta is an Assistant Professor at Thapar Institute of Engineering and Technology in Patiala, India. Aditya has contributed significantly to his field, having published numerous papers in esteemed journals and successfully concluded centrally funded research projects. His research focuses on machine learning, edge computing, and health informatics.
Gunjan is an Assistant Professor at the National Institute of Technology Delhi, India. Her research interests include energy techniques in wireless sensor networks, machine learning, and health informatics.
Jayeeta Chakraborty is an Assistant Professor in the School of Computer Science, Kalinga Institute of Industrial Technology, Bhubaneswar. Her current research interests include human gait analysis, pattern recognition, signal and image processing as well as data mining, recommendation systems, and semantic web.
Preface. PART I: VISUAL COMPUTING. Machine Learning/Deep Learning in Biometric Systems. Rolling Mean-based Real-time Activity Detection and Alert Generation using Deep Learning. PART II: SMART SYSTEMS AND IOT. A Triple Based Lightweight Authentication Scheme for Smart Grid and Smart Metering System. A Comparative Analysis of Cloud-Based Education and Skill Development in Smart Cities Context. Private Blockchain Based Encryption Framework using Computational Intelligence Approaches. Leveraging AI in Smart Healthcare for Future Hospitals: Potential Applications and Challenges. PART III: COMPUTATIONAL ALGORITHMS. Discrete Artificial Bee Colony Optimization Algorithm for Cyber-attack Detection to Mitigate Credential Stuffing in Cyber Defense. Qryptographix: Quantum Image Encryption via Random Gates. PART IV: AI AND HEALTHCARE. Handling Missing Data in Healthcare with Fuzzy Clustering Imputation. A Novel Weighted Ensemble Approach for Improved Brain Tumor Detection and Classification. Glioma Brain Tumour Grade Detection using Clinical Feature, Molecular Feature and ConvLSTM Network. Machine Learning for Ocular Disease Detection in Fundus Images. Index.
