Mathematical Modeling in Computational Intelligence and Generative AI

CIGAI-2025, Hyderabad, India, June 19-20
Erscheinungsjahr 2026
ISBN: 978-981-9245-78-9
Verlag: Springer

Buch, Englisch, 423 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Springer Proceedings in Mathematics & Statistics

This proceedings volume discusses topics on generative AI—one of the most trending topics and application in every field of science and engineering—and machine intelligence. Chapters of this proceedings were presented at the International Conference on Mathematical Modeling in Computational Intelligence and Generative AI (Math-CIGAI), held at Koneru Lakshmaiah Education Foundation, Hyderabad, India, from 19–20 June 2025. The book also discusses how to develop new products and automate the system by generating the new and improved models and improve decision making systems. It also discusses the applications of machine intelligence and generative AI in healthcare decision making, drug discovery, personalized care, synthetic data generation, automations, and many more. Topics on mathematical models such as adversarial networks and variational autoencoders are also discusses which are deployed to produce images for data augmentation, improving disease diagnosis and advanced medical imaging research areas. This volume is intended for researchers, academicians, and professionals.

Zielgruppe

Research

Autoren/Hrsg.

Hu, Yu-Chen

Bhattacharyya, Debnath

Frnda, Jaroslav

Ghosh, Rajib

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

A Hybrid Approach for Failure Detection in Industrial Machinery Using Graphical Neural Network with Random Decision Forest.- HaMobNet: MobileNetV2-Based Hand Gesture Recognition System with NLP Interface for Accessibility.- AI-Enhanced Smart Surveillance Using Autoencoders, YOLOv11, and FaceNet for Anomaly, Unauthorized Access, Unlawful Object, and Tamper Detection.- Transcriptomic Drivers of Asian Breast Cancer.

Über Autor(innen)

Yu-Chen Hu is a Professor in the Department of Computer Science at Tunghai University, Taiwan, and holds a PhD from National Chung Cheng University. He is a senior IEEE member and active in multiple professional societies including the Chinese Cryptology and Information Security Association and Phi Tau Phi. His research spans digital forensics, signal processing, cybersecurity, data compression, computer networks, bioinformatics and deep learning. He has served on editorial boards of journals such as Advances in Multimedia, IET Image Processing, Algorithms, and more. His SCIE-indexed works focus on Covid-19 technologies, medical diagnostics by using CNNs, IoT networks, eHealth security and facial emotion recognition. Recognized among Stanford University’s World’s Top 2% Scientists (2019–2022), he received the 2023 Best Paper Award (Journal of Visual Communication and Image Representation), the 2014 EURASIP award, and was named a Highly Cited Researcher in 2012. He remains deeply committed to advancing intelligent systems and secure computing.

Debnath Bhattacharyya is a Professor in the Department of Information Technology at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, and a Visiting Professor at the University of Johannesburg, South Africa. He previously held invited faculty roles at Lincoln University, Malaysia, and Hannam University, South Korea. He earned his PhD from the University of Calcutta and MTech from West Bengal University of Technology. With over 300 Scopus-indexed and 160 Web of Science papers, he holds H-indices of 24 and 16, respectively. He is a Senior Member of IEEE, ACM, ACM SIGKDD, IACSIT, IAENG and a Life Member of CSI. An ACM Distinguished Speaker (2017–2020), he serves as PhD examiner for top Indian universities. His research spans security engineering, pattern recognition, multimodal biometric authentication, data mining and image processing. He reviews for IEEE, Elsevier, Springer journals and major conferences, and has authored 7 textbooks in Computer Science and Engineering.

Jaroslav Frnda is an Assistant Professor at the University of Žilina, Slovakia, specializing in transport operations and economics. He holds MSc and PhD degrees in communication technology from VŠB–Technical University of Ostrava, Czechia, with doctoral research on video quality in converged networks using machine learning. His industry and academic experience includes consulting for Express Alarm Slovakia and research roles under Czech Ministry-funded projects. Internationally, he has interned and lectured in China, Belgium and Latvia on IP multimedia and AI. His interests include multimedia quality, VoIP, IP services and soft computing. A Senior Member of IEEE, he has published in top journals and serves as a TPC member for major conferences.

Rajib Ghosh is an Assistant Professor in the Department of Computer Science and Engineering at the NIT Patna, India. With over two decades of teaching experience, he holds a PhD (CSE), an MTech in IT and a BE in CSE. His research spans pattern recognition, machine learning and computer vision, focusing on document analysis, object detection, tracking, human movement and video surveillance. His PhD explored machine learning methods for recognizing online handwritten text in Indic scripts such as Bengali, Devanagari, Telugu and Tamil. He has authored over 30 papers in reputed SCI/Scopus-indexed journals and conferences and reviews for several leading publications. A member of IEEE, IUPRAI, ISTE and CSI, he regularly delivers expert lectures at top institutions.