Proceedings of the 9th International Conference on Innovation in Artificial Intelligence

ICIAI 2025, March 13-15, Singapore
Erscheinungsjahr 2026
ISBN: 978-981-950886-0
Verlag: Springer

Buch, Englisch, 517 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 955 g

Reihe: Lecture Notes in Electrical Engineering

This book presents the papers from the 9th International Conference on Innovation in Artificial Intelligence (ICIAI 2025), held 13-15 March in Singapore. The topics covered include fields of mechatronics, robotics, control, and automation and all topics related to thermal fluids. Specifically, this book covers studies in data modeling and prediction system analysis; target detection and machine vision; intelligent image analysis and processing methods; theory, technology and applications of next generation artificial intelligence; intelligent autonomous systems and signal processing; machine learning and intelligent technology in modern information systems; large language models; continual learning and artificial general intelligence (AGI). This caters to academics, researchers and engineers from various industries who work in artificial intelligence systems while utilizing latest technology advances in machine learning and deep learning.

Zielgruppe

Research

Autoren/Hrsg.

Guan, Steven

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

1. Quantum AI Enhanced Blockchain Security for Drug Discovery.- 2. Enhancing Code Vulnerability Detection Using CodeGraphBERT Deep Learning Techniques.- 3. Attention-driven Automatic Speech Recognition: Comparative Analysis of Attention Mechanisms in RNN Architectures.- 4. Predicting Discretionary Accrual: A Comparison between MD&A and Conference Call Scripts.- 5. Detecting Youth Mental Health Issues on Social Media with Deep Learning.- 6. Recommender Systems: Solving cold start using collaborative distance based Siamese Network.- 7. Artificial Intelligence for Factor Investing: A Comprehensive Survey.- 8. Efficient Neural Representations for Videos with Motion-aided Quality Enhancement.

Über Autor(innen)

Steven Guan (Sheng-Uei Guan) received his B.Sc. from Tsinghua University and M.Sc., Ph.D. from the University of North Carolina at Chapel Hill. He is currently a Professor at Xian Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK. Prof. Guan has worked in a prestigious organization for several years, serving as a design engineer, project leader, and department manager. Later he joined the Electrical Computer Engineering Department at National University of Singapore as an associate professor for 8 years.

Prof. Guan’s research interests include: machine learning, computational intelligence, big data analytics, mobile commerce, modeling, networking, personalization, security, coding theory, and pseudorandom number generation. He has published extensively in these areas, with 150+ journal papers and 210+ book chapters or conference papers. He has chaired, delivered keynote speech for 180+ international conferences and served in 190+ international conference committees and 30+ editorial boards. There are quite a few inventions from Prof. Guan including Generalized Minimum Distance Decoding for Majority Logic Decodable Codes, Prioritized Petri Nets, Self-Modifiable Color Petri Nets, Dynamic Petri Net Model for Iterative and Interactive Distributed Multimedia Presentation, Incremental Feature Learning, Ordered Incremental Input/Output Feature Learning, Input/Output Space Partitioning for Machine Learning, Recursive Supervised Learning, Reduced Pattern Training using Pattern Distributor, Contribution-Based Feature Selection, Incremental Genetic Algorithms, Incremental Multi-Objective Genetic Algorithms, Decremental Multi-objective Optimization, Multi-objective Optimization with Objective Replacement, Incremental Hyperplane Partitioning for Classification, etc.