This book presents selected peer-reviewed contributions from the 5th International Conference on Applied and Industrial Mathematics and Statistics (ICoAIMS 2025), focusing on recent advances in modeling, analytics, applied mathematics, and statistics. The topic is timely due to the growing demand for data-driven decision-making and mathematical solutions to complex scientific, industrial, and technological challenges.
The volume covers contemporary theoretical developments, computational techniques, and practical applications across diverse interdisciplinary domains. Its unique strength lies in bringing together current research that bridges mathematical theory with real-world problem solving and industrial relevance.
Designed for researchers, academicians, professionals, and graduate students, the book provides valuable insights into emerging methods, analytical tools, and application-oriented approaches in applied and industrial mathematics and statistics. Readers will gain exposure to current research trends, innovative methodologies, and practical perspectives relevant to modern scientific and technological challenges.
Written at an advanced academic and research level, the book contributes to ongoing developments in modeling, analytics, and data-driven sciences, making it a useful reference for those seeking updated and interdisciplinary research in these rapidly evolving fields.

Discusses the latest advancements in analytics, machine learning, simulation, and predictive modeling Studies decision supports in resource planning, risk monitoring, maintenance scheduling, cost control, and early warning Highlights energy demand, transport flow, healthcare analytics, financial risk, and quality control applications

Autorentext

Nur Haizum Abd Rahman is a senior lecturer at Universiti Malaysia Pahang Al-Sultan Abdullah. She was formerly at Universiti Putra Malaysia. She earned her Ph.D. in mathematics from Universiti Teknologi Malaysia, specializing in statistics, time series modeling, and forecasting. Her research applies statistical and machine learning methods to real-world forecasting in demand prediction, risk modeling, environmental monitoring, and public health. She publishes in peer-reviewed journals and actively engages in conferences, seminars, and research networks. A lifelong member of the Malaysian Institute of Statistics, she has served as a honorary secretary since 2022, promoting statistical thinking nationwide.

Nadirah Mohd Nasir is a senior lecturer at the Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia. She received her Ph.D. in computational mathematics from Universiti Putra Malaysia in 2020 and a master's degree in mathematics from Universiti Sains Malaysia. Her research focuses on numerical analysis, particularly multistep block methods for solving differential equations with boundary value problems.

Adam Shariff Adli Aminuddin is a senior lecturer and head of programme (data analytics) at the Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia. He obtained his B.Sc. in mathematics from Universiti Sains Malaysia and both his M.Sc. and PhD in decision science/operations research from Universiti Utara Malaysia. His research focuses on multi-criteria decision-making, performance analytics, and sustainability applications. He has authored and reviewed papers in international indexed journals and conference proceedings, particularly in the areas of decision analysis, fuzzy-based evaluation, and integrated data-driven approaches for sustainability and management decision support.

Noraini Ibrahim received her Ph.D. in mathematics from Universiti Teknologi Malaysia in 2023, specializing in improving least squares support vector machines for river flow forecasting. She also holds an M.Sc. in computer science and a B.Sc. in industrial mathematics from Universiti Teknologi Malaysia. Her research interests span data analytics, machine learning, artificial intelligence (AI), statistical modeling, and time series forecasting, with particular emphasis on hydrological and environmental applications. She is also interested in the integration of AI and statistical approaches for predictive modeling, optimization of deep learning architectures and the development of explainable AI techniques for decision support in water resource management and related domains. She has contributed as a reviewer, invited speaker and jury member for academic and mathematical events and is a member of the Malaysian Mathematical Sciences Society and the Malaysian Institute of Statistics.

Ali Turab is an associate professor at the School of Software, Northwestern Polytechnical University, China. He earned his Ph.D. in applied mathematics from Thammasat University, Thailand, where he also completed postdoctoral research in nonlinear modeling and dynamical systems. Previously, he was a research associate at the Centre of Excellence for AI and Data Science, Universiti Malaysia Pahang. His research bridges mathematical modeling, AI, and behavioral sciences, focusing on computational modeling of natural phenomena and cognitive behavior. He uses game theory, decision analysis, probabilistic machine learning, and neurodynamics to study adaptive behavior in animals and intelligent systems. His recent work involves hybrid neural-stochastic models and reinforcement learning for decision-making under uncertainty. He actively contributes to interdisciplinary research, mentorship, and high-impact publications across applied mathematics, AI, and behavioral prediction.

Inhalt

Interpreting Random Survival Forest Using SurvSHAP(t) for Patients with Complementary Therapy at Nur Hidayah Hospital, Bantul, Yogyakarta, Indonesia.- Resource Management Analytics Using Linear Regression Model to Maximize Business Outcome.- Cybersecurity and Operation Analytics in the Microsoft Azure Environment.- Design and Evaluation of an Automated Negotiation Protocol for Fairness-Oriented Multiparty Profit-Sharing Negotiations.- Towards a More Detailed Music Genre Classification via Instrument-Based Source.- Identifying Cyber Threat Patterns Using Graph Machine Learning Techniques Separation.- Analysing Worldwide Cryptocurrency Trends.