Assoc. Prof. Nor Erne Nazira binti Bazin, Universiti Teknologi Malaysia, Malaysia

Speech Title: Predictive Intelligence for High-Stakes Environments: Leveraging Machine Learning and Data Imputation in Critical Systems

Abstract: Deploying predictive models in hazardous, high-stakes operational environments requires software systems capable of managing severe data friction and tracking complex, moving risks over time. When industrial disruptions or environmental hazards occur, traditional engineering analysis methods often require extensive computation times, making rapid decision-making difficult. This keynote presents a practical framework designed to achieve rapid, low-latency risk boundary tracking and data optimization. Drawing from research in applied artificial intelligence and data analytics, the session outlines the implementation of time-series predictive modelling and machine learning pipelines to handle volatile telemetry streams. To counter real-world infrastructure constraints, the framework integrates a robust missing-data imputation pipeline that preserves predictive reliability even when active sensor networks experience sudden data loss. This session reveals how structured data engineering can compress processing times, providing a scalable blueprint for reliable decision support systems capable of maintaining operational readiness under volatile real-world conditions.

Biography: Assoc. Prof. Ts. Dr Nor Erne Nazira binti Bazin is an Associate Professor and the Post Graduate Academic Manager at the Faculty of Computing, Universiti Teknologi Malaysia (UTM). She is a registered Professional Technologist (Ts.) with the Malaysia Board of Technologists (MBOT) and holds a PhD in System Dynamics Simulation and Modelling from the University of Salford, UK. Her research focuses on system dynamics simulation, applied artificial intelligence, big data analytics, and predictive modelling for volatile environments. Her current work explores resilient architecture for high-stakes decision intelligence, specifically integrating deep learning frameworks for medical data imputation and spatial analytics. She has also led and contributed to high-impact research grants and innovation projects spanning spatial hazard tracking systems, public policy modelling, and industrial analytics collaborations with the DHL Asia Pacific Innovation Centre.