Dr. Adeleh Asemi Zavareh is a Senior Lecturer in the Department of Software Engineering at the University of Malaya, Malaysia. Her expertise lies in artificial intelligence (AI), the Internet of Things (IoT), and multi-criteria decision-making (MCDM), with a primary focus on developing intelligent decision support systems to address complex challenges across various domains.
Dr. Asemi earned her Ph.D. in Artificial Intelligence from the University of Malaya under the esteemed Malaysian Technical Cooperation Programme (MTCP) Scholarship. She also holds an M.Sc. in Computer Science from Pune University, India, and a B.Sc. in Computer Engineering from Ashrafi Esfahani University, Iran. In 2015, she completed her postdoctoral research in Human-Computer Interaction (HCI) and Human Decision-Making Simulation at the University of Malaya.
With over a decade of academic experience, Dr. Asemi has supervised more than 50 postgraduate students and has taught a diverse range of courses at both undergraduate and postgraduate levels, including Artificial Intelligence, Software Engineering, the Internet of Things, and Big Data Management. Her research contributions encompass fuzzy-based decision support systems, IoT system modeling, and big data analytics, with numerous publications in high-impact ISI and Scopus-indexed journals.
Dr. Asemi is actively engaged in interdisciplinary research collaborations across domains such as healthcare, environmental science, and law, where she leverages AI-driven approaches to address real-world challenges. Since 2019, her research focus has expanded to include the multi-criteria evaluation of big data and IoT technologies, solidifying her role as a leading contributor to smart decision-making frameworks.
Systematic Prioritization of Challenges in IoT Big Data Decision Support
The integration of the Internet of Things (IoT) with Big Data technologies has revolutionized decision support systems, enabling real-time analytics and automation. However, the vast and dynamic nature of IoT-generated data presents numerous challenges, including data scalability, security, interoperability, latency, and processing efficiency. Effectively addressing these challenges requires a systematic prioritization to ensure optimal resource allocation and decision-making efficiency.
This talk explores a structured approach to identifying, categorizing, and prioritizing the key challenges in IoT Big Data decision support systems. By leveraging analytical frameworks, industry case studies, and expert-driven methodologies, we aim to determine which challenges have the most significant impact on system performance and reliability. The session will also discuss best practices and emerging solutions to mitigate these issues, ensuring that IoT decision support systems remain scalable, secure, and effective in handling complex data-driven scenarios.