TRACK 5: Artificial Intelligence (AI) for Data Analytics


AI is useful for analysing and interpreting complex data and for uncovering patterns, correlations, and trends in data that humans may find difficult or impossible to identify. A variety of AI models and methods have proven capable of deriving accurate and dependable insights than traditional data analysis approaches. One of the most important advantages of AI for data analytics is the capability and capacity in managing huge and complicated data sets in a scalable and efficient manner. By leveraging data-based learning algorithms, AI-based tools and systems can assist in finding patterns and relationships in data that are too subtle or laborious for humans to discover. In line with the rapid advances in digital technologies, the value of AI-empowered data analytics will grow as the volume and complexity of data grow over time. Therefore, this conference track is dedicated to advances of general AI methodologies for data analytics and the associated applications to various domains.

Track Chairs:
     Assoc. Prof. Manjeevan Singh Seera, Monash University Malaysia, Malaysia
     Assoc. Prof. Kitsuchart Pasupa, King Mongkut's Institute of Technology Ladkrabang, Thailand

Track Program Chairs:
     Assoc. Prof. Aznul Qalid Md Sabri, University of Malaya, Malaysia
     Assoc. Prof. Tay Kai Meng, Universiti Malaysia Sarawak, Malaysia

Track Technical Committee:
     Dr. Farhad Pourpanah, Queen's University, Canada
     Dr. Tan Choo Jun, Tunku Abdul Rahman University of Management and Technology, Malaysia
     Dr. Praphan Pavarangkoon, King Mongkut's Institute of Technology Ladkrabang, Thailand
     Dr. Zongying Liu, Dalian Maritime University, China

Researchers and practitioners are invited to share their research findings and practical results in utilising AI for data analytics in their domains, which include, but are not limited to:

    ◆ Data Clustering, Classification, and Visualisation
    ◆ Explainable AI and Knowledge Discovery from Databases
    ◆ Fintech, Business Intelligence, Marketing Analytics, Risk Analytics and Decision Support
    ◆ Medical Diagnosis and Prognosis, Biomedical and Healthcare Analytics
    ◆ Predictive Maintenance, Condition Monitoring, Manufacturing Analytics
    ◆ Data Mining in Education, Transportation, Telecommunication, Agriculture, Energy, etc.

Submission Guidelines

Please submit your manuscript via Electronic Submission System (account is needed). (Please choose the track number when you make the submission.)

Important Dates

    ◆ Submission of Full Papers: February 20, 2024
    ◆ Notification of Review Result of Papers from Track: March 20, 2024
    ◆ Registration Deadline: March 30, 2024