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 Chair:
Assoc. Prof. Manjeevan Singh Seera, Monash University
Malaysia, Malaysia
Assoc. Prof. Aznul Qalid Bin Md Sabri, University
of Malaya,
Malaysia
Track Program Chairs:
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:
Jan. 25, 2025
◆ Notification of Review Result
of Papers from Track: Feb. 25, 2025
◆
Registration Deadline: Mar. 25, 2025