• Charles Sturt University will showcase its research capabilities and strengthen its presence in the fields of machine learning and data science at a conference it is co-organising in Sydney in November
• The conference will bring together international and national experts in artificial intelligence (AI), computer vision, future machine learning technologies, cybersecurity and data science
• Leading and emerging researchers, computer practitioners, industry professionals and government participants will exchange ideas and present the latest research

Charles Sturt University is co-organising an international machine learning and data science conference that will bring together leading Australian and international computer experts in Sydney in November.

The conference is convened by the Institute of Electrical and Electronics Engineers (IEEE), the world’s largest technical professional organisation dedicated to advancing technology for the benefit of humanity.

Charles Sturt University, in collaboration with Western Sydney University, is organising the IEEE International Conference on Future Machine Learning and Data Science (FMLDS) from Wednesday 20 to Saturday 23 November.

Associate Professor in Computing Rafiqul Islam (pictured inset above) in the Charles Sturt School of Computing, Mathematics and Engineering is Chair of the conference’s Technical Program Committee (TPC).

Professor Islam said the 2024 FMLDS will bring together international and national experts in artificial intelligence (AI), computer vision, future machine learning technologies, cybersecurity and data science.

“This conference will serve as an important platform for leading and emerging researchers, computer practitioners, industry professionals and government participants to exchange ideas and present the latest research,” Professor Islam said.

“We have already received approximately 170 presentation submissions from around the world, surpassing our expectations and this is an excellent opportunity for Charles Sturt University to showcase its research capabilities and strengthen its presence in the fields of machine learning and data science.

“We look forward to keynote addresses by world leaders in machine learning and data science from both industry and academia and to a successful and impactful conference.”

Topics to be covered include AI, computer vision, future machine learning technologies, pattern recognition, motion tracking, cybersecurity, bioinformatics, internet of things (IoT) and data science.

All papers submitted to 2024 FMLDS will undergo a rigorous review process by at least two independent expert reviewers. Accepted papers will be published in the IEEE Xplore Digital Library.

In addition, selected conference papers will be considered for publication in the Special Issue on Emerging Applications of Machine Learning in Smart Systems, subject to the journal’s acceptance criteria.

Professor Islam said the talent of the University’s computing research students will also be showcased.

“It is so vitally important that anyone interested in a career in advanced computing explores the pathways and options offered by Charles Sturt University because the demand for computer science professionals will only continue to increase,” he said.

“The University offers a range of undergraduate and postgraduate degrees and industry internships, and the Charles Sturt University and IBM Australia Internship Agreement facilitates work integrated learning opportunities for students in regional NSW.”

Examples of some Charles Sturt University research papers to be presented at the IEEE conference:

• Mitigating cybersecurity risk of threat actors through Dark Web browser fingerprinting. In a digital world where cybersecurity is crucial, this paper investigates various cyber-attack types on both the Surface Web and Dark Web, with a focus on browser fingerprinting, which captures metadata from users’ browsers.

• Privacy-preserving federated incremental learning for spatial crowdsourcing: a survey of challenges and methods. This paper explores Spatial Crowdsourcing (SC), an emerging crowdsourcing model that allocates tasks based on real-time user locations to perform spatiotemporal relevant tasks.

• A comparative study on permissioned based blockchain implementation on healthcare data: from security and privacy perspective. Blockchain technology securely stores transaction histories using a cryptographic system in a decentralized platform. This paper delves into permissioned blockchain frameworks in the healthcare sector, examining their consensus range, modularity, language support, privacy, transaction rate, and currency and analysing their adoption rates through these lenses.