[DataLAB | AI/Data-Driven: Cyber & Business Analytics - SMEs]

***** "In God we Trust. All others must bring DATA."***** - William Edwards Deming

Welcome to this global research platform, specifically designed to support potential students and young researchers exploring cutting-edge research in the broad field of Data-Driven, Ethical and Responsible AI, Machine Learning, LLMs, Agentic AI for Business Analytics - SMEs & Cyber Applications. Key Themes:

• AI/Data-Driven Technologies
• Cyber applications with AI/Data-Driven
• Business/SMEs applications with AI/Data-Driven

As AI and data-driven technologies continue to reshape today's business, e.g., Small and medium-sized enterprises (SMEs), and cybersecurity landscape, it is crucial to understand how these emerging technologies operate, their potential impact, and the future research opportunities they present.
The goal is to provide a collaborative environment where students and early-career researchers can explore core AI and Machine Learning algorithms, data-driven modeling techniques, emerging trends, and hands-on experiments with real-world cyber and business datasets.
This platform also offers relevant resources and research guidance, when needed, to help bridge the gap between theoretical understanding and practical application, and to support the development of innovative solutions.

[People - Students/Researchers/Collaborators - National/International]

• Dr. Iqbal H. Sarker [Adviser]

  [Biography] Dr. Iqbal H. Sarker received his PhD in Computer Science from Swinburne University of Technology, Melbourne, Australia, in 2018. He is currently a Research Fellow at the Centre for Securing Digital Futures, Edith Cowan University (ECU), Australia. He has also worked with the Cyber Security Cooperative Research Centre (CSCRC) through academia–industry collaborations, including with CSIRO’s Data61. In addition, Dr. Sarker is an Adjunct Fellow at the DigiSAS Lab, School of Computer Science, University of Technology Sydney (UTS), Australia.
  
  Dr. Sarker’s research focuses on Cybersecurity, AI & Data-driven technologies, Digital twins and AI applications for Business Transformation and SMEs. He has published 100+ Journal and Conference papers in various reputed venues published by Elsevier, Springer Nature, IEEE, ACM, Oxford University Press, etc. Moreover, he is a LEAD author of two research monograph BOOKs titled "Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making", Springer Nature, Switzerland, and "AI-Driven Cybersecurity and Threat Intelligence: Cyber Automation, Intelligent Decision-Making and Explainability", Springer Nature, Switzerland. His research impact has been recognized through ranking by ScholarGPS and inclusion in the world’s top 2% of most-cited scientists, as published by Elsevier and Stanford University.
  
  In addition to his research activities, Dr. Sarker has extensive teaching experience in courses such as AI, Machine Learning, Data Science and Analytics, Database Management Systems, Project Management and Programming. He is actively involved in research leadership and engagement roles, including journal editorial boards, international conference program committees, postgraduate student supervision, visiting scholar appointments, and academia-industry collaborations. He is also engaged with the Industry Mentoring Network in STEM (IMNIS) program and collaborates closely with industry partners across Australia. Dr. Sarker is a member of ACM, IEEE, and the Australian Information Security Association (AISA).
  (ORCID Link)

• [National/International Collaborators]
• [Students/Young Researchers]
• [Academic-Industry Professionals]

[Some Helpful Resources for the Students/Young Researchers - Published by Sarker et al.

The publications listed below may assist Students to select their Research/Thesis work. So you can read your preferred one and find out your interested Research TOPIC/ Issues/ Questions/ Contributions and enjoy your work!

[Note: After selecting their topic, interested students can explore/read more relevant online resources (e.g., Google Scholar, Elsevier, Springer Nature, IEEE Explore, ACM DL, etc.) to enhance their background knowledge.]

Type	Major Domain	Sample Paper to Read and Select your Interested Topic for Research
Journal	Business Analytics & SMEs	SME-TEAM: Leveraging Trust and Ethics for Secure and Responsible Use of AI and LLMs in SMEs, npj Artificial Intelligence, Nature. (Online Link)
Journal	Digital Twin & CyberAI	Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects (24 pages), ICT Express, Elsevier, South Korea. (Online Link)
Journal	Critical Infrastrucutre & CyberAI	Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures, Elsevier, USA. (Online Link)
Journal	Data Science	Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective (22 pages), SN Computer Science, Springer Nature, Germany. (Online Link)
Perspective	Data-Driven & Cyber	Data-Driven Intelligence can Revolutionize Today’s Cybersecurity World: A Position Paper, Springer Nature. (Online Link)
Perspective	Human-AI Teaming & Cyber	AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity, Springer Nature. (Online Link)
Journal	Machine Learning Algorithms	Machine Learning: Algorithms, Real-World Applications and Research Directions (21 pages), SN Computer Science, Springer Nature, Germany . (Online Link)
Journal	Deep Learning Techniques	Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (20 pages), SN Computer Science, Springer Nature, Germany . (Online Link)
Journal	AI Techniques	Artificial Intelligence (AI)-based Modeling: Techniques, Applications and Research Issues towards Automation, Intelligent and Smart Systems (20 pages), SN Computer Science, Springer Nature, Germany . (Online Link)
Perspective	AI/LLM	LLM potentiality and awareness: a position paper from the perspective of trustworthy and responsible AI modeling, Springer Nature. (Online Link)