Nurhadhinah Nadiah Ridzuan | Artificial Intelligence | Research Excellence Award

Ms. Nurhadhinah Nadiah Ridzuan | Artificial Intelligence | Research Excellence Award

Student | Universiti Brunei Darussalam | Brunei Darrussalam

Ms. Nurhadhinah Nadiah Ridzuan is a researcher at Universiti Brunei Darussalam specializing in artificial intelligence applications in the financial sector, with a strong focus on regulation, ethics, and governance. Her research examines the balance between technological innovation and responsible financial practices, particularly in FinTech ecosystems and regulatory sandboxes. She has authored multiple peer-reviewed publications, accumulating 141 citations, with an h-index of 3 and an i10-index of 2. Her highly cited 2024 work on AI, regulation, and ethical responsibility reflects significant scholarly impact. Ridzuan actively collaborates with international researchers across finance, digital governance, and Industry 4.0 studies. Her work contributes to policy-relevant insights that support ethical AI adoption, inclusive financial systems, and sustainable digital transformation in emerging and global financial markets.

Citation Metrics (Google Scholar)

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i10-index

2

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h-index

i10-index

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Featured Publications


Modelling Individual Performance in Industry 4.0 with Artificial Intelligence and Organisational Strategies in the Financial Sector

– In Multi-Industry Digitalization and Technological Governance in the AI Era (2025). | Citations: 2


Exploratory Study of the FinTech Regulative Sandbox: Opportunities and Challenges

– In Promoting Inclusivity and Accessibility with FinTech (2026).

Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

Prof. Dr. Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

Professor of Computer Science | King Abdulaziz University | Saudi Arabia

Prof. Dr. Ahmed Hamza Osman Ahmed is a distinguished computer scientist and cybersecurity expert whose research bridges artificial intelligence, information security, and data privacy. With over 70 peer-reviewed publications in prestigious journals such as IEEE, Elsevier, and Springer, his scholarly impact is evidenced by 709 citations across 658 documents and an h-index of 14, underscoring his significant contributions to the field. His research encompasses AI-driven cybersecurity systems, intrusion detection, digital forensics, and blockchain-based data integrity, with several funded projects advancing intelligent threat prediction and misinformation detection. Prof. Ahmed has played a pivotal role in developing ABET-aligned curricula, integrating machine learning into cybersecurity education, and supervising more than 25 postgraduate theses in cybersecurity and data science. Internationally recognized for academic excellence, he has received awards such as the Gold Medal at PECIPTA 2011 and Best Postgraduate Student at Universiti Teknologi Malaysia. His extensive collaborations across Saudi Arabia, Malaysia, and Sudan reflect his commitment to fostering global research partnerships and advancing secure, AI-empowered digital ecosystems. Through his leadership in teaching, research, and academic service, Prof. Ahmed continues to contribute to shaping the future of cybersecurity and artificial intelligence with profound educational and societal impact.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Elssied, N. O. F., Ibrahim, O., & Osman, A. H. (2014). A novel feature selection based on one-way ANOVA F-test for e-mail spam classification. Research Journal of Applied Sciences, Engineering and Technology, 7(3), 625–638. Citations: 223

  2. Elhadi, A. A. E., Maarof, M. A., & Osman, A. H. (2012). Malware detection based on hybrid signature behaviour application programming interface call graph. American Journal of Applied Sciences, 9(3), 283–293. Citations: 137

  3. Osman, A. H., Salim, N., Binwahlan, M. S., Alteeb, R., & Abuobieda, A. (2012). An improved plagiarism detection scheme based on semantic role labeling. Applied Soft Computing, 12(5), 1493–1502. Citations: 128

  4. Osman, A. H., & Aljahdali, H. M. (2020). An effective ensemble boosting learning method for breast cancer virtual screening using neural network model. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2976149 Citations: 93

  5. Abuobieda, A., Salim, N., Albaham, A. T., Osman, A. H., & Kumar, Y. J. (2012). Text summarization features selection method using pseudo genetic-based model. In Proceedings of the 2012 International Conference on Information Retrieval & Knowledge Management (pp. 84–89). Citations: 84