Amrita Ganguly | Computer Science and Artificial Intelligence | Research Excellence Award

Published on by Forensic Scientist

Mrs. Amrita Ganguly | Computer Science and Artificial Intelligence | Research Excellence Award

George Mason University | United States

Mrs. Amrita Ganguly is a researcher working at the intersection of Responsible and Ethical Artificial Intelligence, Human–AI Interaction, and AI applications in education. Her scholarly contributions focus on ethical governance of generative AI, human-centered AI design, and collaborative intelligence frameworks. According to Scopus, she has 7 peer-reviewed publications, 19 citations, and an h-index of 2. Her work appears in high-impact international journals and premier conferences in AI ethics, human–computer interaction, and computing education. Her research emphasizes evidence-based guidelines, design frameworks, and ethical considerations that shape responsible AI deployment in academic and socio-technical contexts.

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View Scopus Profile  View Google Scholar Profile  View ORCID Profile

Featured Publications


Collaborative job seeking for people with autism: Challenges and design opportunities

– CHI ’24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems · Cited by 18


ShadowMagic: Designing Human-AI Collaborative Support for Comic Professionals’ Shadowing

– UIST ’24: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology · Cited by 5

Nafiz Fahad | Computer Science | Best Researcher Award

Published on by Forensic Scientist

Mr. Nafiz Fahad | Computer Science | Best Researcher Award

Multimedia University | Malaysia

Mr. Nafiz Fahad is an emerging AI researcher at Multimedia University, Cyberjaya, Malaysia, recognized for his growing contributions to artificial intelligence in healthcare, computer vision, and natural language processing. His research focuses extensively on explainable AI, clinical decision support systems, and data-driven healthcare intelligence. According to Scopus, he has 19 indexed publications, 122 citations, and an h-index of 6, reflecting the influence and visibility of his scholarly work within the global research community. His scientific output spans chronic disease prediction, dementia analytics, lung disease classification, hypertension ontology development, wound-image segmentation, obesity prediction, and precision public health. These studies incorporate techniques such as deep learning, transfer learning, ensemble learning, hybrid architectures, and explainable machine learning to advance diagnostic accuracy and interpretability in medical AI systems. Beyond health-focused research, Fahad has also contributed high-impact work in fake news detection, generative AI, machine learning security, student performance prediction, agricultural disease detection, vision transformers for physics data, and federated learning enhanced with homomorphic encryption. His ongoing research extends to mental health analytics, EEG decoding models, diabetic retinopathy detection, and agentic AI solutions for healthcare innovation. Fahad’s growing academic recognition includes research awards, best paper achievements, and contributions to high-impact journals and conferences. His multidisciplinary scholarship positions him as a promising young researcher advancing applied AI at the intersection of healthcare, societal well-being, and intelligent systems.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

1. Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., … (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges and student perception. IEEE Access.

2. Mahamud, E., Fahad, N., Assaduzzaman, M., Zain, S. M., Goh, K. O. M., & Morol, M. K. (2024). An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning. Decision Analytics Journal, 12, 100499.

3. Ahmed, R., Fahad, N., Miah, M. S. U., Hossen, M. J., Morol, M. K., Mahmud, M., … (2024). A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction. Healthcare Analytics, 6, 100362.

4. Fahad, N., Goh, K. M., Hossen, M. I., Shopnil, K. M. S., Mitu, I. J., Alif, M. A. H., & Tee, C. (2023). Stand up against bad intended news: An approach to detect fake news using machine learning. Emerging Science Journal, 7(4), 1247–1259.

5. Hossain, M. N., Fahad, N., Ahmed, R., Sen, A., Al Huda, M. S., & Hossen, M. I. (2024). Preventing student’s mental health problems with the help of data mining. International Journal of Computing, 23(1), 101–108.