Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

Published on by Forensic Scientist

Mrs. Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

Satbayev University | Kazakhstan

Mrs. Gulnaz Zakariya is a researcher specializing in Machine Learning, Data Science, and Geospatial Analytics, with strong interdisciplinary contributions spanning FinTech and remote sensing. Her research focuses on the development of intelligent data-driven models for credit risk assessment, antifraud systems, and decision-support frameworks using machine learning and deep learning techniques. She has also made notable contributions to object-based image analysis and spatial change detection, particularly in the study of informal settlements using satellite imagery. Her work bridges applied artificial intelligence, geoinformatics, and financial technology, emphasizing model robustness, interpretability, and real-world deployment. She has published in peer-reviewed international journals and conferences indexed in major scholarly databases, contributing to innovation at the intersection of data science, spatial analytics, and applied AI-driven solutions.

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Featured Publications


Innovative Credit Scoring and Sales Accounting Solutions for SMEs in Kazakhstan

– Gulnaz Zakariya, Olzhas Akylbekov, Aiman Moldagulova,· FinTech, 2025


Object-based change detection of informal settlements

– P. Hofmann, G. Bekkarnayeva · JURSE 2017 (IEEE)

Olzhas Akylbekov | Data Science and Analytics | Research Excellence Award

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Mr. Olzhas Akylbekov | Data Science and Analytics | Research Excellence Award

Satbayev University | Kazakhstan

Mr. Olzhas Akylbekov is a researcher in data science and machine learning with a strong focus on applied artificial intelligence for spatial analysis and intelligent systems. His scholarly work emphasizes deep learning architectures, hybrid neural networks, and spatial data modeling for real-world decision support systems. According to Scopus, he has 2 peer-reviewed publications, with a total of 20 citations and an h-index of 2. His research contributions are published in internationally recognized journals indexed in Web of Science and Scopus, reflecting impact in areas such as urban spatial analytics and AI-driven content analysis.

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Featured Publications


Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

– International Journal of Advanced Computer Science and Applications(IJACSA), 2023 · Cited by 19

Nafiz Fahad | Computer Science | Best Researcher Award

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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.