Best Academic Researcher Award

Pabon Shaha is a researcher whose documented scholarly work focuses on artificial intelligence, machine learning, feature optimization, cybersecurity, and data-driven approaches to health-related prediction. His publication record includes studies addressing chronic kidney disease diagnosis, DRDoS attack identification, and explainable artificial intelligence for cardiovascular risk assessment, demonstrating research activity across applied artificial intelligence domains. [1] [2] [3]

Abstract

Pabon Shaha’s documented research activity is centered on applied artificial intelligence and machine learning, with particular attention to feature optimization, cybersecurity, and health-oriented predictive analytics. His publications examine machine-learning methods for chronic kidney disease diagnosis, DRDoS attack identification, and explainable cardiovascular disease assessment. The available scholarly record indicates interdisciplinary application of computational methods to practical problems in healthcare and cybersecurity. His Scopus record lists 16 documents, 85 citations, and an h-index of 5, providing quantitative indicators of scholarly activity and citation impact. [1] [2] [3]

Keywords

Artificial Intelligence; Machine Learning; Feature Optimization; Explainable AI; Cybersecurity; DRDoS Detection; Chronic Kidney Disease; Cardiovascular Disease; Risk Assessment; Predictive Analytics. [1] [2] [3]

Introduction

Artificial intelligence and machine learning provide computational methods for analyzing complex datasets and developing predictive models across healthcare and cybersecurity. Shaha’s documented publications illustrate applications involving feature optimization, attack detection, disease diagnosis, and explainable predictive modeling. These studies position machine learning as a practical research tool for addressing data-intensive problems. [1] [2] [3]

Research Profile

The research profile represented by the supplied publication record is interdisciplinary within computer science, particularly artificial intelligence and machine learning. The documented work combines classification, feature optimization, dimensionality reduction, ensemble learning, and explainability techniques. Applications span healthcare and cybersecurity, indicating an emphasis on computational methods that can be evaluated through measurable predictive performance. [1] [2] [3]

Research Contributions

The documented contributions include investigating feature optimization for machine-learning-based chronic kidney disease diagnosis, evaluating machine-learning algorithms for DRDoS attack detection, and applying explainable artificial intelligence to cardiovascular disease detection and risk assessment. Together, these studies demonstrate the use of computational modeling, feature engineering, optimization, and interpretability techniques across distinct applied research challenges. [1] [2] [3]

Publications

The supplied publication record includes peer-reviewed and scholarly works addressing machine-learning applications in healthcare and cybersecurity. The 2022 study on chronic kidney disease examines feature optimization and ensemble classification, while the DRDoS study evaluates machine-learning classifiers with PCA-based feature reduction. A 2025 arXiv work examines explainable AI for cardiovascular disease detection and risk assessment. [1] [2] [3]

Research Impact

The supplied metrics report 16 documents, 85 citations, and an h-index of 5, which provide quantitative indicators of the researcher’s documented scholarly activity. The publications also address practical problems in healthcare and cybersecurity, including disease prediction and network attack detection. Such application-oriented research can contribute methods and evidence for subsequent investigations in these areas. [1] [2] [3]

Award Suitability

The documented research record aligns with an academic recognition category focused on artificial intelligence and applied computer science. Evidence includes publications using machine learning for healthcare diagnosis, cybersecurity attack identification, feature optimization, and explainable AI. The reported scholarly metrics and multidisciplinary applications provide identifiable evidence that can be considered during an award evaluation process. [1] [2] [3]

Conclusion

Pabon Shaha’s documented scholarly work demonstrates research activity in artificial intelligence and machine learning, with applications spanning healthcare and cybersecurity. The selected publications illustrate work on feature optimization, attack detection, and explainable predictive modeling, while the supplied bibliometric indicators provide additional measures of research activity. [1] [2] [3]

External Links

• Scopus Author Profile
• Global CSE Awards

References

1. Hossain, M. M., Swarna, R. A., Mostafiz, R., Shaha, P., Pinky, L. Y., Rahman, M. M., Rahman, W., Hossain, M. S., Hossain, M. E., & Iqbal, M. S. (2022). Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease. Machine Learning with Applications, 9, 100330.
   https://www.sciencedirect.com/science/article/pii/S2666827022000421
2. Shaha, P., Khan, M. S. I., Rahman, A., Hossain, M. M., Mammun, G. M., & Nasir, M. K. (2025). A prevalent model-based on machine learning for identifying DRDoS attacks through features optimization technique. Statistics, Optimization and Information Computing, 13(1), 409–433.
   https://iapress.org/index.php/soic/article/view/2042
3. Sourov, M. E. A., Hossen, M. S., Shaha, P., Hossain, M. M., & Iqbal, M. S. (2025). An explainable AI-enhanced machine learning approach for cardiovascular disease detection and risk assessment. arXiv.
   https://arxiv.org/abs/2507.11185