Innovative Research Award
Shakila Rahman – American International University – Bangladesh
Shakila Rahman is affiliated with American International University – Bangladesh and has contributed to research in deep learning, intelligent systems, machine learning, and applied artificial intelligence. Her scholarly publications demonstrate interdisciplinary applications of advanced computational methods in engineering and healthcare while supporting practical industrial and environmental solutions.[1]
Abstract
This article presents an academic overview of Shakila Rahman’s research achievements supporting her recognition for the Innovative Research Award. Her scholarly work focuses on deep learning, federated learning, intelligent decision systems, environmental monitoring, and industrial automation. Through peer-reviewed publications, she has demonstrated practical applications of artificial intelligence for water quality assessment, privacy-preserving distributed learning, and automated defect detection. Her research combines computational innovation with real-world impact while contributing to scientific advancement, interdisciplinary collaboration, and technology-driven solutions across engineering and data science disciplines.[1]
Keywords
- Deep Learning
- Machine Learning
- Federated Learning
- Artificial Intelligence
- Water Quality Prediction
- Industrial Automation
Introduction
Shakila Rahman’s research emphasizes practical artificial intelligence solutions addressing engineering and environmental challenges through deep learning, intelligent analytics, and data-driven methodologies. Her work integrates computational efficiency with real-world implementation, supporting reliable decision-making, predictive modeling, and technological innovation while strengthening interdisciplinary collaboration across modern scientific and industrial research domains.[2]
Research Profile
Her scholarly profile demonstrates sustained contributions to deep learning, federated learning, computer vision, and intelligent engineering applications. With peer-reviewed publications indexed in recognized databases, measurable citation impact, and interdisciplinary collaborations, she continues advancing artificial intelligence research while supporting practical implementations across healthcare, manufacturing, and environmental monitoring systems.[1]
Research Contributions
Her research contributions include stacking ensemble learning for drinking water assessment, carbon-aware federated learning with privacy preservation, and deep learning models for automated printed circuit board inspection. These studies demonstrate methodological innovation while improving prediction accuracy, computational efficiency, security, and intelligent industrial quality assurance.[2][3]
Publications
Her publications highlight research spanning environmental analytics, federated artificial intelligence, computer vision, and industrial inspection. Published through internationally recognized venues, these studies demonstrate rigorous methodology, practical validation, and reproducible findings while contributing valuable knowledge to machine learning, engineering, and intelligent computational systems research.[2]
Research Impact
Her research has achieved measurable scholarly visibility through publications, citations, and interdisciplinary influence. The practical orientation of her studies supports environmental sustainability, privacy-aware distributed learning, and industrial automation, encouraging broader adoption of artificial intelligence techniques while inspiring continued innovation within academic and applied research communities.[1]
Award Suitability
Recognition through the Innovative Research Award appropriately reflects her documented academic productivity, interdisciplinary research excellence, and commitment to developing impactful artificial intelligence solutions. Her scholarly achievements demonstrate originality, practical significance, and sustained contributions that align with the objectives of the Global Tech Excellence Awards.[1]
Conclusion
Shakila Rahman’s academic record reflects continuous advancement in deep learning and intelligent computational research through impactful publications and measurable scholarly influence. Her contributions demonstrate scientific rigor, practical relevance, and interdisciplinary collaboration, supporting recognition as a deserving recipient of the Innovative Research Award for sustained excellence in research.[1]
External Links
References
- Elsevier. (n.d.). Scopus author details: Shakila Rahman, Author ID 57218573687. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57218573687
- Rahman, S., et al. (2025). Evaluating the Potability of Drinking Water Using Stacking Ensemble Machine Learning Technique.
https://ieeexplore.ieee.org/document/11546276/
- Rahman, S., et al. (2025). FedEPL+: Carbon-Aware Client Selection With Valid Differential Privacy in Federated Learning.
https://ieeexplore.ieee.org/document/11545760
- Rahman, S., et al. (2025). Real-Time Detection of Printed Circuit Board and Soldering Defects Using Deep Learning Techniques.
https://ieeexplore.ieee.org/document/11545912