Research Excellence Award
Faten Alamri
Princess Nourah Bint Abdulrahman University, Saudi Arabia
Faten Alamri is a researcher at Princess Nourah bint Abdulrahman University whose scholarly work spans artificial intelligence, healthcare analytics, system modeling, reliability engineering, and computational analysis. Her publications demonstrate interdisciplinary research addressing medical diagnosis, predictive modeling, deep learning, and engineering reliability. With a substantial citation record and an established Scopus profile, her contributions illustrate the integration of advanced computational methods with practical scientific and healthcare applications. These achievements reflect continuing engagement in internationally relevant research across computer science and engineering disciplines.[1]
Abstract
Faten Alamri’s research emphasizes intelligent computational methods for healthcare diagnostics and engineering system analysis. Her work combines ensemble learning, deep neural networks, reliability assessment, and predictive analytics to improve disease detection and optimize complex systems. These interdisciplinary investigations support practical applications in medicine and engineering while advancing data-driven decision-making through robust analytical frameworks.[2]
Keywords
Artificial Intelligence, Deep Learning, Alzheimer’s Disease, Parkinson’s Disease, Reliability Engineering, System Modeling, Predictive Analytics, Healthcare Computing.
Introduction
Recent advances in artificial intelligence have transformed healthcare diagnostics and engineering optimization. Machine learning algorithms now enable accurate disease prediction while mathematical reliability models improve system performance and operational safety. Research combining these fields contributes significantly to scientific innovation and practical problem solving.[2]
Research Profile
According to available bibliometric information, Faten Alamri maintains a Scopus profile with an h-index of 20 and more than 1,300 citations. Her scholarly activities encompass artificial intelligence, medical image analysis, system reliability, and computational modeling, reflecting sustained interdisciplinary collaboration and international research visibility.[1]
Research Contributions
Her featured publications include an ensemble deep-learning framework for Alzheimer’s disease detection, a hybrid LSTM-GRU model for Parkinson’s disease classification, and analytical modeling of hot and cold standby redundant systems. Collectively, these studies demonstrate expertise in combining computational intelligence with engineering analysis to improve diagnostic accuracy and system performance.[3]
Publications
- An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning. Diagnostics, 2023.
- Novel Analysis between Two-Unit Hot and Cold Standby Redundant Systems with Varied Demand. Symmetry, 2023.
- Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model. Electronics, 2023.
Research Impact
The research has contributed to advancing intelligent healthcare systems and engineering reliability by demonstrating practical applications of deep learning and mathematical modeling. Its interdisciplinary character supports future developments in precision medicine, predictive maintenance, and computational decision-support technologies.[2]
Award Suitability
Faten Alamri’s scholarly achievements, interdisciplinary publications, strong citation performance, and contributions to artificial intelligence and system analysis demonstrate qualities commonly associated with international research recognition. Her work illustrates innovation, scientific rigor, and practical impact across healthcare and engineering applications.
Conclusion
The research portfolio of Faten Alamri reflects meaningful contributions to computational intelligence, healthcare analytics, and reliability engineering. By integrating advanced machine learning techniques with practical engineering methodologies, her work continues to support scientific progress and interdisciplinary innovation in modern computing and applied research.
External Links
References
- Elsevier. (n.d.). Scopus Author Details: Faten S. Alamri, Author ID 57219754933.
https://www.scopus.com/authid/detail.uri?authorId=57219754933
- Mujahid, M., Rehman, A., Alam, T., Alamri, F. S., et al. (2023). An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.
https://doi.org/10.3390/diagnostics13152489
- Rehman, A., Saba, T., Mujahid, M., Alamri, F. S., et al. (2023). Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model.
https://doi.org/10.3390/electronics12132856