Research Excellence Award
Fahimeh Ghasemian
Shahid Bahonar University of Kerman, Iran
Fahimeh Ghasemian, affiliated with Shahid Bahonar University of Kerman, Iran, is a researcher in the field of Computer Science with recognized scholarly contributions in artificial intelligence, machine learning, natural language processing, medical informatics, and computational optimization. Her academic record reflects interdisciplinary research activity across healthcare analytics, deep learning, and intelligent systems, supported by indexed publications, citation impact, and international research visibility.[1]
Abstract
Fahimeh Ghasemian has established a research portfolio centered on artificial intelligence and data-driven computational methodologies, with particular emphasis on healthcare applications, medical imaging, and predictive analytics. Her work integrates deep learning architectures, natural language processing systems, machine learning models, and optimization algorithms to address challenges in disease diagnosis, prognosis, and intelligent healthcare management. Published research associated with her academic profile demonstrates interdisciplinary collaboration and methodological innovation across computer science and medical informatics domains.[2]
Keywords
- Artificial Intelligence
- Deep Learning
- Natural Language Processing
- Machine Learning
- Medical Informatics
- Social Network Analysis
Introduction
The increasing role of artificial intelligence in biomedical and healthcare research has created opportunities for interdisciplinary investigations integrating computational intelligence with clinical decision-making. Researchers in computer science are contributing to this transformation through the development of predictive algorithms, intelligent diagnostic systems, and advanced analytical models. Within this context, Fahimeh Ghasemian has contributed to research exploring machine learning applications in healthcare environments, including COVID-19 prognosis, CT image classification, and natural language processing systems for medical analysis.[3]
Her scholarly activities further extend into optimization algorithms and computational modeling, reflecting broader interests in intelligent systems and data-centric problem solving. The combination of applied healthcare analytics and theoretical algorithmic research demonstrates an interdisciplinary framework aligned with contemporary trends in artificial intelligence and computer engineering.[4]
Research Profile
Fahimeh Ghasemian is affiliated with Shahid Bahonar University of Kerman in Iran. Her academic background includes studies in Computer Engineering, including graduate and postgraduate qualifications from Amirkabir University of Technology and doctoral research at the University of Isfahan. Her research output indexed in Scopus includes journal articles, reviews, and interdisciplinary computational studies with citation activity across medical and engineering domains.[1]
The Scopus profile associated with Author ID 57190766313 indicates documented contributions across healthcare-oriented machine learning systems, predictive modeling, image analysis, and metaheuristic optimization algorithms. Citation metrics and h-index indicators demonstrate measurable scholarly engagement and international visibility within the scientific literature.[5]
Research Contributions
Among the notable areas of contribution is the application of machine learning for healthcare diagnostics and prognostic systems. Research publications involving COVID-19 mortality prediction and hospitalization duration employed data mining and machine learning approaches to support clinical analytics and decision-making processes.[6]
Additional contributions include deep learning frameworks for computerized tomography image classification. The AFEX-Net model introduced adaptive feature extraction strategies within convolutional neural network architectures, demonstrating the integration of artificial intelligence methods into medical image interpretation workflows.[7]
Her research also includes systematic reviews of machine learning models for image-based diagnosis and prognosis in COVID-19 contexts, contributing to evidence synthesis within medical informatics literature. These studies provide comparative perspectives on computational methodologies applied in healthcare analytics and intelligent diagnostics.[8]
Beyond healthcare applications, Fahimeh Ghasemian participated in the development of optimization approaches such as the Human Urbanization Algorithm, a metaheuristic framework designed for solving optimization problems through population-based search mechanisms.[9]
Publications
- Possibilistic–Probabilistic Consumer Participation Modelling and Cybersecure Demand Response Enabled by Convolutional–Bidirectional Long Short‐Term Memory Forecasting, IET Smart Grid (2026).
- AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images, DIGITAL HEALTH (2024).
- Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms, DIGITAL HEALTH (2023).
- Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review, JMIR Medical Informatics (2021).
- Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review, Frontiers in Health Informatics (2021).
Research Impact
The research contributions associated with Fahimeh Ghasemian reflect the growing integration of computational intelligence into healthcare and biomedical sciences. Citation records indicate scholarly engagement with her work, particularly in studies involving machine learning-based diagnostics, COVID-19 predictive systems, and medical image analysis.[5]
Her publications in peer-reviewed journals and interdisciplinary outlets contribute to discussions on artificial intelligence methodologies applicable to clinical support systems, predictive healthcare analytics, and intelligent data processing. The combination of systematic reviews and methodological studies enhances the visibility and applicability of her research within computer science and health informatics communities.[8]
Award Suitability
Fahimeh Ghasemian demonstrates qualifications aligned with recognition in international scientific and research award contexts through her interdisciplinary scholarship, publication record, and contributions to intelligent healthcare systems. Her research activity integrates advanced computational methodologies with real-world healthcare applications, reflecting the objectives commonly associated with research excellence and scientific innovation awards.[2]
The diversity of her research topics, including artificial intelligence in medicine, machine learning, natural language processing, and optimization algorithms, further supports the relevance of her profile for academic recognition programs emphasizing scientific impact, interdisciplinary collaboration, and technological advancement.[9]
Conclusion
Fahimeh Ghasemian has contributed to the advancement of computer science and medical informatics through interdisciplinary investigations in artificial intelligence, machine learning, and intelligent healthcare systems. Her scholarly record includes research on predictive analytics, medical image classification, natural language processing, and optimization methodologies. Indexed publications, citation activity, and participation in computational healthcare research collectively demonstrate an established academic profile suitable for scholarly recognition within international research communities.[1]
External Links
References
- Elsevier. (n.d.). Scopus author details: Fahimeh Ghasemian, Author ID 57190766313. Scopus.
www.scopus.com/authid/detail.uri?authorId=57190766313
- ORCID. (n.d.). Fahimeh Ghasemian ORCID profile.
orcid.org/0000-0002-2176-7089
- Ghasemian, F., et al. (2021). Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review. Frontiers in Health Informatics.
- Ghasemian, F., et al. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
https://doi.org/10.1016/j.matcom.2020.05.023
- Scopus Preview. (n.d.). Citation metrics and indexed documents associated with Fahimeh Ghasemian.
https://www.scopus.com/authid/detail.uri?authorId=57190766313
- Ghasemian, F., et al. (2023). Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. DIGITAL HEALTH.
https://doi.org/10.1177/20552076231170493
- Ghasemian, F., et al. (2024). AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images. DIGITAL HEALTH.
https://doi.org/10.1177/20552076241232882
- Ghasemian, F., et al. (2021). Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review. JMIR Medical Informatics.
https://doi.org/10.2196/25181
- Elsevier. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
https://doi.org/10.1016/j.matcom.2020.05.023
