Fahimeh Ghasemian | Computer Science | Research Excellence Award

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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]

Fahimeh Ghasemian
Affiliation Shahid Bahonar University of Kerman
Country Iran
Scopus ID 57190766313
Documents 26
Citations 235
h-index 9
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0000-0002-2176-7089

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]

References

  1. Elsevier. (n.d.). Scopus author details: Fahimeh Ghasemian, Author ID 57190766313. Scopus.
    www.scopus.com/authid/detail.uri?authorId=57190766313
  2. ORCID. (n.d.). Fahimeh Ghasemian ORCID profile.
    orcid.org/0000-0002-2176-7089
  3. Ghasemian, F., et al. (2021). Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review. Frontiers in Health Informatics.
  4. 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
  5. Scopus Preview. (n.d.). Citation metrics and indexed documents associated with Fahimeh Ghasemian.
    https://www.scopus.com/authid/detail.uri?authorId=57190766313
  6. 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
  7. 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
  8. 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
  9. Elsevier. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
    https://doi.org/10.1016/j.matcom.2020.05.023

Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

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Dr. Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

University of Sydney | Australia

Dr. Mehnaz Tabassum is an accomplished researcher in Computational Neurosurgery and Health Innovation, with core expertise in medical image analysis, artificial intelligence, and brain tumor diagnostics. Her research integrates deep learning, radiomics, and neuroimaging to enhance the precision of tumor segmentation, classification, and recurrence prediction in neuro-oncology. Her scholarly contributions include 14 Scopus-indexed publications, with a total of 87 citations and an h-index of 4 (Scopus metrics). She has published in prestigious journals such as Cancers, European Radiology, and Neuro-Oncology Advances, and has presented her findings at leading international conferences including IEEE EMBC and IEEE ISBI. Dr. Mehnaz Tabassum’s recent research explores cross-modality medical image synthesis, MRI-to-PET generation using diffusion and GAN-based models, and meta transfer learning for brain tumor segmentation. Her innovative work advances computational solutions for precision medicine and AI-assisted neuroimaging. She has received multiple distinctions, including the Pro-Vice Chancellor’s Research Excellence Scholarship and the Henry Sutton Postgraduate Research Scholarship, alongside a Best Paper Award for excellence in scientific contribution. Her interdisciplinary research continues to impact the fields of AI-driven diagnostics, eye-tracking in medical imaging, and computational modeling for neurosurgical innovation, reflecting her commitment to advancing data-driven healthcare and translational neuroscience.

Profiles: Scopus | Google Scholar | ORCID | ResearchGate | Staff Page

Featured Publications

  • Tabassum, M., Suman, A. A., Suero Molina, E., Pan, E., Di Ieva, A., & Liu, S. (2023). Radiomics and machine learning in brain tumors and their habitat: A systematic review. Cancers, 15(8), Article 2034. https://doi.org/10.3390/cancers15082034

  • Ghose, P., Alavi, M., Tabassum, M., Ashraf Uddin, M., Biswas, M., Mahbub, K., … & Hassan, M. (2022). Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Frontiers in Genetics, 13, 980338. https://doi.org/10.3389/fgene.2022.980338

  • Moradizeyveh, S., Tabassum, M., Liu, S., Newport, R. A., Beheshti, A., & Di Ieva, A. (2024). When eye-tracking meets machine learning: A systematic review on applications in medical image analysis. arXiv preprint arXiv:2403.07834. https://arxiv.org/abs/2403.07834

  • Tabassum, M., Suman, A. A., Russo, C., Di Ieva, A., & Liu, S. (2023). A deep learning framework for skull stripping in brain MRI. Neurocomputing (Under review).

  • Afrin, F., Al-Amin, M., & Tabassum, M. (2015). Comparative performance of using PCA with K-means and fuzzy C means clustering for customer segmentation. International Journal of Scientific and Technology Research, 4(8), 70–74.

Weimin Huang | Engineering | Best Researcher Award

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Assist. Prof. Dr. Weimin Huang | Engineering | Best Researcher Award

Shandong University of Science and Technology | China

Dr. Weimin Huang, Academic Associate Professor at the College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, is a leading expert in mechanical manufacturing and automation, with a focus on high-speed cutting technology, friction and fatigue wear mechanisms, and advanced agricultural machinery design. He earned his Ph.D. in Mechanical Manufacturing and Automation from Shandong University, establishing a strong foundation for his research and academic contributions. Dr. Weimin Huang has successfully led over 10 major research projects, including funding from the National Natural Science Foundation of China, and the Natural Science Foundation of Shandong Province, and has directed more than 20 industry-sponsored consultancy projects, effectively translating scientific insights into practical engineering solutions. His pioneering work on surface texture preparation via ball-end milling has significantly enhanced wear resistance and tribological performance of mechanical components, while his studies on sliding fatigue wear mechanisms have improved the durability and efficiency of industrial and agricultural equipment. He has published 37 Scopus-indexed journal articles, with 311 citations and an H-index of 11. Through his sustained research, innovation, and applied engineering contributions, Dr. Weimin Huang has established himself as a prominent scholar and a driving force in advancing mechanical manufacturing technologies.

Profile: Scopus

Featured Publications

1. Wang, G., Li, H., Wang, Z., & Jiang, D. (2025, May). Research on surface integrity and corrosion performance in high-speed ball-end milling of NiTi shape memory alloys.

2. Yang, J., Gong, C., Li, A., & Wang, P. (2025, March). Research on NiTi shape memory alloy electrolyte based on optimization of corrosion performance.

3. Huang, W., Huang, Y., Li, A., & Wang, G. (2024, November). Generation mechanism and anti-friction effect evaluation of continuous micro-groove texture machined by ball-end milling process.

4. Gao, L., Zhou, X., Huang, W., & Xia, H. (2024, February). Generation method and antifriction performance evaluation of discrete micro-pit surface texture based on high speed ball-end milling process.

5. Wang, G., Gong, C., Yang, J., & Wang, P. (2024, February). Electrochemical reaction mechanism of milled surface of NiTi shape memory alloy.

6. Gao, L., Wang, J., Huo, H., & Wang, Z. (2024, February). Residual height of surface topography in milling nickel-titanium shape memory alloy using a small-diameter cutter.

Tian Zhang | Engineering | Best Researcher Award

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Dr. Tian Zhang | Engineering | Best Researcher Award

Xi’an University of Architecture and Technology | China

Dr. Zhang Tian, a Master’s student in Structural Engineering at Xi’an University of Architecture and Technology, has built an impressive academic and research profile distinguished by consistent excellence, leadership, and early scholarly impact. He completed his undergraduate studies at Huanghuai University, where he was recognized as a “Three Good Student” for four consecutive years, awarded multiple academic scholarships, and graduated as an Outstanding Graduate. His achievements also include winning the third prize in the Challenge Cup of the School of Civil Engineering and being honored as an Outstanding Communist Youth League Member, distinctions that reflect his ability to combine academic rigor with innovation and service. At the graduate level, he has continued to excel, receiving an academic scholarship in 2022–2023 while advancing research in seismic-resistant structures, sustainable construction materials, and structural design optimization, areas vital to the development of safe and environmentally responsible infrastructure. Despite being in the early stage of his research career, Dr. Zhang Tian has already made notable scholarly contributions, with 6 publications indexed in Scopus, accumulating 69 citations from 68 documents, and achieving an h-index of 5. These metrics demonstrate that his work is not only visible but also valued within the global academic community. Combining strong academic performance, proven research productivity, and a clear vision for advancing structural engineering, Dr. Zhang Tian exemplifies the qualities of an emerging scholar whose contributions are poised to strengthen the safety, resilience, and sustainability of modern construction.

Profile: Scopus

Featured Publications

Xu, Y., Xu, Z.-D., Hu, H., Guo, Y.-Q., Huang, X.-H., Zhang, Z.-W., Zhang, T., & Xu, C. (2025). Experiment, simulation, and theoretical investigation of a new type of interlayer connections enhanced viscoelastic damper. International Journal of Structural Stability and Dynamics, 25(5), Article 2550045.

Yuan Rao | Computer Science | Best Researcher Award

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Dr. Yuan Rao | Computer Science | Best Researcher Award

Lecturer | School of Artificial Intelligence, Guangzhou University | China

Yuan Rao is a dedicated researcher and lecturer specializing in media forensics and AI security. She has contributed significantly to the field of image forgery detection, focusing on self-supervised learning and domain adaptation techniques. Currently a Lecturer at the School of Artificial Intelligence, Guangzhou University, Yuan Rao combines academic excellence and practical expertise, earning recognition through various awards and high-impact publications.

Profile

Scopus

Google Scholar

Strengths for the Awards

  • Outstanding Research Contributions 
    • Yuan Rao has published numerous high-impact research articles in leading journals and conferences such as:
      • IEEE Transactions on Pattern Analysis and Machine Intelligence
      • Pattern Recognition
      • IEEE International Conference on Computer Vision (ICCV)
    • Her work on JPEG-resistant image forgery detection and AI security vulnerabilities reflects groundbreaking innovation in media forensics.
  • Leadership in Projects 
    • She has successfully undertaken four research projects as Principal Investigator (PI), funded by prestigious bodies such as:
      • National Natural Science Foundation of China
      • Guangdong Basic and Applied Research Foundation
      • Science and Technology Foundation of Guangzhou

🎓 Education

  • PhD in Information and Communication Engineering (2014.9 – 2021.6)
    • Sun Yat-sen University
    • Supervisor: Professor Ni Jiangqun
  • Master’s in Communication and Information Systems (2011.9 – 2014.6)
    • Jinan University
    • Supervisor: Professor Junkai Huang
  • Bachelor’s in Telecommunications Engineering and Management (2007.9 – 2011.6)
    • Beijing University of Posts and Telecommunications

 Work Experience

  • Lecturer (2021.6 – Present)
    • School of Artificial Intelligence, Guangzhou University
    • Yuan Rao conducts research, supervises projects, and inspires students in the evolving fields of AI and media forensics.

🔍 Research Interests On Computer Science

Yuan Rao’s primary research interests focus on:

  • Media Forensics: Detecting and localizing image forgeries using deep learning and self-supervised techniques.
  • AI Security: Exploring vulnerabilities and robustness of AI models in practical scenarios.

🏆 Awards

  • 🥇 Champion and 3rd Place in “Guangzhou Pazhou Algorithm Competition” – Algorithm Safety Track, 2023
  • 🥇 First Prize in the National “Challenge Cup” Black Science and Technology Special Competition, 2023
  • 🏅 6th Rank out of 1471 in “Secure AI Challenger Program: Tamper Detection for Forged Images,” 2020

📚 Publications

1. A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2016
  • Citations: 590

2. Deep Learning Local Descriptor for Image Splicing Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Zhao
  • Publication Year: 2020
  • Citations: 104

3. Multi-semantic CRF-based Attention Model for Image Forgery Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Xie
  • Publication Year: 2021
  • Citations: 53

4. Block-based Convolutional Neural Network for Image Forgery Detection

  • Authors: J. Zhou, J. Ni, Y. Rao
  • Publication Year: 2017
  • Citations: 46

5. Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2021
  • Citations: 29

6. Towards JPEG-Resistant Image Forgery Detection and Localization via Self-Supervised Domain Adaptation

  • Authors: Y. Rao, J. Ni, W. Zhang, J. Huang
  • Publication Year: 2022
  • Citations: 13

7. High-accuracy Current Sensing Circuit with Current Compensation Technique for Buck–Boost Converter

  • Authors: Y. Rao, W.L. Deng, J.K. Huang
  • Publication Year: 2015
  • Citations: 5

8. A Trigger-Perceivable Backdoor Attack Framework Driven by Image Steganography

  • Authors: W. Tang, J. Li, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

9. Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography

  • Authors: W. Tang, H. Yang, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

10. Exploring the Vulnerability of Self-supervised Monocular Depth Estimation Models

  • Authors: R. Hou, K. Mo, Y. Long, N. Li, Y. Rao
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

11. MKD: Mutual Knowledge Distillation for Membership Privacy Protection

  • Authors: S. Huang, Z. Liu, J. Yu, Y. Tang, Z. Luo, Y. Rao
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

12. Deep Multi-image Hiding with Random Key

  • Authors: W. Zhang, W. Tang, Y. Rao, B. Li, J. Huang
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

Research Projects

Yuan Rao has led multiple projects as Principal Investigator (PI):

  1. National Natural Science Foundation of China: Research on Self-supervised Transfer Learning Based Image Forgery Forensics.
  2. Guangdong Basic and Applied Basic Research Foundation: Image Forgery Localization Combining Self-supervised Learning and Knowledge Distillation.
  3. Science and Technology Foundation of Guangzhou: Verifiable Robustness of Object Detection Models Using Domain Knowledge.
  4. Science and Technology Foundation of Guangzhou: Short-term Heavy Precipitation Forecasting with Super-resolution Reconstruction.

📝 Conclusion

Yuan Rao is a prominent researcher and educator in media forensics and AI security. With a robust academic background, impressive research achievements, and numerous accolades, she is committed to advancing technologies that ensure the integrity and security of digital media.