Leila Malihi | Knowledge Distillation | Research Excellence Award

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Dr. Leila Malihi | Knowledge Distillation | Research Excellence Award

Research Assistant | Osnabrück University | Germany

Dr. Leila Malihi is an emerging scholar whose work advances the intersection of medical image analysis, digital health technologies, and clinical decision-support systems. With a developing portfolio of 10 scholarly publications, 88 citations and 5 h-index her research demonstrates both growing influence and clear relevance to contemporary healthcare challenges. Her primary focus lies in applying machine learning and computer-vision techniques to improve diagnostic accuracy, particularly in the context of wound analysis and healing-complication detection, including notable contributions to the automatic classification of wound images and the optimisation of algorithms to detect maceration—an area critical for improving patient care, reducing clinical workload, and supporting early intervention. Disseminated through open-access venues, this work reflects a strong commitment to practical, clinically meaningful impact. Malihi’s collaborative record, involving more than 20 co-authors across computer science, biomedical engineering, and clinical research, highlights her active engagement in interdisciplinary teams that blend methodological rigour with clinical insight, enhancing the translational quality of her contributions. Despite being in an early career stage, she has already established measurable academic impact through consistent citation uptake and growing recognition within the health-technology community. Her research carries significant societal value by enabling more accurate and automated assessment of wound healing, supporting the development of scalable healthcare solutions, strengthening telemedicine workflows, and ultimately contributing to improved patient outcomes, particularly in resource-limited environments.

Profile: Scopus

Featured Publication

1. Dührkoop, E., Malihi, L., Erfurt-Berge, C., Heidemann, G., Przysucha, M., Busch, D., & Hübner, U. H. (2024). Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration. In R. Rohrig et al. (Eds.), German Medical Data Sciences 2024.
Cited by: 2

Dr. Malihi’s research advances intelligent medical-image analysis tools that strengthen diagnostic precision and support clinicians in delivering timely, data-driven care. Her vision is to develop globally accessible digital-health solutions that reduce healthcare disparities and promote more efficient, technology-enhanced clinical workflows.

Ushba Rasool | Generative AI | Best Researcher Award

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Dr. Ushba Rasool | Generative AI | Best Researcher Award

Research Instructor | Zhengzhou University | China

Dr. Ushba Rasool, affiliated with Zhengzhou University, China, is a rising researcher specializing in educational psychology, digital pedagogy, and artificial intelligence (AI) in education. With 11 publications, 68 citations, and an h-index of 5, her work integrates theoretical frameworks such as UTAUT (Unified Theory of Acceptance and Use of Technology) and TPACK (Technological Pedagogical Content Knowledge) to investigate teachers’ and students’ perceptions, attitudes, and adoption behaviors toward emerging educational technologies. Her recent publication in Acta Psychologica (2025), “Perceptions of Generative AI in Teaching and Learning,” highlights her innovative approach in merging psychological insights with technology acceptance models to explore the transformative potential of generative AI in learning environments. Through collaborations with 18 co-authors across international institutions, Dr. Rasool contributes to advancing global understanding of digital transformation in education, addressing key issues of AI ethics, digital literacy, and pedagogical innovation. Her research provides valuable implications for educational policy, technology integration strategies, and the enhancement of learner engagement, thus creating meaningful social and academic impact in the digital age.

Profiles: Scopus | Google Scholar

Featured Publications

1. Rasool, U., Qian, J., & Aslam, M. Z. (2023). An investigation of foreign language writing anxiety and its reasons among pre-service EFL teachers in Pakistan. Frontiers in Psychology, 13, 947867. 
Cited by: 64

2. Barzani, S. H. H. (2022). The effects of online supervisory feedback on student-supervisor communications during the COVID-19. European Journal of Educational Research, 11(3), 1569–1579. 
Cited by: 31

3. Barzani, S. H. H. (2021). Teachers and students’ perceptions towards online ESL classrooms during COVID-19: An empirical study in North Cyprus. The Journal of Asia TEFL, 18(4), 1423–1431. 
Cited by: 21

4. Rasool, U., Mahmood, R., Aslam, M. Z., Barzani, S. H. H., & Qian, J. (2023). Perceptions and preferences of senior high school students about written corrective feedback in Pakistan. SAGE Open, 13(3), 21582440231187612. 
Cited by: 17

5. Rasool, U., Aslam, M. Z., Mahmood, R., Barzani, S. H. H., & Qian, J. (2023). Pre-service EFL teachers’ perceptions of foreign language writing anxiety and some associated factors. Heliyon, 9(2), e13705. 
Cited by: 15

Dr. Ushba Rasool’s research fosters responsible and inclusive integration of generative AI in education, driving innovation in digital pedagogy and shaping global educational practices that empower both teachers and learners for a technologically adaptive future.

Minoru Sasaki | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Minoru Sasaki | Artificial Intelligence | Best Researcher Award

Organizing Committee | Gifu University | Japan

Prof. Dr. Minoru Sasaki, a distinguished Emeritus Professor at Gifu University, has made significant contributions to the fields of mechanical engineering, control systems, and mechatronics throughout his academic and professional career. With a Ph.D. in Mechanical Engineering from Tohoku University (1985), he has held various academic positions in Japan and internationally, including visiting professorships at UCLA, Georgia Institute of Technology, and King Mongkut’s University of Technology Thonburi. He has also served in numerous leadership roles at Gifu University, such as Department Chair, Assistant President, and Director of the Career Center. His professional affiliations include IEEE Life Senior Member, ASME, JSME, SICE (Fellow), RSJ, JSASS, and others. He has actively contributed to global academic and research communities through editorial roles in prestigious journals and program committees of international conferences. His involvement extends to advisory roles and leadership positions within key engineering societies in Japan and abroad. A prolific researcher, Dr. Sasaki has authored 202 publications, which have been cited by 966, reflecting a strong academic impact with an h-index of 14. These metrics highlight the depth and relevance of his research in intelligent mechanical systems and applied electromagnetics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Taheri, S. M., Matsushita, K., & Sasaki, M. (2017). Virtual reality driving simulation for measuring driver behavior and characteristics. Journal of Transportation Technologies, 7(02), 123.
Cited by 84.

2. Takayama, K., & Sasaki, M. (1983). Effects of radius of curvature and initial angle on the shock transition over concave and convex walls. Report of the Institute of High Speed Mechanics, 46, 1–30.
Cited by 66.

3. Yoshida, T., Sasaki, M., Ikeda, K., Mochizuki, M., Nogami, Y., & Inokuchi, K. (2002). Prediction of coal liquefaction reactivity by solid state 13C NMR spectral data. Fuel, 81(11-12), 1533–1539.
Cited by 64.

4. Endo, T., Sasaki, M., Matsuno, F., & Jia, Y. (2016). Contact-force control of a flexible Timoshenko arm in rigid/soft environment. IEEE Transactions on Automatic Control, 62(5), 2546–2553.
Cited by 61.

5. Takeda, K., Sasaki, M., Kieda, N., Katayama, K., Kako, T., Hashimoto, K., … (2001). Preparation of transparent super-hydrophobic polymer film with brightness enhancement property. Journal of Materials Science Letters, 20(23), 2131–2133.
Cited by 56.