Shahina Begum | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Shahina Begum | Artificial Intelligence
| Best Researcher Award

Prof. Dr. Shahina Begum | Mallardalen University | Sweden

Prof. Dr. Shahina Begum, a Swedish national, is a distinguished Professor of Artificial Intelligence at the Artificial Intelligence and Intelligent Systems Research Group, Mälardalen University, Sweden, where she has been serving since 2019 after progressing through roles as Senior Lecturer  and Postdoctoral Researcher . She holds a Ph.D. ,Licentiate (2009), and Docent (2015) in Artificial Intelligence from Mälardalen University, as well as an M.Sc. in Computer Science (Intelligent Systems) from Dalarna University (2005). With over a decade of teaching and research experience, she has made significant contributions to AI through pioneering work in machine learning, intelligent decision support, and AI applications for health and well-being. Prof. Begum has demonstrated exceptional leadership in academia by securing substantial external research funding, amounting to approximately 224.4 MSEK as Principal Investigator and Co-PI, enabling groundbreaking multidisciplinary projects bridging AI with real-world applications. Her scholarly impact is reflected in her Google Scholar record, with an h-index of 30 and around 3,689 citations, and her Scopus record, with 88 indexed publications, 1,497 citations from 1,292 documents, and an h-index of 18. A passionate educator and mentor, she continues to inspire the next generation of AI researchers while actively contributing to the global AI research community through publications, collaborations, and leadership in funded projects.

 Profile: Scopus | Google Scholar | Linked IN | Staff Page | Orcid

Featured Publications

Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022). A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Applied Sciences, 12(3), 1353.

Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Folke, M. (2010). Case-based reasoning systems in the health sciences: A survey of recent trends and developments. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(2), 241–257.

Barua, S., Ahmed, M. U., Ahlström, C., & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Systems with Applications, 115, 121–135.

Chen, R. Y., Kung, V. L., Das, S., Hossain, M. S., Hibberd, M. C., Guruge, J., … & Begum, S. (2020). Duodenal microbiota in stunted undernourished children with enteropathy. New England Journal of Medicine, 383(4), 321–333.

Begum, S., Ahmed, M. U., Funk, P., Xiong, N., & Von Schéele, B. (2009). A case-based decision support system for individual stress diagnosis using fuzzy similarity matching. Computational Intelligence, 25(3), 180–195.

Degas, A., Islam, M. R., Hurter, C., Barua, S., Rahman, H., Poudel, M., Ruscio, D., … & Begum, S. (2022). A survey on artificial intelligence (AI) and explainable AI in air traffic management: Current trends and development with future research trajectory. Applied Sciences, 12(3), 1295.

Cyrille Feybesse | Data Science | Best Researcher Award

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Dr. Cyrille Feybesse | Data Science | Best Researcher Award

Dr. Cyrille Feybesse | Guillaume Regnier Hospital | France

Dr. Cyrille Feybesse, a French psychologist and Maître de Conférences (MCF, 2022), is a researcher and clinical psychologist specializing in love, creativity, and cross-cultural psychology. He earned his Ph.D. in Psychology (2015) at Université Paris Descartes under the supervision of Professors Geneviève Coudin and Todd Lubart, with additional mentorship from Professor Elaine Hatfield (University of Hawai‘i). His academic background spans clinical, health, and social psychology, with training in France, Portugal, and Brazil. Dr. Feybesse has held postdoctoral fellowships at Université Paris Descartes and the University of Porto, collaborating with the Portuguese Foundation for Science and Technology (FCT) on pioneering research into passionate love and creativity. Since 2022, he has been conducting clinical and research activities at the Centre Hospitalier Guillaume Régnier, Rennes, focusing on child psychiatry, creativity, and high-potential intelligence. He has authored over 14 publications, contributed to multiple book chapters, and presented at numerous international conferences. His work has been cited more than 350 times (Scopus Author ID: 57191835134; ORCID: 0000-0001-7795-568X) with an h-index of 6. Multilingual in English, Portuguese, and Spanish, Dr. Feybesse also serves as Assistant Editor for Interpersona and reviewer for leading journals including Sexuality & Culture and European Psychologist, advancing research at the intersection of love, creativity, and human development.

 Profile: Scopus | Orcid  | Researchgate 

Featured Publications

Feybesse, C., Forthmann, B., Neto, F., Holling, H., & Hatfield, E. (2025). Measuring love around the world: A cross-cultural reliability generalization. Sexuality & Culture. Advance online publication.

Feybesse, C. (2024). Social representation of passionate love among Brazilian and French youngsters. Trends in Psychology. Advance online publication.

Feybesse, C., Fu, S., Lubart, T., Rasa, L., Ossom, C., Cavasino, V., Jacob, J., & Lemonnier, T. (2020). Social representation of fair price among professional photographers. PLOS ONE, 15(12), e0243547.

Feybesse, C. (2018). Assessing passionate love: Italian validation of the PLS (reduced version). Sexual and Relationship Therapy, 33(2), 221–229.

Feybesse, C. (2016). Sensory values in romantic attraction in four European countries: Gender and cross-cultural comparison. Journal of Cross-Cultural Research, 50(2), 109–123.

Elahe Karampour | Artificial Intelligence | Best Researcher Award

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Ms. Elahe Karampour | Artificial Intelligence| Best Researcher Award

 

Ms. Elahe  Karampour  K.N.Toosi University Of Technology, Iran

Elahe Karampour is a dedicated researcher specializing in Geodesy and Geomatics Engineering with a focus on spatial-temporal data analysis and network science. She is currently pursuing her Master of Science at K.N. Toosi University of Technology, Tehran, Iran, with a thesis on analyzing location-based social networks using geometric curves. She has served as a research assistant, contributing to advancements in community detection and link prediction in spatial networks. Elahe is also an experienced educator, having taught GIS, spatial databases, and social network analysis. She has received national recognition for her academic excellence and was awarded a fully funded research visit to ScaDS.AI in Germany. Her expertise spans programming, spatial modeling, and AI-driven geospatial analytics.

🌍 Professional Profile:

Orcid

🏆 Suitability for the Best Researcher Award

 

Elahe Karampour is an outstanding researcher with significant contributions to geospatial data analysis, particularly in network science and location-based social networks. Her pioneering research integrates Ricci curvature and hyperbolic geometry for community detection and link prediction, leading to novel advancements in spatial-temporal modeling. She has published influential research in high-impact journals, demonstrating her expertise and innovation. Elahe’s research excellence is further recognized through a prestigious fully funded research visit to ScaDS.AI in Germany. With strong technical proficiency in R, Python, PostgreSQL, and GIS software, she bridges theory and application, developing intelligent recommender systems for urban planning. Her exceptional academic record, teaching excellence, and innovative research make her a prime candidate for the Best Researcher Award.

🎓 Education 

Elahe Karampour holds a Master of Science in Geodesy and Geomatics Engineering from K.N. Toosi University of Technology, Tehran, Iran (2021–2024). Her research focuses on spatial-temporal data analysis and social network modeling, with a thesis titled “Analysis of Location-Based Social Networks with Geometric Curves,” receiving a perfect grade of 20/20. She completed her Bachelor of Science in Geodesy and Geomatics Engineering at the University of Zanjan, Iran (2015–2020). Elahe ranked 23rd nationwide in the Iranian Master’s University Entrance Exam, showcasing her academic excellence. Her strong analytical and technical skills, coupled with expertise in GIS, AI, and network analysis, enable her to make significant contributions to the field of geospatial research and urban data science.

🏢 Work Experience 

Elahe Karampour has extensive research and teaching experience in geospatial data analysis and network science. As a Research Assistant (2022–2024) at K.N. Toosi University of Technology, she developed advanced models for community detection and link prediction in location-based social networks using geometric methods. She also worked as a Lecturer (2024) at Babol Noshirvani University of Technology, teaching spatial analysis and visualization to undergraduate students. Additionally, she served as a Teaching Assistant (2023–2024), guiding master’s and PhD students in GIS, social network analysis, and spatial databases. Her technical expertise in R, Python, PostgreSQL, and QGIS, combined with her ability to integrate AI with geospatial analysis, has positioned her as a leader in her research domain.

🏅 Awards and Honors 

Elahe Karampour has received multiple accolades for her academic and research excellence. She ranked 23rd nationwide in the Iranian Master’s University Entrance Exam (2021), demonstrating her strong academic foundation. She was awarded a fully funded research grant for a short-term visit to ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Germany (2023), recognizing her contributions to AI-driven geospatial analysis. Additionally, she was listed among the top-ranked teachers by her students for her exceptional teaching performance. Her work in network science and geospatial modeling has led to publications in high-impact journals, further cementing her status as a leading researcher. These achievements underscore her dedication and outstanding contributions to the field of geospatial and network science.

🔬 Research Focus 

Elahe Karampour’s research centers on spatial-temporal data analysis, network science, and AI-driven geospatial modeling. She specializes in analyzing location-based social networks using advanced mathematical frameworks such as Ricci curvature and hyperbolic geometry for community detection and link prediction. Her work integrates graph-based modeling with GIS technologies to enhance urban planning, mobility analysis, and personalized recommender systems. Elahe has developed innovative approaches to analyzing complex data structures, utilizing machine learning and AI techniques for geospatial applications. She is particularly interested in the intersection of mathematics, computer science, and geospatial technologies, aiming to create data-driven solutions for urban analytics and smart city development. Her research has been recognized internationally, reinforcing her expertise in geospatial data science.

📊 Publication Top Notes:

  • Karampour, E., Malek, M.R., & Eidi, M. (2025). Discrete Ricci flow: A powerful method for community detection in location-based social networks. Computers and Electrical Engineering, 123, 110302.