Adimias Wendimagegn | Data Science | Best Researcher Award

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Mr. Adimias Wendimagegn | Data Science | Best Researcher Award

Kotebe University of Education | Ethiopia

Mr. Adimias Wendimagegn is a data scientist and biostatistician specializing in statistical modeling, distribution theory, and applications in public health. His research primarily focuses on advanced regression frameworks, including classical and Bayesian modeling approaches under GAMLSS and BAMLSS structures. He has introduced novel models such as the Alpha Power Transformed Beta (APTBeta) regression for analyzing antenatal care utilization and developed methodological frameworks for Gamma and Beta-type distributions. He has published multiple peer-reviewed journal articles indexed in Scopus and Web of Science, contributing significantly to statistical theory and biostatistical applications. His works include studies on antenatal care utilization, maternal age at first birth, and advanced distributional models for proportion data. He is also the author of two academic books published by LAP Lambert Academic Publishing, covering joint modeling of longitudinal and survival data and determinants of teenage fertility in Ethiopia. His scholarly contributions extend to the development of R packages for regression modeling and diagnostics, enhancing reproducibility and accessibility in statistical analysis. Mr. Adimias Wendimagegnserves as a reviewer for leading international journals such as PLOS ONE and Value in Health, and his publications have received multiple citations reflecting his growing impact in the field. Through his research, he continues to advance innovative statistical methodologies and their applications in health and demographic studies.

Profile: ResearchGate

Featured Publications

  • Wendimagegn, A., & Arero, B. G. (2025, October). Alpha power transformed beta regression with application on antenatal care visit proportions among Ethiopian women. Journal of Statistical Theory and Applications. https://doi.org/10.1007/s44199-025-00135-w

  • Wendimagegn, A., Goshu, A. T., & Arero, B. G. (2024, September). New alpha power transformed beta distribution with its properties and applications. Frontiers in Applied Mathematics and Statistics, 10. https://doi.org/10.3389/fams.2024.1433767

  • Wendimagegn, A. (2019, March). Determinant of solid-waste management in Debre Birhan Town. American Journal of Theoretical and Applied Statistics, 8(1), 26–33. https://doi.org/10.11648/j.ajtas.20190801.14

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.

Ruo Hu | Computer Science | Best Keynote Speaker

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Prof. Ruo Hu | Computer Science | Best Keynote Speaker

Guangdong Polytechnic Normal University | China

Professor Hu Ruo is a distinguished academician with a prolific background in artificial intelligence (AI) and medical image recognition. For over a decade, he has been leading graduate research in AI-driven medical diagnostics, with an emphasis on image-based disease classification and predictive systems. As a seasoned professor at Guangdong Polytechnic Normal University, his groundbreaking contributions have helped bridge clinical practice with intelligent systems, yielding innovations that benefit both academia and healthcare industries.

Professional profile👤

Scopus

Strengths for the Awards✨

Professor Hu Ruo brings a compelling blend of academic depth, technological innovation, and real-world collaboration that makes him exceptionally suitable for the “Best Keynote Speaker” award. With over a decade of leadership in AI-driven medical image recognition, his research is not only pioneering but also highly relevant to global health and AI advancements.

He holds three advanced degrees in computer science and information systems, complemented by long-standing teaching and research roles at esteemed Chinese institutions. His work spans key domains like deep learning, neuro-fuzzy systems, medical big data, and Internet of Things (IoT), with numerous peer-reviewed publications in top journals like Medical Physics and IEEE Transactions on Geoscience and Remote Sensing.

Prof. Hu has been PI of several government-funded high-impact projects, such as the Guangzhou Industry-University-Research Science & Tech project, with funding up to 2 million RMB, and has contributed to cross-sector collaborations with tech companies and hospitals. These collaborations have led to clinical applications, including tumor prediction systems and medical IoT platforms, underscoring his applied vision.

🎓 Education

Prof. Hu Ruo’s academic journey began with a Master of Science in Computer Science and Technology at Shaanxi Normal University (1987–1991). He then pursued a Master of Engineering in Computer Application at East China University of Science and Technology (1998–2002). Culminating his academic pursuits, he earned a Doctor of Management in Information System Management from the University of Shanghai for Science and Technology (2003–2006). His diverse interdisciplinary background forms a strong foundation for his research in AI applications.

🏢 Experience

Starting his career as a faculty member at the School of Mathematics and Computer Science, Ningxia University (1992–2007), Dr. Hu transitioned to the School of Computer Science, Guangdong Polytechnic Normal University in 2007. He was promoted to Associate Professor in 2006 and then to Full Professor in 2013. With over 30 years of teaching and research experience, he has played a pivotal role in shaping AI education and research methodologies in China.

🔬 Research Interests On Computer Science

Professor Hu’s research centers on AI in medical image recognition, big data semantic conflict resolution, neuro-fuzzy systems, and deep learning applications in disease diagnosis. He is particularly invested in the development of smart systems for tumor classification, sleep disorder diagnosis, and colorectal cancer detection. His work reflects a robust integration of knowledge-driven and data-driven models, contributing significantly to intelligent healthcare technologies.

🏅 Awards & Honors

Professor Hu Ruo has been the recipient of multiple prestigious research grants, including:

  • 2017 Guangzhou Industry-University-Research Major Science and Technology Project (¥2 million)

  • 2015 Guangdong Natural Science Foundation Project on big data and semantic conflict modeling (¥100,000)
    He has also received acclaim for his collaboration with high-tech companies and top medical institutions, cementing his reputation as a leader in interdisciplinary research.

📚 Selected Publications

Here are some of Professor Hu Ruo’s significant publications with citation details:

  1. Vision Transformer-based recognition of diabetic retinopathy grade, Medical Physics, 2021 — cited by numerous works in AI diagnostics.

  2. Diagnosis of sleep disorders in traditional Chinese medicine based on adaptive neuro-fuzzy inference system, Biomedical Signal Processing and Control, 2021 — contributed to fuzzy modeling in TCM.

  3. Research on data classification and feature fusion method of cancer nuclei image, International Journal of Imaging Systems and Technology, 2021 — applied deep learning in oncology.

  4. Locality Regularized Robust-PCRC framework for hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, 2020 — innovative feature extraction method.

  5. Key technologies for medical image knowledge discovery, International Journal of Pattern Recognition and Artificial Intelligence, 2020 — on intelligent knowledge systems.

  6. “A Mechanism for Healthy Big Data System Confliction Detection”, Basic & Clinical Pharmacology & Toxicology, 2016.

  7. “A New Efficiency Judging Method for Healthy Big Data”, Basic & Clinical Pharmacology & Toxicology, 2016.

  8. “Sensor Network Component Searching Method”, Journal of Investigative Medicine, 2015.

  9. “Semantic Data Network Analysis System”, Journal of Computer, 2013.

  10. “Stability Analysis of IoT Service via Data Stream Methods”, Applied Mathematics & Information Sciences, 2012.

🤝 Collaborations

Prof. Hu collaborated with Guangzhou Jinglian Information Technology Co., Ltd. to develop an AI-powered cloud platform for tumor prediction and postoperative classification. This was based on his earlier Guangdong-funded big data project. He also engaged in clinical-level partnerships with Sun Yat-sen University Cancer Prevention Center and Sixth Affiliated Hospital, providing valuable datasets and expertise for AI training and medical validation.

🎓 Supervision & Mentorship

Professor Hu has mentored numerous graduate theses, including work on deep learning for colorectal cancer, fuzzy systems for sleep disorders, and image-based diagnostics for lung and brain tumors. His students have published in top-tier journals and contributed to a total of 10 high-impact papers and 5 patents under his leadership.

🏁 Conclusion

In conclusion, Professor Hu Ruo exemplifies the spirit of innovation and interdisciplinary excellence. With a solid academic background, pioneering research in medical AI, and extensive industry and clinical collaborations, he stands out as a prime candidate for the Best Keynote Speaker. His work has not only advanced academic knowledge but also translated into tangible tools that enhance modern healthcare.