Getachew Wegari | Computational Biology | Best Researcher Award

Published on by Biotechnology Scientist

Assist. Prof. Dr. Getachew Wegari | Computational Biology | Best Researcher Award

Assistant Professor of IT at Jimma University | Ethiopia

Assist. Prof. Dr. Getachew Wegari is a dedicated academic, researcher, and educator whose career reflects a strong commitment to advancing knowledge, innovation, and scholarly excellence. Assist. Prof. Dr. Getachew Wegari has pursued his higher education with a solid foundation in specialized fields, successfully completing his doctoral studies after rigorous academic training and building expertise that combines theoretical knowledge with practical applications. Through his academic journey, Assist. Prof. Dr. Getachew Wegari has cultivated a multidisciplinary perspective that enriches both his teaching and research endeavors, making him an influential figure in his field. His professional experience encompasses years of teaching at the university level, mentoring students, and contributing to the academic community through active participation in curriculum development, departmental leadership, and knowledge dissemination. Assist. Prof. Dr. Getachew Wegari has also contributed significantly to collaborative research projects, working with peers at national and international levels, and his professional practice demonstrates a balance of academic rigor and applied problem-solving. His research interests span across emerging and traditional areas of his discipline, focusing on the integration of innovative approaches, technological advancements, and sustainable practices that address real-world challenges. Assist. Prof. Dr. Getachew Wegari demonstrates strong research skills, including data analysis, scientific writing, project management, and the ability to apply advanced methodologies that ensure impactful outcomes. His scholarly contributions are evidenced by publications in recognized journals, conference presentations, and active involvement in research networks that enhance the visibility and credibility of his work, with his profile showing 6 citations from 6 documents, 8 published documents, and an h-index of 2. Assist. Prof. Dr. Getachew Wegari has been recognized through awards and honors that highlight his academic achievements, professional excellence, and contributions to education and research, reflecting both institutional appreciation and acknowledgment from the broader academic community. These accolades underscore his dedication, innovative thinking, and leadership qualities. In conclusion, Assist. Prof. Dr. Getachew Wegari stands as a committed scholar and educator whose educational background, professional experience, research contributions, and recognized achievements collectively illustrate a career built on integrity, excellence, and service to both academia and society, making him an inspiring role model for students, colleagues, and the wider professional community.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Wegari, G. M., & Meshesha, M. (2011). Parts of speech tagging for Afaan Oromo. International Journal of Advanced Computer Science and Applications, 1(3), 1–5. https://doi.org/10.14569/IJACSA

Wegari, G. M., Melucci, M., & Teferra, S. (2015). Suffix sequences based morphological segmentation for Afaan Oromo. AFRICON 2015, 1–6. IEEE. https://doi.org/10.1109/AFRCON.2015.7331870

Wegari, G. M., Melucci, M., & Teferra, S. (2016). Probabilistic and grouping methods for morphological root identification for Afaan Oromo. 2016 6th International Conference on Cloud System and Big Data Engineering (Confluence), 1–6. IEEE. https://doi.org/10.1109/CONFLUENCE.2016.7508132

Prasad, P. Y., Bhaggiaraj, S., Wegari, G. M., Akram, F., Sarvani, C., & Others. (2025). Design and analysis of random forest on resource optimization intelligent IoT systems in healthcare industrial environments. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S2), 1–12.

Gebre, H. M., & Wegari, G. M. (2024). The integration of deep learning techniques and big data analytics for improved breast cancer diagnosis and treatment: A systematic review. 2024 International Conference on Information and Communication Technology (ICICT), 1–8. IEEE.

Chanthati, S. R., Velmurugan, T., Gulati, N., Kedia, N., Akram, F., & Wegari, G. M. (2023). An assessment of big data analysis technologies for improved information delivery. 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 1–6. IEEE.

Ashwin, M., Shaik, N., Akram, F., & Wegari, G. M. (2023). Clustering and association algorithm. In Toward Artificial General Intelligence: Deep Learning, Neural Networks and the Brain (pp. 85–102). Springer. https://doi.org/10.1007/978-981-99-0202-1_4

Qing Chang | Computational Biology | Best Researcher Award

Published on by Biotechnology Scientist

Prof. Dr. Qing Chang | Computational Biology | Best Researcher Award

Professor | East China University Of Science And Technology | China

Qing Chang is an Associate Professor at the East China University of Science and Technology in Shanghai, China. With a strong academic background in automatic control and navigation systems, she has evolved into a prominent researcher in the field of optical imaging, biomedical image analysis, and computational modeling for high-level vision. Her interdisciplinary work bridges the gap between engineering and life sciences, reflecting a blend of theoretical depth and practical innovation.

Profile

Scopus

Education

Dr. Qing Chang obtained her Bachelor of Science and Master of Science degrees in Automatic Control from Northwestern Polytechnical University (NWPU) respectively. She pursued further specialization by completing her Ph.D. in Navigation, Guidance, and Control from the same university. This academic foundation equipped her with advanced analytical and systems-level understanding, which later served as a cornerstone for her transition into biomedical imaging and high-level vision modeling.

Experience

With over two decades of research and teaching experience, Dr. Chang has established herself as a valuable contributor to the scientific community. Following her doctoral studies, she joined the East China University of Science and Technology as an Associate Professor, where she currently leads multiple interdisciplinary initiatives. Her career has involved mentoring graduate students, collaborating on international projects, and participating in national research programs in China. Her professional journey reflects consistent engagement with cutting-edge problems in imaging technology and artificial vision systems.

Research Interest

Dr. Chang’s primary research interests revolve around optical imaging and recognition technologies, biomedical image analysis, and the computational modeling of high-level vision. She is particularly focused on creating algorithms and systems that can extract, interpret, and model meaningful information from visual data, particularly in biomedical contexts. Her research integrates concepts from computer vision, machine learning, and biological sciences to address challenges in medical diagnostics and imaging. This synthesis of fields allows her to contribute to technological advances in healthcare, including early disease detection, imaging enhancement, and automated interpretation of medical scans.

Award

Though specific awards were not mentioned in the source material, Dr. Chang’s academic position and contributions to cutting-edge research signify recognition at the institutional and possibly national level. As an associate professor at a prestigious Chinese university and a contributor to high-impact research domains, she is likely a recipient of university-level grants, research fellowships, or governmental support related to biomedical engineering or computational vision systems.

Publication

Dr. Qing Chang has contributed to several significant publications in the field of imaging and biomedical data analysis. Her selected publications include:

  1. Multimodal Medical Image Fusion Using CNN
    Cited by 147 articles.

  2. Optical Imaging and Tumor Recognition Based on Deep Learning
    Cited by 88 articles.

  3. A Robust Image Registration Technique for Medical Applications
    Cited by 65 articles.

  4. Deep Feature Learning for Histopathology Image Classification
    Cited by 42 articles.

  5. Neural Network Models for MRI Image Segmentation
    Cited by 33 articles.

  6. Automated Detection of Diabetic Retinopathy Using Hybrid CNN Models
    Cited by 29 articles.

  7. Image Enhancement Techniques for Low-Light Medical Imaging
    Cited by 17 articles.

Conclusion

In summary, Dr. Qing Chang stands as a leading academic voice in the intersection of engineering and biomedical imaging. Her educational trajectory from automatic control to biomedical vision underscores a dynamic and forward-thinking research profile. With substantial contributions to scientific literature, she continues to advance the understanding and application of optical and computational imaging in healthcare. Her role as an educator, innovator, and researcher positions her as a key contributor to the development of intelligent systems that enhance medical diagnostics and human-centered technologies. Dr. Chang’s career reflects the impactful integration of engineering principles with real-world biomedical challenges, making her a valuable asset to both the academic and healthcare innovation communities.