Assist. Prof. Dr Getinet Yilma| Image processing |Best Researcher Award

Getinet Yilma at Adama science and technology university

Abawatew Getinet Yilma is an assistant professor of software engineering at Adama Science and Technology University, Ethiopia. He earned his Ph.D. in Software Engineering from the University of Electronic Science and Technology of China, specializing in plant disease recognition using deep learning. With over 15 years of teaching and research experience, Getinet has led innovative projects in machine learning, big data analytics, and e-learning systems. His contributions include designing predictive models for power distribution networks and enhancing e-learning applications via social networks. He has guided numerous undergraduate and postgraduate research projects and has a strong academic and professional footprint in software engineering and IT systems.

Professional Profile

• ORCID
• Google Scholar
• Scopus

Education 🎓

• Ph.D. in Software Engineering (2018–2022)
  University of Electronic Science and Technology of China, Chengdu, China
  Thesis Title: “Plant Disease Recognition Based on Deep Learning”
• Master of Computer Applications (2009–2012)
  College of Engineering, Osmania University, Hyderabad, India
  Thesis Title: “Enhancing E-learning Application Based Social Networks”
• Bachelor’s Degree in Information Technology (2002–2006)
  Institute of Technology, Jimma University, Jimma, Ethiopia

Research Interests 

• Deep learning and machine learning applications in agriculture and industry.
• Big data analytics and predictive analytics for the airline and power distribution sectors.
• E-learning platforms and community service-based software solutions.

Professional Experience

• Assistant Professor, Software Engineering (Sept 2018–Present)
  Adama Science and Technology University, Ethiopia
  • Teaching core courses such as machine learning, deep learning, big data, cloud computing, software architecture, and advanced programming.
  • Served as Associate Dean for the School of Electrical Engineering and Computing.
  • Supervised postgraduate research and undergraduate senior projects.
  • Contributed to curriculum development and participated in national-funded research initiatives.
• Lecturer, Computer Science and Engineering (Sept 2013–Sept 2018)
  Adama Science and Technology University, Ethiopia
  • Taught advanced courses including database systems, data structures, and software requirement engineering.
  • Led university-funded research projects.
• Lecturer, Information Technology (Jan 2009–Sept 2013)
  Debremarkos University, Ethiopia
  • Delivered undergraduate and postgraduate courses in programming, databases, and software development.
  • Advised capstone projects for undergraduate students.
• Assistant Lecturer, Information Technology (Jan 2008–Jan 2009)
  Debremarkos University, Ethiopia
  • Taught foundational courses in programming, operating systems, and software development methods.
• Technical Expert (July 2006–Jan 2008)
  Jimma University, Ethiopia
  • Managed IT equipment procurement, bid evaluation, and network system administration.

Top Notes Publications

• “Self-Supervised Scene-Debiasing for Video Representation Learning via Background Patching”
  Authors: M. Assefa, W. Jiang, K. Gedamu, G. Yilma, B. Kumeda, M. Ayalew
  IEEE Transactions on Multimedia, 2023, 25, pp. 5500–5515
  Citations: 13
  Abstract: This study proposes a self-supervised method for scene-debiasing in video representation learning by leveraging background patching. This approach reduces the bias of the background in video datasets, improving the quality of representation learning.
• “Self-Supervised Multi-Label Transformation Prediction for Video Representation Learning”
  Authors: M. Assefa, W. Jiang, G. Yilma, M. Ayalew, M. Seid
  Journal of Circuits, Systems, and Computers, 2022, 31(9), 2250159
  Citations: 6
  Abstract: This paper introduces a self-supervised multi-label transformation prediction technique aimed at enhancing video representation learning. It improves the learning process by predicting transformations across multiple labels in a self-supervised manner.
• “Actor-Aware Contrastive Learning for Semi-Supervised Action Recognition”
  Authors: M. Assefa, W. Jiang, K. Gedamu, M. Ayalew, M. Seid
  Proceedings of the International Conference on Tools with Artificial Intelligence (ICTAI), 2022, October, pp. 660–665
  Citations: 2
  Abstract: This conference paper proposes an actor-aware contrastive learning method for semi-supervised action recognition, focusing on improving the recognition of actions in video sequences by emphasizing actor-specific features.
• “Self-Supervised Representation Learning for Motion Control of Autonomous Vehicles”
  Authors: M. Ayalew, S. Zhou, M. Assefa, K. Gedamu, G. Yilma
  2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2022
  Citations: 0
  Abstract: This paper presents a self-supervised representation learning approach for the motion control of autonomous vehicles. The model aims to improve decision-making and motion control by learning representations without labeled data.
• “Spatio-temporal Dual-Attention Network for View-invariant Human Action Recognition”
  Authors: K. Gedamu, G. Yilma, M. Assefa, M. Ayalew
  Proceedings of SPIE – The International Society for Optical Engineering, 2022, 12342, 123420Q
  Citations: 5
  Abstract: This paper introduces a spatio-temporal dual-attention network for view-invariant human action recognition. The method uses both spatial and temporal attention mechanisms to enhance recognition accuracy, regardless of the viewing angle.

Conclusion

Dr. Getinet Yilma is undoubtedly a strong contender for the Best Researcher Award due to his deep expertise in software engineering, machine learning, and AI applications in diverse sectors. His innovative contributions to deep learning, along with his leadership in academic teaching and mentoring, set him apart as a pioneering researcher. With a few enhancements in interdisciplinary collaboration and broader international engagement, Dr. Yilma could further elevate his research to global prominence. He is highly deserving of recognition for his impactful contributions to both academia and industry.