Dagne Walle Girmaw | Artificial Intelligence | Best Scholar Award

Mr. Dagne Walle Girmaw | Artificial Intelligence | Best Scholar Award

Lecturer at Department of Information Technology, Haramaya University, Ethiopia.

Mr. Dagne Walle Girmaw is an experienced academic professional and researcher in the field of Information Technology, currently serving as a Lecturer at Haramaya University, Ethiopia. With over seven years of teaching and research experience, he specializes in artificial intelligence (AI), machine learning (ML), and deep learning (DL), with a strong emphasis on their applications in healthcare and agriculture. Mr. Girmaw has published more than ten research articles in peer-reviewed journals and has actively contributed as a reviewer for over fifteen reputable international journals. His academic journey is marked by consistent excellence, innovative project leadership, and dedicated service to both his institution and the wider community. In addition to his teaching duties, he has taken on leadership roles such as internship coordinator and project adviser within the Department of Information Technology. Mr. Girmaw is also engaged in community outreach, having delivered various certified trainings to students and professionals in software development and digital literacy. His deep technical expertise, combined with effective communication, problem-solving, and team leadership skills, makes him a valuable contributor to academic and applied research in emerging digital technologies. Mr. Girmaw aims to continue advancing knowledge and innovation through interdisciplinary collaboration and cutting-edge technological solutions.

📝Publication Profile

Scopus

Orcid

Google Scholar

🎓Education

Mr. Dagne Walle Girmaw holds a Master of Science (MSc) degree in Information Technology from the University of Gondar, Ethiopia, awarded in 2021. His master’s thesis, titled “Deep Convolutional Neural Network Model for Classifying Common Bean Leaf Diseases,” reflects his early engagement in artificial intelligence and deep learning applications in agriculture. During his MSc studies, he developed strong competencies in areas such as advanced networking, big data analysis, IT project management, and machine learning algorithms. Prior to his postgraduate education, he earned a Bachelor of Science (BSc) degree in Information Technology from Haramaya University in 2017. His undergraduate project, “Online Car Selling and Renting System for Diredawa Red Star Trading,” showcased his practical skills in web development. Both academic programs provided him with a solid foundation and advanced expertise in software development, database systems, and computer networks.

💼Professional Experience

Mr. Dagne Walle Girmaw has accumulated over seven years of professional experience at Haramaya University, where he has served in ascending academic positions. Currently, he works as a Lecturer in the Department of Information Technology since October 2021. He teaches courses such as Advanced Database, Data Structures and Algorithms, Event-Driven Programming, and Object-Oriented Programming. Previously, from October 2018 to October 2021, he served as an Assistant Lecturer, delivering courses in Internet Programming, Network Design, Programming II (C++), and Emerging Technologies. His academic career began as a Graduate Assistant-II (October 2017–October 2018), teaching Computer Applications and Fundamental Database Systems. Mr. Girmaw has also undertaken administrative responsibilities including serving as the Internship Coordinator and Acting Department Head Delegate. He has guided students in their final-year research and practical projects. Beyond the classroom, he actively contributes to institutional development through training programs, curriculum reviews, and outreach initiatives. His dual role as an educator and researcher reflects his commitment to both academic excellence and student empowerment. His work ethic, leadership in departmental affairs, and technical proficiency have earned him recognition within the academic community and positioned him as a role model for aspiring IT professionals.

🔬Research Interest

Mr. Dagne Walle Girmaw’s research interests lie primarily in artificial intelligence (AI), machine learning (ML), deep learning (DL), data science, and big data technologies. He has focused particularly on deep learning applications in medical imaging and agricultural diagnostics, areas with significant social and economic implications in Ethiopia and other developing countries. His MSc thesis introduced a deep convolutional neural network model to detect bean leaf diseases, demonstrating the potential of AI in enhancing agricultural productivity. Mr. Girmaw is keen on developing and optimizing AI-driven tools that can support decision-making processes in resource-limited settings, particularly in healthcare and agriculture. His long-term objective is to bridge the gap between technological advancement and practical applications that directly benefit communities. He is also interested in reinforcement learning, generative adversarial networks (GANs), and other emerging AI frameworks. These interests are aligned with his goal to explore multidisciplinary collaborations and contribute to AI-based innovations that promote sustainability and improved quality of life. Through his research, Mr. Girmaw strives to create real-world impact by solving pressing challenges using intelligent systems, while also expanding the scope of academic contributions in African higher education.

🧠Research Skills

Mr. Girmaw possesses a robust set of research skills in modern artificial intelligence and data processing methodologies. He is highly proficient in machine learning and deep learning frameworks such as TensorFlow, Keras, PyTorch, and Scikit-learn. His technical expertise includes working with various neural network architectures such as CNN, RNN, LSTM, GRU, and GANs. He is skilled in computer vision tasks and the use of ImageJ for image preprocessing and annotation. In terms of programming, he is fluent in Python, Java, C++, C#, MATLAB, Visual Basic, and web development tools like PHP, HTML, CSS, JavaScript, JSP, and ASP.NET. He also has database management experience with MySQL, MS Access, SQL Server, and MongoDB. Mr. Girmaw has conducted extensive research using structured data analysis techniques and data visualization libraries such as Pandas, Numpy, and Matplotlib. His background includes developing real-world applications, implementing AI models, and performing empirical validations. These competencies are complemented by his ability to write, review, and present scientific work in reputed journals and academic settings. His research skills enable him to work independently and collaboratively on complex projects that involve both theoretical modeling and practical implementation.

🏆Awards and Honors

Mr. Dagne Walle Girmaw has received numerous awards and recognitions for his scholarly contributions and community service. He has been honored with peer review awards from several prestigious journals, including Journal of Signal, Image, and Video Processing, BMC Plant Biology, PLOS ONE, Scientific Reports, Cloud Computing and Data Science (CCDS), Discover Sustainability, Earth Science Informatics, Discover Computing, Crop Health, and Bulletin of Electrical Engineering and Informatics (BEEI). These acknowledgments affirm his role as a dedicated reviewer and contributor to academic quality assurance. He was also awarded the Best Scholar Award-2025 by Science Father for his outstanding research achievements. In the realm of student engagement, Mr. Girmaw served as Vice President of the Anti-Drug Club at Haramaya University (2015–2016), promoting a healthy and drug-free campus culture. He has received several certificates of participation for his involvement in community dialogues and training programs, including the Ethiopian Campus Sustainable Dialogue initiative. His consistent record of excellence in research, teaching, and community outreach reflects a well-rounded academic profile, with both local and international recognition for his efforts.

📈Author Metrics

  • Total Publications: 10+ peer-reviewed journal articles (2023–2025)

  • Total Citations: 20+

  • h-index: 3 (At least 3 papers with 3 or more citations each)

  • Primary Research Areas:

    • Deep learning in agriculture and plant pathology

    • Biomedical image analysis

    • Mobile ad hoc networks (MANETs)

    • Optical character recognition (OCR)

    • Wireless sensor networks (WSNs)

📌Publications Top Notes

1. Deep Convolutional Neural Network Model for Classifying Common Bean Leaf Diseases

  • Authors: D.W. Girmaw, T.W. Muluneh

  • Year: 2024

  • Journal: Discover Artificial Intelligence (Springer Nature)

  • Citations: 2

2. Field Pea Leaf Disease Classification Using a Deep Learning Approach

  • Authors: D.W. Girmaw, T.W. Muluneh

  • Year: 2024

  • Journal: PLOS ONE

  • Citations: Not listed

3. Energy Aware Stable Path Ad Hoc On-Demand Distance Vector Algorithm for Extending Network Lifetime of Mobile Ad Hoc Networks

  • Authors: T. Legesse, D.W. Girmaw, E. Yitayal, E. Admassu

  • Year: 2025

  • Journal: PLOS ONE

  • Citations: 1

4. Livestock Animal Skin Disease Detection and Classification Using Deep Learning Approaches

  • Author: D.W. Girmaw

  • Year: 2025

  • Journal: Biomedical Signal Processing and Control (Elsevier)

  • Citations: Not listed

5. Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning

  • Authors: E.A. Nigus, G.B. Taye, D.W. Girmaw, A.O. Salau

  • Year: 2024

  • Journal: Multimedia Tools and Applications (Springer Nature)

  • Citations: 14

6. MobileNetV2 Model for Detecting and Grading Diabetic Foot Ulcer

  • Authors: D.W. Girmaw, G.B. Taye

  • Year: 2025

  • Journal: Discover Applied Sciences (Springer Nature)

  • Citations: Not listed

7. A Novel Deep Learning Model for Cabbage Leaf Disease Detection and Classification

  • Authors: D.W. Girmaw, A.O. Salau, B.S. Mamo, T.L. Molla

  • Year: 2024

  • Journal: Discover Applied Sciences (Springer Nature)

  • Citations: 3

8. Energy efficient inter-cluster multi-hop communication routing protocol for wireless sensor network based on centralized energy efficient clustering routing protocol

  • Authors: Tibebu Legesse, Dagne Walle Girmaw, * Esubalew Yitayal, Engida Admassu

  • Year: 2025

  • Platform: PLOS ONE

  • Citations: Not listed

9. Deep Learning-Based Potato Leaf Disease Classification and Severity Assessment

  • Authors: T.A. Dame, G.B. Adera, D.W. Girmaw

  • Year: 2025

  • Journal: Discover Applied Sciences (Springer Nature)

  • Citations: Not listed

10. Character Recognition of Ancient Ethiopic Ge’ez Manuscripts Using Deep Convolutional Neural Networks

  • Authors: Kasaye Akanie Guangul, Dagne Walle Girmaw, Million Meshesha

  • Journal: Discover Imaging (Springer Nature)

  • Year: 2025

  • Citation Count: Not listed

🧾Conclusion

Mr. Dagne Walle Girmaw is a passionate educator, skilled researcher, and dedicated contributor to the advancement of technology in Ethiopia. His expertise in artificial intelligence and its application to real-world problems positions him as a valuable asset in both academic and professional settings. Over the years, he has demonstrated a unique blend of technical proficiency, leadership, and commitment to community development. His teaching philosophy is rooted in bridging theoretical knowledge with hands-on practice, inspiring students to pursue excellence in the ever-evolving field of Information Technology. Mr. Girmaw’s research endeavors have led to impactful publications and significant peer recognition. His awards and outreach activities further emphasize his dedication to fostering positive change through education and innovation. Moving forward, he seeks to collaborate on interdisciplinary research projects and contribute to technology-driven solutions in healthcare, agriculture, and beyond. With a firm foundation in both theory and practice, Mr. Girmaw remains committed to lifelong learning and empowering future generations through transformative education and applied research.

Namita Bajpai | Artificial Intelligence | Best Researcher Award

Ms. Namita Bajpai | Artificial Intelligence | Best Researcher Award

Research Scholar at Indian Institute of Technology, India.

Ms. Namita Bajpai is an AI researcher and academic, currently pursuing a PhD at IIT Kharagpur. With expertise in unsupervised learning and subclass classification, she is passionate about leveraging AI for healthcare applications and large-scale data analysis. She has prior experience as an Assistant Professor and Research Assistant, contributing to AI-based knowledge discovery platforms.

Publication Profile

Scopus

Google Scholar

Educational Details

Ms. Namita Bajpai is a dedicated researcher in Artificial Intelligence, specializing in unsupervised learning and subclass classification. She is currently pursuing a Doctor of Philosophy (PhD) in the Department of Artificial Intelligence at the Indian Institute of Technology (IIT) Kharagpur, India, focusing on hidden stratification in classification using scalable clustering. She holds a Master of Technology (M.Tech.) in Computer Science from IIT (Indian School of Mines), Dhanbad with an impressive GPA of 9.28 and a Bachelor of Engineering (B.E.) in Computer Science from Government Engineering College, Raipur, where she graduated with 75.21%.

Professional Experience

Ms. Namita has a strong background in academia and research. She served as a Research Assistant at IIT Kharagpur (2022–2023), where she contributed to the Integrated Information System and Knowledge Discovery Platform for ONGC (Oil and Natural Gas Corporation) of India. Before that, she worked as an Assistant Professor at C.V. Raman College of Engineering, Bhubaneswar (2017–2018), teaching Data Mining and C programming while also taking on administrative roles such as Time Table Coordinator and First-Year Coordinator.

Research Interest

Namita’s research focuses on unsupervised learning, subclass classification, and AI applications in healthcare. Her work aims to enhance AI models for more effective classification and pattern recognition, contributing to fields such as medical diagnostics, data-driven decision-making, and scalable AI solutions.

Author Metrics

Namita has contributed to AI and machine learning research, focusing on classification models, clustering techniques, and AI-driven insights. Her academic contributions are reflected in research publications and projects, advancing the field of unsupervised learning and its real-world applications.

Top Noted Publication

Ms. Namita Bajpai has contributed to high-impact AI and data science research, publishing in renowned journals and conferences. Her most cited works include:

  1. A Novel Distributed Energy-Efficient Routing Algorithm Based on Clustering Mechanism in WSN
    📄 2019 International Conference on Intelligent Computing and Remote Sensing
    🔹 Cited 4 times | Focus: Wireless Sensor Networks & Energy-Efficient Routing

  2. Raw Data Redundancy Elimination on Cloud Database
    📄 Computational Intelligence in Pattern Recognition (CIPR 2020)
    🔹 Cited 3 times | Focus: Cloud Computing & Data Redundancy Optimization

  3. Balanced Seed Selection for K-means Clustering with Determinantal Point Process
    📄 Pattern Recognition (2025)
    🔹 Cited 1 time | Focus: Clustering Optimization & AI Model Improvement

  4. A Stratified Seed Selection Algorithm for K-means Clustering on Big Data
    📄IEEE Transactions on Artificial Intelligence
    🔹 Upcoming | Focus: Big Data Processing & AI-Driven Clustering

Conclusion

Ms. Namita Bajpai is a highly suitable candidate for the Best Researcher Award based on her strong academic record, impactful AI research, and interdisciplinary contributions. Her work in AI-driven clustering, big data analysis, and healthcare applications makes her a promising leader in the field. By further expanding collaborations, citations, and funding opportunities, she can solidify her position as a leading researcher in AI and machine learning.

Her dedication to advancing AI for real-world applications, combined with her teaching experience and research achievements, makes her a top contender for this prestigious award.