Dagne Walle Girmaw | Artificial Intelligence | Best Scholar Award

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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.

Chao Zhang | Machine Learning | Best Researcher Award

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Prof. Chao Zhang | Machine Learning | Best Researcher Award

Professor at Shanghai University, China.

Prof. Chao Zhang is a distinguished mechanical engineer and academic based at Shanghai University. With a career spanning over four decades, Prof. Zhang has made pioneering contributions to tribology, engine wear modeling, and lubrication science. His expertise lies in the integration of quantum chemical molecular dynamics, artificial intelligence, and machine learning into mechanical systems analysis, particularly in the development of kinetic models and digital twins. He has collaborated with renowned experts such as Profs. H.S. Cheng and Qian Wang during his tenure as a senior research associate at Northwestern University, USA (1997–2002), and has held professorships at Tongji University, Shanghai University, and Kunming University of Science and Technology. Prof. Zhang’s research has played a transformative role in understanding tribocorrosion, antiwear mechanisms, and mechanochemical processes in engine components. His innovative work applies computational intelligence and quantum-level simulation to practical engineering problems, improving lubrication performance, predicting scuffing failures, and optimizing engine durability. He has led multiple high-impact national and industrial projects, contributing significantly to both theoretical and applied tribology. Prof. Zhang is also involved in industry-academic partnerships, editorial boards, and international scientific collaborations. His career embodies the intersection of advanced theory, simulation, and engineering practice, making him a leader in mechanical engineering innovation.

Publication Profile

Scopus

Education

Prof. Chao Zhang holds three advanced degrees in Mechanical Engineering. He earned his Bachelor of Arts (B.A.) in Mechanical Engineering from Shanghai Railway University in 1983, where he laid the foundation for his lifelong commitment to mechanical systems design and analysis. He pursued a Master of Science (M.S.) degree at the Shanghai Internal Combustion Engine Research Institute, graduating in 1989. His master’s research centered on internal combustion engine performance and lubrication, paving the way for his deeper exploration into tribology and wear mechanisms. Prof. Zhang completed his Doctor of Philosophy (Ph.D.) in Mechanical Engineering at Shanghai University in 1997. His doctoral work focused on advanced wear modeling and lubrication processes in engine systems. In addition to his formal education, Prof. Zhang expanded his research acumen as a senior research associate at Northwestern University from 1997 to 2002, working under the mentorship of tribology pioneers Prof. H.S. Cheng and Prof. Qian Wang. His education has equipped him with a robust theoretical foundation and practical expertise in mechanics, materials, and computational modeling. The interdisciplinary and global nature of his academic background uniquely positions Prof. Zhang to contribute significantly to high-impact, cutting-edge engineering research and education.

Professional Experience

Prof. Chao Zhang has had a remarkable professional journey across academia and international research institutions. After completing his Ph.D. in 1997, he joined Northwestern University (USA) as a senior research associate, working with Profs. H.S. Cheng and Qian Wang on pioneering tribological studies. From 1997 to 2002, he contributed to foundational research in tribocorrosion, lubrication modeling, and engine wear, integrating both experimental and computational approaches. Returning to China, Prof. Zhang held faculty positions at several leading institutions including Tongji University, Kunming University of Science and Technology, and his current post at Shanghai University. As a professor, he has led numerous research projects funded by national foundations and industry sponsors. He has supervised postgraduate students, developed novel tribological models, and built cross-disciplinary collaborations in mechanical engineering, materials science, and computational chemistry. Prof. Zhang’s expertise in applying quantum molecular dynamics and AI to wear modeling has earned him recognition across scientific communities. His work has contributed significantly to tribology research in internal combustion engines, scuffing failure analysis, and antiwear film formation. Throughout his career, Prof. Zhang has demonstrated academic leadership, innovation in applied mechanics, and a commitment to developing next-generation engineering solutions.

Research Interest

Prof. Chao Zhang’s research interests lie at the confluence of mechanical engineering, computational chemistry, and artificial intelligence. His primary focus is on tribology, particularly in the modeling of engine wear, boundary lubrication, and tribocorrosion processes. He has developed advanced multi-phase and multi-scale models that utilize quantum chemical molecular dynamics (QCMD), machine learning, and electron-phonon coupling to simulate friction, wear, and lubrication at the molecular level. His work also investigates scuffing mechanisms, offering predictive insights through kinetic models and scuffing failure mapping techniques. Prof. Zhang has a strong interest in mechanochemical processes, especially in relation to antiwear additive behaviors, lubricant chemistry, and film formation under operational engine conditions. His digital twin frameworks for tribological systems apply big data analytics and cloud computing for real-time simulation and optimization. Additional areas of interest include boundary film behavior, nano-tribology, AI-assisted diagnostics, and advanced simulation of component surface interactions. His interdisciplinary approach integrates physics-based modeling, chemical reaction kinetics, and machine learning algorithms, pushing the boundaries of traditional mechanical engineering and offering valuable insights for industries such as automotive, energy, and materials engineering.

Research Skills

Prof. Chao Zhang possesses a unique blend of research skills that merge classical mechanical engineering with cutting-edge computational techniques. His expertise in quantum chemical molecular dynamics (QCMD) enables him to simulate tribological interactions at atomic and molecular scales, allowing for a deeper understanding of wear and lubrication mechanisms. He is proficient in developing multi-scale models and applying machine learning (ML) algorithms for pattern recognition, prediction, and optimization in tribological systems. Prof. Zhang also has advanced skills in digital twin development, where he uses big data and cloud computing to model the real-time behavior of engine components under tribocorrosive conditions. His methodological toolkit includes SOL (state-of-lubrication) modeling, electron-phonon interaction theory, reaction kinetics modeling, and mechano-chemical simulation techniques. He is adept at integrating artificial intelligence (AI) with physics-based simulations to derive hybrid models that offer high accuracy and predictive capability. Additionally, Prof. Zhang’s experimental experience in tribometry and engine component testing complements his theoretical models, resulting in comprehensive and validated research outputs. His cross-domain skills enable him to bridge engineering applications with fundamental science, making his contributions innovative and impactful across both academic and industrial sectors.

Awards and Honors

Prof. Chao Zhang has received numerous accolades in recognition of his outstanding contributions to mechanical engineering and tribological research. While specific award names are not detailed in the provided material, his career achievements reflect a high level of scholarly and industrial impact. His tenure at Northwestern University and subsequent professorships at major Chinese universities are testaments to his international reputation and academic excellence. The research projects led by Prof. Zhang have been consistently funded by prestigious national science foundations and industry stakeholders, reflecting the high relevance and quality of his work. His interdisciplinary methods—integrating quantum simulation, machine learning, and real-time modeling—have earned him a respected place in tribology and lubrication science communities. He is frequently invited to collaborate on high-impact projects and contribute to strategic research in engine wear reduction and surface engineering. Prof. Zhang’s work on scuffing failure mapping and digital twin development for tribocorrosion systems has also been featured in leading engineering forums. While further documentation is needed for a comprehensive list, it is evident that Prof. Zhang’s career is distinguished by innovation, academic leadership, and impactful contributions to tribological science.

Author Metrics

  • Total publications: 24 (1995–2025)
  • Total citations: 8,470 (as of June 2025)
  • h‑index: 51
  • i10‑index: 60

Publications Top Notes

📘 Multi‑phase and multi‑scale engine wear modeling via quantum chemical molecular dynamics and machine learning

  • Authors: Zhang, C.

  • Title: Multi‑phase and multi‑scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework

  • Journal: Wear, Volume 571, Article 205771

  • Date: 15 June 2025

📘 Lubricant–Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning

  • Authors: Zhang, C.

  • Title: Lubricant–Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning

  • Conference: STLE 2023 Annual Meeting Digital Proceedings

  • Year: 2023

📘 Scuffing behavior of piston‑pin bore bearing in mixed lubrication

  • Authors: Zhang, C.

  • Title: Scuffing behavior of piston‑pin bore bearing in mixed lubrication

  • Publisher: Springer

  • Year: 2022

  • Published Online: 30 March 2022

📘 Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives

  • Authors: Zhang, C.

  • Title: Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives

  • Conference: Proceedings of I4SDG Workshop 2021, MMS 108, pp. 1–9

  • Year: 2022 (Proceedings published 2022)

📘 Scuffing factor and scuffing failure mapping

  • Authors: Zhang, C.

  • Title: Scuffing factor and scuffing failure mapping

  • Conference: Proceedings of the 2nd World Congress on Internal Combustion Engine, April 21–24, 2021, Jinan, China

  • Year: 2021

Conclusion

Prof. Chao Zhang is an eminent researcher and educator in the field of mechanical engineering, with a distinguished career characterized by innovation, interdisciplinary research, and academic leadership. Through his pioneering work in tribology, engine wear modeling, and lubrication science, Prof. Zhang has bridged the gap between molecular-level simulations and real-world engineering applications. His integration of quantum chemical molecular dynamics, machine learning, and cloud-based digital twins has opened new pathways in tribocorrosion analysis, scuffing prediction, and antiwear film development. With decades of experience in both the United States and China, he has nurtured academic collaborations and guided research with significant scientific and industrial impact. His ability to combine theoretical insight with practical application exemplifies the future of smart mechanical systems and intelligent design frameworks. Prof. Zhang’s contributions extend beyond individual research projects to shaping the next generation of engineering solutions and researchers. As a leader in mechanical science and tribology, his work continues to influence engine design, lubricant formulation, and component life prediction globally. Prof. Zhang’s dedication to excellence, research rigor, and scientific advancement positions him as a valuable asset to both academia and industry in addressing complex mechanical and material challenges of the modern era.

Namita Bajpai | Artificial Intelligence | Best Researcher Award

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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.