Manickam S | Artificial Intelligence and Machine Learning | Research Excellence Award

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Mr. Manickam S | Artificial Intelligence and Machine Learning | Research Excellence Award

Assistant Professor | Saveetha Engineering College | India

Mr. Manickam S is an emerging researcher in Artificial Intelligence and Machine Learning, with focused contributions spanning data analytics, secure systems, intelligent networks, and applied AI for real-world optimization. His scholarly output includes peer-reviewed journal and international conference publications addressing graph-based road network optimization, learning-assisted pathfinding, and cryptographic multi-server authentication using elliptic curve digital signatures. His research demonstrates strong integration of machine learning algorithms with networking, security, and intelligent transportation systems. Mr. Manickam S has an active innovation portfolio, with multiple Indian patents published and granted in domains such as IoT-enabled robotics, smart agriculture, edge-AI energy monitoring, cloud-integrated IoT resource allocation, solar panel automation, and AI-driven healthcare analytics. His work reflects a translational R&D orientation, emphasizing scalable, deployable intelligent systems. According to Google Scholar, he has 8 citations across 3 research documents with an h-index of 1. He has received recognition for academic innovation and contributes to the research ecosystem through conference participation, interdisciplinary AI research, and technology-driven problem solving.

Citation Metrics (Google Scholar)

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8

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View Scopus Profile   View ORCID Profile   View Google Scholar   View ResearchGate

Featured Publications

Secure multi server authentication system using elliptic curve digital signature
– IEEE ICCPCT Conference Proceedings, 2016 | Citations: 8

Optimizing Road Networks: A Graph-Based Analysis with Path-finding and Learning Algorithms
– International Journal of Intelligent Transportation Systems Research, 2024

Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

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Dr. Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

Doctor | Beijing University of Posts and Telecommunications | China

Dr. Boyuan Bai is an emerging researcher in advanced visual computing, with a focused contribution to 3D reconstruction, Gaussian Splatting, multi-view scene modeling, and uncertainty-aware machine learning. His work integrates computer graphics, deep learning, and computational geometry to develop intelligent systems capable of producing highly accurate and stable indoor scene reconstructions. With 83 citations, 4 Scopus-indexed publications, and an h-index of 3, he is rapidly establishing a strong research footprint. Dr. Boyuan Bai’s notable scientific contribution centers on UncertainGS, an uncertainty-aware indoor reconstruction framework published in Neurocomputing (SCI/Scopus). This research introduces a novel pipeline that integrates cross-modal uncertainty prediction to guide the optimization of Gaussian Splatting. His methodological innovation improves the fidelity of reconstructed surfaces, especially in textureless or geometrically ambiguous indoor regions. His incorporation of Manhattan-world constraints into the Gaussian Splatting process represents a significant leap forward in aligning 3D surface geometry with real-world structural patterns. His research areas broadly span multi-view 3D reconstruction, Gaussian Splatting, uncertainty modeling, scene understanding, and deep reinforcement learning for geometric perception. He actively contributes to the development of next-generation 3D vision technologies, with applications in robotics, digital twins, AR/VR environments, and autonomous spatial intelligence. His work shows strong potential for large-scale deployment in real-time virtual reconstruction and simulation systems. Dr. Boyuan Bai’s scholarly output includes peer-reviewed journal publications, research project leadership, and scientific contributions that address fundamental challenges in computational imaging. His research achievements demonstrate clear innovation, technical depth, and growing influence in the fields of computer vision and graphics. Through ongoing academic collaborations and continued focus on high-impact research problems, he is emerging as a promising researcher in intelligent 3D scene modeling and uncertainty-aware visual computing.

Profiles: Scopus | IEEE Xplore | ACM Digital Library 

Featured Publications

1. Bai, B., Qiao, X., Lu, P., Zhao, H., Shi, W., & others. (2025). Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors. Neurocomputing, 618(C). https://doi.org/10.1016/j.neucom.2024.129118

2. Huang, Y., Bai, B., Zhu, Y., Qiao, X., Su, X., Yang, L., & others. (2024). ISCom: Interest-aware semantic communication scheme for point cloud video streaming on Metaverse XR devices. IEEE Journal on Selected Areas in Communications, 42(4). https://doi.org/10.1109/JSAC.2023.3345430

3. Zhu, Y., Huang, Y., Qiao, X., Tan, Z., Bai, B., & others. (2023). A semantic-aware transmission with adaptive control scheme for volumetric video service. IEEE Transactions on Multimedia, 25. https://doi.org/10.1109/TMM.2022.3217928

4. Huang, Y., Zhu, Y., Qiao, X., Tan, Z., & Bai, B. (2021). AITransfer: Progressive AI-powered transmission for real-time point cloud video streaming. In Proceedings of the 29th ACM International Conference on Multimedia (MM ’21). https://doi.org/10.1145/3474085.3475624

Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

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Assist. Prof. Dr. Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Assistant Professor | Ferdowsi University of Mashhad | Iran

Assist. Prof. Dr. Amir R. Masoodi is a highly accomplished structural engineering researcher whose work spans nonlinear mechanics, composite structures, vibration analysis, finite element modeling, and advanced material systems. With 1,789 Scopus citations, 77 publications, and an h-index of 29, he has established a strong international research footprint in computational mechanics, structural stability, soil–structure interaction, wave propagation, and multiscale modeling of advanced composites. His research contributions include developing novel finite element formulations for beams, plates, and shells, particularly for functionally graded materials (FGMs), carbon nanotube (CNT)-reinforced composites, graphene nanocomposites, and porous structural systems. Assist. Prof. Dr. Amir R. Masoodi’s work on nonlinear dynamic analysis, thermal–mechanical coupling, shell instability, and multiscale behavior of nano-engineered materials has been widely cited and influential in advancing modern structural design methodologies. He has published extensively in leading journals such as Composite Structures, Engineering Structures, Mechanics of Advanced Materials and Structures, Aerospace Science and Technology, Scientific Reports, and Applied Sciences. His publications address cutting-edge topics including vibration of hybrid nano-reinforced shells, multiscale characterization of nanocomposites, nonlinear buckling behavior of tapered beams, thermomechanical modeling of composite cables, and smart materials incorporating shape-memory alloys. Assist. Prof. Dr. Amir R. Masoodi has presented his findings at numerous international conferences and contributed several book chapters, including work on nanofillers and thermal properties in advanced materials. His research output extends to R&D projects, predictive modeling, and computational innovations in structural and nano-engineered systems. He has been recognized with multiple distinguished researcher awards, national elite recognitions, and research excellence honors. His expertise is further reflected in his editorial board memberships and contributions as a reviewer for reputable journals. Overall, Assist. Prof. Dr. Amir R. Masoodi’s research stands at the intersection of computational mechanics, smart materials, and multiscale structural engineering, offering impactful advances for next-generation civil, mechanical, and aerospace systems.

Profiles: Scopus | ORCID | Google Scholar | Sci Profiles | Web of Science

Featured Publications

1. Sobhani, E., Masoodi, A. R., & Ahmadi-Pari, A. (2021). Vibration of FG-CNT and FG-GNP sandwich composite coupled conical–cylindrical–conical shell. Composite Structures, 273, 114281.

2. Sobhani, E., Masoodi, A. R., Civalek, O., & Ahmadi-Pari, A. R. (2021). Agglomerated impact of CNT vs. GNP nanofillers on hybridization of polymer matrix for vibration of coupled hemispherical–conical–conical shells. Aerospace Science and Technology, 120, 107257.

3. Rezaiee-Pajand, M., Sobhani, E., & Masoodi, A. R. (2020). Free vibration analysis of functionally graded hybrid matrix/fiber nanocomposite conical shells using multiscale method. Aerospace Science and Technology, 105, 105998.

4. Rezaiee-Pajand, M., Arabi, E., & Masoodi, A. R. (2019). Nonlinear analysis of FG-sandwich plates and shells. Aerospace Science and Technology, 87, 178–189.

5. Rezaiee-Pajand, M., Masoodi, A. R., & Mokhtari, M. (2018). Static analysis of functionally graded non-prismatic sandwich beams. Advances in Computational Design, 3(2), 165–190.

Wei Pan | Artificial Intelligence | Best Researcher Award

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Dr. Wei Pan | Artificial Intelligence | Best Researcher Award

Researcher | OPT Machine Vision | Japan

Dr. Wei Pan is an accomplished researcher specializing in machine vision, 3D imaging, computational geometry, and optical metrology, currently contributing to OPT Machine Vision Corporation in Japan. His research is positioned at the intersection of machine learning, geometric learning, and computer-aided design, with applications in precision manufacturing, intelligent inspection, and automation. With 26 Scopus-indexed publications, 146 citations, and an h-index of 7, Dr. Wei Pan’s research has advanced computational methodologies for 3D reconstruction, point cloud processing, mesh denoising, phase-shifting profilometry, and surface metrology. His works have appeared in leading journals including Advanced Photonics, Optics Express, Computer-Aided Design, Automation in Construction, and The Visual Computer. Notably, his 2024 publications explore deep-learning-embedded structured light imaging and topology-aware transformers for point cloud registration, reflecting his pioneering integration of AI and optical engineering. Dr. Wei Pan has demonstrated exceptional innovation through 39 patents across domains such as 3D data filtering, surface defect detection, structured light reconstruction, and intelligent robotic calibration. These inventions strengthen industrial imaging precision and automation efficiency. His patent WO-2022057250-A1 on mesh denoising and CN-118397020-A on image segmentation and contour extraction exemplify impactful R&D contributions to intelligent vision systems. Beyond publications and patents, Dr. Wei Pan actively engages in collaborative research and R&D leadership, driving algorithmic innovation in structured-light metrology and computer vision. His research excellence has been recognized with multiple distinctions, including the President’s Graduate Fellowship (Singapore) and the Kuang-Chi Young Talents Award (China). Through his interdisciplinary expertise bridging optical design, machine learning, and computational modeling, Dr. Wei Pan continues to advance the frontiers of intelligent manufacturing and vision-based automation technologies.

Profiles: Scopus | ORCID | Google Scholar | ResearchGate

Featured Publications

  • Liu, J., Hao, J., Lin, H., Pan, W., Yang, J., Feng, Y., Wang, G., Li, J., Jin, Z., Zhao, Z., & Liu, Z. (2023). Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction. Patterns, 4(9), Article 100953.

  • Lu, L., Bu, C., Su, Z., Guan, B., Yu, Q., Pan, W., & Zhang, Q. (2024). Generative deep-learning-embedded asynchronous structured light for three-dimensional imaging. Advanced Photonics, 6(4), 046004–046004.

  • Chen, S., Wang, J., Pan, W., Gao, S., Wang, M., & Lu, X. (2023). Towards uniform point distribution in feature-preserving point cloud filtering. Computational Visual Media, 9(2), 249–263.

  • Lu, L., Jia, Z., Pan, W., Zhang, Q., Zhang, M., & Xi, J. (2020). Automated reconstruction of multiple objects with individual movement based on PSP. Optics Express, 28(18), 28600–28611.

  • Si, G. Y., Leong, E. S. P., Pan, W., Chum, C. C., & Liu, Y. J. (2014). Plasmon-induced transparency in coupled triangle-rod arrays. Nanotechnology, 26(2), 025201.

 

Xue-Yao Gao | Computer Vision and Image Recognition | Best Researcher Award

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Prof. Dr. Xue-Yao Gao | Computer Vision and Image Recognition | Best Researcher Award

Professor and Ph.D. Supervisor (Ph.D.), Harbin University of Science and Technology, China

Prof. Dr. Xue-Yao Gao is a Professor and Ph.D. Supervisor at the School of Computer Science and Technology, Harbin University of Science and Technology, where he also serves as Vice Dean and Deputy Director of the Heilongjiang Key Laboratory of Intelligent Information Processing and Applications. He holds a Ph.D. in Computer Application Technology (2009), an M.Sc. in Computer Software and Theory (2006), and a B.Sc. in Computer and Applications (2002), all from Harbin University of Science and Technology. His primary research focuses on computer graphics, CAD, natural language processing (NLP), artificial intelligence (AI), pattern recognition, and deep learning, with particular expertise in 3D model retrieval, multi-view feature fusion, and cross-view optimization strategies. Over his career, Prof. Dr. Xue-Yao Gao has held key academic positions including Professor (2018–present), Associate Professor (2012–2018), and Lecturer (2010–2012) in the School of Computer Science and Technology at Harbin University of Science and Technology. His contributions include over 60 publications, 16 granted invention patents, and leadership of seven funded projects supported by the National Natural Science Foundation of China, Heilongjiang Provincial Natural Science Foundation, Ministry of Education’s Chunhui Program, and corporate collaborations, totaling research funding exceeding 2.6 million yuan. Prof. Dr. Xue-Yao Gao has been recognized with the university’s “Science and Engineering Talent” award and has contributed as editor and co-author to multiple textbooks and monographs. He holds leadership and committee positions in the China Computer Federation and Heilongjiang Computer Society, including Executive Member, Director, Vice Chairman of the Harbin Branch of CCF YOCSEF, and memberships in specialized committees, actively mentoring doctoral and master’s students and fostering youth scientific engagement. His work advances intelligent information processing, enhances 3D modeling and pattern recognition technologies, and promotes innovative AI applications, impacting academia, industry, and society. Author metrics: 53 documents, 124 citations, h-index 6. Prof. Dr. Xue-Yao Gao’s sustained research excellence, innovation in AI and computer graphics, and global collaborative potential make him highly deserving of recognition for advancing science, technology, and education internationally.

Profile: Scopus | ORCID | ResearchGate | IEEE Xplore | Harbin University of Science and Technology

Featured Publications

1. Gao, X., Zhang, Y., Zhang, C., & Xue, Y. (2025). 3D model classification based on DRSN and multi-view feature fusion. Expert Systems with Applications, 273, 126872.

2. Gao, X., Yan, S., & Zhang, C. (2024). 3D model classification based on RegNet design space and voting algorithm. Multimedia Tools and Applications, 83, 42391–42412.

3. Gao, X.-Y., Li, K.-P., Zhang, C.-X., & Yu, B. (2021). 3D model classification based on Bayesian classifier with AdaBoost. Discrete Dynamics in Nature and Society, 2021, Article 2154762.

4. Zhang, C.-X., Pang, S.-Y., Gao, X.-Y., Lu, J.-Q., Yu, B., & Jia, Y. (2022). Attention neural network for biomedical word sense disambiguation. Discrete Dynamics in Nature and Society, 2022, Article 6182058.

5. Zhang, C.-X., Shao, Y.-L., & Gao, X.-Y. (2023). Word sense disambiguation based on RegNet with efficient channel attention and dilated convolution. IEEE Access.

 

Soufiane Bacha | Artificial Intelligence | Best Researcher Award

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Mr. Soufiane Bacha | Artificial Intelligence | Best Researcher Award

PhD Student, University of Science and Technology Beijing, Algeria

Mr. Soufiane Bacha is a promising young researcher in Artificial Intelligence and Data Quality with a strong academic background and growing international exposure. He is currently pursuing a Ph.D. in Data Quality at the University of Science and Technology Beijing (2023–ongoing) and a Ph.D. in Cancer Epidemiology at the Department of Computer Science, Ibn Khaldoun University of Tiaret, Algeria (2021–2025). He also holds a Master’s degree in Software Engineering (2019–2021), where he ranked first in his class and completed a thesis on imbalanced datasets and boosting methods, and a B.Sc. in Computer Science (2016–2019) with strong foundations in algorithms, cryptography and programming. Professionally, Mr. Soufiane Bacha gained valuable international research experience through an internship at the Faculty of Polytechnic Mons, UMONS University in Belgium, where he worked on Internet of Things (IoT) applications involving Raspberry Pi, Arduino and sensor technologies. He has served as a part-time lecturer in Graph Theory and as an ICT trainer in web development, demonstrating strong teaching, leadership, and communication skills. His research interests span artificial intelligence, data quality, machine learning for imbalanced datasets, cancer epidemiology, distributed applications and business analytics. He is proficient in Python, C/C++, Java, SQL and data analysis tools, with expertise in OLAP, data mining, and deep learning frameworks. His achievements include an NVIDIA Deep Learning Institute Certificate, participation in AI workshops, and a Scopus-indexed publication. With a dual doctoral training and interdisciplinary focus, Mr. Soufiane Bacha is well-positioned to make impactful contributions to AI-driven data quality research and healthcare analytics on a global scale.

Profile: ORCID | Google Scholar | ResearchGate

Featured Publications

1. Bacha, S., Ning, H., Mostefa, B., Sarwatt, D. S., & Dhelim, S. (2025). A novel double pruning method for imbalanced data using information entropy and roulette wheel selection for breast cancer diagnosis (arXiv:2503.12239).

 

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.

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.