Sanghyun Lee | Automotive Design Agency Development | Best Researcher Award

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Dr. Sanghyun Lee | Automotive Design Agency Development | Best Researcher Award

Senior Research Enginner | Hyundai Motor Company | South Korea

Dr. Sanghyun Lee is a senior research engineer at Hyundai Motor Company in the Advanced Vehicle Platform division, specializing in intelligent technology–based vehicle body design, closure mechanisms, sealing systems, and AI-driven design automation. He earned his bachelor’s degree in Mechanical Engineering from Korea University and is currently pursuing a combined MS/Ph.D. in Mechanical Engineering at Sungkyunkwan University. His professional experience spans more than two decades, including door and closure mechanism engineering, sealing system design leadership, and new mobility concept development with integrated intelligent technologies. His research interests focus on ontology-based knowledge graphs, RAG systems, generative and parametric design, and CAD automation applied to advanced automotive systems. He has contributed to several impactful industry and academic projects, collaborating with Yonsei University on knowledge graph with RAG systems and with KAIST on generative tailgate design. His work is widely recognized through more than thirty patents across multiple countries and several publications in reputed international journals such as Materials Today Communications, Advanced Engineering Informatics, and the International Journal of Automotive Technology. He has also contributed to advancing intelligent design frameworks that integrate AI and human-in-the-loop knowledge curation to accelerate automotive innovation. He is a member of the Korean Society of Automotive Engineers and has played a vital role in bridging industrial research with academic progress. His research profile currently reflects 4 citations, 1 document, and an h-index of 1.

Profiles: Google Scholar | Scopus | LinkedIn

Featured Publications

  1. Akay, H., Lee, S. H., & Kim, S. G. (2023). Push-pull digital thread for digital transformation of manufacturing systems. CIRP Annals, 72(1), 401–404.

  2. Shim, M., Choi, H., Koo, H., Um, K., Lee, K. H., & Lee, S. (2025). OmEGa (Ω): Ontology-based information extraction framework for constructing task-centric knowledge graph from manufacturing documents with large language model. Advanced Engineering Informatics, 64, 103001.

  3. Lee, S. H., Yoon, B., Cho, S., Lee, S., Hong, K. M., & Suhr, J. (2023). Multidisciplinary design of door inner belt weatherstrip for simultaneous reduction of wind noise and squeaking in electric vehicles. Materials Today Communications, 37, 107567.

  4. Lee, S. H., Yoon, B., Kwon, H., Seo, C. M., & Suhr, J. (2025). Design optimization for minimizing performance deviations of complex vehicle door systems using virtual manufacturing big data and axiomatic design. International Journal of Automotive Technology, 26(4), 1–25.

  5. Lee, S., Kim, M. K., Kim, M., Hong, K. M., & Suhr, J. (2025). Multidisciplinary tailgate guide bumper design for electric vehicles: Overcoming rattle, separation noise and closure effort trade-offs. Materials Today Communications, 112593.

Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

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Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Karim Dabbabi | Unmanned Aerial Vehicle Tracking | Best Academic Researcher Award

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Dr. Karim Dabbabi | Unmanned Aerial Vehicle Tracking | Best Academic Researcher Award

Assistant Professor | Faculty of Sciences of Tunis | Tunisia

Dr. Karim Dabbabi is an accomplished researcher and assistant professor specializing in artificial intelligence, computer vision, natural language processing, and biomedical signal processing. He holds a Ph.D. in Electronics from the Faculty of Sciences of Tunis and has completed advanced studies in automation, signal processing, and embedded electronics. His professional experience includes teaching courses in image and signal processing, machine and deep learning, AI, coding languages, embedded systems, and IoT across multiple institutions, as well as mentoring and supervising numerous student theses. He has actively contributed to national and international research projects, including UAV real-time tracking, smart grids, COVID-19 patient monitoring, intelligent wheelchairs, and disaster management robotics. His research interests focus on multimodal speech and image analysis, intelligent systems, emotion-aware speech recognition, and healthcare applications such as early detection of Parkinson’s and Alzheimer’s diseases. He has published extensively in reputed journals and conferences, including IEEE, Scopus, and Springer, and holds certifications in AI, machine learning, embedded systems, and data science. His work reflects strong leadership in research supervision, active involvement in academic communities, and commitment to advancing technology for societal benefit. His research skills encompass programming (Python, MATLAB, C, C++, Java, VHDL), machine learning frameworks, embedded systems, and IoT development. Dr. Dabbabi’s contributions are evidenced by 39 citations, 17 documents, and an h-index of 4.

Profiles: Google Scholar | Scopus | ResearchGate 

Featured Publications

  1. Dabbabi, K., Hajji, S., & Cherif, A. (2020). Real-time implementation of speaker diarization system on Raspberry PI3 using TLBO clustering algorithm. Circuits, Systems, and Signal Processing, 39(8), 4094–4109.

  2. Walid, M., Bousselmi, S., Dabbabi, K., & Cherif, A. (2019). Real-time implementation of isolated-word speech recognition system on Raspberry Pi 3 using WAT-MFCC. International Journal of Computer Science and Network Security, 19(3), 42.

  3. Dabbabi, K., Kehili, A., & Cherif, A. (2023). Parkinson detection using VOT-MFCC combination and fully-connected deep neural network (FC-DNN) classifier. In Proceedings of the 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET 2023).

  4. Dabbabi, K., Delleji, T. (2025). Graph neural network-tracker: A graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking. Visual Computing for Industry, Biomedicine, and Art, 8(1), 18.

  5. Dabbabi, K., Mars, A. (2024). Self-supervised learning for speech emotion recognition task using audio-visual features and Distil Hubert model on BAVED and RAVDESS databases. Journal of Systems Science and Systems Engineering, 33(5), 576–606.

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

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Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.

Arron Carter | Plant Breeding | Best Researcher Award  

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Dr. Arron Carter | Plant Breeding | Best Researcher Award

Professor | Wasington State University | United States

Dr. Arron Carter is a leading expert in plant breeding and genetics with a focus on winter wheat cultivar development, gene discovery, molecular markers, and high-throughput phenotyping to enhance global food security. He earned his PhD in Crop and Soil Sciences from Washington State University, where his doctoral work identified key quantitative trait loci and molecular markers for disease and agronomic traits in spring wheat. His professional journey includes serving as Professor and O.A. Vogel Endowed Chair of Wheat Breeding and Genetics at Washington State University, where he has made significant contributions to advancing sustainable agriculture and mentoring the next generation of researchers. His research interests span genomic selection, disease resistance, drought and heat tolerance, and integrating remote sensing and artificial intelligence into crop improvement. Over his career, he has been honored with multiple outstanding paper awards and recognition from professional societies for impactful research that combines innovation with practical agricultural applications. His research skills include advanced genomics, quantitative genetics, data-driven breeding strategies, UAV-based crop monitoring, and interdisciplinary collaborations at both national and international levels. His impact on the scientific community is reflected in 3,889 citations, 132 documents, and an h-index of 30.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Cavanagh, C. R., Chao, S., Wang, S., Huang, B. E., Stephen, S., Kiani, S., Forrest, K., … Carter, A. H., … & Akhunov, E. (2013). Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proceedings of the National Academy of Sciences, 110(20), 8057–8062.

  2. Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Carter, A. H., … Miklas, P. N. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112–123.

  3. Carter, A. H., Chen, X. M., Garland-Campbell, K., & Kidwell, K. K. (2009). Identifying QTL for high-temperature adult-plant resistance to stripe rust (Puccinia striiformis f. sp. tritici) in the spring wheat (Triticum aestivum L.) cultivar ‘Louise’. Theoretical and Applied Genetics, 119(6),

  4. Sandhu, K. S., Lozada, D. N., Zhang, Z., Pumphrey, M. O., & Carter, A. H. (2021). Deep learning for predicting complex traits in spring wheat breeding program. Frontiers in Plant Science, 11, 613325.

  5. Naruoka, Y., Garland-Campbell, K. A., & Carter, A. H. (2015). Genome-wide association mapping for stripe rust (Puccinia striiformis f. sp. tritici) in US Pacific Northwest winter wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 128(6), 1083–1101.

Emmanuel Ukekwe | Data Analytics | Best Researcher Award

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Dr. Emmanuel Ukekwe | Data Analytics | Best Researcher Award

Senior Lecturer | University of Nigeria | Nigeria

Dr. Emmanuel Ukekwe is a dedicated researcher and academic with expertise in artificial intelligence, expert systems, data science, computational programming, and software engineering, with a focus on applying intelligent technologies to solve societal problems. He obtained his Bachelor of Science in Computer/Statistics, Master of Science, and Ph.D. in Computer Science from the University of Nigeria, Nsukka, where he has grown into a respected lecturer and researcher. His professional journey includes roles as Senior Lecturer, Lecturer, and Instructor, as well as administrative positions such as Acting Head of Department and Acting Dean, demonstrating both academic and leadership excellence. His research interests span the application of machine learning and Python programming in data-driven problem solving, optimization models, recommender systems, and educational technologies. He has published extensively in recognized journals and conferences indexed in Scopus, covering healthcare systems, telecommunications, student performance, and COVID-19 analytics. He has been actively involved in university committees, curriculum development, and community-based research projects, and is a member of organizations such as the National Biotechnology Development Agency and the Technical Committee on UNESCO-HP projects. His skills include statistical analysis, software development, and advanced computational modeling, reflecting strong technical and analytical capabilities. His academic and research contributions have been recognized with professional memberships and community service engagements, marking him as an influential contributor to both academia and society. His research profile records 4 citations, 8 documents, and an h-index of 1.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Okereke, G. E., Bali, M. C., Okwueze, C. N., Ukekwe, E. C., Echezona, S. C., & Ugwu, C. I. (2023). K-means clustering of electricity consumers using time-domain features from smart meter data. Journal of Electrical Systems and Information Technology, 10(1), 2.

  2. Ukekwe, E. C., Obayi, A. A., Johnson, A., Musa, D. A., & Agbo, J. C. (2025). Optimizing data and voice service delivery for mobile phones based on clients’ demand and location using affinity propagation machine learning. Journal of the Nigerian Society of Physical Sciences, 7(2), 2109.

  3. Ukekwe, E. C., Ezeora, N. J., Obayi, A. A., Asogwa, C. N., Ezugwu, A. O., Adegoke, F. O., Raiyetumbi, J., & Tenuche, B. (2025). Examining the impact of mathematics ancillary courses on computational programming intelligence of computer science students using machine learning techniques. Computer Applications in Engineering Education, 33(4), e70054.

  4. Ukekwe, E. C., Ogbonna, G. U. G., Adegoke, F. O., Okereke, G. E., & Asogwa, C. N. (2023). Clustering Nigeria’s IDP camps for effective budgeting and re-settlement policies using an optimized K-means approach. African Conflict & Peacebuilding Review, 13(2), 60–85.

  5. Okereke, G. E., Azegba, O., Ukekwe, E. C., Echezona, S. C., & Eneh, A. (2023). An automated guide to COVID-19 and future pandemic prevention and management. Journal of Electrical Systems and Information Technology, 10(1), 16.

Madhuri Rao | Machine Learning | Best Researcher Award

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Dr. Madhuri Rao | Machine Learning | Best Researcher Award

Senior Assistant Professor | MIT World Peace University | India

Dr. Madhuri Rao is a dedicated researcher and academic in computer science with expertise in wireless sensor networks, Internet of Things, artificial intelligence, blockchain, and cybersecurity, with her current work focusing on deep learning, cloud security, and healthcare applications. She earned her Ph.D. in Computer Science and Engineering from Biju Patnaik University of Technology, where her research emphasized energy-efficient object tracking in wireless sensor networks. Over her career, she has gained extensive professional experience as a faculty member, academic coordinator, research supervisor, and editorial board member, contributing significantly to both teaching and research. She has authored and co-authored numerous publications in reputed journals and conferences, including IEEE, Springer, Elsevier, and Scopus-indexed platforms, along with patents and book chapters that highlight her innovative approach. Her research interests span interdisciplinary applications of advanced technologies to address challenges in security, healthcare, and sustainability, with ongoing involvement in collaborative projects and international initiatives. She has received recognition through awards such as best paper honors and a best research scholar award, underscoring her contributions to the academic community. Her research skills include problem-solving, experimental design, data analysis, and guiding students at undergraduate, postgraduate, and doctoral levels, coupled with active roles as session chair, track chair, and guest lecturer in international conferences. She is also a life member of professional societies and holds certifications that strengthen her academic profile. Her impactful contributions are reflected in 116 citations and an h-index of 7.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Rao, M., & Kamila, N. K. (2021). Cat swarm optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network. International Journal of System Assurance Engineering and Management, 1–15.

  2. Rao, M., Kamila, N. K., & Kumar, K. V. (2016). Underwater wireless sensor network for tracking ships approaching harbor. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1098–1102. IEEE.

  3. Rao, M., & Kamila, N. K. (2018). Spider monkey optimisation based energy efficient clustering in heterogeneous underwater wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 29(1–2), 50–63.

  4. Chaudhury, P., Rao, M., & Kumar, K. V. (2009). Symbol based concatenation approach for text to speech system for Hindi using vowel classification technique. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1393–1396. IEEE.

  5. Kumar, K. V., Kumari, P., Rao, M., & Mohapatra, D. P. (2022). Metaheuristic feature selection for software fault prediction. Journal of Information and Optimization Sciences, 43(5), 1013–1020.

Shijie Li | Embodied AI | Best Researcher Award

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Dr. Shijie Li | Embodied AI | Best Researcher Award

Scientist | A*STAR Institute for Infocomm Research | Singapore

Dr. Shijie Li is a computer vision researcher with expertise in 3D perception, embodied AI, and vision-language models, contributing to the development of intelligent systems for real-world applications. He earned his Ph.D. in Computer Science from Bonn University under the supervision of Prof. Juergen Gall, following a master’s degree from Nankai University and a bachelor’s degree in Automation Engineering from the University of Electronic Science and Technology of China. His professional experience includes research positions and internships at A*STAR Singapore, Qualcomm AI Research in Amsterdam, Intel Labs in Munich, Alibaba DAMO Academy in China, and Technische Universität München in Germany, showcasing strong international collaborations and applied research expertise. His research interests lie in 3D scene understanding, motion forecasting, vision-language integration, semantic segmentation, and novel view synthesis. He has published in leading journals and conferences such as ICCV, CVPR, IEEE TPAMI, IEEE TNNLS, WACV, BMVC, ICRA, and IROS, reflecting impactful and consistent contributions. His academic excellence has been recognized through scholarships and awards including the Fortis Enterprise Scholarship, National Inspirational Scholarship, First Class Scholarship, and Outstanding Graduate Award. He has also served as a reviewer for top journals and conferences such as IEEE TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, and AAAI, reflecting his active role in the research community. His skills include deep learning, diffusion models, semantic and motion forecasting, vision-language modeling, and embodied AI, with a focus on interdisciplinary innovation. His research impact is reflected in 183 citations, 10 documents, and an h-index of 7.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

Li, S., Abu Farha, Y., Liu, Y., Cheng, M., & Gall, J. (2023). MS-TCN++: Multi-stage temporal convolutional network for action segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 6647–6658.

Chen, X., Li, S., Mersch, B., Wiesmann, L., Gall, J., Behley, J., & Stachniss, C. (2021). Moving object segmentation in 3D LiDAR data: A learning-based approach exploiting sequential data. IEEE Robotics and Automation Letters, 6(4), 6529–6536.

Qiu, Y., Liu, Y., Li, S., & Xu, J. (2020). MiniSeg: An extremely minimum network for efficient COVID-19 segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(11), 13180–13187.

Li, S., Chen, X., Liu, Y., Dai, D., Stachniss, C., & Gall, J. (2021). Multi-scale interaction for real-time LiDAR data segmentation on an embedded platform. IEEE Robotics and Automation Letters, 7(2), 738–745.

Li, S., Zhou, Y., Yi, J., & Gall, J. (2021). Spatial-temporal consistency network for low-latency trajectory forecasting. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10737–10746.

Huaiqu Feng | Agricultural Robotics | Best Researcher Award

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Dr. Huaiqu Feng | Agricultural Robotics | Best Researcher Award

Huaiqu Feng | Zhejiang University | China

Huaiqu Feng is a skilled researcher with expertise in robotics and electromechanical intelligent equipment, focusing on computer vision, deep learning, and image processing for agricultural automation. He holds a Master of Engineering in Agricultural Mechanization Engineering from Northeast Agricultural University and a Bachelor of Engineering in Automation from Hubei Normal University. Throughout his academic and professional career, he has participated in multiple research projects, including provincial science and technology programs and industrial transformation initiatives, demonstrating strong capability in applying AI and robotics to practical agricultural problems. He has contributed to several high-impact publications, patents, and software developments, showcasing his innovative approach and technical proficiency. His professional experience includes leading research teams, mentoring students, and managing projects that integrate advanced technologies into real-world applications. His research interests span robotics, precision agriculture, intelligent equipment, and AI-based image analysis. He is proficient in Matlab for algorithm development, microcontroller programming with STM32, and 3D modeling and simulation using Creo and Pro/E. Huaiqu Feng also actively engages in community and leadership roles through student organizations, innovation competitions, and volunteer initiatives, highlighting his commitment to fostering collaboration and advancing the research community. 426 Citations, 20 Documents, 8 h-index.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., & Yuan, Z. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN. Biosystems Engineering, 184, 1-23.

  2. Zhao, G., Quan, L., Li, H., Feng, H., Li, S., Zhang, S., & Liu, R. (2021). Real-time recognition system of soybean seed full-surface defects based on deep learning. Computers and Electronics in Agriculture, 187, 106230.

  3. Li, D., Li, B., Long, S., Feng, H., Xi, T., Kang, S., & Wang, J. (2023). Rice seedling row detection based on morphological anchor points of rice stems. Biosystems Engineering, 226, 71-85.

  4. Wei, C., Li, H., Shi, J., Zhao, G., Feng, H., & Quan, L. (2022). Row anchor selection classification method for early-stage crop row-following. Computers and Electronics in Agriculture, 192, 106577.

  5. Li, D., Li, B., Long, S., Feng, H., Wang, Y., & Wang, J. (2023). Robust detection of headland boundary in paddy fields from continuous RGB-D images using hybrid deep neural networks. Computers and Electronics in Agriculture, 207, 107713.

Tuğba Özge Onur | Image Reconstruction | Best Researcher Award

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Assoc. Prof. Dr. Tuğba Özge Onur | Image Reconstruction | Best Researcher Award

Associate Professor | Zonguldak Bülent Ecevit University | Turkey

Assoc. Prof. Dr. Tuğba Özge Onur is a distinguished researcher specializing in signal processing, image reconstruction, and optimization. She earned her Ph.D. in electrical and electronics engineering from a leading university, where she developed a strong foundation in computational imaging and algorithm design. Her professional experience includes leading research projects, coordinating international collaborations, and mentoring students in both academic and applied research settings. Her research interests span computer vision, optimization techniques, and advanced signal processing methods, with a focus on developing innovative solutions for real-world challenges. She possesses a diverse set of research skills, including algorithm development, data analysis, experimental design, and implementation of complex computational models. She is actively engaged in the scientific community through professional memberships and collaborative initiatives. Her work has been widely recognized and published in reputed journals and conferences, demonstrating both the depth and impact of her contributions. Her commitment to advancing knowledge, mentoring emerging researchers, and participating in collaborative projects underscores her influence in the field. 98 Citations, 23 Documents, 6 h-index.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Onur, T. Ö. (2022). Improved image denoising using wavelet edge detection based on Otsu’s thresholding. Acta Polytechnica Hungarica, 19(2), 79–92.

  2. Onur, Y. A., İmrak, C. E., & Onur, T. Ö. (2017). Investigation on bending over sheave fatigue life determination of rotation resistant steel wire rope. Experimental Techniques, 41(5), 475–482.

  3. Narin, D., & Onur, T. Ö. (2022). The effect of hyperparameters on the classification of lung cancer images using deep learning methods. Erzincan University Journal of Science and Technology, 15(1), 258–268.

  4. Kaya, G. U., & Onur, T. Ö. (2022). Genetic algorithm based image reconstruction applying the digital holography process with the Discrete Orthonormal Stockwell Transform technique for diagnosis of COVID-19. Computers in Biology and Medicine, 148, 105934.

  5. Onur, T. (2021). An application of filtered back projection method for computed tomography images. International Review of Applied Sciences and Engineering, 12(2), 194–200.