Ean Teng Khor | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Ean Teng Khor
National Institute of Education, Nanyang Technological University, Singapore
Emmanuel Omeje
Affiliation National Institute of Education, Nanyang Technological University
Country Singapore
Scopus 54393424800
Documents 30
Citations 176
h-index 6
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0001-6817-9332
Google Scholar Ra1HNaIAAAAJ

Ean Teng Khor is an education researcher, lecturer, and academic leader whose work integrates artificial intelligence, learning analytics, educational data mining, and technology-enhanced learning. Based at the National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore, she has contributed to the advancement of AI-enabled educational innovation through research, teaching, academic leadership, and externally funded projects. Her research portfolio demonstrates sustained engagement with personalised learning systems, educational predictive analytics, generative artificial intelligence, conversational agents, and human–AI collaboration for learning.[1]

Abstract

This article presents an academic profile of Ean Teng Khor and evaluates her scholarly contributions in the fields of Artificial Intelligence in Education, Learning Analytics, Educational Data Mining, and Technology-Enhanced Learning. Through interdisciplinary research, externally funded projects, academic leadership, and international engagement, she has contributed to the development of evidence-based educational innovations designed to support personalised learning, educator development, and digital transformation across educational sectors. Her work demonstrates a sustained focus on integrating advanced computational methods with learning sciences to improve educational outcomes and learner experiences.[1]

Keywords

Artificial Intelligence in Education; Learning Analytics; Educational Data Mining; Generative AI; Conversational AI; Personalised Learning; Machine Learning; Educational Technology; Adaptive Learning; Human–AI Collaboration.

Introduction

The increasing integration of artificial intelligence into educational environments has created opportunities for innovative approaches to teaching, learning, assessment, and learner support. Within this evolving landscape, Ean Teng Khor has established a research profile focused on leveraging data-driven methods and intelligent technologies to support educational improvement. Her academic work spans generative AI, learning analytics, educational data mining, predictive modelling, adaptive learning systems, and technology-enhanced pedagogy.[1]

Her contributions have been developed through collaborations with educators, policymakers, researchers, and institutions across multiple educational sectors. This combination of technical expertise and educational insight has enabled the translation of research findings into practical educational applications that address contemporary challenges in teaching and learning.[1]

Research Profile

Ean Teng Khor earned a Bachelor of Information Technology (Honours) in 2005 and a Master of Science in Information Technology in 2007 from Multimedia University, followed by a Doctor of Philosophy from Universiti Sains Malaysia in 2015. Her academic appointments include positions at Wawasan Open University, East Asia Institute of Management, Nanyang Technological University, and the National Institute of Education.[1]

Her research expertise encompasses AI-enabled learning design, conversational AI, educational predictive analytics, personalised learning systems, machine learning applications in education, digital learning ecosystems, and data literacy development. She currently serves as Founding Leader of the Learning Analytics Special Interest Group at NIE and participates in multiple editorial, reviewing, and conference leadership roles within the international learning sciences community.[1]

Research Contributions

A defining characteristic of Ean Teng Khor’s research is the application of artificial intelligence and analytics to support personalised and adaptive learning. As Principal Investigator and Co-Principal Investigator, she has led and contributed to multiple competitive research projects funded by organizations including Singapore’s Ministry of Education, SkillsFuture Singapore, and AI Singapore.[1]

  • Development of AI-mediated learning systems using retrieval-augmented generation and intelligent interventions.
  • Research on teacher data literacy and evidence-informed educational decision-making.
  • Learning analytics frameworks for workplace and lifelong learning environments.
  • Adaptive AI-supported language learning and multilingual tutoring technologies.
  • Educational predictive modelling for identifying and supporting at-risk learners.

Beyond funded research, she has contributed to academic community development through editorial board memberships, peer review activities, keynote presentations, conference leadership, educator professional development initiatives, and academic assessment roles across international institutions and scholarly organizations.[1]

Publications

Ean Teng Khor’s publication record includes journal articles and conference papers addressing learning analytics, educational technology adoption, microlearning, predictive modelling, and personalised learning. Several publications have appeared in internationally recognized journals and have contributed to scholarly discussions surrounding data-informed educational practice and AI-supported learning environments.[2][3][4][5]

Research Impact

According to the Scopus author profile, Ean Teng Khor has accumulated 176 citations across indexed publications with an h-index of 6. These metrics reflect measurable scholarly engagement within fields related to educational technology, learning analytics, and artificial intelligence in education.[1]

Her impact extends beyond publication metrics through successful supervision of student researchers, leadership of collaborative research initiatives, development of AI-enabled educational tools, and professional engagement with educators and policymakers. The practical orientation of her research has supported knowledge transfer between academic scholarship and educational practice.[1]

Award Suitability

Ean Teng Khor’s academic profile demonstrates several characteristics commonly associated with recognition through research and innovation awards. These include sustained scholarly productivity, leadership of externally funded projects, contributions to educational innovation, international academic service, successful mentorship of emerging researchers, and recognition through multiple research awards and distinctions.[1]

Her research agenda aligns with contemporary priorities in education and digital transformation by addressing responsible applications of artificial intelligence, personalised learning, educator capacity building, and data-informed decision-making. The combination of research excellence, practical impact, and academic leadership provides a substantive basis for consideration within academic recognition programs such as the Top Teachers Awards.[1]

Conclusion

Ean Teng Khor has established a multidisciplinary academic profile at the intersection of artificial intelligence, learning sciences, educational technology, and analytics. Through research leadership, funded projects, scholarly publications, teaching innovation, and service to the international academic community, she has contributed to advancing understanding of how intelligent technologies can support learning and educational improvement. Her record of scholarship and professional engagement reflects an ongoing commitment to evidence-based educational innovation and the responsible integration of AI within learning environments.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Ean Teng Khor, Author ID 54393424800. Scopus. https://www.scopus.com/pages/authors/54393424800
  2. Khor, E. T., & K. M. (2023). A systematic review of the role of learning analytics in supporting personalized learning. Education Sciences, 14(1), 51. DOI: https://doi.org/10.3390/educsci14010051
  3. Teng, K. E. (2014). An Analysis of ODL Student Perception and Adoption Behaviour using Technology Acceptance Model. International Review of Research in Open and Distance Learning, 15(6), 275–288. DOI: https://doi.org/10.19173/irrodl.v15i6.1732
  4. Puah, S., Khalid, M. I. S., Looi, C. K., & Khor, E. T. (2022). Investigating working adults’ intentions to participate in microlearning using the decomposed theory of planned behaviour. British Journal of Educational Technology, 53(2), 367–390. DOI: https://doi.org/10.1111/bjet.13170
  5. Khor, E. T. (2022). A data mining approach using machine learning algorithms for early detection of low-performing students. The International Journal of Information and Learning Technology, 39(2), 122–132. DOI: https://doi.org/10.1108/IJILT-09-2021-0144

Huili Zhang | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Huili Zhang
Shanghai University, China

Huili Zhang
Affiliation Shanghai University
Country China
Scopus ID 58607120700
Documents 17
Citations 223
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0002-3336-1756

The Innovative Research Award recognizes researchers whose scholarly activities demonstrate originality, methodological rigor, and measurable impact within their respective disciplines. Huili Zhang of Shanghai University has established a research profile centered on artificial intelligence, medical image analysis, radiomics, and intelligent diagnostic systems. Through interdisciplinary collaboration and the application of advanced machine learning methods to healthcare challenges, Zhang has contributed to the development of computational frameworks that support disease detection, classification, and clinical decision-making.[1]

Abstract

Huili Zhang’s research integrates artificial intelligence and medical imaging technologies to improve diagnostic accuracy and predictive modeling in healthcare. Her publications address multimodal ultrasound analysis, radiomics, deep learning, and knowledge distillation techniques, emphasizing clinically relevant solutions for cancer diagnosis and treatment evaluation. The body of work demonstrates a consistent focus on translating computational innovation into practical medical applications.[2]

Keywords

Artificial Intelligence, Deep Learning, Medical Imaging, Radiomics, Ultrasound Diagnostics, Knowledge Distillation, Computer-Aided Diagnosis, Healthcare Analytics.

Introduction

The convergence of artificial intelligence and healthcare has created opportunities for improved diagnostic efficiency and personalized treatment strategies. Within this evolving landscape, Huili Zhang has contributed to research that applies machine learning and image-based analytics to complex clinical problems. Her studies demonstrate the growing importance of data-driven methodologies in modern medical practice.[3]

Research Profile

As a researcher affiliated with Shanghai University, Zhang has developed expertise in computer science and artificial intelligence with a strong emphasis on biomedical applications. Her scholarly record includes peer-reviewed publications focused on multimodal imaging, radiomics-based prediction models, and intelligent healthcare systems. The available bibliometric indicators demonstrate growing academic influence across interdisciplinary domains.[1]

Research Contributions

  • Development of multi-view and multimodal deep learning frameworks for liver cancer diagnosis using ultrasound imaging.
  • Advancement of generalized knowledge distillation approaches for medical image interpretation.
  • Creation of MRI-based radiomics models for differentiating spinal multiple myeloma from metastatic lesions.
  • Application of dual-modal ultrasound and molecular data integration for predicting chemotherapy response in breast cancer patients.
  • Research into deep learning radiomics for distinguishing benign and malignant breast conditions.

Publications

  1. Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities (2026).
  2. Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images (2024).
  3. Radiomics Model Based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study (2024).
  4. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer (2023).
  5. Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer (2023).

Research Impact

The research output attributed to Zhang reflects a commitment to improving diagnostic workflows through advanced computational techniques. By combining machine learning, radiomics, and multimodal imaging data, her work contributes to enhanced disease characterization and supports evidence-based clinical decision-making. Citation activity and publication placement indicate recognition within the scientific community.[4]

Award Suitability

Huili Zhang’s research portfolio aligns with the objectives of the Innovative Research Award due to its interdisciplinary nature, methodological innovation, and relevance to healthcare technology. The integration of artificial intelligence with clinical imaging illustrates a forward-looking approach that addresses contemporary challenges in medical diagnostics while contributing to scientific advancement.[5]

Conclusion

Huili Zhang represents a growing cohort of researchers leveraging artificial intelligence to transform healthcare diagnostics. Her contributions to medical imaging, radiomics, and deep learning demonstrate both scholarly rigor and practical relevance. These achievements support recognition through the Innovative Research Award and reflect continued potential for future scientific impact.

References

  1. Elsevier. (n.d.). Scopus author details: Huili Zhang, Author ID 58607120700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58607120700
  2. Zhang, H. (2026). Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities.
    DOI: https://doi.org/10.1016/j.engappai.2026.115252
  3. Zhang, H. (2024). Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images.
    DOI: https://doi.org/10.1016/j.ipm.2024.103855
  4. Zhang, H. (2024). Radiomics Model based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study.
    DOI: https://doi.org/10.1016/j.jbo.2024.100599
  5. Zhang, H. (2023). Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
    DOI: https://doi.org/10.1016/j.acra.2023.03.036
  6. Zhang, H. (2023). Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer.
    DOI: https://doi.org/10.3233/CH-221608

Jisheng Dang | Computer Science | Research Excellence Award

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Research Excellence Award

Jisheng Dang
Professor, Lanzhou University, China
Jisheng Dang
Affiliation Lanzhou University
Country China
Scopus ID 57216844335
Documents 36
Citations 207
h-index 10
Subject Area Computer Science
Event Top Teachers Awards
IEEE Xplore 37088932779

Jisheng Dang is a Chinese computer scientist and academic researcher affiliated with the School of Information Science and Engineering at Lanzhou University. His research primarily focuses on multimodal learning, video understanding, computer vision, video object segmentation, and embodied intelligence. He has contributed to several peer-reviewed publications in internationally recognized journals and conferences, including IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IJCAI, AAAI, and ICME.[1] His scholarly activities additionally include professional reviewing services for major international conferences and journals such as IEEE TPAMI, CVPR, ICML, NeurIPS, ACM MM, and ICLR.[2]

Abstract

Jisheng Dang is a faculty member at Lanzhou University specializing in computer vision, multimodal large language models, and video understanding. His research focuses on video object segmentation, adaptive memory networks, and intelligent visual reasoning systems. He has published scholarly work in internationally recognized journals and conferences, contributing to advancements in multimodal artificial intelligence and efficient video analysis technologies.[3] The article further evaluates his academic contributions and professional suitability for recognition within the framework of the Top Teachers Awards initiative.

Keywords

Computer Vision, Multimodal Learning, Video Understanding, Video Object Segmentation, Artificial Intelligence, Long-Video Understanding, Adaptive Memory Networks, Large Language Models, Intelligent Transportation Systems, Video Data Processing, IEEE Transactions on Image Processing, Neural Networks.

Introduction

The rapid advancement of artificial intelligence and multimodal machine learning has increased the demand for efficient video understanding systems. In this field, Jisheng Dang has contributed to research on long-video processing, adaptive memory modeling, and unified video segmentation frameworks, supporting developments in computer vision and intelligent multimedia analysis.[4]

Jisheng Dang completed his doctoral research at Sun Yat-sen University under Professor Jianhuang Lai and Professor Huicheng Zheng, and later served as a Research Fellow at the NExT++ Laboratory, National University of Singapore, under Professor Tat-Seng Chua. These experiences strengthened his international collaborations in video analysis and multimodal intelligence research.[2]

Research Profile

Jisheng Dang is a tenured Associate Professor at the School of Information Science and Engineering, Lanzhou University, China. His research focuses on multimodal learning, video understanding, video object segmentation, embodied intelligence, and multimodal large language models, with additional contributions in adaptive memory networks and spatiotemporal information processing.[1]

Jisheng Dang actively serves as a reviewer for leading journals and conferences, including IEEE TPAMI, CVPR, ICML, NeurIPS, AAAI, and IJCAI. He has also collaborated with prominent institutions such as the National University of Singapore, Tsinghua University, and Peking University, along with industry partners including Tencent and Huawei.[2]

  • Research Areas: Computer Vision, Multimodal Large Language Models, Video Object Segmentation
  • Professional Reviewing Experience Across International AI Conferences and Journals

Research Contributions

Jisheng Dang has contributed to efficient and scalable frameworks for video segmentation and multimodal reasoning. His research on adaptive memory systems and spatio-temporal propagation methods improves computational efficiency in long-video processing and video analysis.[3]

His scholarly contributions include the proposal of innovative frameworks such as TW-GRPO, DeSa2VA, and MUPA, designed to improve segmentation accuracy and contextual reasoning in multimodal systems. These approaches attempt to bridge theoretical machine learning research with practical industrial applications involving intelligent transportation systems and advanced multimedia analysis.[5]

  • Research on unified video segmentation frameworks for accurate and efficient video object tracking.
  • Development of quality-guided dynamic memory approaches for long-video understanding systems.
  • Investigation of hallucination mitigation techniques in large video-language models.
  • Contributions to multimodal reasoning and adaptive contextual memory architectures.
  • Participation in interdisciplinary collaborations linking AI theory with industrial applications.

Publications

Selected publications associated with Jisheng Dang include journal articles and conference papers published in IEEE Transactions on Image Processing, IEEE ICME proceedings, and Neural Networks. Several publications focus on efficient video segmentation, dynamic memory networks, and multimodal understanding systems.[3]

  1. Dang, J., Zheng, H., Guo, Y., Lai, J., Hu, B., & Chua, T.-S. (2026). Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation. IEEE Transactions on Image Processing, Volume 35.
  2. Dang, J., Zheng, H., Chen, Z., Li, Z., Guo, Y., & Chua, T.-S. (2026). Fast Track Anything With Sparse Spatio-Temporal Propagation for Unified Video Segmentation. IEEE Transactions on Image Processing, Volume 35.
  3. Wang, B., Wen, F., Dang, J., He, H., Wang, X., Zhu, N., & Weng, J. (2025). Mitigating Hallucination in Large Video-Language Models with Injected Semantics. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME).
  4. Wang, B., Jiao, J., Dang, J., Jiang, Q., Lin, J., Chen, Z., Wang, T., & Yang, J. (2025). Quality-Guided Dynamic Memory for LLMs-based Long-Term Video Understanding. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME).
  5. Zhang, L., Dang, J., Zhang, S., Gan, W., Wang, J., Hu, B., Feng, G., & Peng, H. (2026). Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition. Neural Networks, Volume 198, Article 108609.

Research Impact

The research impact associated with Jisheng Dang is reflected through peer-reviewed publications, scholarly citations, interdisciplinary collaborations, and professional service activities. His work has contributed to research discussions surrounding multimodal large language models, long-video understanding, and scalable segmentation systems.[1]

His publications in IEEE Transactions on Image Processing and conference proceedings have contributed to ongoing advancements in efficient visual processing architectures and multimodal reasoning systems. The application relevance of his research additionally extends to intelligent transportation systems, automated visual understanding, and multimedia analytics.[4]

  • Peer-reviewed publications in internationally indexed journals and conferences.
  • International collaborations with academic and industrial institutions.
  • Reviewer contributions to high-impact AI and computer vision venues.
  • Research contributions in video understanding and multimodal AI systems.
  • Academic recognition through thesis awards and institutional honors.

Award Suitability

Jisheng Dang has made sustained contributions to computer vision and multimodal machine learning through scholarly publications, collaborative research, and academic service. His publication record and participation in leading international conferences and journals reflect active engagement in the global artificial intelligence research community.[2]

His research in video segmentation, adaptive memory networks, and multimodal understanding systems reflects strong contributions to research excellence and academic innovation. His scholarly achievements and professional collaborations support his recognition within the Top Teachers Awards program.[5]

Conclusion

Jisheng Dang has established a research profile centered on multimodal learning, computer vision, and video understanding technologies. Through scholarly publication, international collaboration, and professional academic service, he has contributed to advancements in video segmentation frameworks and adaptive memory systems for artificial intelligence applications. His academic activities, publication record, and interdisciplinary research collaborations collectively reflect a sustained engagement with contemporary developments in artificial intelligence and multimedia computing research.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Jisheng Dang, Author ID 57216844335. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57216844335
  2. IEEE. (n.d.). IEEE Xplore Author Profile: Jisheng Dang. IEEE Xplore Digital Library. https://ieeexplore.ieee.org/author/37088932779
  3. Dang, J., Zheng, H., Guo, Y., Lai, J., Hu, B., & Chua, T.-S. (2026). Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation. IEEE Transactions on Image Processing, Volume 35. https://doi.org/10.1109/TIP.2025.3649360
  4. Dang, J., Zheng, H., Chen, Z., Li, Z., Guo, Y., & Chua, T.-S. (2026). Fast Track Anything With Sparse Spatio-Temporal Propagation for Unified Video Segmentation. IEEE Transactions on Image Processing, Volume 35. https://doi.org/10.1109/TIP.2025.3649365
  5. Zhang, L., Dang, J., Zhang, S., Gan, W., Wang, J., Hu, B., Feng, G., & Peng, H. (2026). Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition. Neural Networks, Volume 198, Article 108609. https://doi.org/10.1016/j.neunet.2026.108609

Sema Servi | Computer Science | Best Research Article Award

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Assist. Prof. Dr. Sema Servi | Computer Science | Best Research Article Award

Selçuk University | Turkey

Asst. Prof. Dr. Sema Servi is a researcher in computer engineering with a strong foundation in applied mathematics, specializing in machine learning, artificial intelligence, and numerical methods for complex problem solving. Her work focuses on data-driven approaches, including clustering algorithms, optimization techniques, and computer vision applications in healthcare and engineering. She has contributed to interdisciplinary research spanning digital competence analysis, bioinformatics, and intelligent systems. Asst. Prof. Dr. Sema Servi actively supervises postgraduate research and advises innovative, technology-driven projects supported by national programs. She has a solid research impact with 62 Scopus citations, 15 indexed documents, and an h-index of 5.

Citation Metrics (Scopus)

100
80
60
40
20
0

Citations
62

h-index
5

Documents
15

Citations

h-index

Documents

Featured Publications

Weidong Ji | Computer Science | Editorial Board Member

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Dr. Weidong Ji | Computer Science | Editorial Board Member

Harbin Normal University | China

Dr. Weidong Ji is a distinguished Chinese scholar known for his innovative contributions to artificial intelligence in education, learning analytics, and intelligent recommendation technologies, with a strong research presence reflected across international journals and conferences. His work integrates machine learning, knowledge graphs, deep learning models, and cognitive theories to enhance personalized learning, online education systems, student engagement assessment, and data-driven educational decision-making. With a Scopus record of 39 documents, 102 citations from 100 citing documents, and an h-index of 6, Dr. Ji’s scholarly influence continues to grow as his recent works advance emerging domains such as quantum-constructivism-based knowledge tracing, lightweight human–computer interaction models, and user-preference-driven recommendation algorithms. Google Scholar metrics (if available) would further extend his citation visibility, demonstrating the expanding global use of his models in adaptive learning, course recommendations, and real-time student behavior analysis. He also contributes to the academic community through participation in peer-review processes and editorial activities that support the development of high-quality research in AI-driven educational technology. Dr. Ji’s recent publications showcase cutting-edge computational frameworks such as neural knowledge graph reasoning, lightweight vision models for engagement analytics, and personalized prediction architectures for sparse learning environments. His contributions position him as an emerging leader at the intersection of educational psychology and computational intelligence, emphasizing practical applicability, algorithmic efficiency, and innovative pedagogical design, demonstrating excellence aligned with this award category as an Editorial Board Member.

Publication Profile

Scopus

Featured Publications

  • Authors unavailable. (2025). Knowledge graph convolutional networks with user preferences for course recommendation. Scientific Reports.

  • Authors unavailable. (2025). CQSA-KT: Research on personalized knowledge tracing based on quantum-constructivism in sparse learning environments. Knowledge Based Systems.

  • Authors unavailable. (2025). LightNet: A lightweight head pose estimation model for online education and its application to engagement assessment. Journal of King Saud University Computer and Information Sciences.

Guangxuan Song | Neural Networks | Best Researcher Award

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Feudjio Ghislain | Deep Learning | Best Research Article Award

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Mr. Feudjio Ghislain | Deep Learning | Best Research Article Award

Academician at University of Dschang, Cameroon.

Feudjio Ghislain, born on March 24, 1986, in Batcham, Cameroon, is a dedicated educator and researcher in electronics and applied physics. He is a Technical and Professional Education Teacher at Government Technical High School of Bangou and a Part-time Lecturer at Fotso Victor University Institute of Technology, University of Dschang. With a strong background in electronics, he has mentored students in various technical domains and supervised numerous academic projects. His research focuses on image classification, segmentation, deep learning, and machine learning. Ghislain has actively participated in multiple academic conferences and seminars. His passion for education, research, and technological advancement drives his contributions to academia and industry. Beyond academia, he is actively involved in community development initiatives and enjoys reading, music, and sports.

Professional Profiles📖

Scopus 

Education 🎓

Feudjio Ghislain is currently in his third year of a Doctorate/Ph.D. in Physics, specializing in Electronics at the University of Dschang, Cameroon. He holds a Master of Science in Physics (2019–2020) from the same university, where he specialized in Electronics with a ‘Good’ grade. His academic journey includes a DIPET II (2011) from HTTTC Douala, University of Douala, specializing in Electronics, a Bachelor of Science in Physics (2007) from the University of Dschang with a ‘Fairly good’ grade, and a Baccalaureate in Science (2004) from Lycée de Batcham. His academic progression showcases a consistent focus on electronics and applied physics, equipping him with in-depth expertise in his field.

work Experience💼

Feudjio Ghislain has been a part-time and professional teacher at the Fotso Victor University Institute of Technology (IUT-FV of Bandjoun) at the University of Dschang since 2018, teaching courses in Electrotechnics, Electronic Systems Maintenance, and Electrical Engineering. His subjects range from electronic construction to telecommunications and microcontroller applications. Since 2012, he has also served as an Electronics Teacher at the Government Technical High School of Bangou, teaching digital circuits, solar energy, and maintenance troubleshooting. His responsibilities extend to supervising final-year projects and serving on examination committees. Between 2020 and 2022, he was a part-time lecturer at the Evangelical University of Cameroon, teaching Biomedical Engineering. His expertise in teaching spans various educational levels, contributing significantly to the professional and technical development of students.

Research Focus

Feudjio Ghislain‘s research interests encompass image classification and segmentation, deep learning, and machine learning. His Ph.D. research focuses on the real-time analysis of medical images using second-generation wavelets, under the guidance of Professor TCHIOTSOP Daniel at the University of Dschang. His Master’s thesis explored embedded image processing systems for medical diagnostics, while his DIPET II research delved into spectral texture analysis using wavelets. His projects reflect a strong inclination toward practical and impactful applications of artificial intelligence in medical imaging and electronics. Through participation in conferences and research collaborations, he remains at the forefront of innovation in AI-based image processing.

Awards & Honors🏆 

Feudjio Ghislain has been recognized for his outstanding contributions to technical education and research. His dedication to advancing electronics education has earned him several professional affiliations, including membership in the SciPinion Scientific Community since 2024. He has also played a significant role in academic committees, acting as a referee for esteemed international journals like Heliyon and Computers in Biology and Medicine. His influence extends beyond the classroom, where he has contributed to organizing and evaluating national examinations. His research and teaching contributions have solidified his reputation as a committed educator and researcher in applied electronics and image processing.

Conclusion✅

Feudjio Ghislain is a strong candidate for the Best Researcher Article Award due to his expertise in electronics, image processing, and machine learning, alongside his teaching, peer-review, and project supervision roles. However, expanding his publication record in indexed journals, securing research funding, and increasing global collaborations would further strengthen his application for such a prestigious award.

Publications to Noted 📚

Title: “An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model”

Authors: Feudjio Ghislain​, Saha Tchinda Beaudelaire​, Tchiotsop Daniel

Kil soo Lee | Intelligent Control | Best Researcher Award

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Dr. Kil soo Lee | Intelligent Control | Best Researcher Award

Principal Researcher at Korea Construction Equipment Technology Institute/Automation System Design Research Group , South Korea

Dr. Kilsoo Lee is a distinguished researcher in mechanical engineering, currently serving as the Group Leader and Principal Researcher at the Korea Construction Equipment Technology Institute (KOCETI). His expertise spans intelligent construction machinery, robotics, and autonomous vehicle technology. Dr. Lee has played a pivotal role in numerous research projects, leading innovations in electric and construction machinery. He has also been a vital contributor to international conferences and professional journals. A dedicated professional, he is a member of the Korean Society of Mechanical Engineers, the Institute of Control, Robotics and Systems, and the Korean Institute of Navigation and Port Research. He has received multiple accolades for his contributions, including awards at autonomous vehicle competitions. His research aims to enhance intelligent construction equipment, furthering advancements in automation and safety technologies.

professional profiles📖

Scopus Profile

Education 🎓

Dr. Kilsoo Lee obtained his PhD in Engineering from Pusan National University, where he specialized in mechanical engineering and automation systems. His academic journey began with undergraduate and master’s degrees in mechanical engineering, during which he developed a strong foundation in robotics and control systems. He later worked as a junior researcher at the Research Institute of Mechanical Technology (RIMT) and the Mechanical, Architectural, and Traffic Engineering Research Information Center (MATERIC). His academic training has equipped him with expertise in intelligent machinery and vehicle automation. Through his doctoral studies, Dr. Lee focused on the integration of advanced control mechanisms, laying the groundwork for his future contributions in autonomous and intelligent construction equipment. His educational background has been instrumental in shaping his career as a leader in automation research and system design.

work Experience💼

Dr. Lee has accumulated extensive experience in engineering research and leadership. Since 2014, he has been leading research at KOCETI’s Automation System Design Research Group, focusing on construction and agricultural machinery innovations. Prior to this, he served as a postdoctoral researcher at Pusan National University (2013–2014), where he advanced research on autonomous vehicle technologies. From 2010 to 2013, he was the CEO and CTO of e-Metro Technology, Inc., where he spearheaded projects in intelligent transportation systems. His involvement in major research projects includes developing machine control kits for excavators and autonomous control mechanisms for construction machinery. Dr. Lee’s expertise has also been recognized in the defense sector, where he has contributed to unmanned vehicle technology. His career reflects a commitment to advancing intelligent and automated engineering solutions.

Awards and Honors 

Dr. Kilsoo Lee has received multiple awards recognizing his excellence in engineering research and technological innovation. Notably, he led Pusan National University’s team at the 1st and 2nd Autonomous Vehicle Competitions, securing the Challenge Award for successfully completing all courses. His work in developing intelligent construction machinery has earned him recognition in industry and academia. He has also been honored for his contributions to automation and robotics research through awards from professional organizations, including the Korean Society of Mechanical Engineers. His patents and research contributions have been acknowledged as significant advancements in safety and control mechanisms for machinery. Additionally, his work in autonomous vehicle technology has been cited in international conferences, further solidifying his reputation as a leader in the field. His achievements continue to drive advancements in automation and intelligent machinery.

Research Focus

Dr. Lee’s research focuses on the development of intelligent control systems for construction machinery, electric vehicles, and autonomous systems. His projects aim to enhance efficiency, safety, and automation in industrial machinery. A key area of his research is the application of artificial intelligence in machine control, particularly for excavators and autonomous vehicles. He has contributed to the design of robust lateral controllers and functional safety systems for unmanned vehicles. His work also extends to the integration of LiDAR-based navigation for transport robots, improving object detection and autonomous movement capabilities. Through his research, Dr. Lee aims to advance automation technologies that optimize machinery performance while ensuring operational safety. His efforts in developing novel control methodologies contribute significantly to the evolving landscape of intelligent engineering solutions.

 

Conclusion✅

Dr. Kilsoo Lee is a highly qualified and impactful researcher with significant contributions to automation, intelligent construction systems, and control engineering. His leadership, strong publication record, patents, and national project success make him a strong contender for the Best Researcher Award. By expanding international collaborations, industry partnerships, and citation visibility, he can further solidify his global reputation in the field.

 

📚Publications to Noted

 

Study on the Near-Distance Object-Following Performance of a 4WD Crop Transport Robot: Application of 2D LiDAR and Particle Filter

Authors: Eun-Seong Pak, Byeong-Hun Kim, Kil-Soo Lee, Yong-Chul Cha, Hwa-Young Kim

Year: 2025

“On the Synthesis of an Underwater Ship Hull Cleaning Robot System”

Authors: Man Hyung Lee, Kil Soo Lee, Won Chul Park, Seok Hee Lee, Sinpyo Hong, Hyung Gyu Park, Jae Won Choi, Ho Hwan Chun

Year: 2012

“Lateral Controller Design for an Unmanned Vehicle via Kalman Filtering”

Authors: Man Hyung Lee, Kil Soo Lee, Hyung Gyu Park, Young Chul Cha, Dong Jin Kim, Byung Il Kim, Sinpyo Hong, Ho Hwan Chun

Year: 2012

“Implementation of Electric Power Assisted Steering System via Hardware-In-Loop-Simulation System”

Authors: Kil Soo Lee, Hyung Gyu Park, Myoung Kook Kim, Jung Hyen Park, Man Hyung Lee

Year: 2011

“4WS Unmanned Vehicle Lateral Control Using PUS and Gyro Coupled by Kalman Filtering”

Authors: Kil Soo Lee, Hyung Gyu Park, Man Hyung Lee

Year: 2011

“Robust Lateral Controller for an Unmanned Vehicle via a System Identification Method”

Authors: Man Hyung Lee, Kil Soo Lee, Hyung Gyu Park, Ho Hwan Chun, Jae Heon Ryu

Year: 2010