Sema Servi | Computer Science | Best Research Article Award

Published on 09/04/202609/04/2026 by TOT Awards

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

Citations
62

h-index

Documents
15

Citations

h-index

Documents

Scopus Google Scholar

Featured Publications

Reduced differential transform method for solving Klein Gordon equations
– Proceedings of the World Congress on engineering, 2011

The approximate solution of high-order linear fractional differential equations with variable coefficients in terms of generalized Taylor polynomials
– Mathematical and Computational Applications, 2011

A Lévy flight based chaotic black winged kite algorithm for solving optimization problems
– Scientific Reports, 2025

A hypothetical method for calculation of the access point, direction angle and access angle for percutaneous nephrolithotomy
– Medical hypotheses, 2019

Reduced differential transform method for improved Boussinesq equation
– AIP Conference Proceedings, 2015

Weidong Ji | Computer Science | Editorial Board Member

Published on 19/11/202519/11/2025 by TOT Awards

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

Published on 26/06/202526/06/2025 by TOT Awards

Mr. Guangxuan Song | Neural Networks | Best Researcher Award

Ph.D. Candidate at University of Science and Technology Beijing, China

Mr. Guangxuan Song, is an accomplished Ph.D. student at the University of Science and Technology Beijing (USTB), specializing in knowledge graphs and graph neural networks applied to scientific data. His research focuses on applying advanced AI techniques to complex, noisy real-world datasets, particularly in materials science and corrosion prediction. He is proficient in Python, PyTorch, Torch Geometric, and LangChain, and also skilled in C++ and embedded systems. With a solid background in both theoretical and applied research, Mr. Song has developed web platforms, published cutting-edge papers, and participated in national projects like China’s Environmental Corrosion Platform. He is recognized for his leadership and innovation, having won national awards, including the Baidu Lingjing Developer title and multiple scholarships. Outside academia, he is passionate about photography, running, and mentorship. Mr. Song exemplifies a new generation of interdisciplinary researchers blending science, data, and innovation.

Professional Profile

Google Scholar

Education

Guangxuan Song began his academic journey at the University of Science and Technology Beijing (USTB), earning his Bachelor’s degree in Automation with distinction. During his undergraduate studies, he demonstrated exceptional academic performance and leadership, earning the National Scholarship in 2017 and recognition as the Beijing Outstanding Student in 2019. He continued at USTB for his Ph.D. in Control, focusing on cutting-edge AI research in scientific knowledge systems. His Ph.D. studies emphasized data-driven solutions for complex challenges in materials science and corrosion research, integrating machine learning, knowledge graph embeddings, and uncertainty modeling. Throughout his academic tenure, he participated in national science projects and received the National Scholarship for Ph.D. students in 2022. His education not only emphasized academic rigor but also highlighted leadership, public speaking, and collaborative research, evidenced by roles like Vice President of the Youth League Committee and other student leadership positions.

Work Experience

Guangxuan Song has been deeply involved in national and institutional research projects in China. He contributed to the National Environmental Corrosion Platform of China and the “Belt and Road” Corrosion Big Data Platform, building data analysis frameworks and web platforms supporting scientific infrastructure. He has also played an integral role in projects for the State Grid Corporation of China, using AI and data mining to understand atmospheric corrosion in power grid materials. Mr. Song has led efforts in developing 3 major web platforms, applied for 6 invention patents, and earned 7 software copyrights. He has proposed novel AI models (MPNNs, TSNet, numerical-semantic embeddings), setting new benchmarks in noisy data modeling and metal property prediction. His professional experience is not limited to technical work—he also led interdisciplinary collaborations, mentored peers, and integrated technical outcomes with public policy and industrial needs, proving his capability in both academic and practical environments.

🏆 Awards & Honors

Guangxuan Song has been widely recognized for academic excellence and innovative research. His accolades include the National Scholarship (2017, 2022), Dean’s Medal (2019), and Beijing Outstanding Graduate (2020). He received First Prize in the iCAN International Entrepreneurship Competition and First Prize in the “Internet+” Innovation Competition for his groundbreaking technological innovations. USTB honored him as an Inspirational Figure and Science and Technology Star for his leadership and impact. He was also selected as a Baidu Lingjing Developer in 2023, acknowledging his expertise in large language model applications. As a Huawei Student Developer and Daxing Half Marathon finisher, Mr. Song exemplifies balance in intellectual, physical, and creative domains. His consistent excellence has made him a leading example of China’s next-generation technology scholars—bridging scientific inquiry, innovation, and real-world application.

Research Focus

Mr. Song’s research is centered on the intersection of graph neural networks, knowledge graphs, and scientific data modeling. He is particularly focused on the interpretability and uncertainty quantification of machine learning models under noisy and heteroscedastic conditions, often present in real-world scientific datasets. His major contributions include:

A learnable MPNN-based PageRank for author ranking in AI networks
Fusion of semantic knowledge with structured numerical data for metal property prediction
The TSNet model for decoupling noise in heteroscedastic environments, enabling better data quality and active learning
He is passionate about creating AI models that are not only accurate but also transparent, interpretable, and applicable to practical challenges in materials science, corrosion engineering, and scientific discovery. His work addresses the urgent need for smarter data pipelines and analytical tools in science, with potential applications in national infrastructure, defense, and industrial R&D.

🧠 Skills

Guangxuan Song is a highly technical and versatile researcher with advanced skills in:

Python Programming (expert in PyTorch, Torch Geometric, LangChain)
C++ Development for microcontrollers and embedded systems
Data Science: Uncertainty estimation, heteroscedastic modeling, multivariate analysis
Machine Learning: Graph Neural Networks (GNNs), message-passing models, interpretability
Web Development: Scientific data platforms and UI/UX for research tools
Software Engineering: Holds 6 patents and 7 software copyrights
Research Tools: GPT-3.5 for scientific data pipelines, LangChain for literature parsing
Project Leadership: Guided cross-functional teams on national research projects
He also demonstrates exceptional communication, strategic planning, and collaborative leadership, supported by his experience as a student leader and mentor. His hobbies in photography and marathon running reflect discipline, creativity, and endurance—traits that further enrich his academic and professional life.

Conclusion

Mr. Guangxuan Song embodies the qualities of an emerging top-tier researcher: innovative, cross-disciplinary, and dedicated to solving real-world problems with advanced data-driven techniques. His balance of deep technical skills, pioneering research, national-level impact, and entrepreneurial achievements make him highly suitable for the Research for Best Researcher Award. With further global exposure and continued publishing excellence, he is positioned to become a future leader in AI-driven scientific discovery.

Publications to Noted

Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model
🧑‍🔬 Authors: J. Dai, D. Fu, G. Song, L. Ma, X. Guo, A. Mol, I. Cole, D. Zhang
📊 Citations: 17
📅 Year: 2022
📰 Journal: Corrosion Science 209, 110780

From Knowledge Graph Development to Serving Industrial Knowledge Automation: A Review
🧑‍🔬 Authors: G. Song, D. Fu, D. Zhang
📊 Citations: 6
📅 Year: 2022
📰 Conference: 2022 41st Chinese Control Conference (CCC), pp. 4219–4226

Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction
🧑‍🔬 Authors: G. Song, D. Fu, Z. Qiu, Z. Yang, J. Dai, L. Ma, D. Zhang
📊 Citations: 2
📅 Year: 2023
📰 Repository: arXiv preprint arXiv:2312.09744

A Named Entity Extraction Method for Commonly Used Steel Knowledge Graph
🧑‍🔬 Authors: Z. Ma, L. Ma, D. Fu, G. Song, D. Zhang
📊 Citations: 2
📅 Year: 2022
📰 Conference: 2021 Chinese Intelligent Systems Conference: Volume III, pp. 724–732

Corrosion Resistant Performance Prediction in High-Entropy Alloys: A Framework for Model, Interpretation and Multi-Dimensional Visualization
🧑‍🔬 Authors: G. Song, D. Fu, W. Chang, Z. Fu, L. Ma, D. Zhang
📊 Citations: Not available (recent publication)
📅 Year: 2025
📰 Journal: Corrosion Science, 113105

A Message Passing Neural Network Framework with Learnable PageRank for Author Impact Assessment
🧑‍🔬 Authors: S. Guangxuan, D. Fu, X. Wu
📊 Citations: Not available (recent publication)
📅 Year: 2025
📰 Journal: Advances in Electrical & Computer Engineering 25 (1)

Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data
🧑‍🔬 Authors: G. Song, D. Fu, Z. Qiu, J. Meng, D. Zhang
📊 Citations: Not available (recent preprint)
📅 Year: 2024
📰 Repository: arXiv preprint arXiv:2409.07942

A Data-Driven Hybrid Machine Learning and Transfer Learning Algorithm for Long Consecutive Atmospheric Environment Missing Data Imputation
🧑‍🔬 Authors: H. Meng, L. Shao, D. Fu, G. Song, J. Meng
📊 Citations: Not available (recent publication)
📅 Year: 2024
📰 Conference: 2024 43rd Chinese Control Conference (CCC), pp. 6728–6733

Extraction of Key Environmental Factors and Construction of the Data-Driven Model for Atmospheric Aging of Polyurethane Coatings
🧑‍🔬 Authors: Z. Bai, D. Fu, G. Song, L. Shao, D. Zhang
📊 Citations: Not available
📅 Year: 2023
📰 Conference: 2023 42nd Chinese Control Conference (CCC), pp. 6713–6718

Feudjio Ghislain | Deep Learning | Best Research Article Award

Published on 01/04/202502/04/2025 by TOT Awards

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

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

Published on 06/02/202506/02/2025 by TOT Awards

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

Publication Profile

Featured Publications

Guangxuan Song | Neural Networks | Best Researcher Award

Mr. Guangxuan Song | Neural Networks | Best Researcher Award

Professional Profile

Education

Work Experience

🏆 Awards & Honors

Research Focus

🧠 Skills

Conclusion

Publications to Noted

Feudjio Ghislain | Deep Learning | Best Research Article Award

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

Academician at University of Dschang, Cameroon.

Education

work Experience

Awards & Honors🏆

Kil soo Lee | Intelligent Control | Best Researcher Award

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

professional profiles

Education

work Experience

Awards and Honors

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