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

Published on 25/07/2026 by Top Teachers

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]

Contents

Abstract
Keywords
Introduction
Research Profile
Research Contributions
Publications
Research Impact
Award Suitability
Conclusion
External Links
References

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]

External Links

References

Elsevier. (n.d.). Scopus author details: Ean Teng Khor, Author ID 54393424800. Scopus. https://www.scopus.com/pages/authors/54393424800
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
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
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
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

Mingyue Zhang | Artificial Intelligence | Best Researcher Award

Published on 11/11/202511/11/2025 by Top Teachers

Dr. Mingyue Zhang | Artificial Intelligence | Best Researcher Award

University of South China | China

Dr. Mingyue Zhang, Ph.D., is a dynamic researcher in computer vision, intelligent systems, and human–computer interaction, currently serving in the field of computer science with a focus on gesture recognition and lightweight deep learning models for edge devices. His research integrates advanced computer vision algorithms with human–machine collaboration, emphasizing intelligent gesture recognition for rehabilitation training, embedded AI, and Internet of Things (IoT)-enabled systems. Dr. Zhang has significantly contributed to developing efficient algorithms such as lightweight convolutional neural networks, adaptive Kalman filtering, and multi-sensor fusion frameworks, which enhance real-time performance in gesture estimation, object tracking, and assistive technologies. His innovative work bridges the gap between deep learning theory and practical deployment on embedded systems and mobile platforms. With over 25 publications in high-impact journals indexed in SCI and EI, including IEEE Internet of Things Journal, Expert Systems with Applications, IEEE Access, and Journal of Supercomputing, his research has achieved growing academic recognition. Dr. Zhang’s work has been widely cited across the global impact of his contributions in computer vision and artificial intelligence. His research is currently supported by the Hunan Provincial Department of Science and Technology, focusing on intelligent gesture recognition in rehabilitation. In recognition of his outstanding contributions to scientific innovation and scholarly excellence, Dr. Zhang is honored with the Best Researcher Award for his pioneering advancements in AI-driven human–computer interaction and lightweight network modeling.

Publication Profile

Orcid

Featured Publications

Jiang, C., Zhang, M., Wang, Y., & Zhang, A. (2025). AHMOT: Adaptive Kalman Filtering and Hierarchical Data Association for 3D Multi-Object Tracking in IoT-Enabled Autonomous Vehicles. IEEE Internet of Things Journal.
Zhang, M., Zhou, Z., Tao, X., & Deng, M. (2023). Hand pose estimation based on fish skeleton CNN: Application in gesture recognition. Journal of Intelligent & Fuzzy Systems, 44, 8029–8042.
Zhang, M., Zhou, Z., Wang, T., & Zhou, W. (2023). A lightweight network deployed on ARM devices for hand gesture recognition. IEEE Access, 11, 45493–45503.
Zhang, M., Zhou, Z., & Deng, M. (2022). Cascaded hierarchical CNN for 2D hand pose estimation from a single color image. Multimedia Tools and Applications, 81, 25745–25763.
Zhang, M., & Zhou, Z. (2025). Speed-accuracy trade-off in lightweight-based hand pose estimation. The Journal of Supercomputing, 81, 1212

Guangxuan Song | Neural Networks | Best Researcher Award

Published on 26/06/202526/06/2025 by Top Teachers

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

Abdelmoaty Mahmoud | Computer Science | Best Research Article Award

Published on 18/06/202518/06/2025 by Top Teachers

Assist. Prof. Dr. Abdelmoaty Mahmoud | Computer Science | Best Research Article Award

Assistant Professor at Ajloun National University, Egypt

Dr. Abdelmoty Mahmoud Mohamed Ahmed is a distinguished academic with over 20 years of teaching and research experience across Egypt, Saudi Arabia, and Jordan. Currently serving as an Associate Professor at the College of Information Technology, Ajloun National University, Jordan, he also holds the Vice Dean position at Al-Nahda University in Egypt. Dr. Abdelmoty specializes in Artificial Intelligence, Machine Learning, Computer Vision, and E-learning. His notable contributions include systems to support individuals with hearing and speech impairments, as well as AI applications in public health. With a passion for quality education, he leads academic accreditation and quality assurance efforts. He has supervised numerous projects and published extensively in SCI and Scopus-indexed journals. His career reflects a commitment to innovation, student development, and community service.

professional profile

ORCID

Scopus Profile

Google Scholar

Education

Dr. Abdelmoty earned all his academic degrees from Al-Azhar University, Cairo, Egypt.

Ph.D. (2014–2021) in Systems and Computers Engineering: Focused on developing an Arabic sign language translation system, integrating Artificial Intelligence and Computer Vision.
M.Sc. (2004–2007) in Systems and Computers Engineering: Developed an electronic business system using GIS technologies.
B.Sc. (1998–2003) in Systems and Computers Engineering: Graduated with a commendable grade of 80.93%.
His education reflects a progressive deepening in systems engineering, with specialization in AI-based assistive technologies, particularly for those with special needs.
🎓📊💻

Work Experience

Dr. Abdelmoty has held academic roles from Lecturer to Associate Professor at top institutions including King Khalid University (Saudi Arabia), Al-Nahda University (Egypt), and Ajloun National University (Jordan). Over the past two decades, he has taught a wide range of subjects—Artificial Intelligence, Python, IoT, Computer Architecture, and more. As Vice Dean and Quality Assurance Head at Al-Nahda University, he led academic planning and assessment. His work includes supervising graduation projects like smart gloves for sign language and IoT-based health systems. His teaching methodology blends modern pedagogy with technological innovation.

🏆 Awards & Honors

Dr. Abdelmoty has been a recipient of multiple funded research grants from King Khalid University, Saudi Arabia, totaling over 300,000 SAR. These projects focused on cutting-edge technologies, including:

AI-based Sign Language Translators
COVID-19 Prediction Models
Smart Hadith Classification Systems
IoT-driven Plant Disease Detection
His projects reflect real-world impact and community service relevance. Additionally, he is honored for his leadership roles in quality assurance and curriculum development. His contributions to deaf-mute assistance technologies have received community recognition.

Research Focus

Dr. Abdelmoty’s research revolves around:

Artificial Intelligence & Machine Learning 🤖
Computer Vision & Pattern Recognition 👁️
Image Processing 🖼️
Human-Computer Interaction (HCI) 🧠💻
Internet of Things (IoT) 🌐
E-Learning Technologies 🧑‍🎓
He is particularly passionate about developing intelligent assistive technologies for people with special needs. His work blends engineering precision with a humanitarian outlook. Through AI-enhanced systems for translation, education, and medical prediction, his research consistently addresses social challenges and enhances accessibility.
🔍🌍📡

🛠️ Skills

Technical Skills:

Programming: Python 🐍, Java ☕, C++, Visual Basic
Databases: SQL, Access 📊
Software: MATLAB, SPSS, MS Office, WordPress
Tools: HTML, CMS platforms 🌐
Soft Skills:
Effective Communicator 📣
Strategic Planner 🧭
Team Collaborator 🤝
Quick Learner and Problem Solver 🔍
He is proficient in modern teaching techniques, curriculum development, assessments, and quality assurance. His dual technical and interpersonal strengths make him a holistic academic leader.
🧑‍💻🔧📘

Conclusion

Dr. Abdelmoty M. M. Ahmed is a highly qualified candidate for the Research for Best Research Article Award, especially if a standout article is formally submitted as part of the application. His rich academic background, broad research interests, social relevance, and dedication to technology for special needs communities showcase his innovative and applied impact in computing and AI.

Publications to Noted

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

Authors: AMJAL Moustafa, MS Mohd Rahim, B Bouallegue, MM Khattab, AM Ahmed

Citations: 17

Year: 2023

A Model for Qur’anic Sign Language Recognition Based on Deep Learning Algorithms

Authors: HA AbdElghfar, AM Ahmed, AA Alani, HM AbdElaal, B Bouallegue

Citations: 16

Year: 2023

Towards the Design of Automatic Translation System from Arabic Sign Language to Arabic Text

Authors: AM Ahmed, RA Alez, G Tharwat, M Taha, W Ghribi, AS Badawy

Citations: 13

Year: 2017

A Secure Hardware Implementation for Elliptic Curve Digital Signature Algorithm

Authors: M Bedoui, B Bouallegue, AM Ahmed, B Hamdi, M Machhout, Mahmoud

Citations: 12

Year: 2023

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

Authors: MM Khattab, AM Zeki, AA Alwan, B Bouallegue, SS Matter, AM Ahmed

Citations: 12

Year: 2023

An Innovative Hybrid Method for Islanding Detection Using Fuzzy Classifier

Authors: YA Elshrief, AD Asham, B Bouallegue, A Ahmed, DH Helmi, BA Abozalam

Citations: 12

Year: 2022

Assessing the Relation Between Family Background and Juvenile Delinquency Using Data Mining

Authors: SB Changalasetty, B Belgacem, AS Badawy, W Ghribi, AM Ahmed

Citations: 11

Year: 2019

Arabic Sign Language Translator

Authors: AM Ahmed, RA Alez, G Tharwat, M Taha, B Belgacem, AM Al Moustafa

Citations: 11

Year: 2019

SAR Image Matching Based on Local Feature Detection Using CNN

Authors: M Elwan, AS Amein, A Mousa, AM Ahmed, B Bouallegue, AS Eltanany

Citations: 10

Year: 2022

Regularized Multiframe Super‐Resolution Image Reconstruction Using Linear and Nonlinear Filters

Authors: MM Khattab, AM Zeki, AA Alwan, B Bouallegue, SS Matter, AM Ahmed

Citations: 10

Year: 2021

Rule-Based Mining of Juvenile Delinquency

Authors: LS Thota, K Shireesha, A Sravani, S Rajender, SB Changalasetty, AM Ahmed

Citations: 10

Year: 2020

QSLRS-CNN: Qur’anic Sign Language Recognition System Based on CNN

Authors: HA AbdElghfar, AM Ahmed, AA Alani, HM AbdElaal, B Bouallegue

Citations: 9

Year: 2024

Rahaf Alsuhaimi | Machine Learning | Best Researcher Award

Published on 04/11/202404/11/2024 by Top Teachers

Ms. Rahaf Alsuhaimi | Machine Learning | Best Researcher Award

Master’s candidate at King Abdulaziz University, Saudi Arabia

Rahaf AlSuhaimi is a highly motivated researcher specializing in data analysis, data science, and computational modeling with a focus on machine and deep learning applications. Currently a Master’s candidate at King Abdulaziz University, Rahaf has worked on several innovative projects, including using explainable AI for sentiment analysis, evaluating the decision factors for solar energy adoption in Saudi Arabia, and developing strategies for open-source adoption. Her strong technical background includes data visualization, model development, and extensive research experience. Her publications and a “Best Paper Award” showcase her commitment to advancing data science and technology.

professional profile

ORCID

Education

Rahaf AlSuhaimi is currently pursuing a Master’s degree in Computer Information Systems at King Abdulaziz University (2021-present). Her studies focus on utilizing data science methodologies to explore various areas, including sentiment analysis, energy policy, and AI-driven user behavior analysis. During her program, she built advanced models like GRU-LSTM with Explainable AI to identify learner confusion on online forums, developed a BERT model for sentiment classification, and investigated factors influencing solar energy adoption in Saudi Arabia. Rahaf also has a Bachelor’s degree in Information Systems from Taibah University (2016-2020), where she developed foundational skills in data science and computational modeling. Through her education, she has built a solid foundation in machine learning, data manipulation, and visualization, which she applies in her ongoing research work.

work Experience

Rahaf AlSuhaimi has a diverse portfolio of experiences in data science and research. Currently, she serves as a Data Management Officer at Salama Cooperative Insurance (2024-present), where she manages and analyzes data to enhance decision-making processes. Previously, she worked on a one-year research project with the Prince Naif Chair and King Abdulaziz University as an ML/DL Researcher (2023-2024). In this role, she collected and preprocessed data, developed Python-based feature extraction methods, and built machine and deep learning models, contributing to a research paper. Rahaf also engaged in voluntary work at the Deanship of Graduate Studies at King Abdulaziz University, where she assisted in developing a user interface for the deanship site. Her experience includes an internship at Taibah University’s IT Deanship, further strengthening her skills in data management and visualization.

Research Focus

Rahaf AlSuhaimi’s research focuses on using data science and machine learning to solve real-world problems in various domains, including education, sustainability, and public sector technology. Her work involves building explainable AI models like GRU-LSTM to identify patterns of confusion in online learning environments, developing sentiment analysis models using BERT for classifying online reviews, and exploring the factors influencing solar energy adoption in Saudi Arabia. Rahaf is also interested in open-source technology strategies, having conducted surveys and analyzed data to improve adaptation methods in the government sector. Her research is characterized by a commitment to innovation, with applications in environmental sustainability and public service improvement, contributing valuable insights to academia and society.

Awards and honors

Rahaf AlSuhaimi has been recognized for her contributions to data science and research, notably receiving the Best Paper Award at the 6th International Conference on Recent Innovations in Computing and Technology (IRICT) in 2021. This award celebrated her paper’s impact and rigor, underscoring her ability to address complex data challenges with innovative approaches. Rahaf’s academic excellence and commitment to research have earned her acknowledgment within the academic community, encouraging her to continue exploring new dimensions in data science and machine learning. Her achievements reflect a blend of technical skill and dedication, marking her as a rising talent in data-driven research and setting a strong foundation for future accomplishments.

Conclusion

Rahaf AlSuhaimi is a promising candidate for the Best Researcher Award, thanks to her strong technical skills, commendable research output, and innovative projects. Her work demonstrates both practical impact and scholarly contribution, with recognition for her research quality. Enhancing her publication reach and fostering interdisciplinary collaborations could further solidify her standing in the research community. Overall, Rahaf’s accomplishments and proactive approach make her a worthy contender for this award.

Publications to Noted

Identifying Learners’ Confusion in a MOOC Forum Across Domains Using Explainable Deep Transfer Learning

Authors: Rahaf AlSuhaimi

Year: 2024

Citation Count: Not available (recently published)

DOI: 10.3390/info15110681

DEVELOPMENT OF A DECISION-SUPPORT FRAMEWORK OF FACTORS AFFECTING SOLAR SYSTEM INSTALLATION IN SAUDI ARABIA

Authors: Rahaf AlSuhaimi

Year: 2023

Citation Count: Not available

Automatic Saudi Arabian License Plate Detection and Recognition Using Deep Convolutional Neural Networks

Authors: Rahaf AlSuhaimi

Year: 2022

Citation Count: Not available

DOI: 10.1007/978-3-030-98741-1

Taha Etem | Deep Learning | Best Researcher Award

Published on 13/09/202413/09/2024 by Top Teachers

Assist Prof Dr. Taha Etem | Deep Learning | Best Researcher Award

Academician at Cankiri Karatekin University, Turkey

Taha Etem, an Assistant Professor at Çankırı Karatekin University, specializes in computer engineering, cryptography, and electromagnetic wave propagation. His research focuses on developing encryption algorithms, investigating electromagnetic pollution, and advancing random number generation for cryptographic applications. With a Ph.D. from Fırat University and an extensive academic and research background, he has contributed significantly to the field through national and international publications, presentations, and project involvement.

professional profile

Scopus Profile

Google Scholar

ORCID

Education

Taha Etem has pursued an extensive academic journey in engineering, with a focus on cryptography, information security, and electromagnetic waves. He completed his doctoral degree at Fırat University in November 2022, with his thesis centered on the development of a random number generator for cryptography and information security, under the guidance of Associate Professor Dr. Turgay Kaya. Prior to his PhD, he earned his master’s degree from İnönü University in July 2017. His master’s thesis focused on the measurement, mapping, and evaluation of high-frequency electromagnetic wave propagation in the Muş region. His academic journey began with a Bachelor’s degree from Fırat University, completed in February 2013. This foundational degree, earned at the Faculty of Engineering, laid the groundwork for his subsequent research and professional endeavors in areas such as image encryption, random number generation, and electromagnetic pollution.

work Experience

Taha Etem, currently serving as an Assistant Professor at Çankırı Karatekin University’s Faculty of Engineering, has accumulated a wealth of academic and research experience. His primary role at the university began in April 2023, where he contributes to the Department of Computer Engineering, particularly in the field of Computer Hardware. His teaching responsibilities include a range of subjects such as Cryptography, Information Security, and Advanced Time Series Analysis, demonstrating his proficiency in computer programming, cybersecurity, and numerical methods. Before joining Çankırı Karatekin University, Taha Etem held the position of Research Assistant at Mus Alparslan University’s Faculty of Engineering and Architecture in the Department of Electrical-Electronics Engineering. From 2014 to 2023, he focused on Electromagnetic Fields and Microwave Theory. During this time, he worked on various scientific research projects, including studies on electromagnetic wave propagation, low-frequency electromagnetic fields, and the associated health risks in various environments. His involvement in these national research initiatives reflects his ability to collaborate on large-scale projects and his dedication to advancing knowledge in his field.

Research Focus

Dr. Taha Etem’s research primarily focuses on cryptography, information security, and electromagnetic wave propagation. His doctoral work involved developing random number generators for cryptographic applications, aiming to enhance security in digital communication systems. His earlier work includes the measurement, mapping, and evaluation of high-frequency electromagnetic waves, particularly in the Muş region, where he examined the effects of electromagnetic fields on various environments. Dr. Etem has contributed to the field of image encryption, proposing novel algorithms and encryption models that use random bit generators, as seen in his publications in prominent international journals. He has also explored the impact of electromagnetic pollution, conducting detailed studies on low and high-frequency electromagnetic waves in urban and campus environments. His recent projects include innovative approaches to electric vehicle energy consumption using machine learning, showcasing his interdisciplinary expertise in combining computational techniques with engineering applications.

Awards and honors

Taha Etem, an accomplished academic and researcher, has made significant contributions to the fields of cryptography, information security, and electromagnetic field research. His work has been recognized both nationally and internationally through various awards and honors.

He played a key role in multiple national scientific research projects, including his contributions to measuring and evaluating high-frequency electromagnetic waves and assessing electromagnetic pollution risks in university campuses. Taha Etem has also been involved in groundbreaking work on encryption models and random number generation, leading to his recognition in the academic community. His article on a fast image encryption algorithm with random structures, published in International Journal of Computers and Applications, stands out as one of his most impactful contributions to the field of cryptography.

Conclusion

Taha Etem is a strong contender for the Best Researcher Award. His contributions to cryptography and electromagnetic research highlight his potential for continued innovation. While his research is solid, expanding his interdisciplinary reach and international visibility could further bolster his candidacy.

Publications to Noted

A novel true random bit generator design for image encryption
Authors: Taha Etem, T Kaya
Journal: Physica A: Statistical Mechanics and its Applications
Citations: 20
Year: 2020

Self-generated encryption model of acoustics
Authors: Taha Etem, T Kaya
Journal: Applied Acoustics
Citations: 11
Year: 2020

Trivium-linear congruential generator based bit generation for image encryption
Authors: Taha Etem, T Kaya
Journal: Fırat Üniversitesi Mühendislik Bilim Dergisi
Citations: 9*
Year: 2020

Estimation of low frequency electromagnetic values using machine learning
Authors: Z Pala, İ Bozkurt, Taha Etem
Conference: XIIIth International Conference on Perspective Technologies and Methods
Citations: 6
Year: 2017

Electromagnetic pollution measurement in the system rooms of a university
Authors: Taha Etem, Z Pala, İ Bozkurt
Conference: XIIIth International Conference on Perspective Technologies and Methods
Citations: 5
Year: 2017

Electric field measurements of a base station at 2G and 3G frequencies
Authors: Taha Etem, T Abbasov
Conference: XII International Conference on Perspective Technologies and Methods
Citations: 3
Year: 2016

Fast image encryption algorithm with random structures
Authors: Taha Etem, T Kaya
Journal: International Journal of Computers and Applications
Citations: 2
Year: 2023

Measurement and evaluation of low frequency electromagnetic field in camera observation rooms
Authors: I Bozkurt, Z Pala, Taha Etem
Conference: XIIIth International Conference on Perspective Technologies and Methods
Citations: 2
Year: 2017

Measurements Of High Frequency Electromagnetic Waves In Center Of Mus
Authors: Taha Etem, T Abbasov
Journal: International Journal of Applied Mathematics Electronics and Computers
Citations: 1
Year: 2016

Electric Field Measurements of a Base Station at 2G and 3G Frequencies
Authors: Taha Etem, T Abbasov
Conference: MEMSTECH
Citations: 1
Year: 2016

Deming Li | Machine learning | Best Researcher Award

Published on 21/08/202423/08/2024 by Top Teachers

Assoc Prof Dr. Deming Li | Machine learning | Best Researcher Award

Associate professor at Jilin international studies university, China

Dr. Deming Li is an accomplished researcher with a strong focus on digital education, particularly in the realm of preschool education and the application of advanced technologies like machine learning, blockchain, and IoT. He holds a Doctorate in Business Administration (DBA) and serves as an Associate Professor and Master’s Supervisor at Jilin International Studies University. He also holds significant academic part-time positions as Chief Editor and Associate Editor of international journals related to education. Dr. Li has authored several high-impact articles in prestigious journals and has led various educational projects supported by notable organizations.

📝professional profile

ORCID

🎓Educational Details:

Dr. Deming Li’s educational background is rooted in advanced studies that have significantly contributed to his expertise in digital education. He earned his Doctorate in Business Administration (DBA) from the prestigious Université de Montpellier. This advanced degree has provided him with a strong foundation in research methodologies and educational leadership, which he has applied throughout his academic career. In addition to his doctoral studies, Dr. Li has continued to enhance his knowledge and skills through various professional development opportunities, allowing him to stay at the forefront of the rapidly evolving field of digital education. His educational journey has equipped him with the tools necessary to integrate cutting-edge technologies into educational practices, particularly in the realm of preschool education, where his work has made a profound impact.

👨‍🏫Experience:

Dr. Deming Li is an esteemed researcher and academic with a significant focus on digital education, particularly in the area of preschool education. He currently holds the position of Associate Professor and Master’s Supervisor at Jilin International Studies University. In addition to his academic roles, Dr. Li is a Researcher at the Shanghai Institute of Lifelong Education at East China Normal University. His academic journey includes earning a Doctorate in Business Administration (DBA) from Université de Montpellier.

Dr. Li has made notable contributions to the academic community through his extensive research and publications. He has authored numerous articles in high-impact journals, including SSCI, SCI, and EI-indexed journals, where his work on machine learning, neural networks, blockchain, and IoT technology in education has gained significant recognition. Some of his papers have been highlighted as hot papers, reflecting their influence in the academic world.

Beyond his research, Dr. Li has demonstrated leadership in the academic publishing arena. He serves as the Chief Editor of the international journal “Innovation and Practice of Teaching Methods” and as an Associate Editor for the “Journal of Education Theory and Management.” These roles underscore his influence in shaping the discourse within the field of education.

Awards and Honors:

Dr. Deming Li has earned numerous awards and honors throughout his distinguished career in digital education. His contributions have been recognized by various academic and professional organizations, reflecting his leadership and innovation in the field. He has been the recipient of several prestigious grants, including those from the Ministry of Education, the Chinese Society of Education, and the Shanghai Municipal Education Commission, underscoring the impact and relevance of his research. Additionally, Dr. Li’s work has been acknowledged with accolades for his high-impact publications, some of which have been classified as hot papers, placing them among the top 1% in their field. His editorial roles in leading international journals also speak to his esteemed reputation within the academic community, further solidifying his status as a thought leader in digital education. These awards and honors not only highlight Dr. Li’s exceptional contributions to advancing educational practices through technology but also reinforce his standing as a pioneering researcher in the global academic landscape.

Conclusion:

Dr. Deming Li is a strong candidate for the Research for Best Researcher Award. His prolific contributions to digital education, particularly his innovative application of advanced technologies in preschool education, set him apart in his field. His leadership in academic publishing and project management further highlights his suitability for this award. Addressing the areas for improvement, such as expanding the practical impact of his research and fostering more interdisciplinary and international collaborations, could elevate his work even further. Overall, Dr. Li’s achievements and ongoing contributions make him a deserving contender for this prestigious recognition.

📚Publications to Noted

Title: An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm
Journal: Computers in Human Behavior
Year: 2024
DOI: 10.1016/j.chb.2024.108211
ISSN: 0747-5632
Author: Deming Li
Title: Research on Family Safety Education for Pre-school Children in China
Journal: The Educational Review, USA
Year: 2024
DOI: 10.26855/er.2024.01.023
ISSN: 2575-7938, 2575-7946
Author: Deming Li
Title: Performance Evaluation of Special Fund for Chinese Private Education: Logic and Index System
Journal: Journal of Humanities, Arts and Social Science
Year: 2023
DOI: 10.26855/jhass.2023.11.025
ISSN: 2576-0556, 2576-0548
Author: Deming Li
Title: Theory and Practice of Preschool Education Digitalization
Publisher: UK Scientific Publishing
Year: 2023
Type: Book
Author: Deming Li
Title: Computerized Data Analysis of the Current Situation of Children’s Psychological Education Using Big Data
Journal: Computer-Aided Design and Applications
Year: 2023
DOI: 10.14733/cadaps.2024.S9.152-160
Source: Crossref
Author: Deming Li
Title: Visual Analysis of the Integration of Traditional Chinese Culture into Preschool Education
Journal: The Educational Review, USA
Year: 2023
DOI: 10.26855/er.2023.08.024
ISSN: 2575-7938, 2575-7946
Author: Deming Li
Title: Research Hotspots and Future Trends of Digital Competence of Preschool Teachers Based on Citespace Analysis
Journal: Heilongjiang Science
Year: 2023
Author: Deming Li
Title: Exploring the Computational Effects of Advanced Deep Neural Networks on Logical and Activity Learning for Enhanced Thinking Skills
Journal: Systems
Year: 2023
DOI: 10.3390/systems11070319
Source: Crossref
Author: Deming Li
Title: Research map of the hot spots and future trends of foreign basic education reform in recent 20 years
Journal: Journal of Comparative Education
Year: 2023
Author: Deming Li
Title: The Concept of Higher Education Quality Evaluation based on Big Data Analysis
Journal: BCP Social Sciences & Humanities
Year: 2022
DOI: 10.54691/bcpssh.v20i.2158
ISSN: 2692-6172
Author: Deming Li