Jana Al Haj Ali | Computer Science and Artificial Intelligence | Best Researcher Award

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Mrs. Jana Al Haj Ali | Computer Science and Artificial Intelligence | Best Researcher Award

Mrs. Jana Al Haj Ali | Computer Science and Artificial Intelligence | PhD Student | University of Lorraine | France

Mrs. Jana Al Haj Ali is an accomplished researcher and PhD student in Computer Engineering, specializing in the design and implementation of cognitive digital twins for industrial applications. Her work integrates neuro-symbolic AI approaches to enable intelligent, adaptive, and human-centric human-robot interaction within cyber-physical systems. Through her innovative research, she has contributed to advancing the understanding of cognitive architectures, simulation models, and interoperability protocols, aiming to improve automation, safety, and efficiency in Industry 5.0 contexts. She is known for combining technical expertise, scientific rigor, and collaborative spirit to drive impactful solutions at the intersection of artificial intelligence, robotics, and cognitive systems.

Professional Profile 

Education

Mrs. Jana Al Haj Ali holds a Bachelor’s degree in Mathematics, followed by a Master’s degree in Mathematical Engineering for Data Science, which provided her with an interdisciplinary foundation in mathematical modeling, machine learning, and computational techniques. She is currently pursuing her doctoral studies in Computer Engineering at a leading research institute in France, where she is actively engaged in high-impact research focusing on cognitive digital twin technologies. Her educational background bridges mathematics, data science, and computer engineering, allowing her to approach complex research problems from both theoretical and applied perspectives.

Experience

Mrs. Jana Al Haj Ali has extensive research experience in the development of modular architectures for cognitive digital twins, focusing on emulation, cognition, and simulation functionalities. She has implemented cognitive exchange protocols between industrial robots and human operators, enabling adaptive reconfiguration of cyber-physical systems based on real-time cognitive feedback. She also completed a visiting research project at a prominent European research institute, where she designed cognitive models and integrated them into simulation environments to evaluate collaborative performance. Additionally, she has experience in data analysis, machine learning modeling, and physical risk estimation from her earlier research internships.

Research Interest

Her primary research interests include cognitive cyber-physical systems, cognitive digital twins, neuro-symbolic AI, knowledge representation, and human-robot collaboration. She is particularly focused on enhancing cognitive interoperability, developing architectures that combine deep learning with symbolic reasoning, and designing intelligent simulation frameworks that predict system behavior in real-time. Her work aims to address key challenges in Industry 5.0 by creating more resilient, adaptive, and human-centric automation solutions.

Award

Mrs. Jana Al Haj Ali has been recognized for her contributions through opportunities to present her research at prestigious international conferences, summer schools, and national symposia. Her participation in scientific events and collaboration with international research teams reflects her growing impact in the academic community. She is highly regarded for her ability to translate complex cognitive models into practical implementations, earning acknowledgment from peers and mentors for her innovative approach.

Selected Publication

  • Human Digital Twins: A Systematic Literature Review and Concept Disambiguation for Industry 5.0 (2025) – 45 citations

  • Cognition in Digital Twins for Cyber-Physical Systems and Humans: Where and Why? (2024) – 30 citations

  • Cognitive Architecture for Cognitive Cyber-Physical Systems (2024) – 28 citations

  • Cognitive Systems and Interoperability in the Enterprise: A Systematic Literature Review (2024) – 33 citations

Conclusion

Mrs. Jana Al Haj Ali is an outstanding candidate for this award, with a strong academic background, impactful research contributions, and a commitment to advancing the field of cognitive digital twins and human-robot collaboration. Her work demonstrates a unique combination of theoretical innovation and practical application, contributing to the future of intelligent and adaptive industrial systems. With a growing publication record, active participation in international collaborations, and dedication to knowledge dissemination, she is well positioned to emerge as a leader in cognitive cyber-physical systems research.

 

Jaehyun Chung| Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Jaehyun Chung| Computer Science and Artificial Intelligence | Best Researcher Award

Computer Science and Artificial Intelligence | Korea University | South Korea

Jaehyun Chung is a highly promising M.S. student at Korea University’s Artificial Intelligence and Mobility Laboratory, specializing in Generative AI, Reinforcement Learning, and Quantum AI applications. His research focuses on autonomous systems, AI-based mobility, defense technologies, and intelligent control, reflected in his involvement in over ten major R&D projects funded by prestigious Korean institutions. He has co-authored several high-impact journal articles and conference papers, including works published or under review in IEEE Transactions and ACM venues, and has earned multiple student paper awards, such as the IEEE Seoul Section Bronze Paper Award. Jaehyun demonstrates strong interdisciplinary capability, applying advanced AI techniques to fields as diverse as torpedo evasion, space rocket stabilization, and stock market prediction. Although early in his academic career, his rapid research output, national recognition, and contributions to innovative, real-world AI applications position him as an outstanding young researcher with exceptional potential for future impact.

Professional Profile 

Educational 

Jaehyun Chung pursued both his undergraduate and graduate studies at Korea University, one of South Korea’s leading institutions. He earned his Bachelor of Science (B.S.) in Electrical and Computer Engineering from the College of Engineering, completing the program between March 2017 and August 2023. Following his undergraduate success, he continued at Korea University to pursue a Master of Science (M.S.) in Electrical and Computer Engineering, starting in September 2023, where he is currently engaged in advanced research in Artificial Intelligence and Mobility. His academic path reflects a strong and consistent focus on engineering and cutting-edge AI technologies.

Professional Experience 

Jaehyun Chung is currently serving as a Research Assistant at the Artificial Intelligence and Mobility Laboratory at Korea University since September 2023, under the guidance of Professor Joongheon Kim. In this role, he actively contributes to a wide range of advanced R&D projects focused on AI-based autonomous systems, reinforcement learning, and quantum AI technologies. His professional experience includes hands-on involvement in nationally funded initiatives such as the Quantum Hyper-Driving Project, AI Bots Collaborative Platform, and Learning-Based Swarm Mission Planning Algorithms, among others. Jaehyun’s work spans across various sectors including defense, mobility, construction, and education, highlighting his ability to apply AI innovations to real-world challenges through practical, cross-disciplinary research collaborations.

Research Interests 

Jaehyun Chung’s research interests lie at the intersection of Artificial Intelligence, Autonomous Systems, and Quantum Computing. He is particularly focused on Generative AI technologies, including Transformer-based architectures, and their application in dynamic environments. A key area of his work involves AI-based Autonomous Control, where he utilizes advanced Reinforcement Learning techniques to optimize decision-making in systems such as autonomous vehicles, robotic platforms, and defense mechanisms. Additionally, his growing involvement in Quantum Reinforcement Learning and Federated Learning reflects a forward-looking approach to building scalable and intelligent systems. His research is deeply interdisciplinary, targeting real-world problems in mobility, finance, aerospace, and military applications through the lens of cutting-edge AI innovation.

Awards and Honors 

Jaehyun Chung has received several prestigious awards recognizing his innovation and excellence in research at an early stage in his academic career. In December 2024, he was honored with the IEEE Seoul Section Best Student Bronze Paper Award for his impactful work on stock prediction using correlation graph-based proximal policy optimization. In November 2024, he received the Outstanding Paper Award from the Korean Institute of Communications and Information Sciences (KICS) for his research on reinforcement learning-based countermeasure tactics against torpedo threats. Additionally, he earned another Bronze Paper Award at the IEEE Seoul Section Student Paper Contest in December 2023 for developing reinforcement learning strategies for aircraft taxi routing. These accolades reflect Jaehyun’s strong analytical skills, innovative thinking, and significant contributions to the fields of AI and autonomous control.

 Publications 

Title: Joint Quantum Reinforcement Learning and Stabilized Control for Spatio-Temporal Coordination in Metaverse
Authors: S. Park, J. Chung, C. Park, S. Jung, M. Choi, S. Cho, J. Kim
Year: 2024
Cited by: 19

Title: Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach
Year: 2024
Cited by: 17

Title: Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks
Authors: G. S. Kim, Y. Cho, J. Chung, S. Park, S. Jung, Z. Han, J. Kim
Year: 2024
Cited by: 4

Title: DDPG-based Deep Reinforcement Learning Tactics for Defending Torpedo Attacks
Authors: J. Chung, C. Im, J. Choi, Y. Yoon, S. Park
Year: 2024
Cited by: 1

Title: Correlation-Assisted Spatio-Temporal Reinforcement Learning for Stock Revenue Maximization
Year: 2025

Title: Multi-Modal LLM-Based Fully-Automated Training Dataset Generation Software Platform for Mathematics Education
Year: 2025

Title: Trends in Reinforcement Learning Methods for Stock Prediction
Year: 2024

Conclusion 

Jaehyun Chung is an exceptionally strong early-career researcher who demonstrates intellectual depth, research versatility, and practical relevance across AI domains. He possesses all the qualities sought in a Best Researcher Award.

Badr Machkour | Artificial Intelligence | Best Researcher Award

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Dr. Badr Machkour | Artificial Intelligence | Best Researcher Award

Professor, Faculty of Legal, Economic and Social Sciences, Morocco 

Dr. Badr Machkour is a Moroccan researcher and academic with deep expertise in economics, finance, and digital transformation. Currently holding a Ph.D. in Economic and Management Sciences, he has contributed to multiple domains including Industry 4.0, financial digitalization, and educational innovation. With a robust blend of academic and consulting experience, Dr. Machkour bridges theory and practice to drive impactful research. 🌐📊

Profile

Scopus

Google Scholar

Orcid

🎓 Education

Dr. Machkour holds a Doctorate in Economic and Management Sciences (2018–2023) from the FSJES of Agadir. His academic foundation includes a degree in Audit and Management Control from ENCG Agadir (2014–2017), post-preparatory classes in Economics and Commerce (2012–2014), and a Mathematics Baccalaureate (2011–2012). His diverse training forms a strong base for multidisciplinary research. 📚🧠

💼 Professional Experience

Dr. Machkour has extensive experience as a financial auditor, consultant, and trainer. He currently serves as a trainer at the Cité des Métiers et des Compétences (OFPPT), delivering finance and management programs. His previous roles span auditing and consulting at prominent firms like Augeco, MAZARS Maroc, and Agadir Conseil, involving sectors from agriculture to banking. 🏢💼📈

🔬 Research Interests

His research explores the intersection of Industry 4.0, digital banking solutions, customer experience, AI in education, and entrepreneurship. Dr. Machkour is particularly interested in how technology transforms economic relationships, pedagogical structures, and corporate strategies. 🤖📱🏫

🏆 Awards & Honors

While specific awards are not listed, Dr. Machkour’s work has been featured in indexed journals and cited internationally — notably his highly cited paper on Industry 4.0’s implications in finance. His academic contributions reflect both quality and influence. 🥇🌍

📑 Publications

Industry 4.0 and its Implications for the Financial Sector, Procedia Computer Science, 2020 — Cited by 151 🏦

The Rise of Artificial Intelligence in Educational Management: A Prospective Analysis on the Role of the Virtual Educational Director, Procedia Computer Science, 2025 🧠

Internet of Things in Education: Transforming Learning Environments, Enhancing Pedagogy, and Optimizing Resource Management, Data and Metadata, 2024 🏫

L’impact de l’adoption des solutions digitales sur la relation banque-client, Revue Française d’Economie et de Gestion, 2024 — Cited by 2 📲

Les facteurs d’adoption des solutions digitales bancaires par les consommateurs marocains, IJAFAME, 2022 — Cited by 1 📱

The Uses of Connected Objects and Their Influence on the Customer Experience, Test Engineering and Management, 2020 — Cited by 1 🌐

Etude exploratoire du développement de l’esprit Entrepreneurial et des compétences Entrepreneuriales auprès des étudiants au Maroc, Alternatives Managériales Economiques, 2024 👨‍🎓

Entrepreneurship 4.0 and Success Factors in the Context of Industry 4.0: A literature review, African Scientific Journal, 2024 🚀

✅ Conclusion

Overall, Dr. Badr Machkour is a promising and accomplished researcher whose work bridges digital innovation and economic practice with scholarly insight. His growing citation record, topical relevance, and interdisciplinary reach make him a strong candidate for the Best Researcher Award. With continued international engagement and broader collaborative networks, his impact is poised to grow even further. 🌍📈

Yunqiang Sun | Artificial Intelligence | Best Researcher Award

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Yunqiang Sun | Artificial Intelligence | Best Researcher Award

Prof. Dr Yunqiang Sun, 中北大学, China

Prof. Dr. Yunqiang Sun🌐📡 is a distinguished scholar specializing in automatic modulation recognition (AMR), wireless communications, and intelligent sensor networks. He has contributed groundbreaking research, including the development of the Multimodal Parallel Hybrid Neural Network (MPHNN), which achieves 93.1% recognition accuracy with reduced complexity. His expertise spans spatio-temporal signal processing, attention mechanisms, and hybrid neural networks. Prof. Sun has published extensively, with works featured in prestigious journals like Electronics (Switzerland) and IEEE Access. His research also explores gait recognition algorithms, millimeter-wave cavity filters, and ultrasonic signal transmission. A dedicated innovator, Prof. Sun’s work advances technologies in communication and sensing systems. 📊📖✨

Publication Profile

Scopus

Proposed Solution 🤖✨

The Multimodal Parallel Hybrid Neural Network (MPHNN) is an advanced model designed to address limitations in processing modulated signals. It preprocesses these signals in multimodal formats, enhancing data interpretation. By combining Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Gated Recurrent Units (Bi-GRU) for temporal feature processing, MPHNN efficiently captures both spatial and temporal dependencies. This innovative approach enables more accurate and robust signal processing, making it highly effective in various applications. Prof. Dr. Yunqiang Sun’s work highlights the power of integrating multiple neural network models for improved performance. 🧠🔧📡📊

Attention Mechanisms 🎯🔗

Prof. Dr. Yunqiang Sun’s research leverages advanced deep learning techniques to enhance recognition accuracy. By integrating the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA), his work in the Multi-Path Hierarchical Neural Network (MPHNN) effectively combines both temporal and spatial features. This fusion allows for improved recognition performance in complex tasks, as the model focuses on the most relevant information across time and space. Prof. Sun’s innovative approach showcases the power of attention mechanisms in modern neural networks. 🤖📊🧠🔍

Results 📊✅

Prof. Dr. Yunqiang Sun, MPHNN, has achieved an impressive 93.1% accuracy across multiple datasets, setting a new benchmark in model performance. His work stands out due to its lower complexity and reduced number of parameters compared to existing models, making it more efficient and scalable. This breakthrough represents a significant advancement in the field, offering a solution that balances high accuracy with computational efficiency. Prof. Sun’s innovative approach holds great promise for a wide range of applications, offering potential improvements in performance and resource utilization. 🔬📊💡📈

Diverse Publication Record

Prof. Dr. Yunqiang Sun is an accomplished researcher with a focus on AMR, gait recognition algorithms, and plasmonic waveguide-coupled systems. He has published extensively in prestigious journals such as IEEE Access, Electronics (Switzerland), and Advanced Composites and Hybrid Materials. Notable works include impactful publications like CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network and Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism. Prof. Sun’s research continues to push the boundaries of technology, contributing significantly to the fields of signal processing and machine learning. 📚🔬📈💡

Citations and Recognition

Prof. Dr. Yunqiang Sun has contributed significantly to the field, with some recent works gaining traction and fewer citations, while others, like his paper on MEMS sensors in Cluster Computing, showcase a higher citation count, reflecting their enduring influence. His research spans various areas, where his innovative approaches and technical expertise continue to shape discussions and advancements in the field. Despite the varying citation impact, Prof. Sun’s work maintains its relevance and continues to inspire future developments in the areas he studies. 🌟📚🔬🧠📈

Research Focus

Prof. Dr. Yunqiang Sun’s research focuses on advanced signal processing, modulation recognition, and sensor technologies. He explores machine learning models like transformers and convolutional neural networks (CNNs) for automatic modulation recognition and signal analysis, with applications in communication systems. His work also extends to gait recognition using algorithms based on compressed sensing and MEMS sensors, which contribute to innovations in human-computer interaction and health monitoring. Prof. Sun’s expertise spans across ultrasonic wave transmission in negative refractive materials and advanced filter designs in millimeter-wave systems, with a strong emphasis on the intersection of signal processing and emerging technologies. 📡🤖📊

Publication Top Notes

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Quadrule-passband millimeter-wave cavity filter based on non-resonant node

Transmission characteristics of ultrasonic longitudinal wave signals in negative refractive index materials

Numerical calculus solution of gait recognition algorithm based on compressed sensing

Application and research of MEMS sensor in gait recognition algorithm

 

 

Cheikh Abdelkader Ahmed Telmoud | Computer Science and Artificial Intelligence | Best Researcher Award

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Cheikh Abdelkader Ahmed Telmoud | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Cheikh Abdelkader Ahmed Telmoud, Nouakchott University, Mauritania

Based on Mr. Cheikh Abdelkader Ahmed Telmoud’s academic and professional background, he appears to be a strong candidate for a “Best Researcher Award.

Publication profile

google scholar

Education and Expertise

Mr. Telmoud has a solid educational foundation, currently pursuing a Ph.D. in Computer Sciences with a focus on AI applications in healthcare, food security, similarity learning, and NLP. His Master’s and Bachelor’s degrees in Computer Science with specializations in Data Science, Networks, and Information Systems further reinforce his expertise in these critical fields.

Professional Experience

As a Communication Support Manager, Project Manager, and Full Stack Developer at Next Technology, Mr. Telmoud has demonstrated significant practical experience in the tech industry, managing multiple FinTech projects. His role in developing innovative solutions like CrossPay and BCIpay showcases his capability to apply his academic knowledge to real-world problems.

Research Contributions

Mr. Telmoud’s research contributions are impressive, spanning various domains such as AI in healthcare and agriculture, similarity learning, and natural language processing. His published articles and presentations at international conferences demonstrate his commitment to advancing knowledge in these areas. Notably, his work on optimizing ML models for rice yield prediction and heart disease diagnosis highlights his focus on impactful research with tangible benefits.

Scientific Impact

The breadth and depth of Mr. Telmoud’s research, especially in AI-driven solutions for healthcare and agriculture, reflect his potential to make significant contributions to these fields. His involvement in cutting-edge research projects, such as similarity learning and Arabic dialect identification, further underline his research capabilities.

Conclusion

Mr. Cheikh Abdelkader Ahmed Telmoud’s blend of academic excellence, practical experience, and impactful research makes him a deserving candidate for the Best Researcher Award. His work not only advances scientific knowledge but also addresses real-world challenges, making a positive difference in society.

Publication top notes

Elevating Cardiac Health with ECG Classification Using Machine Learning

Cutting-Edge Predictive Models: A Comparative Study on Heart Disease Diagnosis

EcoSense: A Smart IoT-Based Digital Twin Monitoring System for Enhanced Farm Climate Insights

Harvesting Insights: AI-driven Rice Yield Predictions and Big Data Analytics in Agriculture

REVOLUTIONIZING RICE YIELD PREDICTION: A DATA-DRIVEN APPROACH IN MAURITANIA

ADVANCING HEART DISEASE DIAGNOSIS AND ECG CLASSIFICATION USING MACHINE LEARNING

DeepSL: Deep Neural Network-based Similarity Learning.

Optimizing Machine Learning Models for Enhanced Rice Yield Prediction

Optimizing ML Models for Enhanced Rice Yield Prediction and Development of an Integrated Platform for Monitoring, and Rice Yield Prediction In Precision Agriculture

Revolutionizing Heart Disease Prediction: A Machine Learning Approach

Emmanuel Mutabazi | Computer Science and Artificial Intelligence | Best Researcher Award

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Emmanuel Mutabazi | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Emmanuel Mutabazi, Hohai University, China

Based on the information provided, Mr. Emmanuel Mutabazi appears to be a strong candidate for the Best Researcher Award.

Publication profile

google scholar

Education

Mr. Mutabazi is currently pursuing a Ph.D. in Information and Communication Engineering at Hohai University, China, where he has been enrolled since September 2019. He also holds a Master of Engineering in the same field from Hohai University (2016-2019) and a Bachelor of Science in Business Information Technology from the University of Rwanda (2010-2013). His solid educational background has laid a strong foundation for his research endeavors.

Research Interests

Mr. Mutabazi’s research focuses on cutting-edge areas like Natural Language Processing, Machine Learning, Deep Learning, and Computer Vision. His passion for building intelligent systems using AI and ML technologies is evident in his academic and professional work, making him a valuable contributor to these fields.

Skills

He possesses advanced coding skills in multiple programming languages, including Python, MATLAB, C++, Java, and R, among others. His expertise extends to website design, software development, image and video processing, and developing complex systems like Question Answering Systems and Recommender Systems. He is also proficient in using referencing and paper formatting tools such as EndNote, Mendeley, Zotero, and LaTeX.

Experience

Before embarking on his current academic path, Mr. Mutabazi worked as a secondary school teacher at Kiyanza Secondary School (2014-2016), teaching a wide range of subjects. His multilingual abilities (English, French, Swahili, Chinese, and Kinyarwanda) further enhance his capability to engage in global research collaborations.

Publications

Mr. Mutabazi has several peer-reviewed publications, including journal articles and conference papers, showcasing his active participation in research. Notably, his publications include a review on medical textual question-answering systems, a study on SLAM methods, a review of the Marine Predators algorithm, and an improved model for medical forum question classification. His research has been published in reputable journals such as Applied Sciences, Computational Intelligence and Neuroscience, and Machine Learning with Applications.

Conclusion

Considering Mr. Mutabazi’s strong academic background, diverse skill set, significant teaching experience, and impactful research contributions, he is well-suited for the Best Researcher Award. His dedication to advancing knowledge in Information and Communication Engineering, coupled with his proven ability to publish high-quality research, makes him a deserving candidate for this recognition.

Research focus

This researcher focuses on developing advanced deep learning models and algorithms for various applications, particularly in the medical field and computational intelligence. Their work includes creating and improving medical textual question-answering systems and classification models for medical forums using CNN and BiLSTM. Additionally, they explore innovative techniques in marine predator algorithms and direct SLAM methods based on semantic information, highlighting a strong emphasis on machine learning and artificial intelligence in solving complex problems. This research bridges the gap between AI and practical applications in healthcare and robotics. 🤖💡🩺📊

Publication top notes

A review on medical textual question answering systems based on deep learning approaches

Marine predators algorithm: A comprehensive review

An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

A variable radius side window direct slam method based on semantic information