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.

 

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

 

Ioannis Chatzilygeroudis | Computer Science and Artificial Intelligence | Best Researcher Award

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Ioannis Chatzilygeroudis | Computer Science and Artificial Intelligence | Best Researcher Award

Prof Ioannis Chatzilygeroudis, University of Patras, Greece

Prof. Emeritus at the University of Patras, Greece, with a rich educational background in Mechanical and Electrical Engineering (NTUA), Theology (University of Athens), MSc in Information Technology, and a PhD in Artificial Intelligence (University of Nottingham). Fluent in Greek and English, he specializes in AI, KR&R, knowledge-based systems, theorem proving, intelligent tutoring, e-learning, machine learning, natural language generation, sentiment analysis, semantic web, and educational robotics. His prolific research includes a PhD thesis, 18 edited volumes, 21 book chapters, 46 journal papers, 115 conference papers, 8 national conference papers, and 14 workshop papers. 📚🤖💻🌐

Publication profile

Orcid

Education

📚 From September 1968 to June 1974, completed secondary education, earning a Certificate of High School Graduation in Science. 🎓 Pursued a Diploma in Mechanical and Electrical Engineering with a specialization in Electronics at the National Technical University of Athens from October 1974 to July 1979. 📜 From February to June 1983, obtained a Certificate of Educational Studies from PATES of SELETE, Greece. 📖 Achieved a Bachelor in Theology from the University of Athens, completed between October 1979 and December 1987. 🎓 Earned an MSc in Information Technology from the University of Nottingham in 1989, followed by a PhD in Artificial Intelligence from the same university in 1992. 🧠 Thesis: “Integrating Logic and Objects for Knowledge Representation and Reasoning.”

Experience

📘 From Feb. 1982 to June 1982, I served as a part-time lab professor at PALMER Higher School of Electronics in Greece, teaching Electronics Lab. My full-time teaching journey began at TEI of Athens (1982-84), where I covered courses like Electrotechnics and Circuit Theory. My secondary education tenure (1984-92) focused on electrical engineering subjects. I then transitioned to higher education, teaching at TEI of Kozani and Chalkida, and later at the University of Nottingham (1990-92). From 1995-2006, I was a senior researcher and lecturer at the University of Patras, ultimately becoming a professor (2009-2023). Now, I am a Professor Emeritus. 🎓🔬

Projects

From June 1993 to November 1995, I managed the CTI team for the DELTA-CIME project, developing a knowledge-based production control system. I led several initiatives, including the MEDFORM project for multimedia education and the national project for educational software in chemistry. As a senior researcher, I contributed to intelligent systems for tele-education and hybrid knowledge representation. I led multiple European projects like MENUET, AVARES, and TESLA, focusing on innovative education through virtual reality. My work aims to enhance learning experiences across disciplines, involving collaboration with various international partners. 🌍📚💻🎓

Research focus

Ioannis Hatzilygeroudis specializes in artificial intelligence and its applications in various domains, particularly in agriculture and healthcare. His research includes intelligent systems for diagnosing farmed fish diseases, employing deep learning techniques for image analysis, and exploring natural language processing methods. He has contributed significantly to the development of expert systems and reinforcement learning approaches to improve disease prediction in aquaculture. Additionally, his work in sentiment analysis and e-learning demonstrates a commitment to advancing educational technologies and user experience. Hatzilygeroudis’s interdisciplinary approach combines computer science with practical applications, making significant strides in health and environmental management. 🌱🐟💻📊

Publication focus

Using Level-Based Multiple Reasoning in a Web-Based Intelligent System for the Diagnosis of Farmed Fish Diseases

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

A Convolutional Autoencoder Approach for Boosting the Specificity of Retinal Blood Vessels Segmentation

Evaluating Deep Learning Techniques for Natural Language Inference

A Brief Survey of Sim2Real Methods for Robot Learning

E-Learning Courses Evaluation on the Basis of Trainees’ Feedback on Open Questions Text Analysis

Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application