Prof. Ouajdi Korbaa | AI in Engineering | Innovative Research Award

Published on 29/09/202529/09/2025 by Superior Awards

Prof. Ouajdi Korbaa | AI in Engineering | Innovative Research Award

Prof. Ouajdi Korbaa | University of Sousse | Tunisia

Prof. Ouajdi Korbaa is a distinguished researcher and professor at the Institute of Computer Science and Communication Techniques, University of Sousse, Tunisia, and a member of the Modeling of Automated Reasoning Systems Laboratory. His research focuses on modeling, discrete optimization, scheduling, and artificial intelligence, contributing significantly to the development of advanced methodologies in these areas. He has supervised numerous Master’s and PhD students and actively participates in academic juries, reflecting his commitment to mentoring the next generation of researchers. Prof. Korbaa has authored 157 documents cited by 998 sources, achieving an h-index of 18, demonstrating his strong impact and influence in the field. His work integrates theoretical foundations with practical applications, advancing computational techniques for problem-solving and decision-making. Recognized for his expertise in optimization and AI, he has made substantial contributions to both the academic community and the broader field of computer science, fostering innovation in modeling and automated reasoning systems.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

Nssibi, M., Manita, G., & Korbaa, O. (2023). Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Computer Science Review, 49, 100559.
Jemili, F., Meddeb, R., & Korbaa, O. (2024). Intrusion detection based on ensemble learning for big data classification. Cluster Computing, 27(3), 3771–3798.
Benzarti, S., Triki, B., & Korbaa, O. (2017). A survey on attacks in Internet of Things based networks. In Proceedings of the 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1–7).
Meddeb, R., Jemili, F., Triki, B., & Korbaa, O. (2023). A deep learning-based intrusion detection approach for mobile Ad-hoc network. Soft Computing, 27(14), 9425–9439.
Abid, A., Jemili, F., & Korbaa, O. (2024). Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques. Cluster Computing, 27(2), 2217–2238.
Korbaa, O., Camus, H., & Gentina, J. C. (1997). FMS cyclic scheduling with overlapping production cycles. In Proceedings of the 18th International Conference on Application and Theory of Automation in Technology (pp. 1–10).
Lee, J., & Korbaa, O. (2004). Modeling and scheduling of ratio-driven FMS using unfolding time Petri nets. Computers & Industrial Engineering, 46(4), 639–653.
Meddeb, R., Triki, B., Jemili, F., & Korbaa, O. (2017). A survey of attacks in mobile ad hoc networks. In Proceedings of the 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1–7).

Dr. Ren Jianji | AI in Engineering | Best Researcher Award

Published on 22/09/202522/09/2025 by Superior Awards

Dr. Ren Jianji | AI in Engineering | Best Researcher Award

Dr. Ren Jianji | Henan Polytechnic University | China

Dr. Ren Jianji is an Associate Professor at the School of Software, Henan University of Technology. She earned her Doctoral and Master degrees in Computer Science and Technology from Dong-A University and her Bachelor degree in Information Management and Information Systems from Jinan University. Since joining Henan University of Technology in 2013, she has advanced from Lecturer to Associate Professor, making significant contributions to computer science and software engineering education and research. Over the past 5 years, she has led several major research projects, including a key provincial project on federated learning in edge computing, a collaborative algorithm study for edge intelligence based on complex networks, and multiple industrial projects focused on industrial big data analysis, digital twin systems, and Internet of Vehicles technologies. Dr. Ren’s research interests include edge computing, intelligent algorithms, digital twin systems, and applied big data analytics, reflecting a strong combination of theoretical innovation and practical implementation. She has authored 45 research documents, cited 976 times by 740 documents, with an h-index of 16. Her work has advanced intelligent computing applications in both academic and industrial settings, demonstrating her leadership in developing algorithms and systems that address real-world challenges and establishing her as a leading figure in intelligent computing in China.

Profile: Scopus

Featured Publications

Ren, J. (2025). A novel ensemble network based on CNN-AM-BiLSTM learner for temperature prediction of distillation columns. Canadian Journal of Chemical Engineering.
Ren, J. (2025). Short-term power load forecasting based on SKDR hybrid model. Electrical Engineering.
Ren, J. (2025). A method for intelligent information extraction of coal fractures based on µCT and deep learning. Meitiandizhi Yu Kantan Coal Geology and Exploration.
Ren, J. (2025). Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine. Quality and Reliability Engineering International.

Mr. Bimal Kumar Dora | AI in Engineering | Best Researcher Award

Published on 19/09/202519/09/2025 by Superior Awards

Mr. Bimal Kumar Dora | AI in Engineering | Best Researcher Award

Mr. Bimal Kumar Dora | Visvesvaraya National Institute of Technology | India

Mr. Bimal Kumar Dora is a dedicated researcher in Electrical Engineering, currently pursuing his Doctor of Philosophy at Visvesvaraya National Institute of Technology, Nagpur, after completing his Master of Technology in Control, Power and Electric Drives from the National Institute of Technology, Sikkim, and a Bachelor of Technology in Electrical Engineering from Biju Patnaik University of Technology, Odisha. He recently broadened his international research experience as a Visiting Researcher at the Montefiore Institute, University of Liège, Belgium, where he contributed to advanced studies in renewable energy integration and the development of global electricity grids. His doctoral research, titled Global Electricity Interconnection with Renewable Energy Generation, emphasizes methods such as the Enhanced Critical Time Window Framework, Weibull distribution analysis, and temporal variability indexing to identify and optimize renewable energy sites across Indian onshore and offshore regions. He has designed several innovative hybrid algorithms including Modified Pelican Optimization Algorithm, Novel Modified Pelican Driven Optimization Algorithm, Enhanced Pelican Foraging Algorithm, Enhanced Dragonfly and Moth Optimization Algorithm, Modified Reptile Optimization Algorithm, Modified Harris Hawk and Pelican Optimization Algorithm, and Enhanced Harris Hawk and Pelican Optimization Algorithm. With 97 citations from 75 documents, 16 publications, and an index rating of 7, he is building a growing academic reputation that combines computational intelligence, renewable energy, and futuristic large-scale power system design.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

Dora, B. K., Rajan, A., Mallick, S., & Halder, S. (2023). Optimal reactive power dispatch problem using exchange market based butterfly optimization algorithm. Applied Soft Computing, 147, 110833.
Halder, S., Bhat, S., & Dora, B. K. (2022). Inverse thresholding to spectrogram for the detection of broken rotor bar in induction motor. Measurement, 198, 111400.
Halder, S., Bhat, S., & Dora, B. (2023). Start-up transient analysis using CWT and ridges for broken rotor bar fault diagnosis. Electrical Engineering, 105(1), 221–232.
Halder, S., Dora, B. K., & Bhat, S. (2022). An enhanced pathfinder algorithm based MCSA for rotor breakage detection of induction motor. Journal of Computational Science, 64, 101870.
Dora, B. K., Bhat, S., Halder, S., & Srivastava, I. (2024). A solution to multi objective stochastic optimal power flow problem using mutualism and elite strategy based pelican optimization algorithm. Applied Soft Computing, 158, 111548.
Dora, B. K., Bhat, S., Halder, S., & Sahoo, M. (2023). Solution of reactive power dispatch problems using enhanced dwarf mongoose optimization algorithm. 2023 International Conference for Advancement in Technology (ICONAT), 1–6.

Prof. Dr. Liudmyla Tereikovska | Neural Network Applications | Cybersecurity Excellence Award

Published on 03/03/202503/03/2025 by Superior Awards

Prof. Dr. Liudmyla Tereikovska | Neural Network Applications | Cybersecurity Excellence Award

Prof. Dr. Liudmyla Tereikovska , Kyiv National University of Construction and Architecture , Ukraine.

Prof.Dr. Liudmyla Tereikovska, is a distinguished Ukrainian scientist in information technology and cybersecurity. She holds a Ph.D. in Technical Sciences and a Doctor of Technical Sciences degree. Currently, she is a Professor at Kyiv National University of Construction and Architecture. Her expertise spans information security, voice signal recognition, biometric authentication, and cybersecurity. Passionate about innovation, she has contributed significantly to the field through extensive research and teaching. With a deep commitment to technological advancement, she continues to inspire future generations in the IT sector.

Publication Profile

Scopus

Orcid

Education & Experience

MSc in Technological Engineering – State Academy of Light Industry of Ukraine (1997)
Ph.D. in Technical Science – Kyiv National University of Construction and Architecture (2016)
Doctor of Technical Sciences (Dr. Eng.) – National Transport University (2023)
Professor of Information Technology Design & Applied Mathematics (2024)

Professional Experience:

2023 – Present: Professor, Kyiv National University of Construction and Architecture
2019 – 2023: Associate Professor, Kyiv National University of Construction and Architecture
2019 – 2021: Doctoral Student, Kyiv National University of Construction and Architecture
2017 – 2019: Associate Professor, Department of Cybersecurity & Computer Engineering
2012 – 2016: Postgraduate Student, Kyiv National University of Construction and Architecture
2010 – 2012: Assistant, Department of Information Technologies
2004 – 2010: Assistant, Department of Informatics & Control Systems

Suitability summary

Prof.Dr. Liudmyla Tereikovska, is a highly esteemed researcher in cybersecurity and information technology, making her a strong candidate for the Cybersecurity Excellence Award. With a Doctor of Technical Sciences degree and extensive experience in biometric authentication, facial recognition, and AI-driven cybersecurity solutions, she has significantly contributed to enhancing digital security frameworks. Her research has played a crucial role in developing intelligent security systems, voice signal recognition, and emotion detection, ensuring innovative advancements in critical infrastructure protection. Her dedication to cutting-edge technology has had a profound impact on biometric authentication methods and AI-based cybersecurity solutions.

Professional Development

Prof.Dr. Liudmyla Tereikovska, has made remarkable strides in the field of cybersecurity and information technology. With expertise in voice signal recognition, biometric authentication, and intelligent information security systems, she has contributed extensively to research and education. As a Professor, she mentors students, shaping the next generation of IT professionals. Her commitment to technological advancements is reflected in her research on emotion recognition and cyber protection. Dedicated to innovation, she collaborates on international projects and continuously develops advanced cybersecurity solutions. Her work significantly impacts the future of digital security and AI-driven authentication.

Research Focus

Prof.Dr. Liudmyla Tereikovska, research delves into cutting-edge cybersecurity advancements, biometric authentication, and artificial intelligence applications. She specializes in voice signal recognition, emotion detection, and intelligent security systems, ensuring innovative solutions in cyber protection. Her work also explores AI-driven biometric authentication, enhancing security measures in digital environments. With a keen interest in data protection and cryptographic technologies, she develops smart solutions for safeguarding sensitive information. Collaborating on global projects, she contributes to enhancing cybersecurity frameworks worldwide. Her research directly influences next-gen security technologies and AI-driven risk detection systems.

Awards & Honors

Associate Professor Diploma – Department of Cybersecurity & Computer Engineering (2018)
Doctor of Technical Sciences Degree – National Transport University (2023)
Professor Title – Department of Information Technology Design & Applied Mathematics (2024)
Recognized for Excellence in Cybersecurity Research
Honored for Contributions to Biometric Authentication and AI Development

Publication Top Notes

“Modular Neural Network Model for Biometric Authentication of Personnel in Critical Infrastructure Facilities Based on Facial Images” – Applied Sciences (2025) | DOI: 10.3390/app15052553

“Conceptual model of the face recognition process based on the image of the face and iris of personnel of critical infrastructure facilities” – Conference Paper (2024) | DOI: 10.23939/IW_itpm2024.278

“Концептуальна модель процесу прогнозування навантаження на вебсервер” – Computer-Integrated Technologies: Education, Science, Production (2024) | DOI: 10.36910/6775-2524-0560-2024-54-09

“Multiple Effectiveness Criteria of Forming Databases of Emotional Voice Signals” – Cybersecurity: Education, Science, Technique (2023) | DOI: 10.28925/2663-4023.2023.21.6574

“Infrastructure Projects of Road Traffic Management: Management Methodology Selection” – Management of Development of Complex Systems (2023) | DOI: 10.32347/2412-9933.2023.56.24-30

Conclusion

Prof.Dr. Liudmyla Tereikovska, outstanding contributions to cybersecurity, biometric authentication, and AI-driven security systems make her an ideal recipient of the Best Researcher Award. Her research not only strengthens global cybersecurity standards but also drives forward next-generation technological advancements. Through her groundbreaking work, publications, and leadership in academia, she continues to shape the future of information security and artificial intelligence. .