Prof. Ebenezer Esenogho | Artificial Intelligence | Best Researcher Award

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Prof. Ebenezer Esenogho | Artificial Intelligence | Best Researcher Award

Prof. Ebenezer Esenogho | University of South Africa | South Africa

Prof. Ebenezer Esenogho (NRF C2-rated) is a distinguished academic and research leader recognized for his extensive contributions to Artificial Intelligence, telecommunications engineering, and digital innovation. With a strong background in computer, electronics, and communication engineering, he has built an influential career marked by advanced research, academic leadership, and international collaboration. He has produced impactful work across areas such as AI-driven wireless systems, 5G networks, cognitive radio, cybersecurity, IoT, smart grid technologies, software-defined systems, big data, and cloud computing. His scholarly influence is reflected in 920 citations from 762 documents, 40 published documents, and an h-index of 12. Prof. Esenogho has secured numerous competitive grants, awards, and research fellowships in recognition of his excellence, and he has contributed to global research advancement through participation in strategic committees, collaborative initiatives, and high-level innovation programs. He has supervised a wide portfolio of postgraduate researchers, contributed to editorial boards, reviewed international grant proposals, and chaired sessions at reputable conferences. Beyond research, he is committed to academic mentorship, capacity building, and advancing technology-driven development through community-focused initiatives. Prof. Ebenezer continues to play a leading role in shaping the future of intelligent systems, next-generation networks, and multidisciplinary innovation across regional and global research landscapes.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  • Esenogho, E., Mienye, I. D., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access, 10, 16400–16407.

  • Esenogho, E., Djouani, K., & Kurien, A. M. (2022). Integrating artificial intelligence, Internet of Things, and 5G for next-generation smart grid: A survey of trends, challenges, and prospects. IEEE Access, 10, 4794–4831.

  • Sarah, I. D. M., Ebiaredoh-Mienye, A., Esenogho, E., & Swart, T. G. (2022). A machine learning method with filter-based feature selection for improved prediction of chronic kidney disease. Bioengineering, 9(8).

  • Nguyen, N., Duong, T., Chau, T., Nguyen, V. H., Trinh, T., Tran, D., Ho, T., … Esenogho, E. (2022). A proposed model for card fraud detection based on CatBoost and deep neural network. IEEE Access, 10, 96852–96861.

  • Arnaz, A., Lipman, J., Abolhasan, M., Hiltunen, M., … Esenogho, E. (2022). Toward integrating intelligence and programmability in open radio access networks: A comprehensive survey. IEEE Access, 10, 67747–67770.

  • Obaido, G., Ogbuokiri, B., Swart, T. G., Ayawei, N., Kasongo, S. M., Aruleba, K., … Esenogho, E. (2022). An interpretable machine learning approach for hepatitis B diagnosis. Applied Sciences, 12(21), 11127.

 

Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

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Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

Assist. Prof. Dr. Yan Zeng | Hangzhou Dianzi University | China

Assist. Prof. Dr. Yan Zeng, an accomplished associate professor at the School of Computer Science, Hangzhou Dianzi University, has made significant contributions in the fields of distributed and parallel computing, distributed machine learning, and big data analytics. After earning her PhD from the Institute of Software, Chinese Academy of Sciences in 2016, her research has focused on advancing large-scale computation and data-intensive systems.  The Key Research and Development Program of Zhejiang Province, the Yangtze River Delta Project, and the Natural Science Foundation of Zhejiang Province. Her academic influence is reflected in 173 citations by 161 documents, 42 published papers, and an h-index of 9, demonstrating strong research impact and visibility. With 10 peer-reviewed publications in SCI and Scopus-indexed journals, Yan Zeng’s scholarly output showcases innovation in computational frameworks and distributed systems. Furthermore, she has been actively involved in practical technological advancements, holding 34 patents that bridge theoretical insights with industrial applications. Through her extensive research, publication record, and innovation-driven approach, Yan Zeng continues to play a pivotal role in shaping advancements in computer science and data engineering.

Profile: Scopus

Featured Publications

Zeng, Y., et al. (2025). FedAMM: Federated learning for brain tumor segmentation with arbitrary missing modalities [Conference paper]. Proceedings of the International Conference on Artificial Intelligence and Machine Learning.

Zeng, Y., et al. (2025). TransAware: An automatic parallel method for deep learning model training with global model structure awareness [Conference paper]. Proceedings of the International Conference on Advanced Computing and Applications.

Zeng, Y., et al. (2025). A correlation analysis-based federated learning framework for defending against collusion-free-riding attacks. Cybersecurity, 2025(1), 1–12.

Zeng, Y., et al. (2025). FedAEF: Optimizing federated learning with mining and enhancing local data features. Cluster Computing, 2025(1), 1–15.

Dr. Zahra Beheshti | AI in Engineering | Best Researcher Award

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Dr. Zahra Beheshti | AI in Engineering | Best Researcher Award

Dr. Zahra Beheshti | Islamic Azad University | Iran

Dr. Zahra Beheshti is an Assistant Professor at the Islamic Azad University, Najafabad Branch, with a distinguished background in computer engineering and artificial intelligence. She holds a B.Sc. and M.Sc. in Computer Engineering (Software) and a Ph.D. in Computer Science with a focus on Artificial Intelligence, followed by postdoctoral research in Soft Computing. Dr. Beheshti has made significant contributions to the field, including the compilation of the book Centripetal Accelerated Particle Swarm Optimization and Applications. Her academic excellence has been recognized through scholarships awarded to top international Ph.D. students. She is actively involved in knowledge dissemination, having conducted multiple workshops on advanced topics such as Machine Learning, Fuzzy Expert Systems and their application in algorithm parameter determination, and Introduction to Fuzzy Logic along with the Design and Implementation of Fuzzy Expert Systems. Her research output includes 34 documents, cited 1,865 times by 1,698 publications, with an h-index of 20, reflecting the significant impact of her work. Through her teaching, research, and publications, Dr. Beheshti demonstrates a strong commitment to advancing computational intelligence, fostering innovation, and mentoring the next generation of researchers in AI and soft computing, combining both academic rigor and practical application.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

  • Z Beheshti, SMH Shamsuddin, A review of population-based meta-heuristic algorithms, Int. J. Adv. Soft Comput. Appl 5 (1), 1-35, 744 citations, 2013

  • H Abedinpourshotorban, SM Shamsuddin, Z Beheshti, DNA Jawawi, Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm, Swarm and Evolutionary Computation 26, 8-22, 414 citations, 2016

  • M Jafarzadegan, F Safi-esfahani, Z Beheshti, Combining Hierarchical Clustering approaches using the PCA Method, Expert Systems with Applications 137, 1-10, 156 citations, 2019

  • Z Beheshti, SM Shamsuddin, S Hasan, Memetic binary particle swarm optimization for discrete optimization problems, Information Sciences 299, 58-84, 129 citations, 2015

  • Z Beheshti, SMH Shamsuddin, CAPSO: centripetal accelerated particle swarm optimization, Information Sciences 258, 54-79, 120 citations, 2014

  • M Banaie-Dezfouli, MH Nadimi-Shahraki, Z Beheshti, R-GWO: Representative-based grey wolf optimizer for solving engineering problems, Applied Soft Computing 106, 1-28, 108 citations, 2021

 

 

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

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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).

 

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

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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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Ihor Tereikovskyi | Neural Network Applications | Cybersecurity Excellence Award

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Prof. Dr. Ihor Tereikovskyi | Neural Network Applications | Cybersecurity Excellence Award

Prof. Dr. Ihor Tereikovskyi , at National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” ,Ukraine.

Prof.Dr. Ihor Anatolyevich Tereikovskyi  is a distinguished professor and researcher in the field of Information Security and Computer Science 🖥️🔐. With over three decades of academic and research experience, he has contributed significantly to biometric authentication, cybersecurity, and voice signal recognition 🎤📊. He holds a Doctor of Technical Sciences degree and serves as a professor at multiple universities, including the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” 🎓. His expertise extends to managing emotions using sound and video technologies 🎥🎶, making groundbreaking advancements in AI-driven security systems 🤖🔒.

Publication Profile

Orcid
Scopus
Google Scholar

Education & Experience

🎓 Education

✅ MSc in Aircraft Maintenance (1984–1992) – Kyiv International University of Civil Aviation ✈️
✅ Ph.D. in Technical Science (1992–1995) – Kyiv International University of Civil Aviation 📚
✅ Diploma of Associate Professor in Computer Science (2008) – Kyiv, Ukraine 🏆
✅ Doctorate of Technical Science (2013–2015) – National Aviation University 🏅
✅ Doctor of Technical Sciences (2015) – National Aviation University 🎖️
✅ Professor of System Programming (2018) – National Aviation University 🏛️

💼 Work Experience

✅ Professor – National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (2015–Present) 🖥️
✅ Professor – Al-Farabi Kazakh National University (2020–Present) 🌍
✅ Professor – National Aviation University (2015–Present) ✈️
✅ Associate Professor – National Technical University of Ukraine (2011–2013) 🏛️
✅ Associate Professor – Kiev Slavonic University (2006–2011) 📖
✅ Associate Professor – Kiev National Trade and Economic University (2003–2006) 📊
✅ Associate Professor – European University (2002–2003) 🌐
✅ Associate Professor – Kyiv International University of Civil Aviation (1999–2002) ✈️
✅ Assistant Professor – Kyiv International University of Civil Aviation (1995–1999) 📚

Suitability Summary

Prof.Dr. Ihor Anatolyevich Tereikovskyi is a distinguished researcher and cybersecurity expert whose groundbreaking work in biometric authentication, AI-driven security systems, and voice signal recognition has significantly advanced the field of cybersecurity and information protection. His dedication to developing secure information systems and enhancing cybersecurity resilience makes him an ideal candidate for the Cybersecurity Excellence Award. With over 30 years of academic and research experience, he has contributed to critical infrastructure protection, facial and voice recognition, and AI-based security solutions, demonstrating a profound impact on the global cybersecurity landscape.

Professional Development

Prof.Dr. Ihor Anatolyevich Tereikovskyi  has dedicated his career to advancing cybersecurity, biometric authentication, and artificial intelligence 🔐🤖. As a professor and researcher, he has contributed to multiple international universities, focusing on developing secure information systems 🏛️📡. His work in voice signal recognition, emotion detection, and AI-based security solutions has gained global recognition 🎤🎭. With a strong foundation in aviation and IT security, he bridges the gap between engineering and modern cybersecurity innovations ✈️💻. His research has shaped next-generation security systems, integrating AI, biometrics, and real-time monitoring for enhanced digital protection 🛡️🔍.

Research Focus

Prof.Dr. Ihor Anatolyevich Tereikovskyi specializes in Information Security and Cybersecurity 🔐💻, particularly in biometric authentication, voice recognition, and AI-driven security solutions 🎤📊. His research explores the intersection of technology and human emotions, focusing on managing emotions through sound and video technologies 🎶🎥. He has developed intelligent security systems that integrate machine learning, behavioral analysis, and deep learning 🤖📡. His contributions extend to protecting digital assets, detecting cyber threats, and improving authentication methods 🔒📈. With a passion for enhancing AI-based security protocols, he continuously explores new strategies for securing sensitive data 🛡️📊.

Awards & Honors 🏆

🏅 Doctor of Technical Sciences – National Aviation University (2015)
🏆 Professor of System Programming & Computer Systems (2018)
🎖 Associate Professor in Computer Science & Control Systems (2008)
🌍 International Recognition in AI-driven Cybersecurity Research
🥇 Outstanding Contributions to Biometric Authentication & Voice Recognition
🏅 Honorary Professor at Al-Farabi Kazakh National University (2020)

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 (Cited by: N/A) 🧑‍💻🔐

📖 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 (Cited by: N/A) 🏛️📸

📖 Концептуальна модель процесу визначення емоційної тональності тексту – Computer-Integrated Technologies: Education, Science, Production, 2024, DOI: 10.36910/6775-2524-0560-2024-55-14 (Cited by: N/A) 📜🔍

📖 Формалізація процесу розпізнавання ключових слів у голосовому сигналі – Computer-Integrated Technologies: Education, Science, Production, 2024, DOI: 10.36910/6775-2524-0560-2024-55-09 (Cited by: N/A) 🎤🧠

📖 Концептуальна модель процесу прогнозування навантаження на вебсервер – Computer-Integrated Technologies: Education, Science, Production, 2024, DOI: 10.36910/6775-2524-0560-2024-54-09 (Cited by: N/A) 🌐📊.

Conclusion

Prof. Ihor Tereikovskyi’s pioneering research, technological innovations, and academic contributions place him among the top cybersecurity and AI researchers globally. His groundbreaking work in biometric authentication, facial and voice recognition, and AI-driven security solutions has not only strengthened critical infrastructure protection but also set new standards in cybersecurity. As a visionary in digital security and artificial intelligence, he is an outstanding candidate for the Best Researcher Award and the Cybersecurity Excellence Award, with a legacy of innovation, impact, and leadership in the field.