Csaba Makó | Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Csaba Makó
Ludovika University of Public Service, Hungary

Csaba Makó
Affiliation Ludovika University of Public Service
Country Hungary
Scopus ID 6508289335
Documents 49
Citations 585
h-index 10
Subject Area Artificial Intelligence
Event Superior Engineering Research Awards
ORCID 0000-0002-2597-3103

Csaba Makó is a researcher affiliated with the Ludovika University of Public Service in Hungary. The researcher record supplied for this recognition profile identifies 49 documents, 585 citations, and an h-index of 10 in the Scopus-indexed record. The stated subject area is Artificial Intelligence, providing the disciplinary context for consideration under the Innovative Research Award category. [1]

Abstract

This academic recognition profile presents the documented research information of Csaba Makó in relation to the Innovative Research Award. His supplied bibliometric record contains 49 documents, 585 citations, and an h-index of 10. The profile identifies Artificial Intelligence as the relevant subject area and places the researcher within the academic environment of the Ludovika University of Public Service. Bibliographic identifiers provide a basis for distinguishing the researcher and locating associated scholarly records. [2]

Keywords

  • Artificial Intelligence
  • Innovation
  • Research
  • Bibliometrics
  • Academic Publications
  • Research Impact

Introduction

Research recognition commonly considers scholarly output, documented influence, disciplinary relevance, and identifiable research records. In this context, the Innovative Research Award profile records the available bibliometric indicators associated with Csaba Makó and relates them to the stated field of Artificial Intelligence. Scopus author identifiers and ORCID provide complementary mechanisms for researcher identification and scholarly record management. [1] [3]

Research Profile

The supplied profile associates Csaba Makó with the Ludovika University of Public Service in Hungary and identifies Artificial Intelligence as the principal subject area. The reported publication record consists of 49 documents. Its citation count is 585, while the h-index is reported as 10. These indicators describe bibliometric activity but do not, by themselves, characterize the methodological quality or substantive significance of individual publications. [1]

Research Contributions

Within the information supplied for this profile, the research contribution is framed around scholarly activity in Artificial Intelligence. Relevant contributions may be examined through individual publications, research topics, collaborations, methodologies, and documented applications. A complete assessment requires examination of the underlying publications and their research contexts rather than relying solely on aggregate bibliometric indicators. [4]

Publications

The supplied Scopus record reports 49 documents associated with the researcher identifier 6508289335. Individual publication titles, journals, publication dates, citation distributions, and DOI records should be verified against the corresponding bibliographic databases before being used for detailed publication-level analysis. [1]

Research Impact

The reported total of 585 citations and h-index of 10 provides a quantitative description of the researcher’s citation record at the time represented by the supplied data. Citation indicators can vary between databases and over time, and they may also differ according to indexing coverage. Consequently, the figures should be interpreted together with publication-level and disciplinary context. [1] [5]

Award Suitability

The Innovative Research Award profile connects the supplied research record with the Superior Engineering Research Awards event. The documented affiliation, subject area, publication count, citation count, h-index, and persistent researcher identifier provide factual information for an award-review process. Final recognition should be determined according to the applicable award criteria and verification procedures. [6]

Conclusion

Csaba Makó’s supplied academic profile documents a research record associated with the Ludovika University of Public Service, Hungary, and Artificial Intelligence. The reported 49 documents, 585 citations, and h-index of 10 form the principal bibliometric indicators presented in this recognition profile. Persistent identifiers such as Scopus Author ID and ORCID support researcher identification and facilitate further verification of scholarly records.

References

  1. ORCID. (n.d.). Csaba Makó — ORCID record.
    https://orcid.org/0000-0002-2597-3103
  2. Elsevier. (n.d.). Scopus Author Details: Csaba Makó, Author ID 6508289335.
    https://www.scopus.com/authid/detail.uri?authorId=6508289335
  3. Makó, C., Illéssy, M., Pap, J., Farkas, É., & Komlósi, L. (2025). Algorithmic Management in Traditional Workplaces: The Case of High vs. Low Involvement Working Practices. Journal of Labor and Society. https://doi.org/10.1163/24714607-bja10182
  4. Molina, O., Butollo, F., Makó, C., et al. (2023). It takes two to code: A comparative analysis of collective bargaining and artificial intelligence. Transfer: European Review of Labour and Research. https://doi.org/10.1177/10242589231156515
  5. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.

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.

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.