Csaba Makó | Artificial Intelligence | Innovative Research Award

Published on by Superior Engineering

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

Published on by Superior Engineering

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.