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.

 

Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

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Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

Mr. Adizue Ugonna | Budapest University of Technology and Economics | Hungary

Mr. Adizue Ugonna Loveday is a Doctoral Researcher and Laboratory Instructor at the Budapest University of Technology and Economics, specializing in Mechanical Engineering with expertise in industrial and production systems. His research focuses on intelligent modelling and process optimization for ultra-precision machining of hard materials, integrating artificial intelligence, tribological analysis, and thermal modeling to enhance manufacturing precision and efficiency. Professionally, he has contributed to several major research initiatives including the Horizon 2020 Centre of Excellence in Production Informatics and Control (EPIC CoE), the iNext project on industrial digitalization, and multiple Hungarian Scientific Research Fund (OTKA) projects emphasizing AI-based predictive models for advanced machining and intelligent forming processes. His scholarly record demonstrates strong research performance, with 45 citations by 42 documents, 6 documents, and an h-index of 4 in Scopus; and 66 citations, an h-index of 5, and an i10-index of 2 in Google Scholar. In addition, his ORCID profile lists 6 professional activities and 8 published works, reflecting active engagement in international research collaboration, scientific reviewing, and production editing. Through these contributions, Mr. Loveday continues to advance smart and sustainable manufacturing technologies, bridging artificial intelligence and mechanical systems design in alignment with Industry 4.0 innovation goals.

Profiles: Scopus | Orcid | Google Scholar | LinkedIn | ResearchGate

Publication Details

  1. Adizue, U. L., Tura, A. D., Isaya, E. O., Farkas, B. Z., & Takács, M. (2023). Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool. The International Journal of Advanced Manufacturing Technology, 128(1), 1–28.

  2. Adizue, U. L., Nwanya, S. C., & Ozor, P. A. (2020). Artificial neural network application to a process time planning problem for palm oil production. Engineering and Applied Science Research, 47(2), 161–169.

  3. Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study. The International Journal of Advanced Manufacturing Technology, 1–30.

  4. Elly, O. I., Adizue, U. L., Tura, A. D., Farkas, B. Z., & Takács, M. (2024). Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(1), 1–18.

  5. Adizue, U. L., Balázs, B. Z., & Takács, M. (2022). Surface roughness prediction applying artificial neural network at micro machining. IOP Conference Series: Materials Science and Engineering, 1246(1), 012034.

  6. Tura, A. D., Isaya, E. O., Adizue, U. L., Farkas, B. Z., & Takács, M. (2024). Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools. Heliyon, 10(11), e24357.

  7. Adizue, U. L., Agbadah, S. E., Ibeagha, D. C., & Falade, Y. O. (2017). Design and construction of an automated adjustable-can foil sealing machine. International Journal of Engineering and Applied Sciences, 4(9), 257384.

 

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

 

 

Mr. Eze Jude Uche | Artificial Intelligence | Best Researcher Award

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Mr. Eze Jude Uche | Artificial Intelligence | Best Researcher Award

Mr. Eze Jude Uche | The Ohio State University College of Pharmacy | United States

Mr. Eze Jude Uche is a dedicated and accomplished researcher in pharmacoepidemiology, patient-reported outcomes, cancer therapy, infection prevention, and predictive modeling. He is currently pursuing a PhD in Health Services & Outcomes Research at The Ohio State University College of Pharmacy, where he is recognized as a Dean’s Distinguished Fellow and receives advanced training in pharmacoepidemiology, biostatistics, survival analysis, causal inference, and bioinformatics. He holds a Bachelor of Pharmacy (BPharm) from the University of Nigeria, Nsukka, with a distinction in Pharmacy Administration and Management; his thesis focused on assessing malaria treatment patterns and costs in community pharmacies and patent medicine shops in Nsukka and Enugu. He has 2 published documents to his credit. Mr. Uche has extensive research experience, including a tenure as a Graduate Research Assistant and Intern Pharmacist at the National Institute for Pharmaceutical Research and Development in Abuja, where he conducted comprehensive literature reviews, analyzed and interpreted experimental results, and authored detailed research reports. His hands-on experience spans microbial preparation, standardization of bacteria and Candida species, extraction of plant materials, chromatographic separation for pharmacologic screening, in-vitro drug testing using animal models, and qualitative and quantitative pharmaceutical analysis, reflecting a strong commitment to advancing pharmaceutical and healthcare research.

Profile: Scopus

Featured Publication

Uche, E. J.,(2025). Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction. BMC Medical Informatics and Decision Making. Advance online publication.

 

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

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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. 🤖