Eugeny Smirnov | Artificial Intelligence | Innovative Research Award

Published on by Computer Vision Awards

Innovative Research Award

              Eugeny Smirnov
Affiliation K.D. Ushinsky Yaroslavl State Pedagogical University
Country Russia
Scopus ID 55734369900
Documents 28
Citations 85
h-index 6
Subject Area Artificial Intelligence
Event Global Tech Excellence Awards
ORCID 0000-0002-8780-7186

Eugeny Smirnov
K.D. Ushinsky Yaroslavl State Pedagogical University, Russia

Eugeny Smirnov is an academic researcher whose work integrates artificial intelligence, mathematics education, intelligent learning technologies, and computational modeling. His scholarly activities emphasize digital transformation in education, hybrid intellectual environments, and innovative instructional methodologies while contributing to interdisciplinary research addressing contemporary educational and computational challenges.[1]

Abstract

Eugeny Smirnov has developed a research portfolio centered on artificial intelligence, mathematics education, hybrid learning environments, and computational methodologies. His publications investigate intelligent educational systems, digital learning platforms, nonlinear mathematical modeling, and innovative assessment technologies. Through interdisciplinary collaboration, his studies contribute to educational modernization by integrating advanced computational techniques with pedagogical practice. His work supports improved student engagement, research-oriented learning, and technology-enhanced instruction while advancing scholarly understanding of intelligent educational ecosystems and practical digital transformation strategies in higher education and mathematics teaching.[1]

Keywords

Artificial Intelligence, Mathematics Education, Intelligent Tutoring Systems, Educational Technology, Hybrid Learning, Computational Modeling, Digital Education, Research Software, Learning Analytics, Nonlinear Dynamics

Introduction

The research activities of Eugeny Smirnov emphasize the application of artificial intelligence and digital technologies within mathematics education. His investigations combine computational innovation with pedagogical methodology, supporting effective teaching, research participation, and intelligent educational environments that respond to evolving academic and technological requirements.[1]

Research Profile

Smirnov’s scholarly profile demonstrates sustained contributions across artificial intelligence, educational informatics, mathematical modeling, and software-assisted learning. His publications reflect interdisciplinary collaboration, emphasizing practical educational solutions supported by computational intelligence, hybrid instructional systems, and evidence-based approaches that strengthen modern higher education practices.[2]

Research Contributions

His research contributions include intelligent educational software, advanced mathematical assessment methods, nonlinear dynamic analysis, and hybrid research environments. These studies encourage technology-supported learning, improve analytical capabilities among students, and demonstrate the integration of computational techniques with innovative educational methodologies across multiple academic disciplines.[3]

Publications

The publication record includes peer-reviewed journal articles, conference proceedings, and scholarly book chapters focusing on intelligent educational systems, mathematics instruction, computational applications, and digital learning technologies. These publications demonstrate methodological diversity while addressing contemporary challenges in educational innovation and artificial intelligence research.[1][2]

Research Impact

The research has contributed to expanding knowledge in artificial intelligence applications for education while supporting practical improvements in mathematics teaching. Citation activity, collaborative publications, and interdisciplinary investigations demonstrate academic recognition and continuing relevance within educational technology and computational research communities.[2]

Award Suitability

Eugeny Smirnov demonstrates qualities aligned with the Innovative Research Award through sustained scholarly productivity, interdisciplinary collaboration, and contributions to artificial intelligence in education. His research reflects innovation, practical educational relevance, and continuous advancement of digital learning methodologies suitable for international academic recognition.[3]

Conclusion

Overall, Eugeny Smirnov’s academic achievements illustrate a balanced combination of theoretical investigation and practical educational innovation. His interdisciplinary work continues supporting advancements in intelligent learning technologies, computational education, and mathematics instruction while contributing meaningfully to international research and scholarly development.[1][3]

References

  1. Smirnov, E. (2025). Complex Multi-Stage Tasks for Testing Schoolchildren in the Mathematics Course. In Springer Proceedings.
    https://link.springer.com/chapter/10.1007/978-3-031-84039-5_2
  2. Smirnov, E. (2023). Software Package to Support Students’ Research Activities in the Hybrid Intellectual Environment of Mathematics Teaching.Web of Science
    https://www.webofscience.com/wos/woscc/full-record/WOS:000940699300001
  3. Smirnov, E. (2021). Manifestation Technology of Non-linear Dynamics Synergetic Effects of Schwartz Cylinder’s Areas. Springer.
    https://link.springer.com/chapter/10.1007/978-3-030-78273-3_2

Ahmet Kayabaşı| Artificial Intelligence | Best Researcher Award

Published on by Computer Vision Awards

Prof. Dr. Ahmet Kayabaşı | Artificial Intelligence | Best Researcher Award

Professor | Karamanoglu Mehmetbey University | Turkey

Prof. Dr. Ahmet Kayabaşı is a distinguished academic in electrical-electronics engineering with expertise in artificial intelligence, antennas, biomedical signal processing, image processing, fuzzy logic, and power electronics. He earned his PhD in Electrical-Electronics Engineering from Selcuk University and has since built a strong academic career combining teaching, research, and leadership. His professional experience includes serving as Head of Department, Director of the Institute of Graduate Studies, and Senate Member, along with mentoring numerous MSc and PhD students. His research interests span interdisciplinary fields, applying advanced AI techniques in UAV swarm algorithms, smart agriculture, biomedical diagnostics, and energy-efficient power systems. He has been actively involved in TÜBİTAK and institutional projects, contributing to impactful solutions for both academia and industry. Recognized for his excellence, he has received awards such as Best Presenter Award at ICAT and has played vital roles in academic conferences and scientific communities. His research skills include developing intelligent systems, applying machine learning to engineering challenges, and designing novel antenna and biomedical applications. He has published widely in leading international journals indexed in IEEE, Scopus, and Web of Science, with notable contributions in Applied Thermal Engineering, Swarm and Evolutionary Computation, and Computers and Electronics in Agriculture. His academic excellence is reflected in 609 citations by 522 documents, 47 publications, and an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593.

  2. Yigit, E., Sabanci, K., Toktas, A., & Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156, 369–377.

  3. Kayabasi, A., Toktas, A., Yigit, E., & Sabanci, K. (2018). Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. AEU-International Journal of Electronics and Communications, 85, 47–53.

  4. Sabanci, K., Toktas, A., & Kayabasi, A. (2017). Grain classifier with computer vision using adaptive neuro‐fuzzy inference system. Journal of the Science of Food and Agriculture, 97(12), 3994–4000.

  5. Yildiz, B., Aslan, M. F., Durdu, A., & Kayabasi, A. (2024). Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs. Swarm and Evolutionary Computation, 88, 101612.