Alexander Khrulev | Traffic and Transportation Analysis | Editorial Board Member Published on 24/03/202624/03/2026 by Computer Vision Awards Dr. Alexander Khrulev | Traffic and Transportation Analysis | Editorial Board Member Ukrainian State Flight Academy | Ukraine Dr. Alexander Khrulev is a Professor at the Ukrainian State Flight Academy specializing in internal combustion engines, UAV propulsion, and thermodynamic modeling. He has authored , contributing extensively to engine diagnostics, failure analysis, and mathematical modeling of complex engine systems. His recent work advances pulse jet engine modeling through the development of universal frameworks and real-time simulation software. Collaborating with international researchers, he integrates analytical, computational, and applied engineering approaches. His research has significant societal impact by enhancing engine reliability, supporting efficient UAV technologies, and enabling predictive maintenance in automotive and aerospace industries. Citation Metrics (Scopus) 150 100 50 0 Citations 78 Documents 9 h-index 3 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile   View ORCID Profile    View Google Scholar Profile    View ResearchGate Profile Featured Publications Acoustic method for estimation of marine low-speed engine turbocharger parameters. –Journal of Marine Science and Engineering, 9(3), 321. (2021). Cited By: 94 Study of the conrod deformation during piston interaction with liquid in the internal combustion engine cylinder. – Journal of Mechanical Engineering and Sciences, 14(2), 6557–6569. (2020). Cited By: 14 The method of expert assessment of the technical condition of an automobile engine after overheating. – Automobile Transport. (2021). Cited By: 12 Building a mathematical model of the destruction of a connecting rod-piston group in the car engine at hydraulic lock. – Eastern-European Journal of Enterprise Technologies, 117(7). (2022). Cited By: 11 Analysis of pneumatic catapult launch system parameters, taking into account engine and UAV characteristics. – Advanced UAV, 3(1), 10–24. (2023). Cited By: 8
Kesyton Ozegin Oyamenda | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award Published on 24/03/202624/03/2026 by Computer Vision Awards Assoc. Prof. Dr. Kesyton Ozegin Oyamenda | Remote Sensing and Satellite Imagery Analysis | Research Excellence Award Ambrose Alli University | Nigeria Assoc. Prof. Dr. Kesyton Ozegin Oyamenda is an Associate Professor at Ambrose Alli University, Nigeria, specializing in exploration and environmental geophysics with emphasis on groundwater systems, geospatial analysis, and AI-driven modeling. He has produced 15 Scopus-indexed publications with 187 citations h-index: 8, alongside broader global visibility. His highly cited works focus on groundwater potential mapping, vulnerability assessment, and geophysical modeling using GIS, MCDA, and remote sensing. He collaborates extensively with multidisciplinary researchers and has supervised postgraduate theses. His research contributes to sustainable water resource management, infrastructure planning, and environmental protection, addressing critical societal challenges in Nigeria and comparable regions. Citation Metrics (Scopus) 300 200 100 0 Citations 187 Documents 15 h-index 8 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profille   View ORCID Profile    View Google Scholar Profile    View ResearchGate Profile  View Web of Science (WOS) Profile Featured Publications Groundwater exploration in a landscape with heterogeneous geology: An application of geospatial and analytical hierarchical process (AHP) techniques in the Edo North region, Nigeria. – Groundwater for Sustainable Development, 20, 100871. (2023). Cited By: 46 Spatial evaluation of groundwater vulnerability using the DRASTIC-L model with the analytic hierarchy process (AHP) and GIS approaches in Edo State, Nigeria. – Physics and Chemistry of the Earth, Parts A/B/C, 134, 103562. (2024). Cited By: 44 Structural mapping for groundwater occurrence using remote sensing and geophysical data in the Ilesha Schist Belt, Southwestern Nigeria. -Geology, Ecology, and Landscapes, 9(1), 199–216. (2025). Cited By: 32 Assessment of groundwater occurrence in a typical schist belt region in Osun State, Southwestern Nigeria using VES, aeromagnetic dataset, remotely sensed data, and MCDA approaches. – Sustainable Water Resources Management, 9, 29. (2023). Cited By: 29 A triangulation approach for groundwater potential evaluation using geospatial technology and multi-criteria decision analysis (MCDA) in Edo State, Nigeria. – Journal of African Earth Sciences, 209, 105101. (2024). Cited By: 26
Chao He | Benchmark Datasets and Evaluation Methods | Best Paper Award Published on 23/03/202623/03/2026 by Computer Vision Awards Dr. Chao He | Benchmark Datasets and Evaluation Methods | Best Paper Award Chongqing Three Gorges University | China Dr. Chao He is an active researcher in Internet of Vehicles (IoV), UAV-assisted edge computing, and intelligent resource management. His work integrates Generative AI, Digital Twin, and Deep Reinforcement Learning into next-generation network systems. Publications in IEEE Transactions on Network Science and Engineering and Future Generation Computer Systems highlight innovative task offloading and resource allocation using UAV collaboration and dual-timeslot optimization. He focuses on dependency-aware scheduling, V2G systems, and intelligent energy management, advancing adaptive, efficient, and interdisciplinary solutions for dynamic vehicular and smart energy environments. Citation Metrics (Scopus) 150 100 50 0 Citations 107 Documents 26 h-index 6 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile       View ORCID Profile Featured Publications UAV-assisted task offloading and resource allocation in Internet of Vehicles: An integration of digital twin and generative AI. – IEEE Transactions on Network Science and Engineering. (2026). Dual-timeslot optimization and UAV collaboration: A novel task offloading strategy for low-altitude power Internet of Things. – Future Generation Computer Systems. (2026). Coordinated scheduling mechanism of electric vehicle V2G and DR in integrated energy systems via deep reinforcement learning. -Sustainable Energy, Grids and Networks. (2026). Generative AI-based dependency-aware task offloading and resource allocation for UAV-assisted IoV. – IEEE Open Journal of the Communications Society. (2025). Generative AI-enhanced task offloading strategy for the IoV: An RSU–RSU load-balancing perspective. – IEEE Networking Letters. (2025). Â
Abdullah Alshammari | Surveillance and Security | Editorial Board Member Published on 18/03/202618/03/2026 by Computer Vision Awards Assoc. Prof. Dr. Abdullah Alshammari | Surveillance and Security | Editorial Board Member University of Hafr Albatin | Saudi Arabia Assoc. Prof. Dr. Abdullah Alshammari is a researcher at University of Hafr Al-Batin specializing in artificial intelligence, cybersecurity, Internet of Things, and cloud computing. With 16 publications, 186 citations, and an h-index of 8, his work demonstrates consistent contributions to high-impact Q1 journals, including IEEE venues. His research integrates machine learning, blockchain security, and edge computing to address challenges in smart systems, energy efficiency, and digital infrastructure. Collaborating with over 60 international co-authors, he advances interdisciplinary innovation with practical societal impact in secure communication networks, intelligent decision-making systems, and sustainable smart technologies. Citation Metrics (Scopus) 200 150 100 0 Citations 186 Documents 16 h-index 8 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile       View ORCID Profile       View Google Scholar Profile Featured Publications Payoff optimization through wireless network virtualization for IoT applications: A three layer game approach. – IEEE Internet of Things Journal. (2019). Cited By : 70 Intelligent multi-camera video surveillance system for smart city applications. – In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 317–323). (2019). Cited By : 47 Power system monitoring for electrical disturbances in wide network using machine learning. -Sustainable Computing: Informatics and Systems. (2024). Cited By : 26 Sell or HODL cryptos: Cryptocurrency short-to-long term projection using simultaneous classification-regression deep learning framework. – IEEE Access. (2024). Cited By : 14 A perspective study for the assessment of field robots in agriculture: An enhanced fuzzy MADM approach. – Computers and Electronics in Agriculture. (2023). Cited By : 14
Jianjun Zhang | Deep Learning for Computer Vision | Best Innovation Award Published on 17/03/202617/03/2026 by Computer Vision Awards Prof. Jianjun Zhang | Deep Learning for Computer Vision | Best Innovation Award Teacher | Guangzhou Huali College | China Prof. Jianjun Zhang is an academic researcher specializing in business administration education, digital intelligence, and the integration of big data and AI in higher education. He has authored over 30 publications indexed in CNKI, including one SCI paper and two EI-indexed works, reflecting steady scholarly impact. Zhang has led seven national and provincial research projects and collaborates with multidisciplinary teams on digital transformation in education. His work emphasizes virtual simulation and intelligent learning systems to enhance teaching efficiency and practical skill development. Recognized as a “double-qualified” educator, he effectively bridges theory and practice, contributing to workforce readiness and innovation in modern business education. Profiles : ORCID Featured Publications Leveraging big data, AI, and virtual simulation: Research on enhancing teaching efficiency in higher education business administration through digital intelligence empowerment. – In Proceedings of the ACM Conference. (2025).Â
Prof. Jianjun Zhang is an academic researcher specializing in business administration education, digital intelligence, and the integration of big data and AI in higher education. He has authored over 30 publications indexed in CNKI, including one SCI paper and two EI-indexed works, reflecting steady scholarly impact. Zhang has led seven national and provincial research projects and collaborates with multidisciplinary teams on digital transformation in education. His work emphasizes virtual simulation and intelligent learning systems to enhance teaching efficiency and practical skill development. Recognized as a “double-qualified” educator, he effectively bridges theory and practice, contributing to workforce readiness and innovation in modern business education. Profiles : ORCID Featured Publications
Francesco Fiorini | Emerging Trends and Future Directions | Editorial Board Member Published on 13/03/202613/03/2026 by Computer Vision Awards Dr. Francesco Fiorini | Emerging Trends and Future Directions | Editorial Board Member University of Pisa | Italy Dr. Francesco Fiorini is a PhD researcher at the University of Pisa, Italy, and a former Visiting Scholar at the Superconducting Quantum Materials and Systems Center (SQMS) at Fermi National Accelerator Laboratory, Chicago. His research focuses on telecommunications, quantum communication, network security, and queuing analysis. With 11 scholarly publications, 14 citations, and an h-index of 2, he has contributed to reputable venues including IEEE journals and conferences. His work spans quantum key distribution security, AI-driven modeling, and network performance analysis. Through international collaborations and interdisciplinary research, his studies contribute to advancing secure communication systems and intelligent networking technologies for next-generation digital infrastructures. Citation Metrics (Scopus) 20 15 10 5 0 Citations 14 Documents 11 h-index 2 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile       View ORCID Profile     View Google Scholar Profile Featured Publications Estimating interception density in the BB84 protocol: A study with a noisy quantum simulator. – Future Internet. (2024). Cited By : 9 Modelling heavy tailed phenomena using a LogNormal distribution having a numerically verifiable infinite variance. – Mathematics. (2023). Cited By : 4 Fuzzy-augmented neural network for reducing computational complexity: A demonstration in microclimate prediction for smart agriculture. -IEEE Internet of Things Journal. (2025). Cited By : 2 Low-complexity microclimate classification in smart greenhouses: A fuzzy-neural approach. – Future Internet. (2025). Cited By : 2
Dr. Francesco Fiorini is a PhD researcher at the University of Pisa, Italy, and a former Visiting Scholar at the Superconducting Quantum Materials and Systems Center (SQMS) at Fermi National Accelerator Laboratory, Chicago. His research focuses on telecommunications, quantum communication, network security, and queuing analysis. With 11 scholarly publications, 14 citations, and an h-index of 2, he has contributed to reputable venues including IEEE journals and conferences. His work spans quantum key distribution security, AI-driven modeling, and network performance analysis. Through international collaborations and interdisciplinary research, his studies contribute to advancing secure communication systems and intelligent networking technologies for next-generation digital infrastructures. Citation Metrics (Scopus) 20 15 10 5 0 Citations 14 Documents 11 h-index 2 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile       View ORCID Profile     View Google Scholar Profile Featured Publications Estimating interception density in the BB84 protocol: A study with a noisy quantum simulator. – Future Internet. (2024). Cited By : 9 Modelling heavy tailed phenomena using a LogNormal distribution having a numerically verifiable infinite variance. – Mathematics. (2023). Cited By : 4 Fuzzy-augmented neural network for reducing computational complexity: A demonstration in microclimate prediction for smart agriculture. -IEEE Internet of Things Journal. (2025). Cited By : 2 Low-complexity microclimate classification in smart greenhouses: A fuzzy-neural approach. – Future Internet. (2025). Cited By : 2
Prof. Dr. Natalia A. Serdyukova | Deep Metric Learning | Editorial Board Member Published on 13/03/202616/03/2026 by Computer Vision Awards Prof. Dr. Natalia A. Serdyukova | Deep Metric Learning | Editorial Board Member Professor | Gubkin University | Russia Prof. Dr. Natalia A. Serdyukova is an active researcher in Applied Mathematics, Complex Systems, and Digital Transformation, with a strong focus on algebraic and quasi-fractal modeling of intelligent and socio-technical systems. Her scholarly portfolio includes 45+ publications, encompassing conference papers, book chapters, and contributions to international series such as Intelligent Systems Reference Library and E3S Web of Conferences. Collaborating frequently with Vladimir I. Serdyukov, her research develops algebraic quasi-fractal logic, probabilistic modeling, and smart-system dynamics. These contributions advance digitalization, risk analysis, and sustainability modeling in complex technological systems, strengthening interdisciplinary scientific innovation and applications in smart infrastructure and digital ecosystems. Citation Metrics (Scopus) 200 150 100 50 0 Citations 144 Documents 45 h-index 8 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile       View ORCID Profile Featured Publications Algebraic quasi-fractal modeling of complex systems with intermittent links. – Proceedings of the ICEIP Conference. (2025). Algebraic quasi-fractal dynamics: Modeling of the risk system of a complex energy-saving system. – E3S Web of Conferences. (2024). Fixed-point logic: Quasi-fractal aspects. –In Quasi-fractal algebraic systems and smart systems modeling. (2024). Presystem concept: Quasi-fractal probabilistic logic: The conditional digitalization function—Modeling of smart system states using homological algebra method. – In Quasi-fractal algebraic systems and smart systems modeling. (2024).
Prof. Dr. Natalia A. Serdyukova is an active researcher in Applied Mathematics, Complex Systems, and Digital Transformation, with a strong focus on algebraic and quasi-fractal modeling of intelligent and socio-technical systems. Her scholarly portfolio includes 45+ publications, encompassing conference papers, book chapters, and contributions to international series such as Intelligent Systems Reference Library and E3S Web of Conferences. Collaborating frequently with Vladimir I. Serdyukov, her research develops algebraic quasi-fractal logic, probabilistic modeling, and smart-system dynamics. These contributions advance digitalization, risk analysis, and sustainability modeling in complex technological systems, strengthening interdisciplinary scientific innovation and applications in smart infrastructure and digital ecosystems. Citation Metrics (Scopus) 200 150 100 50 0 Citations 144 Documents 45 h-index 8 🟦 Citations 🟥 Documents đźź© h-index View Scopus Profile       View ORCID Profile Featured Publications Algebraic quasi-fractal modeling of complex systems with intermittent links. – Proceedings of the ICEIP Conference. (2025). Algebraic quasi-fractal dynamics: Modeling of the risk system of a complex energy-saving system. – E3S Web of Conferences. (2024). Fixed-point logic: Quasi-fractal aspects. –In Quasi-fractal algebraic systems and smart systems modeling. (2024). Presystem concept: Quasi-fractal probabilistic logic: The conditional digitalization function—Modeling of smart system states using homological algebra method. – In Quasi-fractal algebraic systems and smart systems modeling. (2024).
Dr. Ibrahim Musa Conteh | Machine Learning for Computer Vision | Innovative Research Award Published on 13/03/2026 by Computer Vision Awards After Completing the Registration Process, your Biography will be displayed here. Register by using your Entry ID: 8178 Register Here
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Mr. Félix Hernández de Rojas | Emerging Trends and Future Directions | Global Vision Impact Award Published on 10/03/202610/03/2026 by Computer Vision Awards After Completing the Registration Process, your Biography will be displayed here. Register by using your Entry ID: 8165 Register Here
