Mona Ali | Computer Science and Artificial Intelligence | Research Excellence Award

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Prof. Mona Ali | Computer Science and Artificial Intelligence | Research Excellence Award

King Faisal University | Saudi Arabia

Dr. Mona Abdelbaset Sadek Ali is an Associate Professor of Computer Science specializing in artificial intelligence, machine learning, and image processing. She earned her PhD in Computer Science (Wireless Computer Communications) from Cardiff University, UK, after completing an MSc in Information Technology (Image Processing) and a BSc in Information Technology with honors from Cairo University. With extensive academic experience spanning the UK, Saudi Arabia, and Egypt, her research integrates deep learning, optimization techniques, computer vision, IoT, mobile security, and intelligent healthcare systems. Dr. Ali has authored over 30 peer-reviewed research articles published in high-impact Web of Science-indexed journals and conferences, achieving an h-index of approximately 17, with more than 871 citations and 29 research documents. Her work frequently appears in Q1 and Q2 journals such as Mathematics, Electronics, Sustainability, PLOS ONE, and Applied Sciences. She has led and co-led numerous funded research projects supported by national and institutional bodies and has supervised multiple postgraduate MSc and PhD researchers. Her academic excellence has been recognized through competitive research funding and research poster awards. Overall, Dr. Ali’s career reflects sustained contributions to applied artificial intelligence and data-driven solutions with strong interdisciplinary and societal impact.

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Featured Publications


Tomato leaves diseases detection approach based on support vector machines

11th International Computer Engineering Conference (ICENCO), 246–250, 2015 · Citations: 222


Identifying two of tomatoes leaf viruses using support vector machine

Information Systems Design and Intelligent Applications, 2015 · Citations: 145


Detection of breast abnormalities of thermograms based on a new segmentation method

Federated Conference on Computer Science and Information Systems, 2015 · Citations: 78


Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm

IEEE Engineering in Medicine and Biology Conference, 2015 · Citations: 76


A hybrid segmentation approach based on neutrosophic sets and modified watershed: A case of abdominal CT liver parenchyma

11th International Computer Engineering Conference (ICENCO), 2015 · Citations: 70

Jeremy Carlosama | Biomedical Engineering | Research Excellence Award

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Mr. Jeremy Carlosama | Biomedical Engineering | Research Excellence Award

Yachay Tech University | Ecuador

Jeremy Fabrizio Carlosama Quinatoa is an emerging researcher in biomedical engineering whose work focuses on applying machine learning and intelligent sensing technologies to healthcare and rehabilitation. He holds a degree in Biomedical Engineering from Yachay Tech University, where his academic training emphasized biomechanics, human motion analysis, and data-driven medical systems. His professional experience includes teaching and research activities in biomechanics and biomedical signal processing, as well as participation in interdisciplinary projects that integrate inertial sensors, motion capture systems, neural networks, and clinical data. His research interests span machine learning for healthcare, rehabilitation engineering, inertial sensor systems, neural-network-based medical diagnosis, and assistive technologies. He has authored peer-reviewed research outputs including a journal article in Sensors (MDPI) and international conference book chapters addressing low back pain assessment, sign-language recognition, and heart failure diagnosis using artificial intelligence. As of 2025, his scholarly record includes 4 publications, an h-index of 2, and over 30 citations, reflecting growing academic impact at an early career stage. His work demonstrates a strong commitment to translational research that bridges engineering innovation and clinical application, positioning him as a promising young scientist contributing to the advancement of intelligent healthcare technologies.

Citation Metrics (Google Scholar)

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Featured Publications

Ecuadorian Sign Language Detection in Real Time

J. Carlosama, S. Criollo, C. Játiva, V. Mina, S. Velastegui, J. de-la-A, et al.
International Conference on Computer Science, Electronics and Industrial Engineering, 2023

InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients

J. Carlosama, L. Zhinin-Vera, C. Guevara, C. Cadena-Morejón, et al.
Sensors, Vol. 25(21), Article 6665, 2025

Diagnosis of Heart Failure: Integrating Echocardiographic and Clinical Data Through Neural Networks

V. Arrobo-Sarango, J. Carlosama, D. Almeida-Galárraga, A. Tirado-Espín, et al.
International Conference on Smart Technologies, Systems and Applications, 2024

Alexey Beskopylny | Civil Engineering | Best Researcher Award

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Prof. Dr. Alexey Beskopylny | Civil Engineering | Best Researcher Award

Don State Technical University | Russia

Dr. Alexey N. Beskopylny is a distinguished researcher and Vice Rector at Don State Technical University, serving as a Professor in the Department of Transport Systems. He holds a Doctor of Technical Sciences degree and has made significant contributions to civil and structural engineering, materials science, and transport systems. His studies span concrete technology, geopolymers, recycled construction materials, digital modeling, and structural optimization using AI and machine learning. Dr. Beskopylny’s works are frequently featured in high-impact journals such as Scientific Reports, Polymers, Buildings, and Applied Sciences. He has collaborated extensively on international projects focusing on sustainable materials and innovative construction technologies. Recognized for his academic excellence and leadership, he has received multiple institutional honors for advancing the field of transport infrastructure and sustainable construction. His continued efforts contribute to the modernization of engineering education and the promotion of environmentally responsible building practices worldwide.

Profile : Orcid

Featured Publications

Zubarev, K. P., Razveeva, I., Beskopylny, A. N., Stel’makh, S. A., Shcherban’, E. M., Mailyan, L. R., Shakhalieva, D. M., Chernil’nik, A., & Nikora, N. I. (2025). Predicting the strength of heavy concrete exposed to aggressive environmental influences by machine learning methods. Buildings, 15(21), Article 3998.

Özkılıç, Y. O., Kalkan, İ., Aksoylu, C., Madenci, E., Umiye, O. A., Althaqafi, E., Stel’makh, S. A., Shcherban’, E. M., & Beskopylny, A. N. (2025). Effect of stirrup spacing and recycled steel wires on the shear and energy dissipation of pultruded GFRP hybrid beams. Journal of Engineered Fibers and Fabrics, 20, Article 15589250251380680.

Ecemiş, A. S., Yildizel, S. A., Beskopylny, A. N., Stel’makh, S. A., Shcherban’, E. M., Aksoylu, C., Madenci, E., & Özkılıç, Y. O. (2025). Sustainable concrete with waste tire rubber and recycled steel fibers: Experimental insights and hybrid PINN–CatBoost prediction. Polymers, 17(21), Article 2910.

Özkılıç, Y. O., Başaran, B., Aksoylu, C., Karalar, M., Zeybek, Ö., Althaqafi, E., Beskopylny, A. N., Stel’makh, S. A., Shcherban’, E. M., & Umiye, O. A. (2025). Bending performance of reinforced concrete beams with partial waste glass aggregate replacement assessed by experimental, theoretical and digital image correlation analyses. Scientific Reports, 15, Article 20716.

Stel’makh, S. A., Shcherban’, E. M., Beskopylny, A. N., Mailyan, L. R., Shilov, A. A., Razveeva, I., Oganesyan, S., Pogrebnyak, A., Chernil’nik, A., & Elshaeva, D. (2025). Enhancing the mechanical properties of sulfur-modified fly ash/metakaolin geopolymers with polypropylene fibers. Polymers, 17(15), Article 2119.

Chih-Lyang Hwang | Electrical Engineering | Best Researcher Award

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Prof. Chih-Lyang Hwang | Electrical Engineering | Best Researcher Award

National Taiwan University of Science and Technology | Taiwan

Dr. Chih-Lyang Hwang (SM’08) is a distinguished researcher and academic in the field of electrical and mechanical engineering, currently serving as a Research Fellow at the Intelligent Robot Center, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan. He earned his Ph.D. in Mechanical Engineering from Tatung Institute of Technology  and subsequently held professorial positions at Tatung Institute of Technology, Tamkang University, and NTUST. With an extensive academic career spanning over three decades, he has contributed significantly to robotics, fuzzy neural modeling, nonlinear control, and human–robot interaction. His research also encompasses distributed visual and wireless localization, UAV control, and emotion recognition. Dr. Hwang has been a Visiting Scholar at Georgia Institute of Technology and Auburn University, broadening his international academic collaborations. He has authored numerous influential journal and conference papers, amassing over 3,383 citations, 533 documents, and an H-index of 29. Recognized among the world’s top 2% scientists by Stanford University for multiple years, he has also received Excellent and Outstanding Research Awards from NTUST and 2024. His enduring contributions continue to advance intelligent robotics and control systems research globally.

Profile : Google Scholar

Featured Publications

Hwang, C.-L., Yang, C.-C., & Hung, J.-Y. (2017). Path tracking of an autonomous ground vehicle with different payloads by hierarchical improved fuzzy dynamic sliding-mode control. IEEE Transactions on Fuzzy Systems, 26(2), 899–914.

Hwang, C.-L., Jan, C., & Chen, Y.-H. (2001). Piezomechanics using intelligent variable-structure control. IEEE Transactions on Industrial Electronics, 48(1), 47–59.

Hwang, C.-L., Chang, L.-J., & Yu, Y.-S. (2007). Network-based fuzzy decentralized sliding-mode control for car-like mobile robots. IEEE Transactions on Industrial Electronics, 54(1), 574–585.

Hwang, C.-L., Chiang, C.-C., & Yeh, Y.-W. (2013). Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain underactuated nonlinear dynamic systems. IEEE Transactions on Fuzzy Systems, 22(2), 286–299.

Hwang, C.-L. (2004). A novel Takagi–Sugeno-based robust adaptive fuzzy sliding-mode controller. IEEE Transactions on Fuzzy Systems, 12(5), 676–687

Yosef Wubet | Electrical Engineering | Young Scientist Award

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Mr. Yosef Wubet | Electrical Engineering | Young Scientist Award

University of Gondar Institute of Technology | Ethiopia

Yosef Birara Wubet is a lecturer and researcher in Electrical and Computer Engineering, specializing in power systems and renewable energy. He holds a Master of Science in Power System Engineering and a Bachelor of Science in Electrical Power and Control Engineering, both from Bahir Dar University, Ethiopia. Since joining the University of Gondar as Lecturer (previously Assistant Lecturer), he has taught, supervised undergraduate projects and internships, reviewed journals, managed team projects, and contributed to practical works in both academic and industrial settings. His research interests span transient stability analysis using machine learning (notably artificial neural networks), fault classification and detection, solar power system design, smart grid technologies, modeling and design of controllers, system stability and forecasting, and modeling renewable energy integration. He has published multiple articles in peer-reviewed journals, including “Transient Stability Assessment and Enhancement of Hydropower Plant Using Artificial Neural Network” and “Design and modeling of ANN-based automatic generation control and voltage regulator for integrated hydropower plants in Ethiopia.” His honors include high academic distinctions (CGPA 3.96/4.0 in MSc, “Very great distinction”), the “Best Project of the Year 2018” at Bahir Dar Institute of Technology, and an Electrical Installation License from the Ethiopian Petroleum and Energy Authority. Yosef’s work contributes both theoretically and practically toward more stable, reliable, and renewable-energy-enabled power systems in Ethiopia and beyond.

Profile : Google Scholar

Featured Publications

Wubet, Y. B., Getahun, H. M., Alemu, Y. A., & Gela, T. T. (2025). Transient stability assessment and enhancement of hydropower plant using artificial neural network. Scientific African, Elsevier.

Wubet, Y. B., Gela, T. T., & Getahun, H. M. (2025). Design and modeling of ANN-based automatic generation control and automatic voltage regulator for two integrated hydro power plants in Ethiopia. Scientific African, Elsevier.

Hacene Mellah | Electrical Engineering | Best Researcher Award

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Dr. Hacene Mellah | Electrical Engineering | Best Researcher Award

bouira university | Algeria

Dr. Hacene Mellah is an Associate Professor of Electrical Engineering at Université de Bouira, Algeria. His education includes an Ingenieur degree (2006) with a focus on electrical machine control, a Magister (2009) in electrical machines and control, a PhD (2020) in electrical machines, and his habilitation à diriger des recherches (HDR). He conducts research in estimation techniques of intrinsic machine parameters and thermal behaviour, fault diagnosis, renewable energy systems (wind, PV, hybrid), and control strategies for advanced electrical machines and drives. According to the AD Scientific Index (2025), his total h-index is 7, with about 91 citations and 24 indexed documents. A recent years his work has focused on observer design, neural networks, finite element modelling (FEM), and smart control of doubly fed induction generators among other topics. He has published in a number of peer-reviewed journals as well as international conferences. His contributions have enhanced understanding of sensorless control, fault modelling, and thermal monitoring for electrical machines, particularly under transient or non-linear conditions. Looking forward, he aims to expand his research in intelligent control, sustainable energy integration, and improved diagnostics. He seeks collaborations and impact through both theoretical development and practical applications in electrical drive systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Sahraoui, H., Mellah, H., Drid, S., & Chrifi-Alaoui, L. (2021). Adaptive maximum power point tracking using neural networks for photovoltaic systems according grid. Electrical Engineering & Electromechanics, (5), 57–66.

Mellah, H., Hemsas, K. E., & Taleb, R. (2016). Intelligent sensor based Bayesian neural network for combined parameters and states estimation of a brushed DC motor. International Journal of Advanced Computer Science and Applications (IJACSA), 7(7), 230–235.

Mellah, H., & Hemsas, K. E. (2013). Simulations analysis with comparative study of a PMSG performances for small WT application by FEM. International Journal of Energy Engineering, 3(2), 55–64.

Maafa, A., Mellah, H., Ghedamsi, K., & Aouzellag, D. (2022). Improvement of sliding mode control strategy founded on cascaded doubly fed induction generator powered by a matrix converter. Engineering, Technology & Applied Science Research, 12(5), 9217–9223.

Bounasla, N., Hemsas, K. E., & Mellah, H. (2015). Synergetic and sliding mode controls of a PMSM: A comparative study. Journal of Electrical and Electronic Engineering, 3(1-1), 22–26.