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

Ganiyat Salawu | Engineering and Technology | Research Excellence Award

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Dr. Ganiyat Salawu | Engineering and Technology | Research Excellence Award

University of KwaZulu-Natal,Durban | South Africa

Dr. Ganiyat Abiodun Salawu is a mechanical and mechatronics engineer and researcher with strong expertise in advanced manufacturing systems, robotics, renewable energy technologies, and intelligent engineering solutions. She holds a PhD in Mechanical Engineering (Mechatronics and Robotics) from the University of KwaZulu-Natal, South Africa, following earlier degrees in mechanical engineering and education from Nigerian institutions. Her academic and professional career spans lecturing, postgraduate supervision, research coordination, and postdoctoral research, with extensive experience in both university and polytechnic systems. Her research interests focus on disruptive manufacturing technologies, IoT-based systems, renewable energy optimization, smart automation, tribology, composite materials, and sustainable engineering design. Dr. Salawu has authored over 20 peer-reviewed journal and conference publications, with an approximate h-index of 3, and over 20 cumulative citations based on available scholarly records. She has received multiple competitive research grants, including TETFund institutional and doctoral awards, and was honored with an Award of Excellence as Best Researcher by the University of KwaZulu-Natal. Through impactful research, supervision, and innovation-driven teaching, she continues to contribute significantly to engineering education, sustainable technology development, and applied industrial research in Africa and beyond.

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Featured Publications

Dike Chijindu P.; Sam Obu C.V.; Imuran A.S.; Salawu G.A.; Shuaib A.A. (2025).
Production and Mechanical Evaluation of Bio-Composite Materials from Coconut, Palm Kernel and Periwinkle Shells as an Alternative to Grinding Wheel Production.

International Journal of Applied and Advanced Engineering Research, Vol. 8, No. 5, 2025

Jimoh A.A.; Iromini N.A.; Oladuntoye Q.O.; Ajiteru S.O.; Salawu G.A. (2024).
Design and Deployment of an Internet of Things Based Weather Station for Real-Time Monitoring of Environmental Conditions.

International Journal of Earth Design and Innovation Research, Vol. 3, No. 4, 2024

Dike Chijindu P.; Sam Obu C.V.; Imuran A.S.; Salawu G.A.; Shuaib A.A.; Afolabi A.A. (2025).
Investigating the Mechanical Performance of Offshore Wind Turbine Towers Using Finite Element Analysis for Sustainable Renewable Energy.

Journal of Engineering, Logical and Modelling Research, 2025

Investigation of Technical Feasibility and Efficiency of Wi-Fi Based Internet of Things Remote Monitoring and Control of Home Appliances.

International Journal of Science Research and Technology, Vol. 6, No. 9, pp. 121–135, 2024

Design and Development of Internet of Things Based Weather Station for Real-Time Monitoring of Environmental Conditions.

International Journal of Earth Design and Innovation Research, Vol. 3, No. 4, pp. 66–75, 2024

Ho-jun Song | Computer Science and Artificial Intelligence | Research Excellence Award

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Mr. Ho-jun Song | Computer Science and Artificial Intelligence | Research Excellence Award

Postech | South Korea

Ho-jun Song is a dedicated researcher and Ph.D. candidate in Computer Science and Engineering, specializing in federated learning, edge intelligence, and AIoT systems. With an academic foundation grounded in advanced distributed learning, he has contributed to developing personalized, scalable, and diffusion-based FL frameworks tailored for heterogeneous and resource-constrained environments. He has gained extensive experience through work on edge AI architectures, large-scale experimental pipelines, and applied AI systems for surveillance, security, and military decision support. Professionally, he leads AI initiatives as the Head of AI Development at the Army Artificial Intelligence Center, overseeing deepfake detection, ontology-based LLM systems, and intelligent multi-sensor surveillance solutions. His research interests span federated learning, personalized models, diffusion-based FL, distributed deep learning, and AIoT innovation. His academic journey includes rigorous research under expert mentorship and collaborations with interdisciplinary teams. Although early in his career, he has already contributed impactful ideas such as multidimensional trajectory optimization for FL personalization. He aspires to advance secure, efficient, and adaptive AI systems while contributing to global AI research communities through innovative, mission-driven research.

Profile : Orcid

Featured Publications

Song, H.-J., & Suh, Y.-J. (2025). HyFLM: A hypernetwork-based federated learning with multidimensional trajectory optimization on diffusion paths. Electronics, 14, Article 4704.

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

Jiuping Xu | Petroleum Engineering | Best Researcher Award

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Prof. Jiuping Xu | Petroleum Engineering | Best Researcher Award

Sichuan University, Business School | China

Prof. Jiuping Xu of Sichuan University is a distinguished scholar whose research spans applied mathematics, system science and complex-systems engineering, with a focus on decision and technology innovation for large-scale energy, environment, water-resource, circular economy and health-management systems. Educated with a PhD in applied mathematics from Tsinghua University under Prof. Shutie Xiao and a second PhD in physical chemistry from Sichuan University under Prof. Jiuli Luo, he has built a career at the interface of mathematics and engineering practice. He formulated the “TS-MG-AC” (Theory Spectrum Model Group Algorithm Cluster) paradigm for multivariate-multilevel systems, and developed multilevel dynamic equilibrium approaches in areas such as water allocation, circular economy systems and hydropower project management. His applied work has delivered significant societal and economic impact (for example in post-earthquake ecosystem reconstruction, irrigation-district water allocation and large hydropower construction). With an h-index of approximately 62 and over 14,979 citations, his publication output and influence are substantial. He has held leadership roles in major engineering-science teams and has contributed to policy formulation in China for resource, seismic-ecosystem and environmental systems. His research interests include fuzzy logic, multi-criteria decision making, large-scale system optimisation, circular economy modelling and low-carbon infrastructure innovation. In conclusion, Professor Xu is a prolific and impactful systems-engineer-scientist whose theoretical and applied contributions bridge mathematics, optimisation, engineering and environmental-economics to address pressing global challenges.

Profile : Scopus

Featured Publications

Xu, J., et al. (2025). Comprehensive benefits evaluation of low impact development using scenario analysis and fuzzy decision approach. Scientific Reports.

Xu, J., et al. (2025). Parental expectation and psychological distress of Chinese youth: The chain mediating effects of core self-worth and perceived stress. BMC Public Health.

Xu, J., et al. (2025). A co-gasification strategy of residual municipal solid waste and biomass for electricity generation optimization and carbon reduction. Energy.

Xu, J., et al. (2025). Predict-then-optimise based day-ahead scheduling towards demand response and hybrid renewable generation for wastewater treatment. Applied Energy.

Xu, J., et al. (2025). Industrial prosumption-based energy transition technologies investigation for wastewater sector. Renewable and Sustainable Energy Reviews.

Jingjing Jiang | Robotics and Automation | Best Researcher Award

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Dr. Jingjing Jiang | Robotics and Automation | Best Researcher Award

Loughborough University | United Kingdom

Dr Jingjing Jiang is a distinguished researcher in intelligent mobility and autonomous systems, currently a Senior Lecturer in Intelligent Mobility and Autonomous Vehicles at the Department of Aeronautical and Automotive Engineering, Loughborough University. She holds a BE in Electronic and Electrical Engineering an MSc in Control Engineering , and a PhD in Control Engineering (Imperial College London,  thesis: Shared Control for Systems with Constraints). After her doctoral work she served as a Research Associate in the Department of Electrical and Electronic Engineering at Imperial College London before joining Loughborough University as Lecturer in 2018 and being promoted to Senior Lecturer. Her research interest spans trustworthy control design and rigorous closed-loop performance analysis for intelligent systems and autonomous vehicles, combining classical control theory with modern data-driven models and algorithms, and emphasising both trial-based validation and top-down safety and reliability guarantees. Her work has been recognised for bridging fundamental research and real-world application in mobility systems. She continues to drive innovation in autonomous mobility and system safety, contributing to the future of reliable intelligent transport.

Profiles : Google Scholar | Orcid

Featured Publications

Cao, S., Sun, L., Jiang, J., & Zuo, Z. (2021). Reinforcement learning-based fixed-time trajectory tracking control for uncertain robotic manipulators with input saturation. IEEE Transactions on Neural Networks and Learning Systems, 34(8), 4584–4595.

Jiang, J., & Astolfi, A. (2018). Lateral control of an autonomous vehicle. IEEE Transactions on Intelligent Vehicles, 3(2), 228–237.

Fu, H., Jiang, J., Hu, S., Rao, J., & Theodossiades, S. (2023). A multi-stable ultra-low frequency energy harvester using a nonlinear pendulum and piezoelectric transduction for self-powered sensing. Mechanical Systems and Signal Processing, 189, 110034.

Jiang, J., & Astolfi, A. (2020). Stabilization of a class of underactuated nonlinear systems via underactuated back-stepping. IEEE Transactions on Automatic Control, 66(11), 5429–5435.

Hu, J., Lin, Y., Li, J., Hou, Z., Chu, L., Zhao, D., Zhou, Q., Jiang, J., & Zhang, Y. (2024). Performance analysis of AI-based energy management in electric vehicles: A case study on classic reinforcement learning. Energy Conversion and Management, 300, 117964.

Mehrdad Esmaeilipour | Electrical Engineering | Engineering Talent Award

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Mr. Mehrdad Esmaeilipour | Electrical Engineering | Engineering Talent Award

Arya Plasma Gostar Pars | Iran

Mehrdad Esmaeilipour is an accomplished Electronics Engineer with extensive experience in green technology, digital health, and smart systems innovation. Holding a Bachelor’s degree in Electronics Engineering Technology from Islamic Azad University, he has applied his expertise as a Senior Electronics Engineer at Arya Plasma Gostar Pars Company, leading projects in cold plasma systems, air purification, and wastewater treatment technologies. As the founder and CEO of Parsa Pardazesh Bushehr Sanat Company (PPBS Co.), he has combined technical excellence with entrepreneurial vision, fostering innovation, employment, and social impact. His research spans artificial intelligence, robotics, smart wearable devices, renewable energy systems, and electronic circuit optimization, leading to multiple patents, including a novel smart bracelet for visually impaired individuals. He has authored several books and journal articles on photovoltaic systems, electrical safety, robotics, and AI applications, contributing to both academic and industrial advancements. With conference presentations in Europe, Iran, and beyond, he has gained international recognition through numerous awards such as the Best Wearable Sensing Technology Award 2025 and the Global Recognition Award 2025. Dedicated to leveraging electronics for environmental sustainability and human welfare, Mehrdad continues to integrate research, innovation, and mentorship, shaping the future of smart systems and sustainable engineering.

Profile :  Google Scholar

Featured Publications

Esmaeilipour, M., & Zalzar, M. H. (2025). Design, construction and performance comparison of fuzzy logic controller and PID controller for two-wheel balance robot (smart sensors). Applied Mathematics on Science and Engineering, 2(1), 36–47.

Esmaeilipour, M. (2025, July). Advances and challenges in lithium-ion battery technology. In Proceedings of the 2nd International Conference on Electrical, Mechanical, Information Technology and Computer in Engineering Sciences (Naples, Italy).

Esmaeilipour, M. (2025, May). Design of an optimal proportional-integral-derivative controller utilizing AI techniques for brushless direct current motor with phase shift. In Proceedings of the International Conference on Computer, Electrical, Mechanical and Engineering Science (Eindhoven, Netherlands).

Esmaeilipour, M. (2025, June). Advancements in cold plasma technology for electronic and surface sterilization applications. In Proceedings of the 9th International Conference on Science and Technology of Electrical, Computer and Mechanical Engineering (Iran).

Esmaeilipour, M. (2025, February). Design, construction and performance comparison of fuzzy logic controller and PID controller for two-wheel balance robot. In Proceedings of the 10th International Conference on Electrical Engineering, Computer, Mechanics and Artificial Intelligence (Mashhad, Iran).