Ammar Khaleel l Reinforcement Learning | Research Excellence Award

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Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award

Széchenyi István Egyetem | Hungary

Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.

Citation Metrics (Scopus)

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Citations
12

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Featured Publications


A Multi-Levels RNG Permutation

– Indonesian Journal of Electrical Engineering and Computer Science, 2019

A New Permutation Method for Sequence of Order 28

– Journal of Theoretical and Applied Information Technology, 2019

N/A and Signature Analysis for Malwares Detection and Removal

– Indian Journal of Science and Technology, 2019

Ali Ali | Artificial Intelligence and water Resources Management | Research Excellence Award

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Dr. Ali Ali | Artificial Intelligence and water Resources Management | Research Excellence Award

Brunel University London | United Kingdom

Dr. Ali Ali is an emerging researcher and PhD student in Civil Engineering at Brunel University London, specializing in Artificial Intelligence applications for water resources management. He holds a BEng (Hons) in Civil Engineering from Brunel University and a Diploma with Distinction from Kaplan International College, demonstrating strong academic performance. Dr. Ali has extensive experience with civil engineering and computational software, including AutoCAD, Groundwater Modelling System (GMS), ABAQUS, MATLAB, and programming languages Python and R, enabling advanced modeling, simulation, and data analysis. He has applied his expertise in designing steel and concrete structures while addressing economic, social, and environmental challenges. His professional experience includes multiple Graduate Teaching Assistant roles in Civil Engineering and Computer Science, supporting both undergraduate and master’s students with lab sessions, course material, tutoring, and programming instruction. Additionally, he gained practical industry experience through internships in project management and turbomachinery design, enhancing his skills in project coordination and operational efficiency. Dr. Ali’s research interests focus on optimizing water resources management using AI, sustainable infrastructure design, and hydrological modeling. He has contributed to academic documents, reports, and simulations, with ongoing work aimed at publication in peer-reviewed journals. His work demonstrates a commitment to bridging theoretical research and practical engineering solutions, advancing innovation in civil and environmental engineering.

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Featured Publication

Aquifer-specific flood forecasting using machine learning: A comparative analysis for three distinct sedimentary aquifers – Science of the Total Environment, 2025 | Open Access

Ali Raza Shaikh | Mechanical Engineering | Research Excellence Award

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Mr. Ali Raza Shaikh | Mechanical Engineering | Research Excellence Award

Technical University of Darmstadt | Germany

Ali Raza Shaikh is a dedicated mechanical engineer and researcher specializing in fluid mechanics, heat transfer, ice adhesion, aerodynamics, and surface-interface science. He holds a B.Eng. in Mechanical Engineering, an M.Eng. in Power Engineering and Engineering Thermophysics, and is currently pursuing a Dr.-Ing. in Mechanical Engineering. With extensive research and practical experience, he has developed experimental setups, conducted thermal-fluid and droplet impact studies, and contributed to advancements in superhydrophobic and anti-icing surfaces. He has served as a research assistant in the MSCA-ITN SURFICE project, conducted industrial research internships, and held graduate research positions, collaborating on international projects and presenting findings at leading conferences. His technical expertise spans experimental design, surface characterization, high-speed imaging, and numerical simulations, complemented by proficiency in MATLAB, Python, LabView, CAD, and LaTeX. His research interests focus on ice adhesion dynamics, surface wetting, thermal-fluid interactions, and functional surface design. Ali has actively participated in workshops and training schools worldwide, contributing to knowledge exchange and innovation in engineering applications. His dedication to advancing research and practical solutions in fluid mechanics and surface science underscores his commitment to scientific excellence and technological development.

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Featured Publications


Transient testing of tensile ice adhesion

– Cold Regions Science and Technology, 2025

Ice adhesion dynamics in the tensile mode

– Smart Surface Design for Efficient Ice Protection and Control, 2025

The physics of icing

– Smart Surface Design for Efficient Ice Protection and Control, 2025

Understanding the physics of ice adhesion on complex substrates

– International Conference on Icing of Aircraft, Engines, and Structure, 2023

Helmi Ayari | Data processing | Research Excellence Award

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Mr. Helmi Ayari | Data processing | Research Excellence Award

Université Ibn khaldoun | Tunisia

Helmi Ayari is a doctoral researcher in Artificial Intelligence at the École Polytechnique de Tunisie, focusing on machine learning, deep learning, and intelligent imaging systems, with a research track record that includes 3 published documents, an h-index of 1, and citations from 33 documents. He holds a Master of Research in Intelligent Systems for Imaging and Computer Vision and a fundamental Bachelor’s degree in Computer Science. His work includes contributions to medical image analysis, explainable AI, and optimization techniques, with notable publications such as a comparative study of classical versus deep learning-based computer-aided diagnosis systems published in Knowledge and Information Systems (Q2), and a study integrating genetic algorithms with ensemble learning for enhanced credit scoring presented at the International Conference on Business Information Systems (Class B). Professionally, he has served as a Maître Assistant and teaching assistant, delivering courses in machine learning, deep learning, Python, R, computer architecture, and automata theory, while supervising Master’s research, coordinating academic programs, and contributing to hackathons and university events. His research interests span AI-driven decision systems, interpretable machine learning, evolutionary optimization, and applied deep learning. Recognized for both academic and pedagogical engagement, he continues advancing impactful AI research and education.

Profile : Scopus

Featured Publications

Computer-Aided Diagnosis Systems: A Comparative Study of Classical Machine Learning Versus Deep Learning-Based Approaches. Knowledge and Information Systems, published May 23, 2023. https://doi.org/10.1007/s10115-023-01894-7

Integrating Genetic Algorithms and Ensemble Learning for Improved and Transparent Credit Scoring. International Conference on Business Information Systems, June 25, 2025. (Class B)

Chang Liu | Digital Data | Research Excellence Award

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Mr. Chang Liu | Digital Data | Research Excellence Award

 Xi ’ an University of Posts & Telecommunications Shaanxi | China

Mr. Chang Liu is a researcher specializing in data analysis and its application in the digital economy, enterprise value creation, and policy evaluation. He holds advanced degrees in economics and finance, and has contributed extensively to academic research, national-level projects, and provincial research initiatives. His expertise encompasses the full data analysis workflow, including data collection, cleaning, modeling, and empirical verification, with proficiency in Python, STATA, and SPSS. He has co-authored multiple SSCI and PKU Core papers focusing on digital finance, enterprise digital transformation, ESG impact, and the role of data elements in promoting new quality productive forces. In addition, he has led key data analysis tasks in projects evaluating corporate performance, large model spillovers, digital asset accounting, and the internal transmission mechanism of the digital economy. His research integrates advanced econometric methods, spatial models, panel regressions, and complex system coordination frameworks to ensure robust and scientifically reliable conclusions. Through practical work, including enterprise audits and data asset evaluations, he has applied analytical techniques to real-world problems, producing actionable insights. Mr. Liu’s work demonstrates a commitment to precision, academic rigor, and the advancement of data-driven research, contributing significantly to both theoretical understanding and practical application in the field of digital economy and enterprise innovation.

Profile : Orcid

Featured Publication

How data elements fuel new quality productive forces in enterprises: A perspective from digital finance, 2026

Arun Kumar | Mathematical Modeling | Best Researcher Award

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Dr. Arun Kumar | Mathematical Modeling | Best Researcher Award

IIT Mandi | India

Dr. Arun Kumar is a Research Associate (Postdoctoral) at the Indian Institute of Technology Mandi, specializing in mathematical and computational modeling of complex biological, ecological, and epidemiological systems. He earned his Ph.D. in Mathematics from Banaras Hindu University in 2023, following an M.Sc. in Mathematics from the same institution and a B.Sc. from CCS University, Meerut. His research focuses on nonlinear dynamics, bifurcation theory, delay differential equations, reaction–diffusion systems, Turing patterns, and the integration of deep learning and physics-informed neural networks (PINNs) for solving partial differential equations. Dr. Kumar has published extensively in high-impact journals on topics including SIR/SIRS epidemic models, cross-diffusion models, predator–prey dynamics, and pattern formation in spatial ecological systems. His work bridges theoretical mathematics and practical applications in disease modeling and ecology, offering insights into complex population interactions, control strategies, and spatio-temporal dynamics. Currently, he is developing deep learning algorithms to solve PDEs with applications in ecological and epidemiological systems. His ongoing research explores predator-prey interactions, learning in ecological models, and the forecasting of infectious diseases such as monkeypox. Dr. Kumar’s contributions have advanced the understanding of nonlinear systems, providing both analytical and computational tools for studying complex biological and ecological phenomena.

Profile : Google Scholar

Featured Publications

Gupta, R. P., & Kumar, A. (2022). “Endemic bubble and multiple cusps generated by saturated treatment of an SIR model through Hopf and Bogdanov–Takens bifurcations.” Mathematics and Computers in Simulation, 197, 1–21.

Gupta, R. P., Kumar, A., & Yadav, D. K. (2024). “The complex dynamical study of a UAI epidemic model in non-spatial and spatial environments.” The European Physical Journal Plus, 139(2), 117.

Yadav, D. K., Gupta, R. P., & Kumar, A. (2022). “Nonlinear dynamics of a three species prey-predator system incorporating fear effect and harvesting.” Journal of Mathematical Control Science and Applications.

Kumar, A., Gupta, R. P., & Tiwari, S. (2022). “Influences of nonlinear cross-diffusion on a reduced SI epidemic model with saturated treatment.”

Kumar, A., Kumari, N., Mandal, S., & Tiwari, P. K. (2025). “Autonomous and non-autonomous dynamics of an SIRS model with convex incidence rate.” Journal of the Franklin Institute, 108236.

Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

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Mr. Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Chongqing University of Technology | China

Dr. Xiaoxia Yu is a scholar in mechanical engineering whose work advances intelligent diagnostics and predictive maintenance for large-scale rotating machinery, particularly wind turbines. With a Ph.D. in Mechanical Engineering and earlier degrees in Vehicle Engineering and Armored Vehicle Engineering, she has built a strong interdisciplinary foundation that integrates mechanical systems knowledge with advanced computational modeling. Her research spans fault diagnosis, health assessment, digital twin systems, graph neural networks, reinforcement learning, and signal processing, supported by a growing publication record that includes 29 documents, 477 citations by 448 documents, and an h-index of 7. As a Lecturer, she leads research projects funded by regional scientific agencies and has contributed to national-level R&D initiatives related to machinery health management. Her work appears in high-impact journals, and she has secured patents focused on structural health monitoring, image recognition, and intelligent fault detection. Recognized with competitive grants and academic honors, she continues to influence the fields of renewable energy reliability and smart manufacturing. Through her commitment to innovation, research leadership, and engineering application, she is emerging as a key contributor to the development of intelligent, data-driven mechanical health monitoring systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Yu, X., Tang, B., & Zhang, K. (2021). Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–14.

Yu, X., Tang, B., & Deng, L. (2023). Fault Diagnosis of Rotating Machinery Based on Graph Weighted Reinforcement Networks Under Small Samples and Strong Noise. Mechanical Systems and Signal Processing, 186, 109848.

Zhang, K., Tang, B., Deng, L., & Yu, X. (2021). Fault Detection of Wind Turbines by Subspace Reconstruction‑Based Robust Kernel Principal Component Analysis. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Li, B., Tang, B., Deng, L., & Yu, X. (2020). Multiscale Dynamic Fusion Prototypical Cluster Network for Fault Diagnosis of Planetary Gearbox Under Few Labeled Samples. Computers in Industry, 123, 103331.

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2021). Multi‑block Domain Adaptation with Central Moment Discrepancy for Fault Diagnosis. Measurement, 169, 108516.

Samaneh Saeedinia | Engineering | Best Researcher Award

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Prof.Dr. Samaneh Saeedinia | Engineering | Best Researcher Award

Iran University of Science and Technology | Iran

Dr. Samaneh Alsadat Saeedinia is an accomplished researcher and engineer specializing in Control Electrical Engineering, Computational Neuroscience, Robotics, and Artificial Intelligence. She earned her Ph.D. from Iran University of Science and Technology (IUST), where her thesis focused on designing and stabilizing intelligent decision-making systems to support the treatment of adult focal epilepsy, receiving an excellent grade. She also holds a Master’s degree in Control Electrical Engineering from IUST and a Bachelor’s degree from Imam Khomeini International University. Dr. Saeedinia has extensive experience in signal processing, machine learning, spiking neural networks, EEG and MRI data analysis, and adaptive control systems. She has contributed to numerous high-impact publications in IEEE Transactions, Scientific Reports, and other international journals. Her research interests include computational modeling, intelligent control, data fusion, robotics path planning, and neuroinformatics. Beyond academia, she has led industrial projects in electrical power systems, automation, and instrumentation. She has received multiple awards, including the Khwarizmi International Award and top ranks in academic performance. Dr. Saeedinia is also an experienced educator and mentor, guiding students in advanced control systems, neural networks, and data analysis. Her work bridges engineering, neuroscience, and artificial intelligence, aiming to develop innovative solutions for healthcare and robotic applications.

Profile : Google Scholar

Featured Publications

Saeedinia, S.A., Jahed-Motlagh, M.R., Tafakhori, A., & Kasabov, N.K. (2024). “Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine” in Scientific Reports, 14(1), 10667.

Mohaghegh, M., Saeedinia, S.A., & Roozbehi, Z. (2023). “Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles” in Frontiers in Robotics and AI, 10, 1226028.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2024). “Dynamic-structured reservoir spiking neural network in sound localization” in IEEE Access, 12, 24596–24608.

Saeedinia, S.A., & Tale Masouleh, M. (2022). “The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision” in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2025). “Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks” in IEEE Access.

Mittar Pal | Engineering | Women Researcher Award

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Dr. Mittar Pal | Engineering | Women Researcher Award

Deenbandhu Chhotu Ram University of Science and Technology | India

Dr. Mittar Pal is an experienced academic and researcher in Electronics & Communication Engineering with over twelve years of teaching and research experience, specializing in renewable energy systems and AI‑based engineering applications. He holds a B.Tech in ECE (2003), M.Tech in Nanoscience & Technology (2013) and completed his Pre‑PhD in ECE (2019) from renowned Indian institutions, and recently earned his PhD on barrier identification and prioritisation for solar photovoltaic system deployment in dairy farming in India (2023). His work on techno‑economic analysis of photovoltaic systems in dairy farming and optimization of utility‑integrated solar PV systems has appeared in SCI and Scopus‑indexed outlets, and he has supervised numerous B.Tech and M.Tech projects while contributing to NBA documentation and curriculum development. He is UGC‑NET qualified for Electronic Science (Assistant Professor). His research interests span signal & system theory, digital electronics, power electronics, renewable solar energy modelling (using MATLAB, HOMER Pro, SAM, RETScreen), AI/ML, generative AI, IoT and pattern recognition. With a growing scholarly profile (h‑index ~ 5 and citation count ~5 as per publicly listed profiles), he continues to publish, present at international conferences, and drive innovation in teaching and research. His dedication to transforming traditional dairy farming through solar PV integration underscores his commitment to sustainability and engineering education.

Profile: Scopus 

Featured Publications

Mittarpal. (2018). “Modeling and Simulation of Solar Photovoltaic Module using Matlab-Simulink.” International Journal for Research in Engineering Application & Management .

Mittarpal. (2018). “Modeling Design and Simulation of Photovoltaic Module with Tags Using Simulink.” International Journal of Advance & Innovative Research.

Mittarpal, & Asha. (2018). “Simulation and Modeling of Photovoltaic Module Using Simscape.” National Conference on Advances in Power, Control & Communication Systems .

Mittarpal, Naresh Kumar, & Priyanka Anand, Sunita. (2017). “Realization of Flip Flops Using LabVIEW and MATLAB.”

Mittarpal, Naresh Kumar, & Sunita Rani. (2017). “Realisation of Digital Circuits Systems Using Embedded Function on MATLAB.” International Journal of Engineering and Technology.

Chulhwan Bang | Machine Learning and AI Applications | Best Researcher Award

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Dr. Chulhwan Bang | Machine Learning and AI Applications | Best Researcher Award

Georgia Southern University | United States

Dr. Chulhwan C. Bang is an Assistant Professor of Business Analytics and Information Systems at Georgia Southern University. He earned his Ph.D. in Business Management, specializing in Management Science and Systems with a minor in Communication, from the University at Buffalo, SUNY. With over a decade of academic and professional experience, Dr. Bang has taught a wide range of courses in business programming, information systems, and data analytics, integrating tools such as Python, SAP, Power BI, and Tableau. His research interests include big data analytics, cryptocurrency forecasting, social media analysis, deep learning, and sports analytics. He has published in top-tier journals such as Information Systems Frontiers and International Journal of Information Management and actively collaborates with students on applied data-driven projects. Prior to academia, he worked in the IT industry as a system architect and software developer, contributing to large-scale ERP and BI projects in Korea and the U.S. Dr. Bang has received multiple honors, including the Outstanding Research Award and finalist recognition for accelerator research grants. His work bridges theory and practice, fostering innovation in analytics education and advancing interdisciplinary research in business intelligence and emerging technologies.

Profile : Orcid

Featured Publications

Lee, Y. S., & Bang, C. C. (2022). Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network. Information Systems Frontiers.

Bang, C. C., Lee, J., & Rao, H. R. (2021). The Egyptian protest movement in the twittersphere: An investigation of dual sentiment pathways of communication. International Journal of Information Management.

Bang, C. C. (2020). Predicting Loyalty of Korean Mobile Applications: A Dual-Factor Approach. International Journal of Information and Communication Technology for Digital Convergence (IJICTDC).

Yu, J., Bang, C. C., & Oh, D.-Y. (2020). The Effect of Noncognitive Factors on Information Disclosure on Social Network Websites: Role of Habit and Affect. Southern Business & Economic Journal.

Kwon, K. H., Bang, C. C., Egnoto, M., & Rao, H. R. (2016). Social media rumors as improvised public opinion: semantic network analyses of Twitter discourses during Korean saber rattling 2013. Asian Journal of Communication.