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

Zohaib Khan | Engineering and Technology | Excellence in Research Award

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Dr. Zohaib Khan | Engineering and Technology | Excellence in Research Award

Jiangsu University | China

Zohaib Khan is a PhD candidate in Control Science and Engineering at Jiangsu University, specializing in machine learning–driven perception and control for intelligent robotic systems. With over six years of research and applied experience, his work bridges deep learning, computer vision, and real-time robotic control, with a strong focus on agricultural robotics and precision farming. He has authored more than 10 high-impact SCI-indexed journal articles, achieving an h-index of 6, with 11 research documents and 121 citations. His research interests include object detection and segmentation (YOLO series, transformer-based models, RCNN), vision-guided navigation, precision spraying, and robust control of autonomous robots in unstructured environments. Zohaib has contributed as both first and co-author to leading journals such as Computers and Electronics in Agriculture, Agronomy, Sensors, and IEEE Transactions on Industrial Electronics. Alongside research, he has extensive experience supervising student projects and developing real-time AI pipelines using Python, PyTorch, OpenCV, ROS, and C/C++. His academic excellence is recognized through multiple national and international awards, including innovation, debate, and research excellence honors. Overall, Zohaib Khan represents a strong blend of theoretical rigor and practical AI deployment, aiming to advance large-scale industrial and agricultural perception systems.

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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.

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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

Oussama El Othmani | Computer Science and Artificial Intelligence | Best Innovation Award

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Mr. Oussama El Othmani | Computer Science and Artificial Intelligence | Best Innovation Award

Ecole Polytechnique de Tunisie | Tunisia

Oussama El Othmani is an emerging researcher and software engineer whose work bridges artificial intelligence, explainable machine learning, and applied computer engineering. He is currently pursuing a PhD in ETIC, following a strong academic foundation in computer engineering and preparatory mathematics–physics, with rigorous training in artificial intelligence, advanced learning algorithms, computer architecture, databases, and software methodology. Professionally, he has contributed to complex, mission-critical software systems, working across the full software development lifecycle while applying agile methodologies, object-oriented design, and hardware-aware optimization. His research interests focus on explainable and interpretable AI, machine learning, rough set theory, soft computing, computer vision, natural language processing, and AI applications in healthcare and high-stakes decision systems. He has led and contributed to multiple applied AI projects, including medical chatbots, diagnostic decision-support systems, blood anomaly detection, and antibiotic resistance classification. His scholarly output includes peer-reviewed publications in applied AI, He has also gained recognition through research-driven projects aligned with national and institutional initiatives. Overall, his profile reflects a strong balance of academic research, applied innovation, and real-world impact, positioning him as a promising contributor to the future of trustworthy and explainable artificial intelligence.

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Liu Ying | Mechanical Engineering | Research Excellence Award

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Dr. Liu Ying | Mechanical Engineering | Research Excellence Award

East China Jiaotong University | China

Ying Liu is a dedicated researcher and Lecturer specializing in vehicle engineering with a strong academic trajectory and growing research impact, reflected in an h-index of 2, 4 published documents, and 14 citations across indexed platforms. She earned a combined Master’s and PhD degree in Mechanical Engineering from Shanghai University, followed by a Bachelor’s degree in Mechanical Design, Manufacturing and Automation from Changchun Institute of Technology. Her professional career includes serving as a Lecturer at East China Jiaotong University, where she contributes to teaching, mentoring, and interdisciplinary research. Her work focuses on target optimization, image detection, reinforcement learning, computer vision, intelligent control, and smart vehicle technologies. She has completed five research projects, contributed to two national-level and multiple provincial and horizontal projects, and actively engages in consultancy with ten industry-linked initiatives. She has authored seven SCI-indexed papers, produced twenty-five patents, and participated in major scientific collaborations including a project funded with 5 million yuan. As a recipient of Jiangxi’s Early-Career Young Talents Program, she continues to advance innovation in intelligent vehicle systems. She remains committed to impactful research, advancing engineering applications, and contributing to societal and technological development.

Profile : Scopus

Featured Publications

Liu, Y., & [Co-author(s) if any]. (2025). Continuous path tracking of robots based on positioning error compensation with iterative learning control. IEEE Transactions on Instrumentation and Measurement.

Suk-Ju Kang | Computer Science and Artificial Intelligence | Best Researcher Award 

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Prof. Suk-Ju Kang | Computer Science and Artificial Intelligence | Best Researcher Award 

Sogang University | South Korea

Prof. Suk-Ju Kang is a distinguished Professor in the Department of Electronic Engineering at Sogang University, Seoul, Korea, specializing in visual computing, computer vision, and artificial intelligence. His research spans image synthesis and restoration, real-time 2D/3D human and hand pose estimation, and industrial AI applications such as anomaly detection and remaining useful life prediction. Prior to joining Sogang University in 2015, he served as Assistant Professor at Dong-A University and worked as a Senior Research Engineer at LG Display, contributing to advanced display technologies. He earned his Ph.D. in Electrical Engineering from POSTECH under the supervision of Dr. Young Hwan Kim, and his B.S. in Electronic Engineering from Sogang University. Prof. Kang has authored over 209 peer-reviewed publications, which have collectively garnered over 2,143 citations with an h-index of 25, reflecting his global research impact. He has been recognized with numerous honors, including the 2025 Haedong Best Paper Award, multiple Samsung Best Paper Awards (2023, 2024), the 2022 Merck Young Scientist Award, and the 2020 Young Researcher Award from The Korean Institute of Broadcast and Media Engineers. He also plays an active leadership role in academia, serving as Chairman of the AI and Computational Technology Society for Display, Chairman of the Image Processing Research Society, and Organizing Committee Chair for major international conferences such as ITC-CSCC and AISPC.

Profiles: Scopus | Google Scholar

Featured Publications

“Luminance Compensation for Stretchable Displays Using Deep Visual Feature-Optimized Gaussian-Weighted Kernels.” Journal of the Society for Information Display, 2025.

“DGTFNet: Depth-Guided Tri-Axial Fusion Network for Efficient Generalizable Stereo Matching.” IEEE Robotics and Automation Letters, 2025.

“CRAN: Compressed Residual Attention Network for Lightweight Single Image Super-Resolution.” IEEE Signal Processing Letters, 2025.

“Supervised Denoising for Extreme Low-Light Raw Videos.” IEEE Transactions on Circuits and Systems for Video Technology, 2025.

“Query-Vector-Focused Recurrent Attention for Remaining Useful Life Prediction.” IEEE Transactions on Reliability, 2025.