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

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.

Citation Metrics (Scopus)

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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

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

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.

Citation Metrics (Google Scholar)

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Featured Publications

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

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.

Mohd Asim | Remote Sensing | Best Researcher Award

Dr Mohd Asim | Remote Sensing | Best Researcher Award

Senior Research Fellow, Indian Agricultural Research Institute, India

Mohd Asim is a dedicated Senior Research Fellow at the Laboratory of Drone Remote Sensing and Big Data Analytics, Indian Agricultural Research Institute (IARI) in New Delhi. With a robust educational background in geography, he has recently earned his PhD from Indira Gandhi National Open University (IGNOU). His research primarily revolves around the application of remote sensing and machine learning in environmental monitoring and agricultural practices. Mohd is passionate about addressing pressing environmental challenges, particularly those related to water resources and pollution. He has contributed significantly to the understanding of heavy metal pollution in the Yamuna River, enhancing knowledge in this critical area. Mohd’s innovative approaches and commitment to research are reflected in his growing publication record and citation impact, making him a prominent figure in his field. He aims to leverage advanced technologies to foster sustainable agricultural practices and promote environmental conservation.

Profile

Orcid

Strengths for the Award

  1. Strong Academic Background: Mohd Asim has a solid foundation in geography, culminating in a PhD from IGNOU. His educational background provides a robust framework for his research.
  2. Relevant Research Experience: His work on heavy metal pollution in the Yamuna River and ongoing projects utilizing UAV technology showcase his commitment to addressing significant environmental issues through innovative methods.
  3. Publication Record: With 79 citations for his research on heavy metal pollution and publications in reputable journals like Environmental Monitoring and Assessment and International Journal of River Basin Management, he demonstrates the impact and recognition of his work in the scientific community.
  4. Innovative Approaches: The use of machine learning algorithms for the detection of banana plants using UAV data highlights his ability to integrate technology with traditional research methods, contributing to advancements in agricultural monitoring.
  5. Contribution to Environmental Science: His focus on critical issues such as water pollution and agricultural sustainability aligns with global research priorities, emphasizing the relevance of his contributions.

Areas for Improvement

  1. Expand Collaborative Efforts: Building partnerships with other researchers or institutions could enhance the scope and impact of his projects, potentially leading to interdisciplinary research opportunities.
  2. Engage in Consultancy or Industry Projects: Gaining experience in consultancy could provide practical insights and enhance the applicability of his research findings in real-world scenarios.
  3. Increase Professional Visibility: Joining professional organizations or taking editorial roles in relevant journals could further elevate his profile and foster networking opportunities within the research community.
  4. Broaden Publication Scope: While his publications are significant, aiming for a more diverse range of journals could enhance visibility and reach in different academic circles.

Education

Mohd Asim completed his Bachelor’s and Master’s degrees in Geography, laying a strong foundation for his academic pursuits. He has recently obtained his PhD in Geography from Indira Gandhi National Open University (IGNOU), where he focused on the intersection of environmental science and technology. His doctoral research investigated heavy metal pollution in the Yamuna River, utilizing Geographic Information Systems (GIS) and pollution indices to assess and analyze environmental health. Throughout his education, Mohd has developed a keen interest in remote sensing, machine learning, and their applications in agricultural monitoring and water resource management. His educational journey has equipped him with essential skills and knowledge to tackle complex environmental challenges. By integrating traditional geographical methods with modern technology, he aspires to contribute to sustainable practices in agriculture and environmental management, making his academic background a vital asset in his ongoing research endeavors.

Experience 

Mohd Asim currently serves as a Senior Research Fellow (SRF) at the Laboratory of Drone Remote Sensing and Big Data Analytics within the Division of Agricultural Physics at IARI, New Delhi. His role involves conducting advanced research on environmental monitoring, focusing on the impacts of pollution and the dynamics of river systems. He has successfully completed several projects, including studies on heavy metal pollution in the Yamuna River and channel dynamics, both of which have been published in reputable journals. Currently, he is working on a project that employs UAV multispectral imaging and machine learning algorithms to detect banana plants, showcasing his ability to apply cutting-edge technology in agricultural research. Mohd’s experience in interdisciplinary research and collaboration with experts in remote sensing and GIS has significantly contributed to his understanding of the complex interactions between agricultural practices and environmental sustainability, positioning him as a key researcher in his field.

Research Focus

Mohd Asim’s research focuses on the integration of remote sensing, Geographic Information Systems (GIS), and machine learning in environmental studies, particularly concerning agricultural monitoring and water resource management. He aims to address critical environmental issues, such as pollution and resource sustainability, with a strong emphasis on understanding heavy metal contamination in waterways, especially the Yamuna River. His ongoing project on detecting banana plants using UAV multispectral imagery illustrates his commitment to leveraging technology for agricultural advancements. Mohd’s research encompasses the dynamics of river systems, channel changes, and the impact of anthropogenic activities on water resources. By employing innovative methodologies and interdisciplinary approaches, he seeks to enhance the efficiency and sustainability of agricultural practices while promoting environmental conservation. His work contributes significantly to the understanding of how advanced technologies can facilitate better management of natural resources, aligning with global efforts toward sustainable development and environmental protection.

Publication Top Notes

  • Flow dynamics and channel changes at Yamuna River in Delhi-National Capital Region, India 🌊
  • Assessment of heavy metal pollution in Yamuna River, Delhi-NCR, using heavy metal pollution index and GIS ⚗️

Conclusion

Mohd Asim is a strong candidate for the Best Researcher Award, demonstrating excellence in research and innovation. His contributions to the field of geography, particularly in remote sensing and environmental monitoring, position him as a key figure in addressing pressing ecological challenges. By expanding his collaborative and professional engagements, he can further amplify his impact and recognition in the academic community.