Ms. Ifza Shad | Computer Vision | Research Excellence Award

Published on by Computer Scientists Awards

Ms. Ifza Shad | Computer Vision | Research Excellence Award

University of Central Punjab | Pakistan

Ms. Ifza Shad is a computer vision and artificial intelligence researcher whose work focuses on real-time object detection, medical image analysis, deep learning optimization, and multimodal perception models for complex environments. Her research integrates advanced machine learning architectures, including YOLO-based detectors, attention-driven fusion networks, and lightweight deep learning frameworks designed for resource-efficient deployment in dynamic real-world scenarios. She has contributed to cutting-edge studies in aquatic and surface litter detection, brain tumor diagnosis, protective workwear recognition, and driver-behavior monitoring systems, demonstrating a strong emphasis on safety, healthcare, and environmental sustainability. Her interdisciplinary approach merges computer vision, robotics, and large-scale data processing, allowing her to design algorithms that address challenges in automation, public health, and smart systems. She has authored impactful publications in reputable international journals indexed in Scopus and Web of Science, with her research widely cited and accessible on Google Scholar. Her scholarly record includes peer-reviewed articles, collaborative projects with international researchers, and contributions to academic seminars and conferences. She continues to advance innovative detection models and AI-driven solutions, aiming to enhance real-time decision support systems through robust, interpretable, and computationally efficient algorithms. Her research output reflects a growing citation count, supported by Scopus metrics, Google Scholar indices, and document-level analytics, emphasizing her active role in the global scientific community and her contribution to emerging intelligent systems.

Profile

ORCID

Featured Publications

Shad, I., Zhang, Z., Asim, M., Al-Habib, M., Chelloug, S. A., & Abd El-Latif, A. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences, 18(2), 101534.

Shad, I., Bilal, O., & Hekmat, A. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering & Biosciences, 7(3).

Hekmat, A., Zhang, Z., Khan, S. U. R., Shad, I., & Bilal, O. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control, 101, 107221.

Prof. Joongrock Kim | Computer Vision | Best Researcher Award

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Prof. Joongrock Kim | Computer Vision | Best Researcher Award

Associate Professor | Changwon National University | South Korea

Prof. Joongrock Kim is an accomplished researcher and Associate Professor in Artificial Intelligence Convergence Engineering at Changwon National University, Republic of Korea. His expertise spans computer vision, 3D scene understanding, deep learning-based perception, and intelligent systems for automotive and consumer applications. Over his distinguished career, he has contributed significantly to the development of advanced AI technologies, including driver monitoring systems, 3D reconstruction, food recognition, and smart V2X perception systems. His research focuses on integrating multimodal sensing, neural rendering, and adaptive feature extraction for robust real-world perception, bridging academia and industry to advance AI deployment in smart vehicles and appliances. Dr. Kim’s prolific output includes numerous high-impact publications and international patents on AI-based sensing and perception systems. According to Scopus, he has achieved 212 citations across 207 documents with an h-index of 7, while his Google Scholar profile reflects broader academic engagement and influence. His work continues to drive innovation in perception AI, human–machine interaction, and computational imaging, establishing him as a leading figure in applied artificial intelligence and computer vision research.

Profile

Scopus

Featured Publications

Park, M., Do, M., Shin, Y. J., Yoo, J., Hong, J., Kim, J., & Lee, C. (2024). H2O-SDF: Two-phase learning for 3D indoor reconstruction using object surface fields. International Conference on Learning Representations (ICLR).

Kim, J., Yu, S., Kim, D., Toh, K.-A., & Lee, S. (2017). An adaptive local binary pattern for 3D hand tracking. Pattern Recognition.

Kim, J., Yoon, C. (2016). Three-dimensional head tracking using adaptive local binary pattern in depth images. International Journal of Fuzzy Logic and Intelligent Systems.

Kim, K., Kim, J., Choi, J., Kim, J., & Lee, S. (2015). Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions. Sensors.

Kim, J., Yu, S., & Lee, S. (2014). Random-profiles-based 3D face recognition system. Sensors.

Mr. Zhenduo Meng | Machine Learning | Best Researcher Award

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Zhenduo Meng | Machine Learning | Best Researcher Award

Inner Mongolia University, China

Zhenduo Meng is a graduate student pursuing his M.Sc. in Electronic Information Engineering at the School of Electronic Information Engineering, Inner Mongolia University, with a strong academic foundation built during his B.Eng. studies in Automation at Guangxi University. His research primarily focuses on multi-agent reinforcement learning (MARL), deep reinforcement learning, cooperative control of multi-agent systems, and the broader applications of artificial intelligence in intelligent decision-making. He has actively participated in several research projects, where he contributed to the development of algorithms integrating attention mechanisms and value decomposition methods to improve collaboration efficiency in MARL environments. Recently, his research work, “DDWCN: A Dual-Stream Dynamic Strategy Modeling Network for Multi-Agent Elastic Collaboration,” was accepted for publication in Applied Sciences (2025), highlighting his innovative contributions in the field. Despite being at the early stage of his academic journey, his scholarly output includes 2 documents, and his current citation count stands at zero, reflecting the fresh and emerging nature of his research profile. His h-index is also recorded as zero, consistent with his recent entry into the publication landscape. Proficient in Python, MATLAB, PyTorch, and TensorFlow, along with strong command of both Chinese and English, Meng demonstrates promising potential for impactful contributions in intelligent systems research.

Profile: Scopus

Featured Publications

Meng, Z., Na, X., Wang, T., Liu, J., & Wang, W. (2025). DDWCN: A dual-stream dynamic strategy modeling network for multi-agent elastic collaboration.

Wang, T., Na, X., Nie, Y., Liu, J., Wang, W., & Meng, Z. (2025). Parallel task offloading and trajectory optimization for UAV-assisted mobile edge computing via hierarchical reinforcement learning. Drones, 9(2),

Mr. Kostas Ordoumpozanis | Computer Vision | Best Researcher Award

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Mr. Kostas Ordoumpozanis | Computer Vision | Best Researcher Award

Phd Cand. Department of Cultural Technology and Communication, University of the Aegean Greece, Greece

Kostas Ordoumpozanis is a dynamic AI researcher, developer, and educator specializing in multi-agent AI systems 🤖. Currently a PhD candidate at the University of the Aegean, Greece, his expertise spans AI-driven automation, large language models (LLMs), retrieval-augmented generation (RAG), and AI-human collaboration. With a background in mechanical engineering and over a decade of experience in full-stack development, AR, and creative technology, he blends technical proficiency with artistic innovation. Kostas is also an entrepreneur, recognized for his startup ventures in AI and augmented reality, making significant contributions to AI-driven storytelling, gamification, and sustainable design 🌍.

Publication Profile

ORCID

🎓 Academic Background

Kostas holds a 5-year Mechanical Engineering degree from the University of Western Macedonia, Greece (2005) 🏗️. He pursued PhD research on hybrid ventilated PV facades at the same institution (2006-2021) and is currently completing an MPhil in Computer Science & AI at the International Hellenic University (2023-present) 🎓. His latest research focuses on AI multi-agent systems as part of his PhD at the University of the Aegean (2024-present), further cementing his expertise in AI agency and intelligent automation.

💼 Professional Experience

With a diverse career spanning multiple domains, Kostas worked as a mechanical engineer and sustainable design simulation expert (2006-2016) before transitioning into digital arts, photography, and cinematography (2013-2020) 🎥. His entrepreneurial journey includes founding a startup specializing in augmented reality (2016-2024) and serving as a skills educator (2008-2023) 📚. Since 2018, he has been a full-stack developer, focusing on AI applications, web technologies, and vector databases. Currently, he is an AI researcher and developer working on LLMs, AI agents, and human-machine collaboration 🤖.

🏆 Awards and Honors

Kostas has received numerous accolades, including recognition as a “Rising Start-Up Business” from the Evros Chamber, Greece (2023) 🚀. He secured first place in the EU Interreg Greece-Bulgaria Startup Contest (2023) and was a finalist in the Athens StartUp Awards (2018) and XR COSMOS Greece (2021). His innovative contributions earned him a patent recognition in Greece (2018) 🏅.

🔬 Research Focus

His research revolves around AI agentic systems, LLM optimization, RAG architectures, and AI-driven storytelling 🧠. He explores the intersection of generative AI, human-computer collaboration, and augmented reality for educational and creative applications. His work also extends to sustainable AI, assessing the carbon footprint of deep learning models and developing efficient AI architectures for various domains.

📝 Conclusion

Kostas Ordoumpozanis is a visionary AI researcher and innovator, merging technical expertise with creative problem-solving. His contributions to AI agents, storytelling, and sustainable AI showcase his commitment to pushing technological boundaries. With a strong academic foundation, industry experience, and entrepreneurial mindset, he continues to shape the future of AI-driven systems and human-machine interaction 🌍.

📚 Top Research Publications

Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields (2025) – Applied Sciences
Cited by: Multiple AI research articles

Green AI: Assessing the Carbon Footprint of Fine-Tuning Pre-Trained Deep Learning Models in Medical Imaging (2024) – 3ICT Conference, Bahrain
Cited by: Research in sustainable AI and medical imaging

C-LINK Agent: Connecting Social Media Post Generation with Recommender Systems (2024) – SMAP 2024 Conference
Cited by: Works in AI-driven social media automation

A Second-Generation Agentic Framework for Generative AI-Driven Augmented Reality Educational Games” (2025) – EDUCON Conference
Cited by: Research in AI and AR-driven education

Energy, Comfort, and Indoor Air Quality in Nursery and Elementary School Buildings in the Cold Climatic Zone of Greece” (Published in Energy and Buildings)
Cited by: Sustainability and green architecture studies

Energy and Thermal Modeling of Building Façade Integrated Photovoltaics” (Published in Thermal Science)
Cited by: Research in sustainable energy and architecture