Mr. Sina Rezaei | Computer Vision | Research Excellence Award

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Mr. Sina Rezaei | Computer Vision | Research Excellence Award

Engineer | University of Tehran | Iran

Mr. Sina Rezaei Nafar is a researcher in photogrammetry, computer vision, and geomatics engineering, with a strong focus on 3D reconstruction, semantic segmentation, and AI-driven analysis of spatial data. His work integrates spherical and fisheye camera systems, low-cost imaging sensors, and machine learning models to enhance point cloud accuracy, urban mapping, and cultural heritage documentation. His research has been published in high-impact international journals, including Sensors, addressing camera calibration, network design, and point cloud quality assessment. According to Google Scholar, his scholarly output includes multiple indexed documents with 12 citations and an h-index of 2, reflecting growing academic impact across photogrammetry and computer vision research.

Citation Metrics (Google Scholar)

15

10

5

0

Citations
12
i10-index
0
h-index
2
🟦 Citations    🟥 i10-index    🟩 h-index

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Featured Publications

Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

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Prof. Mengmeng Liao | Computer Vision | Research Excellence Award

Associate Professor | Shanghai University | China

Prof. Mengmeng Liao is an accomplished researcher in artificial intelligence, computer vision, pattern recognition, and image processing, with a strong record of contributions to both foundational and applied aspects of visual computing. His work focuses on developing robust algorithms for face recognition, multi-resolution modeling, adaptive subspace learning, and representation learning, addressing complex challenges in real-world environments such as noise interference, limited samples, and multi-pose variation. He has authored more than 20 SCI/EI-indexed research papers, including publications in leading international journals such as Information Sciences, Neurocomputing, Expert Systems with Applications, Electronics, and IEEE Signal Processing Letters. His research impact is reflected in Scopus metrics, with 170 citations across 159 citing documents and an h-index of 6, alongside a growing presence on Google Scholar. Prof. Liao has also contributed to several major national research initiatives, securing competitive funding from programs such as the National Natural Science Foundation and the Postdoctoral Innovative Talent Support Program. His active engagement with the global academic community includes serving as a technical committee member, session chair, and program chair for numerous international conferences. Through his interdisciplinary approach and sustained research output, Prof. Liao continues to advance the field of artificial intelligence, particularly in intelligent visual perception, pattern learning, and computational recognition systems.

Publication Profile

Scopus

Publications

Fan, X., Liao, M., Chen, L., & Hu, J. (2023). Few-shot learning for multi-POSE face recognition via hypergraph de-deflection and multi-task collaborative optimization. Electronics.

Liao, M., Fan, X., Li, Y., & Gao, M. (2023). Noise-related face image recognition based on double dictionary transform learning. Information Sciences.

Fan, X., Liao, M., Xue, J., Wu, H., Jin, L., Zhao, J., & Zhu, L. (2023). Joint coupled representation and homogeneous reconstruction for multi-resolution small sample face recognition. Neurocomputing.

Liao, M., Li, Y., & Gao, M. (2022). Graph-based adaptive and discriminative subspace learning for face image clustering. Expert Systems with Applications.

Jiang, W., Li, Y., Liao, M., & Wang, S. (2021). An improved LPI radar waveform recognition framework with LDC-Unet and SSR-Loss. IEEE Signal Processing Letters.

 

Ms. Ifza Shad | Computer Vision | Research Excellence Award

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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.

Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award

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Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award

Professor | Shaoyang University | China

Assoc. Prof. Dr. Ammar Oad is an accomplished researcher in Artificial Intelligence with strong expertise in deep learning, computer vision, cybersecurity, and intelligent data-driven systems. His research focuses on designing advanced algorithms for image analysis, object detection, multimodal learning, cross-modal retrieval, and secure AI frameworks capable of addressing modern challenges in threat detection and autonomous systems. Dr. Oad’s scientific contributions span AI-powered fake news detection, plant disease identification using explainable AI, blockchain-enabled cybersecurity mechanisms, sustainable smart grid prediction models, and intelligent pattern recognition. His research impact is reflected in Scopus metrics of 382 citations across 374 documents with an h-index of 9, and Google Scholar metrics of 573 citations, h-index 10, and i10-index 12, demonstrating strong visibility and influence within the scientific community. His work regularly appears in reputable journals such as IEEE Access, Optik, Electronics (MDPI), and leading materials science journals through interdisciplinary collaborations. Dr. Oad also contributes to the academic community as an editorial board member and scientific reviewer for several high-impact journals. His research interests include deep neural architectures, Gaussian mixture models, ensemble learning, blockchain security frameworks, and energy-efficient AI systems for smart cities. By integrating machine learning with cybersecurity principles, he aims to develop intelligent, robust, and transparent AI solutions capable of safeguarding digital infrastructures while advancing the state of automated recognition and decision-making technologies. His growing body of research reflects innovation, rigor, and a commitment to addressing real-world AI challenges.

Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Oad, A., Farooq, H., Zafar, A., Akram, B. A., Zhou, R., & Dong, F. (2024). Fake news classification methodology with enhanced BERT. IEEE Access, 12, 164491–164502.

Oad, A., Abbas, S. S., Zafar, A., Akram, B. A., Dong, F., Talpur, M. S. H., & Uddin, M. (2024). Plant leaf disease detection using ensemble learning and explainable AI. IEEE Access, 12, 156038–156049.

Oad, A., Ahmad, H. G., Talpur, M. S. H., Zhao, C., & Pervez, A. (2023). Green smart grid predictive analysis to integrate sustainable energy of emerging V2G in smart city technologies. Optik, 272, 170146.

Oad, A., Razaque, A., Tolemyssov, A., Alotaibi, M., Alotaibi, B., & Zhao, C. (2021). Blockchain-enabled transaction scanning method for money laundering detection. Electronics, 10(15), 1766.

Li, Y., Liu, W., Pang, X., Oad, A., Liang, D., Zhang, X., Tang, B., Fang, Z., Shi, Z., & Chen, J. (2024). Microwave dielectric properties, Raman spectra and sintering behavior of low loss La7Nb3W4O30 ceramics with rhombohedral structure. Ceramics International.

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.

Yinlei Cheng | Computer Vision | Best Researcher Award

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Dr. Yinlei Cheng | Computer Vision | Best Researcher Award

Beijing Institute Of Fashion Technology | China

Dr. Yinlei Cheng is a dedicated postgraduate researcher at the Beijing Institute of Fashion Technology, specializing in artificial intelligence and innovative design. With a strong academic foundation in engineering and computing, he has developed expertise in deep learning, computer vision, and intelligent image processing. His research journey is marked by active involvement in collaborative projects bridging academia and industry, where he has focused on real-world challenges such as intelligent fabric recognition and fault diagnosis systems. Driven by a passion for research and innovation, he continues to explore advanced computational methods that contribute to both theoretical understanding and practical applications.

Publication Profile

Scopus

Education Background

Dr. Yinlei Cheng completed his undergraduate engineering studies at Shandong Jiaotong University, where he established a strong base in technology and problem-solving. He is currently pursuing a master’s degree at the School of Liberal Arts and Sciences, Beijing Institute of Fashion Technology, advancing his academic career with a focus on artificial intelligence applications. His educational path highlights a consistent pursuit of excellence, blending technical knowledge with practical applications in computer vision and image processing. Through this background, he has been able to integrate academic learning with innovative research contributions, strengthening his expertise in both theory and practice.

Professional Experience

Dr. Yinlei Cheng has been actively engaged in research-driven projects with direct industry relevance, showcasing his ability to apply cutting-edge methods to solve complex problems. His work on the intelligent fabric piece grasping system demonstrated his skill in combining deep learning and machine vision for non-rigid object recognition and automation. He also contributed to developing a portable fault diagnosis software system designed to provide real-time monitoring and predictive analysis of industrial equipment. These experiences reflect his growing professional maturity and highlight his potential to bridge academic research with practical industry solutions, ensuring his contributions have both scientific and applied value.

Awards and Honors

While Dr. Yinlei Cheng is still at an early stage in his research career, he has already achieved recognition through his publication in a peer-reviewed international journal indexed in high-ranking databases. His academic contributions, particularly in advancing activation functions for convolutional neural networks, have been cited by other researchers, reflecting the growing impact of his work. His dedication to refining theoretical insights and combining them with rigorous experimental validation has positioned him as a promising researcher. Although formal awards may not yet fully represent his contributions, his publication record and involvement in impactful projects underline his academic excellence.

Research Focus

The central focus of Dr. Yinlei Cheng’s research lies in computer vision, deep learning, and image processing, with a particular interest in designing intelligent systems for real-world applications. His work explores innovative activation functions to enhance the performance of convolutional neural networks, contributing both theoretical advancements and practical improvements. He also applies these concepts to industrial applications, such as automation in flexible manufacturing and predictive fault detection systems. By balancing theoretical depth with practical deployment, his research adds value to both academia and industry. His ongoing efforts aim to extend these methodologies to more advanced architectures and transformative technologies.

Publication Notes

Title: A Periodic Mapping Activation Function: Mathematical Properties and Application in Convolutional Neural Networks
Published Year: 2025
Citation: 1

Conclusion

Dr. Yinlei Cheng’s academic journey reflects a balance of solid educational grounding, active participation in significant projects, and meaningful contributions to the field of artificial intelligence. His work demonstrates the ability to translate theoretical research into applied solutions that address complex industry challenges. With an expanding publication record and growing recognition, he shows strong potential to emerge as a leading researcher in computer vision and deep learning. His commitment to rigorous research, clarity in academic writing, and focus on future innovations position him as a deserving candidate for recognition in the Best Researcher Award category.

Mr. Ayush Roy | Computer Vision | Young Researcher Award

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Mr. Ayush Roy | Computer Vision | Young Researcher Award

PhD, University at Buffalo, United States

Ayush Roy is an emerging researcher and innovator in the field of Electrical Engineering with a deep interest in AI, computer vision, and biomedical image analysis. Currently pursuing his B.E. at Jadavpur University, he has demonstrated exceptional potential through interdisciplinary research, AI-driven solutions, and impactful contributions to both academia and real-world applications. With multiple international publications and recognitions, Ayush is a dynamic force in the intersection of deep learning, signal processing, and intelligent systems.

Publication Profile

Google Scholar

🎓 Education Background

Ayush Roy is a final-year undergraduate student at Jadavpur University, West Bengal, India, enrolled in the Bachelor of Engineering (Electrical) program with an SGPA of 8.1/10 (2020–2024). He completed his schooling from Bhartiya Vidya Bhavan, West Bengal under the CBSE board, scoring 90.6% in Class 12 and a perfect CGPA of 10 in Class 10.

💼 Professional Experience

Ayush’s research journey began at Jadavpur University, working under renowned professors in Audio Signal Processing, Reinforcement Learning, and Image Segmentation. As a research intern at the Indian Statistical Institute, he contributed to dataset development and text detection models. He furthered his research as an intern at the University of Malaya on transformer-based networks and at IISc Bangalore on CLIP for image quality assessment. His work integrates deep learning models like YOLO, Swin Transformer, UNet, and CLIP with novel architectures and real-world applications.

🏆 Awards and Honors

Ayush has earned several accolades such as the Most Innovative Solution award at Hack-a-Web by NIT Bhopal (2021), 3rd Prize at FrostHack, IIT Mandi (2022), Top 10 in Cloud Community Hackday by GDG Cloud, and became a Finalist in both the IEEE R10 Robotics Competition and 404 Resolved hackathon at IIT Delhi.

🔬 Research Focus

His primary research areas include computer vision, medical image segmentation, scene text detection, and real-time AI systems. He is especially focused on lightweight models, attention mechanisms, domain adaptation, and hybrid approaches combining deep learning and signal processing. He has created multiple datasets for benchmarking including those for drone license plate detection, underwater text, water meter digit recognition, and circuit component recognition.

📌 Conclusion

Ayush Roy stands as a committed and creative researcher, blending electrical engineering fundamentals with cutting-edge AI methodologies. His work not only adds value to academic literature but also paves the way for practical, socially impactful AI systems. With an impressive early-career portfolio, Ayush continues to show immense promise for future contributions to science and technology.

📚 Top Publication Notes 

AWGUNet: Attention-aided Wavelet Guided U-net for nuclei segmentation in histopathology images

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 2 articles (Google Scholar)

A Wavelet Guided Attention Module for Skin Cancer Classification

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 1 article (Google Scholar)

A New Lightweight Attention-based Model for Emotion Recognition Using Distorted Social Media Images

Year: 2023

Journal/Conference: ACPR 2023

Cited By: 3 articles

Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images

Year: 2023

Conference: ICDAR WML 2023

Cited By: 1 article

A Lightweight Script Independent Scene Text Style Transfer Network

Year: 2024

Journal: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

Cited By: 1 article

Identification and Classification of Human Mental Stress using Physiological Data

Year: 2022

Conference: IEEE CATCON 2022

Cited By: 4 articles

Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 2 articles

An Attention-based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 1 article

DAU-Net: Dual Attention-aided U-Net for Segmenting Tumor Region in Breast Ultrasound Images

Year: 2023

Journal: PLOS ONE

Cited By: 6 articles

 

 

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

VINOD KUMAR VERMA | Computer Science | Best Researcher Award

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Assist Prof Dr. VINOD KUMAR VERMA | Computer Science | Best Researcher Award

Assistant Professor, Sant Longowal Institute of Engineering and Technology, Longowal, India

👨‍🏫 Dr. Vinod Kumar Verma was born in Kalka, Haryana, India. He is an esteemed Assistant Professor in the Department of Computer Science and Engineering at SLIET-Deemed to be University. With international teaching and research experience, he has contributed significantly to various academic and research institutions worldwide, including the UK, USA, Japan, Italy, Australia, France, and Greece. Dr. Verma has collaborated with prestigious universities such as the University of Surrey, England, and the University of Nottingham, Malaysia. He has published extensively in renowned international journals, making notable contributions to the fields of wireless sensor networks, IoT, big data, cloud computing, and more.

Profile

Scopus

🎓 Education

BTech in Computer Engineering from Kurukshetra University, 2005. MS Degree from BITS Pilani, Pilani, 2008. PhD in Computer Science and Engineering from Sant Longowal Institute of Engineering and Technology (SLIET), Longowal, India

💼 Experience

Dr. Verma has held various academic positions and has been involved in numerous international collaborations. His teaching and research engagements have taken him to countries such as the UK, USA, Japan, Italy, Australia, France, and Greece. He has worked with the University of Surrey, England, and visited the University of West Attica, Athens, Greece under the ERASMUS+ program. Dr. Verma is currently an Assistant Professor at SLIET-Deemed to be University.

🔍 Research Interests

Dr. Verma’s research interests are diverse and cutting-edge, including wireless sensor networks, the internet of things (IoT), big data, cloud computing, trust and reputation systems, simulation, distributed computing, cryptography, and software systems. His work has been published in various top-tier international journals, showcasing his significant contributions to these fields.

🏆 Awards

Dr. Verma has been recognized for his outstanding contributions to the field of computer science. He received the Session’s Best Paper Award at IMETI-CITSA-2014 in Orlando and the Best Paper Award at NCCN-11 in Longowal in 2011. He has also served on numerous organizing and program committees for international conferences, highlighting his influence and leadership in the academic community.

📚 Publications

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things