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
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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.

 

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.

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.