Cheng Ju | 3D object detection | Research Excellence Award

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Dr. Cheng Ju | 3D object detection | Research Excellence Award

Xi'an Innovation College of Yan'an University | China 

The research profile centers on advancing computer vision through robust, trustworthy, and high-efficiency intelligent systems, addressing both task-specific challenges and foundational methodological limitations. Core contributions focus on object detection, anomaly segmentation, feature representation learning, and adaptive model design, with strong emphasis on real-world deployment reliability. In oriented small object detection, the proposed FDA-DETR framework overcomes weak feature representation and excessive computational cost in transformer-based detectors by integrating multi-scale frequency domain enhancement, density-aware dynamic query generation, and multi-granularity attention fusion, achieving significant gains in accuracy, robustness, and efficiency for dense and complex visual scenes. In zero-shot anomaly segmentation, the explainable recursive ERSF-AS framework resolves prompt dependency, limited anomaly feature learning, and interpretability challenges through a collaborative CLIP-SAM architecture combined with semantic, spatial, and frequency priors. Its recursive reasoning paradigm strengthens cross-domain generalization and reliability in both industrial and medical environments. Collectively, these innovations contribute not only to practical solutions for complex vision tasks but also to broader theoretical advancements in feature learning, attention modeling, adaptive mechanisms, and explainable artificial intelligence. This integrated research direction establishes a scalable foundation for future exploration across diverse computer vision and intelligent perception domains, promoting trustworthy, interpretable, and deployable AI systems for real-world applications.

Profile: Orcid 

Featured Publications 

Ju, C., Xie, Y., Wang, Z., Zhao, Y., Yan, W., Chai, R., Duan, J., Cao, Y., & Chang, Y. (2026). STGFormer: A pyramidal spatio-temporal graph transformer with cross-disciplinary feature fusion for semantic-rich trajectory prediction in heterogeneous autonomy traffic. Expert Systems with Applications.

Ju, C., Xie, Y., Duan, J., Chang, Y., & Wu, M. (2026). KeyGeoFusion: A multi-modal keypoint and geometry-aware framework for small and distant 3D object detection in sparse point clouds. Neurocomputing.

Xiaoqing Wan | Pattern Recognition | Research Excellence Award

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Dr. Xiaoqing Wan | Pattern Recognition | Research Excellence Award

Hengyang Normal University | China

Xiaoqing Wan is a lecturer in computer science and an active member of the global research community in Pattern Recognition artificial intelligence and intelligent information processing. His academic work focuses on pattern recognition and image processing, with particular emphasis on the development of advanced algorithms for remote sensing image analysis. His research integrates deep learning and machine learning techniques to improve classification accuracy, feature extraction, and robustness in complex and large-scale image datasets. In addition, he is deeply involved in the design of computer-aided diagnosis systems, where artificial intelligence is applied to support medical image interpretation and decision-making, aiming to enhance efficiency and reliability in clinical analysis. His scholarly background in signal and information processing and communication systems provides a strong theoretical foundation for interdisciplinary research that bridges engineering, data science, and applied intelligence. As an educator, he contributes to the training of future engineers and researchers through teaching core subjects in artificial intelligence, programming, and software engineering, with a strong focus on practical problem-solving and algorithmic thinking. His ongoing research continues to explore innovative methodologies that combine intelligent computation with real-world applications, contributing to the advancement of intelligent systems in remote sensing, healthcare, and computer vision.

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Featured Publications

Kaili Wang | Machine Learning and Statistics | Best Researcher Award

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Dr. Kaili Wang | Machine Learning and Statistics | Best Researcher Award

university of malaya | Malaysia

Dr. Kaili Wang is an accomplished economist and Doctoral Candidate in Financial Economics at the University of Malaya, with a strong academic foundation in quantitative analysis, holding a master’s degree in Quantitative Economics from Zhongnan University of Economics and Law and a bachelor’s degree in Statistics from Luoyang Normal University. She has extensive teaching experience, having served as a full-time faculty member at the Business School of Nantong University of Technology, where she contributed significantly to both academic research and student mentorship. Her research expertise encompasses financial security, green finance, and the operational efficiency of financial institutions, reflected in her monographs, including Analysis of RMB Internationalization Path from the Perspective of Financial Security (sole author) and Research on the Long-term Mechanism of Green Finance Development (second author). She has also led impactful research projects, such as the Jiangsu Provincial University Philosophy and Social Sciences Research Project on the operational efficiency of city commercial banks. Kaili Wang has demonstrated a strong commitment to student development, guiding participants in national and provincial financial competitions to notable achievements, including second and third prizes in the National ETF Elite Challenge and the “East Money Cup” National College Students’ Financial Challenge, and earning recognition as an Excellent Supervisor. Her work reflects a combination of rigorous empirical analysis and practical engagement with financial markets, emphasizing sustainable finance and strategic economic development. With a focus on integrating academic excellence with real-world financial insights, Kaili Wang continues to advance knowledge in financial economics while nurturing the next generation of economists and financial professionals through research, mentorship, and academic leadership. Her career demonstrates a sustained dedication to both scholarly contributions and fostering student success in competitive financial arenas.

Profile: Orcid

Featured Publication

Wang, K. (2024). An analysis of the RMB internationalization path from the perspective of financial security.