Xinyue Xu | Computer Science | Research Excellence Award

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Ms. Xinyue Xu | Computer Science | Research Excellence Award

Ms. Xinyue Xu | Computer Science | University of Chinese Academy of Sciences | China

Ms Xinyue Xu is a dedicated researcher affiliated with the University of Chinese Academy of Sciences, where she advances her work in intelligent defect detection, robust deep-learning architectures, and computer vision applications for industrial automation. Ms Xinyue Xu completed her doctoral studies in a computing-related discipline at the University of Chinese Academy of Sciences, focusing on lightweight neural network optimization, blur-robust models, and intelligent inspection frameworks that address real-world engineering challenges. Her professional experience includes academic research responsibilities within her institution, participation in collaborative laboratory initiatives, and involvement in international research efforts related to machine vision and high-precision defect-detection systems. Her primary research interests include computer vision, artificial intelligence, defect detection in industrial components, lightweight convolutional models, and the development of high-efficiency architectures suitable for real-time inspection tasks. Ms Xinyue Xu possesses strong research skills in model design, data-driven algorithm development, image processing, neural network compression, performance benchmarking, and experimental evaluation across diverse industrial datasets. Her ORCID profile documents her contribution to the Scopus-indexed journal article FDC-YOLO: A Blur-Resilient Lightweight Network for Engine Blade Defect Detection, where she introduces an enhanced lightweight framework capable of handling blur-induced recognition challenges. This publication demonstrates her technical rigour, methodological clarity, and ability to address practical industrial problems through advanced machine-learning models. Although early in her career, Ms Xinyue Xu exhibits promising scholarly potential and continues to strengthen her academic record. Her research visibility and contributions make her eligible for participation in professional associations such as IEEE and ACM, and her academic accomplishments position her well for future recognition in emerging researcher and innovation-focused award categories. Her honors include her contribution to high-quality peer-reviewed research published in an international journal indexed in Scopus. Moving forward, Ms Xinyue Xu is well positioned to expand her publication portfolio, engage in broader interdisciplinary collaboration, strengthen her global research presence, and contribute to the development of next-generation intelligent inspection technologies. With her commitment to scientific advancement, growing research output, and expertise in applied AI, she continues to demonstrate strong potential for leadership and long-term impact in her field.

Academic Profile: ORCID

Featured Publications:

  1. Xu, X., Li, F., Xiong, L., He, C., Peng, H., Zhao, Y., & Song, G. (2025). FDC-YOLO: A blur-resilient lightweight network for engine blade defect detection. Algorithms.

 

 

Mohammad Badrul Alam Miah | Computer Science | Best Researcher Award

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Prof. Dr. Mohammad Badrul Alam Miah | Computer Science | Best Researcher Award

Prof. Dr. Mohammad Badrul Alam Miah | Computer Science | Professor at Mawlana Bhashani Science and Technology University | Bangladesh

Prof. Dr. Mohammad Badrul Alam Miah is a distinguished academic and research expert whose scholarly contributions span Data and Text Mining, Artificial Intelligence, Machine Learning, Neural Networks, Computer Vision, Photonic Crystal Fiber Design, and Network Communication Systems, making him a prominent figure in multidisciplinary computational research. Prof. Dr. Mohammad Badrul Alam Miah completed his higher education with advanced research training that established a strong methodological foundation for his career, enabling him to engage in both theoretical and applied scientific studies with exceptional depth. His professional experience includes long-standing service as a professor, program director, research supervisor, and senior academic leader, where he contributed extensively to curriculum development, postgraduate training, and international research collaborations across universities and specialized research centers. His research interests are rooted in intelligent systems, biomedical image analysis, AI-driven disease detection, fiber-optic sensing, blockchain-enabled IoT security, and automated information extraction systems, allowing him to integrate emerging technologies with real-world scientific challenges. Prof. Dr. Mohammad Badrul Alam Miah possesses advanced research skills in algorithm development, predictive modeling, feature engineering, neural network architecture design, data preprocessing, photonic fiber simulation, and cross-domain computational analysis, supported by strong proficiency in scientific programming and experimental validation. His recognition includes multiple scholarly honors, editorial review appointments, invited talks, and collaborative engagement with globally recognized researchers, reflecting his reputation as a respected contributor within the academic community. With a citation record exceeding nine hundred, an h-index demonstrating sustained impact, and numerous publications in IEEE, Scopus, Elsevier, and other reputable platforms, he continues to influence both foundational and applied research domains. His scholarly activities highlight his commitment to innovation, mentorship, and interdisciplinary advancement. Prof. Dr. Mohammad Badrul Alam Miah remains actively involved in expanding his research output through international collaborations, exploring advanced AI applications in healthcare, enhancing neural network performance for medical diagnostics, and contributing to knowledge development in data-driven science. His ongoing work positions him as a forward-looking researcher whose efforts significantly enrich global scientific scholarship and technological progress.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. Miah, M. B. A., & Yousuf, M. A. (2015). Detection of lung cancer from CT image using image processing and neural network. 165 citations.

  2. Shamim, S. M., Miah, M. B. A., Sarker, M. R. A., & Jobair, A. A. (2018). Handwritten digit recognition using machine learning algorithms. 140 citations.

  3. Mehedi, S. K. T., Shamim, A. A. M., & Miah, M. B. A. (2019). Blockchain-based security management of IoT infrastructure with Ethereum transactions. 45 citations.

  4. Sethi, R., Kaushik, I., & Miah, M. B. A. (2020). Hand written digit recognition using machine learning. 40 citations.

  5. Chowdhury, S., Sen, S., Ahmed, K., Paul, B. K., Miah, M. B. A., & Asaduzzaman, S. (2017). Porous shaped photonic crystal fiber with strong confinement field in sensing applications: Design and analysis. 39 citations.

 

 

Shilong Lin | Computer Science | Best Researcher Award

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Mr. Shilong Lin | Computer Science | Best Researcher Award

Mr. Shilong Lin | Computer Science | Guangxi Normal University | China

Mr Shilong Lin is a dedicated researcher in computer science, specializing in graph neural networks, representation learning, and advanced machine learning methods, and he is currently affiliated with the School of Computer Science and Engineering at Guangxi Normal University, where he is engaged in both academic research and teaching activities; Mr Shilong Lin has pursued his education within the same institution, building a strong foundation in artificial intelligence, machine learning, and data-driven modeling, and his academic journey has enabled him to develop deep expertise in designing algorithms for node classification, graph similarity learning, contrastive learning, and adaptive feature fusion; throughout his professional experience, Mr Shilong Lin has collaborated with notable international scholars such as Guangquan Lu, Longqing Du, Shichao Zhang, Cuifang Zou, and Xuxia Zeng, contributing to impactful research projects that address challenges in imbalanced data learning, multi-hop contrastive approaches, multi-scale graph feature fusion, and cross-level interaction modeling; his research interests span graph representation learning, deep learning architectures, imbalance-aware modeling, scalable graph algorithms, and AI-driven knowledge discovery, and these interests have shaped his contribution to several high-quality journal and conference publications across reputed platforms including Neurocomputing, Information Processing & Management, and the International Joint Conference on Artificial Intelligence (IJCAI); in terms of research skills, Mr Shilong Lin demonstrates strong capabilities in algorithm design, graph neural network development, experimental evaluation, data preprocessing, model optimization, and collaborative scientific writing, supported by his competence in programming, machine learning frameworks, and scientific communication; while building his research trajectory, Mr Shilong Lin has also contributed to academic community participation through teamwork, collaborative problem solving, and involvement in multi-author research projects that enhance the scientific visibility of his institution; his awards and honors reflect his ongoing contribution to high-quality research, including recognition for publications in top-tier AI and computational intelligence venues and his involvement in internationally indexed works listed under ORCID; overall, Mr Shilong Lin continues to advance his research career with strong dedication, intellectual rigor, and a vision of contributing to the development of efficient, explainable, and scalable graph learning systems, and his growing academic output positions him as a promising scholar who is steadily building an impactful presence in the global artificial intelligence research community.

Academic Profile: ORCID

Featured Publications:

  1. Lin, S., Lu, G., Zhang, S., Du, L., & Luo, Z. (2025). Synthesizing stronger nodes for minority classes in imbalanced node classification.

  2. Zeng, X., Lu, G., Zou, C., Lin, S., Du, L., & Zhang, S. (2025). Multi-hop contrastive learning with feature augmentation for node classification.

  3. Zou, C., Lu, G., Zhang, W., Zeng, X., Lin, S., Du, L., & Zhang, S. (2025). Enhanced graph similarity learning via adaptive multi-scale feature fusion.

  4. Zou, C., Lu, G., Du, L., Zeng, X., & Lin, S. (2025). Graph similarity learning for cross-level interactions.