Gary Wong | Computer Science | Best Researcher Award

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Prof. Dr. Gary Wong | Computer Science | Best Researcher Award

Prof. Dr. Gary Wong | Computer Science | The University of Hong Kong | Hong Kong

Prof. Dr. Gary Wong is a highly accomplished scholar in computer science education whose research contributions have significantly influenced the fields of computational thinking, digital literacy, technology-enhanced learning, and K–12 AI education. With a strong academic foundation supported by advanced degrees from leading institutions, his educational background reflects rigorous training in both pedagogy and computer science, enabling Him to bridge disciplinary boundaries with expertise and innovation. Throughout his professional career, Prof. Dr. Gary Wong has held key academic and leadership roles, contributing to major international research initiatives, collaborating with renowned global scholars, and publishing impactful studies that guide educational technology policies and classroom practices. His research interests encompass computational thinking development, digital literacy assessment, educational data analysis, teacher professional development, AI-in-education integration, and immersive learning design, and he demonstrates strong methodological skills in both qualitative and quantitative research approaches. His scholarly output includes numerous Scopus-indexed and IEEE publications that are extensively cited worldwide, shaping frameworks for digital skills assessment, pedagogical innovation, and technology adoption among educators. His research skills include large-scale data analysis, cross-cultural educational research, experimental design, research instrument development, and systematic reviews with meta-analytic rigor. Prof. Dr. Gary Wong has received multiple recognitions and honors for academic excellence, peer-review contributions, and impactful research, reflecting his prominence in the global education technology community. He has collaborated with leading universities and contributed to funded international research projects focused on curriculum innovation and computational thinking education. Additionally, he is actively involved in academic service, editorial review roles, and professional memberships in major associations such as IEEE and ACM, demonstrating strong engagement with scholarly communities and educational leadership. His substantial citation metrics further reflect the global influence of his work across multiple research domains. In conclusion, Prof. Dr. Gary Wong stands out as a leading expert whose research excellence, innovative contributions, and strong academic leadership continue to advance the global understanding of technology-enhanced learning and computational thinking education, positioning Him as a driving force for future advancements in education research worldwide.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  1. A global framework of reference on digital literacy skills for indicator 4.4.2. (2018). Citation Count: 662

  2. Broadening artificial intelligence education in K-12: Where to start? (2020). Citation Count: 294

  3. Designing unplugged and plugged activities to cultivate computational thinking: An exploratory study in early childhood education. (2020). Citation Count: 260

  4. Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. (2020). Citation Count: 153

  5. The behavioral intentions of Hong Kong primary teachers in adopting educational technology. (2016). Citation Count: 149

 

 

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.