Muhammad Aamir | Artificial Intelligence | Best Researcher Award

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Dr. Muhammad Aamir | Artificial Intelligence | Best Researcher Award

Research Scientist | University of Oxford | United Kingdom

Dr. Muhammad Aamir is a researcher at the University of Oxford, United Kingdom, specializing in Artificial Intelligence and advanced computational modeling. His research focuses on developing intelligent algorithms for data-driven decision-making, machine learning, and real-world AI applications across diverse domains. He has contributed to high-impact studies involving hybrid AI models, neural networks, and intelligent sensing systems. Dr. Aamir’s work emphasizes robustness, scalability, and practical deployment of AI solutions. Through interdisciplinary research, he continues to advance the integration of artificial intelligence into complex scientific and engineering problems.

Citation Metrics (Scopus)

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Citations
926

Documents
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14

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Featured Publications

Danheng Gao | Deep Learning | Research Excellence Award

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Prof. Dr. Danheng Gao | Deep Learning | Research Excellence Award

Associate Researcher at Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences | China

Prof. Dr. Danheng Gao is a distinguished researcher at the Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China, specializing in deep learning and its integration with advanced optical and photonic systems. His research bridges the disciplines of machine learning, surface-enhanced Raman spectroscopy (SERS), nonlinear optics, and ultrafast photonics, with a strong emphasis on intelligent data-driven strategies for real-world analytical applications. Prof. Gao has made notable contributions to the development of rapid identification and sensing technologies by combining artificial intelligence with spectroscopic techniques, significantly enhancing accuracy, speed, and automation in chemical and food analysis. His work in ultrafast photonics further explores the convergence of nonlinear optical phenomena with intelligent control systems, enabling breakthroughs in high-speed optical signal processing and precision measurement. Through high-impact publications in leading journals such as Food Chemistry, his research demonstrates strong interdisciplinary value across photonics, artificial intelligence, and applied chemistry. With growing citation impact, Prof. Gao is recognized for advancing intelligent optical sensing, machine-learning-driven spectroscopy, and next-generation photonic technologies.

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Featured Publications

  1. Gao, D., et al. (2025). A rapid wine brand identification method based on the joint application of SERS and machine learning techniques.

  2. Gao, D., et al. (2025). Advancements in ultrafast photonics: Confluence of nonlinear optics and intelligent strategies.
    Citation Count: 6

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

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Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Lecturer,  Global Banking School, United Kingdom

Mr. Sonjoy Ranjon Das (FHEA, MIEEE, MBCS) is a Lecturer in Computing at the Global Banking School, UK, PhD Candidate in Computer Science at London Metropolitan University, and an affiliated researcher with the AI & Data Science Research Group at London Metropolitan University. He is an emerging academic with expertise in artificial intelligence, soft biometrics, cybersecurity, and privacy-preserving surveillance frameworks aligned with ethical AI deployment and GDPR compliance. Mr. Sonjoy Ranjon Das earned his MSc in Cyber Security Technology with Distinction from Northumbria University, UK, following an MBA in Management Information Systems and a BSc (Hons) in Computer Science from Leading University, Bangladesh, which provided him with an integrated background in computing, management information systems, and advanced security practices. Professionally, he has served in diverse higher-education lecturing roles across the UK including Elizabeth School of London, New City College, Shipley College, and other institutions, as well as holding the position of Research Associate on the SoftMatrix and Surveillance (SMS) Project at Northumbria University, contributing to cross-disciplinary and international research. Mr. Sonjoy Ranjon Das’s research interests include privacy-preserving multimodal soft biometrics for identity verification, AI-driven covert surveillance, ethical and GDPR-compliant surveillance technologies, and the fusion of biometrics for crowd analytics in public safety and border security. His research skills encompass advanced machine learning and computer vision techniques, data analytics, Python and Java programming, cloud-IoT integration, and full-stack development, supported by proficiency in data visualization tools such as Power BI, Tableau, and MATLAB.

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Featured Publications

  • Das, S. R., Kruti, A., Devkota, R., & Sulaiman, R. B. (2023). Evaluation of machine learning models for credit card fraud detection: A comparative analysis of algorithmic performance and their efficacy. FMDB Transactions on Sustainable Technoprise Letters. 12 citations.

  • Thinesh, M. A., Varmann, S. S., Sharmila, S. L., & Das, S. R. (2023). Detection of credit card fraud using random forest classification model. FMDB Transactions on Sustainable Technologies Letters. 9 citations.

  • Pranav, R. P., Prawin, R. P., Subhashni, R., & Das, S. R. (2023). Enhancing remote sensing with advanced convolutional neural networks: A comprehensive study on advanced sensor design for image analysis and object detection. FMDB Transactions on Sustainable Computer Letters. 8 citations.

  • Das, S. R., Hassan, B., Patel, P., & Yasin, A. (2024). Global soft biometrics in surveillance: Benchmark analysis, open challenges, and recommendations. Multimedia Tools and Applications. 6 citations.