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.

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.

Prof. Cristiano Andre da Costa | Interoperability, AI and IoT | Best Researcher Award

Full Professor at University of the Sinos River Valley, Brazil

Cristiano André da Costa is a seasoned academic and researcher with over two decades of contributions in applied computing. He serves as a full professor and heads the SOFTWARELAB, an innovation hub focusing on software solutions. His academic journey spans national and international institutions, including a visiting professorship in Germany, and he holds extensive experience in guiding research, innovation, and industry collaboration.

Profile

Scopus | ORCID | Google Scholar

Best Researcher Award

Cristiano Costa is highly suited for the “Best Researcher Award” due to his exemplary contributions to artificial intelligence and applied computing in healthcare. His research bridges academia and industry, reflected in his leadership of innovation-driven projects, high citation metrics, and impactful scholarly output. His recognition as a CNPq Productivity Researcher further reinforces his eligibility.

Education

He holds both a Master’s and a Ph.D. in Computer Science from Universidade Federal do Rio Grande do Sul (UFRGS), Brazil. These qualifications laid a strong foundation for his career in academic research, teaching, and cross-disciplinary collaboration.

Experience

Since 2000, he has been affiliated with Universidade do Vale do Rio dos Sinos. He has directed several interdisciplinary projects, including digital health and blockchain technologies, and has acted as a consultant for top-tier companies such as Dell, SAP, Siemens Healthineers, and Santander Bank. His academic leadership includes editorial roles and extensive supervision of postgraduate research.

Research Interest

His primary research interests lie in distributed and mobile computing, Internet of Things (IoT), semantic interoperability, and artificial intelligence, with a special emphasis on applications in digital health. He has also contributed significantly to machine learning, computer vision, and deep learning models, particularly in health data analytics.

Publication

2025 – IEEE Pervasive Computing
Title: Breaking Down the Data Path in Digital Health: From Edge to Fog and Beyond
2025 – Clinical & Biomedical Research
Title: Inequalities and risk factors of COVID-19 patients with Down syndrome: a Brazilian cross-sectional, analytical-exploratory study
2025 (February) – The International Journal of Advanced Manufacturing Technology
Title: Digital twin for product design collaboration: a systematic literature review
2024 (December) – Expert Systems with Applications
Title: CheXReport: A transformer-based architecture to generate chest X-ray reports suggestions
2024 (November) – Internet Technology Letters
Title: On proposing an intelligent model for tracking agrochemicals
2024 (August) – Computers and Electrical Engineering
Title: A method to predict the percentage of biodegradation in polymeric materials
2024 (July) – Expert Systems with Applications
Title: SOAP classifier for free-text clinical notes with domain-specific pre-trained language models

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