RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

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Prof. Dr. RaulJavier ChangTam | Data Science and Analytics | Best Researcher Award

Profesor Investigador | Universidad Latina de Costa Rica | Costa Rica

Prof. Dr. RaulJavier ChangTam  is a multidisciplinary researcher whose work connects technology adoption, entrepreneurship, and sustainable innovation in Latin America. His research explores how emerging technologies influence entrepreneurial ecosystems, digital transformation, and user behavior within both business and social contexts. Drawing upon models such as UTAUT2, his studies provide empirical insights into the acceptance and use of new technologies by entrepreneurs, SMEs, and professionals in fields like healthcare and finance. He also contributes to studies on sustainable finance and FinTech, emphasizing the role of digital platforms and neobanks in promoting environmentally responsible entrepreneurship. Beyond business and technology, his work extends into socio-cultural domains examining global consumption patterns such as sneaker culture and analyzing trade dynamics in ornamental species markets. Chang-Tam’s ongoing research reflects a strong orientation toward understanding the intersection of innovation, digitalization, and sustainability, with an applied perspective that links academic insight to real-world economic and cultural transformations. His research has received 3 citations across 9 documents, with an h-index of 1.

Profiles : ORCID | Scopus  | ResearchGate

Featured Publications

1. Taboada Álvarez, J. E., Chang-Tam, R. J., Rueda Varón, M. J., & Hunter Torrealba, R. (2025). Analysis of the entrepreneurial motivational demand in a learning management process in incubators.

2. Araya, J. L. G., Robles Herrera, A. E., & Chang-Tam, R. J. (2025). Analysis of trends in exports and imports of continental and marine ornamental species of aquariums in Costa Rica.

3. Chang-Tam, R. J., Caldera-Gutiérrez, V., & Rivera Shaik, V. (2025). Influence of IT technology on the development of SME entrepreneurs in Costa Rica: Applied study of the adapted model of the Unified Theory of Acceptance and Use of Technology (UTAUT2).

4. Chang-Tam, R. J., Garita Quesada, R., Masís Muñoz, R., & Chang Caldera, A. P. (2025). Influence of new IT technology trends in dental care processes between dental professionals and patients: An analysis of the UTAUT2 theory.

5. Palos-Sanchez, P. R., Chang-Tam, R. J., & Folgado-Fernández, J. A. (2025). The role of neobanks and fintech in sustainable finance and technology: The customer/user perspective for entrepreneurs. Sustainable Technology and Entrepreneurship, 100109.

Deqian Fu | Data Science and Analytics | Best Researcher Award

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Prof. Dr. Deqian Fu | Data Science and Analytics | Best Researcher Award

Professor | Linyi University | China

Prof. Dr. Deqian Fu is a prominent researcher at Linyi University, China, with a strong focus on logistics, data exchange, and trust management in supply chain and intermodal transport systems. His research explores innovative methods for secure and efficient data sharing in the logistics industry, integrating advanced technologies such as blockchain, edge computing, and ontology-based frameworks. Fu has made notable contributions in developing trusted data access control mechanisms and non-intrusive data exchange models that enhance collaboration and operational efficiency across complex logistics networks. He has authored 39 publications, which have collectively garnered 127 citations, reflecting the growing impact of his work in the fields of applied sciences and industrial informatics. His research outputs demonstrate a commitment to advancing the intersection of information technology and logistics, emphasizing both theoretical development and practical applications. With an h-index of 7, Fu’s scholarly contributions have been recognized for their relevance and innovation, particularly in promoting secure and intelligent data-sharing frameworks within the logistics sector. Selected works include “Trusted Data Access Control Based on Logistics Business Collaboration Semantics” in Applied Sciences (2024), alongside conference papers such as “Data Exchange and Sharing Framework for Intermodal Transport Based on Blockchain and Edge Computing” and “Trusted Non-intrusive Data Exchange based on Ontology in Logistics Industry,” underscoring his focus on reliable, technology-driven logistics solutions.

Profiles : ORCID | Scopus 

Featured Publications

1. Wang, W., Li, Q., Jiang, Z., Fu, D., & Camacho, D. (2025). An efficient framework for general long-horizon time series forecasting with Mamba and diffusion probabilistic models. Engineering Applications of Artificial Intelligence.

2.Liu, Z., Shi, Z., Wang, W., Kong, R., Fu, D., & Qiu, J. (2025). Research on data ownership and controllable sharing schemes in the process of logistics data flow.

3.Wang, L., Zhang, X., Xu, L., Fu, D., & Qiu, J. (2024). Data exchange and sharing framework for intermodal transport based on blockchain and edge computing. In Communications in Computer and Information Science. Springer.

4.Zhang, X., Jing, C., Chen, Y.-C., Wang, L., Xu, L., & Fu, D. (2024). Trusted data access control based on logistics business collaboration semantics.

5.Zhang, X., Wang, L., Xu, L., & Fu, D. (2023). A distributed logistics data security sharing model based on semantics and CP-ABE. In Proceedings of the ACM International Conference (pp. 1–8).

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

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Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Prof. Dr. Wei Fang, Nanjing University of Information Science & Technology, China

Prof. Dr. Wei Fang is a Professor in the Department of Computer Science at Nanjing University of Information Science & Technology, China, and a member of the State Key Laboratory for Novel Software Technology, Nanjing University. He holds a Ph.D. and M.Sc. in Computer Science from Soochow University. Wei was a visiting scholar at the University of Florida in 2015-2016. His research interests include Artificial Intelligence, Big Data, Data Mining, and Meteorological Information Processing. He has led several research projects funded by the National Natural Science Foundation of China and is an active reviewer for international journals. Wei is a senior member of the CCF and ACM.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Summary of Suitability for Best Researcher Award – Prof. Wei Fang

Prof. Wei Fang of Nanjing University of Information Science & Technology stands out as a highly meritorious candidate for the Best Researcher Award. With a solid academic foundation, national and international research exposure, and extensive contributions in Artificial Intelligence, Big Data, Computer Vision, and Applied Meteorology, his work bridges theoretical innovation with real-world application.

🎓 Education

  • Ph.D. in Computer Science – Soochow University, China

  • M.Sc. in Computer Science – Soochow University, China

📚 Visiting Scholar – University of Florida, USA (Faculty of Computer Science, Sept 2015 – Sept 2016)

💼 Work Experience

  • 👨‍🏫 Professor, Department of Computer Science, NUIST

  • 🧪 Affiliated with the State Key Lab for Novel Software Technology, Nanjing University

  • 🤝 Program Committee Member for multiple international conferences

  • 📝 Reviewer for various international journals

  • 🌍 International Research Scientist

🏆 Achievements & Honors

  • 🧠 Recognized for impactful research in:

    • Artificial Intelligence 🤖

    • Big Data & Cloud Computing ☁️📊

    • Computer Vision 👁️

    • Applied Meteorology 🌦️

  • 🔬 Project Leader of national and industrial research projects funded by:

    • National Natural Science Foundation of China

    • Guodian Nari Nanjing Control System Co., Ltd.

    • Baoshan Iron and Steel Co., Ltd.

  • 🎖️ Senior Member of CCF (China Computer Federation) & ACM

  • 📈 Cited in SCI-indexed journals

Publication Top Notes:

A rapid learning algorithm for vehicle classification

CITED: 562

A Method for Improving CNN-Based Image Recognition Using DCGAN.

CITED: 230

Efficient feature selection and classification for vehicle detection

CITED: 220

A survey of big data security and privacy preserving

CITED: 117

Survey on research of RNN-based spatio-temporal sequence prediction algorithms

CITED: 100