Jay Kachhadia | Data Science | Data Science Award

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Mr. Jay Kachhadia | Data Science | Data Science Award

Syracuse University | United States

Mr. Jay Kachhadia is a data science professional whose research lies at the intersection of machine learning, natural language processing (NLP), and computational social science. His scholarly work focuses on applying advanced deep learning models—particularly transformer-based architectures such as BERT—to analyze and classify political and social media discourse. He has authored one peer-reviewed conference publication, PoliBERT: Classifying Political Social Media Messages with BERT (SBP-BRIMS 2020), which has received 33 citations, reflecting sustained academic relevance and impact within the field. With an h-index of 1 and an i10-index of 1, his work demonstrates focused contributions with measurable scholarly influence. The publication resulted from interdisciplinary collaboration with researchers in social and behavioral modeling, highlighting his ability to bridge data science with social science research. Beyond academia, his research has broader societal impact by enabling scalable, data-driven analysis of political communication, misinformation, and public opinion, contributing to more informed policy analysis and civic discourse at a global level.

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Muhammad Firoz Mridha | Machine Learning | Best Researcher Award

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Prof. Dr. Muhammad Firoz Mridha | Machine Learning | Best Researcher Award

Professor | American International University | Bangladesh

Prof. Dr. Muhammad Firoz Mridha, a researcher at the American International University–Bangladesh (AIUB), has established a strong scholarly profile in computer science with notable contributions to machine learning, data analytics, cybersecurity, IoT, and applied artificial intelligence. With 319 publications, over 4,629 citations, and an h-index of 33, his work demonstrates sustained academic productivity and global research impact. His studies often address practical and emerging challenges—such as intelligent decision-support systems, secure digital infrastructures, and data-driven solutions for healthcare and smart environments—positioning his contributions at the intersection of theoretical advancement and real-world application. Collaboration is a defining feature of his career, reflected in partnerships with 575 co-authors, enabling multidisciplinary knowledge exchange and strengthening international research networks. His work has supported technological development, digital inclusion, and innovation-oriented problem-solving, particularly in contexts where data-centric technologies can improve societal outcomes.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Mridha, M. F., Keya, A. J., Hamid, M. A., Monowar, M. M., & Rahman, M. S. (2021). A comprehensive review on fake news detection with deep learning. IEEE Access, 9, 156151–156170.

Cited by: 297

2. Mridha, M. F., Das, S. C., Kabir, M. M., Lima, A. A., Islam, M. R., & Watanobe, Y. (2021). Brain–computer interface: Advancement and challenges. Sensors, 21(17), 5746.

Cited by: 296

3. Jim, J. R., Talukder, M. A. R., Malakar, P., Kabir, M. M., Nur, K., & Mridha, M. F. (2024). Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 6, 100059.

Cited by: 271

4. Rayed, M. E., Islam, S. M. S., Niha, S. I., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024). Deep learning for medical image segmentation: State-of-the-art advancements and challenges. Informatics in Medicine Unlocked, 47, 101504.

Cited by: 227

5. Mridha, M. F., Lima, A. A., Nur, K., Das, S. C., Hasan, M., & Kabir, M. M. (2021). A survey of automatic text summarization: Progress, process and challenges. IEEE Access, 9, 156043–156070.

Cited by: 197

Prof. Dr. Muhammad Firoz Mridha’s research advances data-driven intelligence and secure digital systems, contributing to global technological innovation and societal problem-solving. His work supports scalable, real-world applications—particularly in developing regions—promoting inclusive, ethical, and sustainable digital transformation.

Tianyuan Liu | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Tianyuan Liu | Machine Learning | Best Researcher Award

Master’s Supervisor | Donghua University | China

Assoc. Prof. Dr. Tianyuan Liu, affiliated with Donghua University, Shanghai, China, is a distinguished researcher specializing in industrial intelligence, human-centric manufacturing, and vision-based quality inspection. With 43 publications, 1,103 citations, and an h-index of 17, Dr. Liu’s work reflects significant academic impact and steady scholarly growth in intelligent industrial systems. His research integrates cognitive computing, deep learning, and large language models to enhance manufacturing precision, reliability, and adaptability. Notably, his 2025 article “Analysis of causes of welding defects in bridge weathering steel based on large language models” in the Journal of Industrial Information Integration demonstrates his pioneering approach to applying AI-driven diagnostic systems in structural materials engineering. Another major contribution, “Causal deep learning for explainable vision-based quality inspection under visual interference” published in Journal of Intelligent Manufacturing, advances explainable AI (XAI) frameworks for real-time industrial inspection, ensuring transparency and accuracy in automated decision-making. His review, “Towards cognition-augmented human-centric assembly: A visual computation perspective”, underscores his vision for augmenting human intelligence with computational cognition to achieve collaborative, efficient, and sustainable manufacturing systems. Furthermore, his book chapter “Industrial Intelligence: Methods and Applications” provides a comprehensive view of the synergy between AI and industrial processes, shaping the academic and applied discourse in smart factories. Assoc. Prof. Dr. Liu’s contributions collectively enhance the fusion of AI, cognition, and industrial engineering, driving forward the next generation of intelligent, explainable, and human-oriented manufacturing ecosystems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., & Liu, S. (2022). A reinforcement learning method for human-robot collaboration in assembly tasks. Robotics and Computer-Integrated Manufacturing, 73, 102227.
Cited by: 182.

2. Zhou, B., Bao, J., Li, J., Lu, Y., Liu, T., & Zhang, Q. (2021). A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robotics and Computer-Integrated Manufacturing, 71, 102160.
Cited by: 152.

3. Zhou, B., Shen, X., Lu, Y., Li, X., Hua, B., Liu, T., & Bao, J. (2023). Semantic-aware event link reasoning over industrial knowledge graph embedding time series data. International Journal of Production Research, 61(12), 4117–4134.
Cited by: 123.

4. Zhou, B., Li, X., Liu, T., Xu, K., Liu, W., & Bao, J. (2024). CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing. Advanced Engineering Informatics, 59, 102333.
Cited by: 114.

5. Liu, T., Bao, J., Wang, J., & Zhang, Y. (2018). A hybrid CNN–LSTM algorithm for online defect recognition of CO₂ welding. Sensors, 18(12), 4369.
Cited by: 105.

Assoc. Prof. Dr. Tianyuan Liu’s research bridges artificial intelligence and industrial engineering, advancing smart, explainable, and human-centric manufacturing solutions that empower global industry transformation.

Mohammad Amin | Gradient boosting | Young Scientist Award

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Mr. Mohammad Amin | Gradient boosting | Young Scientist Award

Mohammad Amin | RWTH Aachen University | Germany

Mr. Mohammad Yazdi is a distinguished researcher and academic with expertise in information technology, e-learning systems, and research data management. He holds advanced degrees in information systems and has built a robust academic and professional profile through his work in integrating IT solutions for collaborative research, developing interactive e-learning media, and enabling process mining for research data life cycles. Professionally, he has contributed to several high-impact projects, including the implementation of web services for integrated government systems, evaluation of FAIR data management architectures, and the orchestration of research cluster collaborations, demonstrating strong technical and leadership skills. His research focuses on e-learning as an interactive IT-based learning medium, process mining, IT resource management in research projects, and operational support systems, with a publication record spanning reputable conferences and journals. His works, including “E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi,” “How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment,” and “Event Log Abstraction in Client-Server Applications,” have collectively garnered significant citations, underscoring their academic impact. He has been recognized for his scholarly contributions and actively participates in academic dissemination through editorial roles and conference presentations. With a commitment to advancing digital transformation in education and research, Mohammad Yazdi stands out as a thought leader and innovator in his field, with 29 citations across 23 documents, 10 publications, and an h-index of 4

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. M. Yazdi*, E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi. Foristek, 2012, 2(1), 680.

2. M. Yazdi*, Implementasi Web-Service pada Sistem Pelayanan Perijinan Terpadu Satu Atap di Pemerintah Kota Palu. Seminar Nasional Teknologi Informasi & Komunikasi Terapan, 2012, 450–457, 24.

3. M. Politze, F. Claus, B. Brenger, M.A. Yazdi*, B. Heinrichs, A. Schwarz, How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment. 2020, 22.

4. M.A. Yazdi*, P. Farhadi Ghalati, B. Heinrichs, Event Log Abstraction in Client-Server Applications. 13th Int. Conf. Knowledge Discovery and Information Systems, 2021, 13.

5. M.A. Yazdi*, Enabling Operational Support in the Research Data Life Cycle. Proc. 1st Int. Conf. Process Mining – Doctoral Consortium, 2019, 13.