Jay Kachhadia | Data Science | Data Science Award

Published on by Technology Scientists

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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Featured Publication

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.

Chao Li | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Chao Li | Machine Learning | Best Researcher Award

Department Chair | Chengdu University of Technology | China

Assoc. Prof. Dr. Chao Li of Chengdu University of Technology is an expert in geophysical signal processing, seismic data reconstruction, and intelligent subsurface imaging, with a focus on integrating machine learning and advanced computational techniques into geoscience applications. He has authored 31 peer-reviewed publications cited 425 times, reflecting a strong research impact and an h-index of 12. His work includes the development of Generative Adversarial Networks for seismic reconstruction, non-subsampled contourlet transforms for low-amplitude structure detection, and hybrid neural architectures for source deblending, addressing critical challenges in exploration geophysics and subsurface data interpretation. Collaborating with over 50 co-authors, Dr. Li demonstrates a commitment to interdisciplinary and international research, bridging academia and industry. His contributions enhance the accuracy, efficiency, and sustainability of seismic exploration, providing tools for more reliable resource evaluation and environmental monitoring. By combining computational intelligence with applied geophysics, Dr. Li’s research promotes innovation in energy exploration, environmental stewardship, and global geoscience advancement, making significant scientific, industrial, and societal impacts.

Profile: Scopus

Featured Publications

1. Ke, C.-F., Zu, S.-H., Cao, J.-X., Jiang, X.-D., Li, C., & Liu, X.-Y. (2024). A hybrid WUDT‑NAFnet for simultaneous source data deblending. Petroleum Science, 21(3), 1649‑1659.
Cited by: 1

2. Low‑amplitude structure recognition method based on non‑subsampled contourlet transform. Petroleum Science.(2024)
Cited by: 1

3. Seismic Data Reconstruction via Least‑Squares Generative Adversarial Networks With Inverse Interpolation. IEEE Transactions on Geoscience and Remote Sensing.(2025)
Cited by: 1

Assoc. Prof. Dr. Chao Li’s pioneering work at the interface of geophysics and artificial intelligence is reshaping the future of seismic data interpretation, enabling smarter, data-driven exploration. His vision emphasizes leveraging AI-powered geoscience solutions to advance sustainable resource utilization and strengthen global resilience in energy and environmental systems.

Minoru Sasaki | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Minoru Sasaki | Artificial Intelligence | Best Researcher Award

Organizing Committee | Gifu University | Japan

Prof. Dr. Minoru Sasaki, a distinguished Emeritus Professor at Gifu University, has made significant contributions to the fields of mechanical engineering, control systems, and mechatronics throughout his academic and professional career. With a Ph.D. in Mechanical Engineering from Tohoku University (1985), he has held various academic positions in Japan and internationally, including visiting professorships at UCLA, Georgia Institute of Technology, and King Mongkut’s University of Technology Thonburi. He has also served in numerous leadership roles at Gifu University, such as Department Chair, Assistant President, and Director of the Career Center. His professional affiliations include IEEE Life Senior Member, ASME, JSME, SICE (Fellow), RSJ, JSASS, and others. He has actively contributed to global academic and research communities through editorial roles in prestigious journals and program committees of international conferences. His involvement extends to advisory roles and leadership positions within key engineering societies in Japan and abroad. A prolific researcher, Dr. Sasaki has authored 202 publications, which have been cited by 966, reflecting a strong academic impact with an h-index of 14. These metrics highlight the depth and relevance of his research in intelligent mechanical systems and applied electromagnetics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Taheri, S. M., Matsushita, K., & Sasaki, M. (2017). Virtual reality driving simulation for measuring driver behavior and characteristics. Journal of Transportation Technologies, 7(02), 123.
Cited by 84.

2. Takayama, K., & Sasaki, M. (1983). Effects of radius of curvature and initial angle on the shock transition over concave and convex walls. Report of the Institute of High Speed Mechanics, 46, 1–30.
Cited by 66.

3. Yoshida, T., Sasaki, M., Ikeda, K., Mochizuki, M., Nogami, Y., & Inokuchi, K. (2002). Prediction of coal liquefaction reactivity by solid state 13C NMR spectral data. Fuel, 81(11-12), 1533–1539.
Cited by 64.

4. Endo, T., Sasaki, M., Matsuno, F., & Jia, Y. (2016). Contact-force control of a flexible Timoshenko arm in rigid/soft environment. IEEE Transactions on Automatic Control, 62(5), 2546–2553.
Cited by 61.

5. Takeda, K., Sasaki, M., Kieda, N., Katayama, K., Kako, T., Hashimoto, K., … (2001). Preparation of transparent super-hydrophobic polymer film with brightness enhancement property. Journal of Materials Science Letters, 20(23), 2131–2133.
Cited by 56.

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.