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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Featured Publication

Bao Peng | Big Data | Excellence in Research Award

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Prof. Bao Peng | Big Data | Excellence in Research Award

Professor | Shenzhen University of Information Technology | China

Prof. Bao Peng is an expert in millimeter-wave radar sensing, computer vision, and intelligent signal processing, with a focus on device-free human sensing, gesture recognition, and multimodal data fusion. He has published 54 papers, cited over 580 times, 13 h-index and collaborated with more than 110 researchers globally. His key contributions include cross-modal radar frameworks with information-maximization enhancement, lightweight self-attention-free transformer models for gesture recognition, and fusion-driven architectures for end-to-end human motion understanding, enabling efficient, low-data, and interpretable AI solutions. His work also extends to industrial applications, such as intelligent monitoring of unmanned pumping stations and YOLO-based infrastructure inspection, demonstrating broad societal and industrial relevance. By combining advanced signal processing with practical AI deployment, Prof. Peng’s research strengthens human–machine interaction, autonomous systems, and smart sensing technologies, contributing to safer, more efficient, and globally impactful innovations.

Profile: Scopus

Featured Publications

1. (2025). Cross-modal device-free radar sensing with information maximization enhancement and few-shot learning. IEEE Transactions on Microwave Theory and Techniques.

2. (2025). Device-free gesture recognition using multidimensional feature representation and lightweight self attention-free transformer. IEEE Transactions on Consumer Electronics.

3. (2025). End-to-end human motion recognition with multidomain dual attention transformer fusion network and millimeter-wave radar. IEEE Transactions on Consumer Electronics.

Cited by: 7

4. (2024). Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. Eurasip Journal on Advances in Signal Processing.

Cited by: 1

5. (2024). GAM-YOLOv8n: Enhanced feature extraction and difficult example learning for site distribution box door status detection. Wireless Networks.

Cited by: 5

Prof. Bao Peng research transforms radar-based perception into practical AI solutions, advancing intelligent monitoring, autonomous systems, and human–machine interaction to foster safer, smarter, and more sustainable technological ecosystems.

Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

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Mr. Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

Researcher | Kharazmi University | Iran

Mr. Amirhossein Ghasemi Abyaneh is an emerging scholar in the field of artificial intelligence applications in sustainable supply chains, affiliated with Kharazmi University, Tehran, Iran. His academic endeavors focus on integrating advanced data analytics, optimization techniques, and machine learning frameworks to enhance decision-making, efficiency, and sustainability across complex supply chain networks. With 3 published research papers and an h-index of 1, Mr. Abyaneh has begun establishing a scholarly footprint that bridges technology-driven innovation with environmental and operational resilience. His work, including the open-access article “An Analytical Review of Artificial Intelligence Applications in Sustainable Supply Chains” (2025, Supply Chain Analytics), provides critical insights into the evolving intersection of AI and sustainability, emphasizing how digital intelligence can optimize resource utilization, reduce carbon footprints, and strengthen circular economy practices. Having received citations from international scholars, he actively contributes to the global academic dialogue on sustainable logistics, smart manufacturing, and responsible innovation. Mr. Abyaneh’s collaborative research network includes seven co-authors from diverse academic and institutional backgrounds, reflecting a strong interdisciplinary approach that combines engineering, data science, and environmental management. His studies aim to foster both theoretical advancement and practical applicability, offering valuable implications for policymakers, corporations, and researchers seeking to transition toward greener, data-driven supply chains. Beyond academic impact, his contributions align with global sustainability goals, promoting knowledge transfer, digital equity, and responsible AI adoption for societal benefit.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Sharbati, A., Movahed, A. B., Abyaneh, A. G., & Rahmanian, F. (2025). Risk assessment of healthcare systems using the FMEA method: Medication management process. Journal of Future Digital Optimization, 1(1), 71–85.
Cited by: 4

2. Abyaneh, A. G., Movahed, A. B., Abyari, A., Nodehfarahani, A., & Khakbazan, M. (2025). Evaluating the RFID technology in Costco Company: A focus on logistics and supply chain management. Applied Innovations in Industrial Management, 5(2), 34–51.
Cited by: 2

3. Movahed, A. B., Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2025). Smart economy cybersecurity: AI-driven risk management in digital markets. In Dynamic and Safe Economy in the Age of Smart Technologies (pp. 49–72).
Cited by: 2

4. Abyaneh, A. G., Ghanbari, H., Mohammadi, E., Amirsahami, A., & Khakbazan, M. (2025). An analytical review of artificial intelligence applications in sustainable supply chains. Supply Chain Analytics, 100173.
Cited by: 1

5. Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2026). Artificial intelligence in digital marketing: Trends, challenges, and strategic opportunities. In Improving Consumer Engagement in Digital Marketing Through Cognitive AI (pp. 225–260)

Mr. Amirhossein Ghasemi Abyaneh envisions a future where artificial intelligence empowers sustainable industrial transformation, enabling supply chains to become more adaptive, transparent, and environmentally responsible. His research advances the integration of smart analytics and sustainability principles, fostering innovation that supports global climate resilience and ethical technological progress.