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.

Citation Metrics

33
30
20
10
0

Citations

33

h-index

1

i10-index

1

Citations

h-index

i10-index

View Google Scholar Profile

Featured Publication

Bao Peng | Big Data | Excellence in Research Award

Published on by Technology Scientists

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.

Tianyuan Liu | Machine Learning | Best Researcher Award

Published on by Technology Scientists

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.