Xuecheng Xia | Machine Learning | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Xuecheng Xia — National University of Defense Technology

                 Xuecheng Xia
Affiliation National University of Defense Technology
Country China
Documents 3
Citations 2
Subject Area Machine Learning
Event Technology Scientists Awards
ORCID 0009-0002-5820-5095

The Innovative Research Award recognizes emerging scholarly contributions that demonstrate originality, technical rigor, and relevance within advanced scientific disciplines. Xuecheng Xia has contributed to machine learning-enabled waveform design and electronic warfare research through publications addressing robust optimization, deep unfolding methodologies, and multi-target jamming systems, reflecting active engagement in contemporary aerospace and signal processing research.[1]

Abstract

This article presents an academic overview of Xuecheng Xia and evaluates research achievements associated with machine learning-based waveform design, robust optimization, and electronic countermeasure systems. The profile highlights publication records, technical contributions, scholarly influence, and alignment with the objectives of the Innovative Research Award within the Technology Scientists Awards framework.[1][2]

Keywords

Machine Learning, Deep Unfolding Networks, Robust Waveform Design, Signal Processing, Multi-Target Jamming, Electronic Warfare, Aerospace Systems, Optimization Algorithms.

Introduction

Xuecheng Xia conducts research in machine learning and signal processing, focusing on robust waveform design for complex electronic environments. Current studies explore optimization strategies, deep unfolded architectures, and multi-target jamming scenarios that integrate modern artificial intelligence techniques with aerospace and defense-oriented signal analysis applications.[1][2]

Research Profile

Affiliated with the National University of Defense Technology, Xia’s scholarly work centers on waveform optimization, machine learning-enhanced signal processing, and resilient communication strategies. Research outputs demonstrate an emphasis on combining theoretical modeling with computational approaches to improve performance under uncertain and dynamically changing operational conditions.[1][3]

Research Contributions

Major contributions include the development of robust waveform design methodologies for digital arrays and wideband jamming environments. Xia has also investigated deep unfolding frameworks that bridge optimization theory and neural network learning, enabling computationally efficient solutions for challenging multi-target interference and signal management problems.[1][2][3]

Publications

The publication record includes articles in IEEE Transactions on Aerospace and Electronic Systems, Signal Processing, and IEEE conference proceedings. These works address robust waveform optimization, unfolded learning algorithms, and machine learning-assisted jamming strategies, contributing to contemporary discussions in advanced signal processing research.[1][2][3]

Research Impact

The research contributes to ongoing advancements in intelligent signal processing by introducing practical approaches for robust system performance. Integration of deep learning and optimization techniques provides a framework that may support future developments in electronic warfare, communication resilience, and adaptive sensing technologies.[2][3]

Award Suitability

Xia’s research profile aligns with the objectives of the Innovative Research Award through demonstrated engagement in emerging machine learning methodologies and technically rigorous waveform design studies. The combination of originality, interdisciplinary relevance, and publication activity supports consideration within technology-focused scientific recognition programs.[1][2]

Conclusion

Xuecheng Xia has established an emerging research presence through studies addressing robust waveform design, deep unfolding algorithms, and machine learning applications in signal processing. The documented scholarly outputs illustrate a commitment to advancing analytical methodologies while contributing to evolving challenges in aerospace and electronic systems research.[1][2][3]

References

  1. Xia, X., Tang, B., Chen, Y., & Zhang, J. (2026). Robust waveform design for multi-target jamming with digital arrays. IEEE Transactions on Aerospace and Electronic Systems.
    https://doi.org/10.1109/TAES.2026.3650892
  2. Xia, X., Chen, Y., Tang, B., & Zhang, J. (2026). Unfolded robust waveform design algorithm for wideband multi-target jamming. Signal Processing.
    https://doi.org/10.1016/j.sigpro.2026.110709
  3. Xia, X., Wu, W., Wang, X., Zhang, J., Wang, X., & Tang, B. (2025). Deep unfolded network-based robust waveform design for multi-target jamming. IEEE Conference Publication.URL:
    https://ieeexplore.ieee.org/document/11348019

Yujia Sun | Artificial Intelligence | Best Researcher Award

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Best Researcher Award

                                 Yujia Sun
Affiliation Northeastern University
Country China
Scopus ID 60333628400
Documents 1
Subject Area Artificial Intelligence
Event Technology Scientists Awards
ORCID 0009-0007-8431-9156

Yujia Sun is affiliated with Northeastern University, China, and conducts research within the field of Artificial Intelligence, with particular emphasis on advanced medical image analysis, multi-task learning architectures, image interpolation, and segmentation methodologies. The researcher has contributed to the development of intelligent computational frameworks designed to improve diagnostic image processing performance and clinical decision-support applications.[1][2]

Abstract

This article presents an academic overview of Yujia Sun and highlights contributions to Artificial Intelligence research, particularly in medical image segmentation, interpolation, and deep learning-based diagnostic systems. The work demonstrates the application of advanced neural network architectures to improve accuracy, efficiency, and reliability in healthcare imaging workflows and intelligent medical analysis.[1][2]

Keywords

Artificial Intelligence, Medical Imaging, Deep Learning, Image Segmentation, Multi-Task Learning, CT Imaging, MRI Imaging, Computer Vision, Healthcare Analytics, Neural Networks, Image Interpolation, Diagnostic Technologies.[1][2]

Introduction

Yujia Sun’s research focuses on integrating artificial intelligence techniques with medical image analysis to address challenges in segmentation, reconstruction, and diagnostic interpretation. Through innovative deep learning frameworks, the research aims to improve image quality, automate clinical workflows, and enhance the accuracy of healthcare decision-making systems across diverse imaging modalities.[1][2]

Research Profile

The research profile of Yujia Sun is centered on artificial intelligence, computer vision, and biomedical image computing. Areas of investigation include image interpolation, segmentation optimization, attention-based neural networks, and multi-task learning strategies designed to support precise analysis of CT, MRI, and clinical diagnostic imaging datasets.[1][2]

Research Contributions

Significant contributions include the development of task-adaptive multi-task learning frameworks and attention-gated convolutional networks for medical image processing. These approaches improve segmentation performance, enhance image reconstruction quality, and support efficient extraction of clinically relevant information, contributing to advancements in intelligent healthcare technologies and computational medical diagnostics.[1][2]

Publications

Published studies demonstrate expertise in advanced deep learning architectures for healthcare imaging. Research outputs address CT and MRI image interpolation, segmentation accuracy, posterior pharyngeal wall detection, and swab segmentation. These publications illustrate a commitment to developing robust artificial intelligence solutions that improve medical image analysis capabilities.[1][2]

Research Impact

The research contributes to ongoing advancements in AI-assisted healthcare by improving the reliability and efficiency of image processing methodologies. Enhanced segmentation and interpolation techniques can support clinical interpretation, reduce manual effort, and facilitate the adoption of intelligent systems in diagnostic and treatment planning environments.[1][2]

Award Suitability

Yujia Sun demonstrates qualities aligned with the objectives of the Best Researcher Award through contributions to artificial intelligence and medical imaging research. The development of innovative computational frameworks, combined with practical healthcare applications, reflects scholarly excellence, technical innovation, and meaningful contributions to scientific and technological advancement.[1][2]

Conclusion

Yujia Sun’s research activities highlight the growing role of artificial intelligence in modern medical image analysis. Through innovative approaches to segmentation, interpolation, and deep learning optimization, the researcher contributes to the development of efficient healthcare technologies while supporting broader progress in computational intelligence and biomedical engineering research.[1][2]

References

  1. Sun, Y., et al. (2025). TASC-SwinMT: Task-Adaptive Synergistic Cross-Task Swin Multi-Task Framework for CT and MRI Image Interpolation and Segmentation. Forensic Sciences, 12(6), 80. MDPI.
    https://www.mdpi.com/2379-139X/12/6/80
  2. Sun, Y., et al. (2026). AGC-Net: Attention-gated convolution network for posterior pharyngeal wall and swab segmentation. Biomedical Signal Processing and Control. Elsevier.
    https://www.sciencedirect.com/science/article/abs/pii/S1746809426000625
  3. Elsevier. (n.d.). Scopus author details: Yujia Sun, Author ID 60333628400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60333628400

Oliger Veronica Mendoza | Machine Learning | Innovative Research Award

Published on by Technology Scientists

Innovative Research Award

Oliger Veronica Mendoza
University of Science and Technology Beijing, China

                  Oliger Veronica Mendoza
Affiliation University of Science and Technology Beijing
Country China
Documents 3
Subject Area Machine Learning
Event Technology Scientists Awards
ORCID 0009-0006-4319-3908

Oliger Veronica Mendoza is a researcher affiliated with the University of Science and Technology Beijing whose work focuses on machine learning applications in underwater optical wireless communication systems. Her research integrates adaptive optimization, intelligent communication architectures, and machine learning-driven performance enhancement techniques, contributing to emerging developments in secure and efficient underwater networking technologies.[1][2][3]

Abstract

This article presents an overview of Oliger Veronica Mendoza’s research achievements in machine learning-enhanced underwater optical wireless communication systems. Her publications explore adaptive optimization, intelligent reflecting surface technologies, MIMO-NOMA architectures, and machine learning-driven turbulence mitigation strategies, addressing key challenges associated with underwater communication reliability, security, and transmission efficiency.[1][2][3]

Keywords

Machine Learning, Underwater Optical Wireless Communications, Adaptive Optimization, LSTM, NSGA-II, RIS Optimization, Secure Communications, MIMO-NOMA Systems, Adaptive Optics, Turbulence Mitigation, Intelligent Communications, Optical Networks.

Introduction

Machine learning is increasingly transforming communication systems by enabling adaptive decision-making and performance optimization. Oliger Veronica Mendoza’s research investigates how advanced learning algorithms can improve underwater optical wireless communications, a field requiring robust solutions for signal degradation, security, and environmental variability. Her work addresses practical and theoretical communication challenges.[1][2]

Research Profile

The research profile of Oliger Veronica Mendoza centers on intelligent communication technologies, with emphasis on machine learning integration into underwater optical networks. Her studies combine optimization algorithms, adaptive optics, intelligent reflecting surfaces, and advanced wireless architectures to improve communication efficiency, reliability, and security under dynamic underwater environmental conditions.[2][3]

Research Contributions

Her contributions include the development of adaptive optimization frameworks utilizing LSTM and NSGA-II methodologies, secure communication strategies employing reconfigurable intelligent surfaces, and machine learning-based turbulence mitigation mechanisms for underwater MIMO-NOMA optical systems. These studies demonstrate interdisciplinary integration between communication engineering, optimization science, and artificial intelligence techniques.[1]

Publications

  • Real-Time Adaptive Optimization for Underwater Optical Wireless Communications Using LSTM–NSGA-II.
  • Adaptive RIS Optimization for Secure Underwater Optical Communications.
  • Optimizing Underwater MIMO-NOMA Optical Wireless Systems with Adaptive Optics and Machine Learning-driven Turbulence Mitigation.

These publications collectively examine optimization, security enhancement, and adaptive communication techniques for underwater optical wireless systems. The studies contribute methodological advancements that combine machine learning with communication engineering, supporting improved network performance and resilience across challenging underwater transmission environments while addressing practical implementation considerations.[1][2][3]

Research Impact

The research provides valuable insights into the application of machine learning for underwater communication optimization. By addressing efficiency, security, and turbulence-related limitations, these studies support ongoing advancements in intelligent communication infrastructures. The findings may inform future developments in underwater sensing, exploration, environmental monitoring, and maritime communication networks.[1][2]

Award Suitability

Oliger Veronica Mendoza demonstrates strong alignment with the objectives of the Innovative Research Award through contributions that combine machine learning, optimization algorithms, and advanced communication technologies. Her research introduces novel approaches to underwater optical communications while addressing contemporary engineering challenges, reflecting originality, technical rigor, and interdisciplinary scientific relevance.[3]

Conclusion

The scholarly work of Oliger Veronica Mendoza highlights the growing role of machine learning in enhancing underwater optical wireless communication systems. Through research on adaptive optimization, secure communication architectures, and turbulence mitigation, she contributes to advancing intelligent communication technologies and demonstrates meaningful potential for future innovation and scientific development.[1][2][3]

References

  1. Mendoza Betancourt, O. V., & Wang, J. (2025). Real-Time Adaptive Optimization for Underwater Optical Wireless Communications Using LSTM–NSGA-II. Electronics, 15(3), 611.
    https://doi.org/10.3390/electronics15030611
  2. Mendoza Betancourt, O. V., & Peraza, D. (2025). Adaptive RIS Optimization for Secure Underwater Optical Communications. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3602057
  3. Mendoza Betancourt, O. V., & Peraza, D. (2025). Optimizing Underwater MIMO-NOMA Optical Wireless Systems with Adaptive Optics and Machine Learning-driven Turbulence Mitigation. Optical and Quantum Electronics Conference Proceedings.
    http://dx.doi.org/10.1364/optcon.547620

Jingjing Wang | Neural Network | Editorial Board Member

Published on by Technology Scientists

Editorial Board Member

Jingjing Wang
Shandong Normal University
Jingjing Wang
Researcher Jingjing Wang
Affiliation Shandong Normal University
Country China
Scopus ID 57214140268
Documents 79
Citations 726
h-index 15
Subject Area Neural Network
Event Technology Scientists Awards
ORCID 0000-0003-1597-1793

Jingjing Wang is affiliated with Shandong Normal University and has contributed extensively to the field of neural network research, computational imaging, inverse scattering systems, and advanced signal processing methodologies. Her academic profile demonstrates active participation in multidisciplinary research involving microwave imaging, image fusion, radar systems, and machine learning-assisted imaging technologies.[1] Her publication portfolio indexed in Scopus reflects sustained scholarly productivity, citation impact, and international visibility within engineering and intelligent imaging research domains.[2]

Abstract

This article presents an academic overview of Jingjing Wang, focusing on her scholarly contributions to neural network applications, microwave imaging, inverse scattering systems, MIMO-SAR imaging, and image fusion methodologies. Her research demonstrates interdisciplinary integration between computational intelligence and advanced imaging technologies for engineering applications.[2] The analysis highlights her publication impact, research collaborations, technical innovations, and suitability for recognition within the Technology Scientists Awards framework.[3]

Keywords

Neural Network, Microwave Imaging, Inverse Scattering, MIMO-SAR Imaging, Image Fusion, Computational Intelligence, Signal Processing, Deep Learning, Radar Imaging, Artificial Intelligence.[1]

Introduction

The rapid advancement of neural network methodologies has significantly influenced imaging science, signal reconstruction, and computational sensing technologies. Researchers working at the intersection of artificial intelligence and engineering systems have contributed to improving imaging precision, computational efficiency, and multi-source data interpretation.[2] Jingjing Wang’s research profile reflects active engagement in these evolving domains, particularly in inverse scattering imaging, radar imaging optimization, and intelligent image fusion approaches.[3]

Her work combines deep learning principles with advanced engineering models to address practical limitations in high-contrast imaging, nonlinear reconstruction, and multichannel signal integration. Such interdisciplinary contributions align with the broader objectives of modern intelligent sensing and computational imaging research.[1]

Research Profile

Jingjing Wang has established a consistent academic record supported by Scopus-indexed publications, citation impact, and collaborative international research activities.[1] Her research specialization primarily focuses on neural network systems, computational imaging, inverse scattering, radar imaging technologies, and image fusion techniques utilizing machine learning frameworks.[2]

  • Advanced inverse scattering imaging systems
  • Neural network-assisted image enhancement
  • MIMO-SAR computational imaging methodologies
  • Signal processing and nonlinear reconstruction
  • Deep learning-based image fusion frameworks

Her scholarly output demonstrates integration of computational intelligence with practical imaging applications, supporting advancements in engineering visualization and sensing technologies.[3]

Research Contributions

One of Jingjing Wang’s notable research contributions involves the development of an enhanced contrast born iterative cascaded network for high-contrast inverse scattering imaging. This work explores advanced reconstruction strategies capable of improving imaging quality and computational efficiency in inverse scattering environments.[1]

Her research also includes efficient range migration algorithms integrated with chunked nonlinear normalized weights and SNR-based multichannel fusion methods for MIMO-SAR imaging systems. These approaches contribute to improved imaging robustness, enhanced signal integration, and optimization of radar imaging performance under complex conditions.[2]

In the field of image fusion, Jingjing Wang contributed to KCUNET, a framework that combines KAN and convolutional layers for multi-focus image fusion. This contribution reflects the increasing role of hybrid neural architectures in computational imaging and intelligent feature integration.[3]

Publications

  • Enhanced Contrast Born Iterative Cascaded Network for High-Contrast Inverse Scattering Imaging.[1]
  • An Efficient RMA with Chunked Nonlinear Normalized Weights and SNR-Based Multichannel Fusion for MIMO-SAR Imaging.[2]
  • KCUNET: Multi-Focus Image Fusion via the Parallel Integration of KAN and Convolutional Layers.[3]

Research Impact

The research impact of Jingjing Wang is reflected through her Scopus-indexed publication profile, citation record, and ongoing contributions to computational imaging technologies.[1] Her interdisciplinary work supports broader developments in radar imaging, neural network optimization, image reconstruction, and intelligent sensing systems utilized across engineering and applied science disciplines.[2]

Her collaborations with multiple researchers in signal processing and imaging science further indicate active participation in contemporary scientific research networks. The combination of theoretical modeling and practical implementation in her publications contributes to both academic advancement and technological innovation.[3]

Award Suitability

Jingjing Wang demonstrates strong suitability for recognition within the Technology Scientists Awards due to her consistent scholarly productivity, research relevance, and contributions to neural network-enabled imaging technologies.[1] Her work addresses important technical challenges in inverse scattering systems, radar imaging optimization, and intelligent image fusion methodologies.[2]

The interdisciplinary nature of her research aligns with the objectives of technological innovation, computational intelligence advancement, and engineering-oriented scientific development. Her publication metrics and collaborative research activities further support her recognition as an active contributor within the scientific community.[3]

Conclusion

Jingjing Wang’s academic contributions illustrate the integration of neural networks, intelligent imaging systems, and computational sensing methodologies within modern engineering research.[1] Her work in inverse scattering imaging, MIMO-SAR systems, and image fusion demonstrates technical depth and interdisciplinary relevance.[2] Through scholarly publications, collaborative research, and impactful engineering studies, she continues to contribute to advancements in computational intelligence and imaging science.[3]

References

  1. Wang, J., Li, Z., Xu, H., & Hu, N. (2025). Enhanced Contrast Born Iterative Cascaded Network for High-Contrast Inverse Scattering Imaging. IEEE Antennas and Wireless Propagation Letters.
    DOI:https://doi.org/10.1109/LAWP.2025.3593269
  2. Wang, J., Chen, H., Duan, H., Sun, R., Yang, K., Fang, J., Xu, H., & Song, P. (2025). An Efficient RMA with Chunked Nonlinear Normalized Weights and SNR-Based Multichannel Fusion for MIMO-SAR Imaging. Remote Sensing, 17(18), 3232.
    DOI:https://doi.org/10.3390/rs17183232
  3. Fang, J., Wang, R., Ning, X., Wang, R., Teng, S., Liu, X., Zhang, Z., Lu, W., Hu, S., & Wang, J. (2025). KCUNET: Multi-Focus Image Fusion via the Parallel Integration of KAN and Convolutional Layers. Entropy, 27(8), 785.
    DOI:https://doi.org/10.3390/e27080785
  4. Elsevier. (n.d.). Scopus author details: Jingjing Wang, Author ID 57214140268. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57214140268

Sheng Xiang | AI | Research Excellence Award

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Assoc. Prof. Dr. Sheng Xiang | AI | Research Excellence Award

Chongqing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Sheng Xiang is an Associate Researcher at Chongqing University, China, specializing in intelligent battery management systems, energy storage analytics, and data-driven prognostics for lithium-ion batteries. His research expertise lies at the intersection of artificial intelligence, machine learning, and electrochemical energy systems, with a particular focus on remaining useful life prediction, state-of-charge estimation, and lightweight deep learning models for real-world battery applications. He has authored 30 peer-reviewed publications, which have collectively received 1,655 citations, reflecting strong international recognition and an h-index of 17. His recent contributions in high-impact journals such as Energy and Journal of Energy Storage demonstrate methodological innovation and practical relevance, especially for electric vehicles and smart energy systems. With an active collaboration network involving 57 co-authors, his work supports interdisciplinary research and global knowledge exchange. The societal impact of his research is evident in its potential to enhance battery safety, efficiency, sustainability, and lifecycle management in next-generation energy technologies.

Citation Metrics (Scopus)

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Bin Zhang | Artificial Intelligence | Research Excellence Award

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Prof. Bin Zhang | Artificial Intelligence | Research Excellence Award

City University of Hong Kong | China

Prof. Bin Zhang is a Professor and senior engineering expert specializing in computer science and intelligent perception, with a strong focus on brain-inspired intelligence, generative models, small object detection, and deep learning–based visual understanding. His research integrates theory and real-world applications in areas such as infrared tiny object detection, intelligent transportation, panoramic vision, spiking neural networks, and natural language processing. He has authored 6 peer-reviewed publications in reputable international journals and conferences, including Sensors and the International Journal of Pattern Recognition and Artificial Intelligence, with his work attracting growing academic citations and visibility. Zhang Bin has led and contributed to multiple nationally recognized innovation initiatives in China and maintains active collaborations with researchers across academia and industry. His research has demonstrated clear social and industrial impact, particularly in smart cities, intelligent sensing, and decision-support systems, advancing practical AI deployment aligned with national and global technological priorities.


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Top 5 Featured Publications

Ushba Rasool | Generative AI | Best Researcher Award

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Dr. Ushba Rasool | Generative AI | Best Researcher Award

Research Instructor | Zhengzhou University | China

Dr. Ushba Rasool, affiliated with Zhengzhou University, China, is a rising researcher specializing in educational psychology, digital pedagogy, and artificial intelligence (AI) in education. With 11 publications, 68 citations, and an h-index of 5, her work integrates theoretical frameworks such as UTAUT (Unified Theory of Acceptance and Use of Technology) and TPACK (Technological Pedagogical Content Knowledge) to investigate teachers’ and students’ perceptions, attitudes, and adoption behaviors toward emerging educational technologies. Her recent publication in Acta Psychologica (2025), “Perceptions of Generative AI in Teaching and Learning,” highlights her innovative approach in merging psychological insights with technology acceptance models to explore the transformative potential of generative AI in learning environments. Through collaborations with 18 co-authors across international institutions, Dr. Rasool contributes to advancing global understanding of digital transformation in education, addressing key issues of AI ethics, digital literacy, and pedagogical innovation. Her research provides valuable implications for educational policy, technology integration strategies, and the enhancement of learner engagement, thus creating meaningful social and academic impact in the digital age.

Profiles: Scopus | Google Scholar

Featured Publications

1. Rasool, U., Qian, J., & Aslam, M. Z. (2023). An investigation of foreign language writing anxiety and its reasons among pre-service EFL teachers in Pakistan. Frontiers in Psychology, 13, 947867. 
Cited by: 64

2. Barzani, S. H. H. (2022). The effects of online supervisory feedback on student-supervisor communications during the COVID-19. European Journal of Educational Research, 11(3), 1569–1579. 
Cited by: 31

3. Barzani, S. H. H. (2021). Teachers and students’ perceptions towards online ESL classrooms during COVID-19: An empirical study in North Cyprus. The Journal of Asia TEFL, 18(4), 1423–1431. 
Cited by: 21

4. Rasool, U., Mahmood, R., Aslam, M. Z., Barzani, S. H. H., & Qian, J. (2023). Perceptions and preferences of senior high school students about written corrective feedback in Pakistan. SAGE Open, 13(3), 21582440231187612. 
Cited by: 17

5. Rasool, U., Aslam, M. Z., Mahmood, R., Barzani, S. H. H., & Qian, J. (2023). Pre-service EFL teachers’ perceptions of foreign language writing anxiety and some associated factors. Heliyon, 9(2), e13705. 
Cited by: 15

Dr. Ushba Rasool’s research fosters responsible and inclusive integration of generative AI in education, driving innovation in digital pedagogy and shaping global educational practices that empower both teachers and learners for a technologically adaptive future.

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.

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.