Jian-Hong Wang | Computer Technology | Best Researcher Award

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Prof. Jian-Hong Wang | Computer Technology | Best Researcher Award 

Professor | Shandong University of Technology | China

Prof. Jian-Hong Wang is a professor in the Department of Computer Science and Technology at Shandong University of Technology, China, specializing in areas such as bandwidth guarantee of networks, online audio and video synchronization technology, wireless networks admission control, IoT control, efficient management for wireless multimedia devices, smart building, virtual reality human–robot interaction, and big data analytics. He earned his doctorate from National Chung Cheng University, Taiwan, and has since built an extensive academic career marked by research, teaching, and leadership. His professional experience includes serving as a faculty member at Shandong University of Technology, where he has actively contributed to advancing computer science and technology through both theoretical and applied research. His scholarly output includes numerous influential publications in high-impact journals and international conferences, covering topics such as fault-tolerant content management, disease-related SNP detection, brain network community analysis, real-time streaming systems, admission control policies, optical data hiding, and multimedia communication systems. Prof. Wang’s research interests lie in integrating computational intelligence with network systems, smart technologies, and human–robot interaction to address emerging challenges in wireless communications, data management, and digital applications. He has been recognized for his academic contributions through multiple publications, collaborations, and professional activities, establishing his reputation as a dedicated researcher and educator committed to fostering innovation and knowledge dissemination.

Profile:  Orcid 

Featured Publications

Wang, H.-Y., Wang, J.-H., Zhang, J., & Tai, H.-W. (2024). The collaborative interaction with Pokémon-Go robot uses augmented reality technology for increasing the intentions of patronizing hospitality. Information Systems Frontiers. Advance online publication.

Tran, N. C., Wang, J.-H., Vu, T. H., Tai, T.-C., & Wang, J.-C. (2023). Anti-aliasing convolution neural network of finger vein recognition for virtual reality (VR) human–robot equipment of metaverse. The Journal of Supercomputing, 79(2), 1690–1708.

Lo, C.-M., Wang, J.-H., & Wang, H.-W. (2022). Virtual reality human–robot interaction technology acceptance model for learning direct current and alternating current. The Journal of Supercomputing, 78(18), 20229–20249.

Xiao, L., Feng, J., Niu, X., Wang, J.-H., & Lv, J. (2022). Using competitive binary particle swarm optimization algorithm for matching sensor ontologies. Mobile Information Systems, 2022, Article 2207252.

Feng, J., Niu, X., Zhang, J., Wang, J.-H., & Lee, C.-Y. (2022). Gene selection and classification of scRNA-seq data combining information gain ratio and genetic algorithm with dynamic crossover. Wireless Communications and Mobile Computing, 2022, Article 9639304.

Lian, W., Fu, L., Niu, X., Feng, J., Wang, J.-H., & Xue, X. (2022). Solving sensor ontology metamatching problem with compact flower pollination algorithm. Wireless Communications and Mobile Computing, 2022, Article 9662517.

 

 

 

 

Haojie Liu | Machine Vision | Best Researcher Award

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Mr. Haojie Liu | Machine Vision | Best Researcher Award

Ph.D. candidate at Zhejiang University | China

Haojie Liu is a Ph.D. candidate at Zhejiang University, China, specializing in control science and engineering. His research focuses on advanced topics in artificial intelligence, including person re-identification, multi-modal learning, and content-based visual search. He has published extensively in leading international journals such as IEEE TNNLS, IEEE IoT Journal, IEEE JSTSP, IEEE TKDE, and IEEE TCSS, along with multiple papers under review in prestigious venues including IJCV and IEEE TSMC. His contributions have been recognized through innovative approaches such as spectrum-aware feature augmentation, modality bias calibration, and collaborative mixed learning for visible-infrared person re-identification, significantly advancing the field of AI-driven surveillance and smart systems.

Profile Verification

Scopus

Education Details

He is pursuing a doctoral degree in control science and engineering at Zhejiang University under the supervision of Prof. Wei Jiang. He previously completed a joint master’s program in computer science and technology at Xiamen University under Prof. Rongrong Ji and obtained his master’s degree in computer science and technology at Guizhou Normal University under Prof. Daoxun Xia.

Professional Experience

He has gained professional experience as a visual algorithm engineer at the Yuyao Research Center, Zhejiang University Robotics Research Institute in Ningbo, China, where he contributed to the development and application of advanced visual recognition and learning systems.

Research Interests

His primary research interests include person re-identification, multi-modal learning, and content-based visual search, with a focus on bridging modality gaps, enhancing model robustness, and advancing real-world applications in intelligent visual perception and surveillance.

Awards and Honors

He has been recognized with multiple awards for academic excellence and innovation, including provincial-level prizes in national innovation and entrepreneurship competitions, honors as an outstanding graduate, and distinctions such as the university-level three-good student award.

Publication Top Notes

SFANet: A Spectrum-Aware Feature Augmentation Network for Visible-Infrared Person Reidentification. IEEE Transactions on Neural Networks and Learning Systems, 2023.

Visible-Thermal Person Reidentification in Visual Internet of Things with Random Gray Data Augmentation and A New Pooling Mechanism. IEEE Internet of Things Journal, 2023.

Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification. IEEE Journal of Selected Topics in Signal Processing, 2023.

Inter-Intra Modality Knowledge Learning and Clustering Noise Alleviation for Unsupervised Visible-Infrared Person Re-Identification. IEEE Transactions on Knowledge and Data Engineering, 2024.

Modality Bias Calibration Network via Information Disentanglement for Visible-Infrared Person Re-Identification in Social Surveillance System. IEEE Transactions on Computational Social Systems, 2024.

Conclusion

Through his strong academic background, impactful research contributions, and recognized achievements, Haojie Liu has established himself as a promising researcher in the fields of artificial intelligence, computer vision, and intelligent surveillance, with significant potential for advancing multi-modal learning and real-world applications in AI-driven systems.

Sajad Rezvani | Computer vision | Excellence in Research

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Mr Sajad Rezvani | Computer vision | Excellence in Research

Shahrood University of Technology , Iran

Sadjad Rezvani is a highly qualified candidate for the Research for Excellence in Research award. His impressive academic achievements, impactful research contributions, technical expertise, and leadership in mentoring make him a strong contender. His work in masked face recognition, medical image analysis, and image segmentation reflects both the depth and relevance of his research in today’s rapidly evolving tech landscape.

Publication Profile
scopus

Education :

Sadjad Rezvani holds a Master of Science in Computer Engineering with a specialization in Artificial Intelligence from Shahrood University of Technology, Iran. He completed his master’s degree between September 2020 and September 2022, graduating with a GPA of 4/4 (18.59/20). His thesis was titled “Masked Face Recognition Using Deep Learning,” under the guidance of Professor Mansoor Fateh. Prior to this, Sadjad earned his Bachelor of Science in Computer Engineering, specializing in Software Engineering, from Shahrood University of Technology, completing his degree between September 2015 and September 2019 with a GPA of 3.53/4 (16.92/20). His undergraduate thesis was titled “Profiling Web Applications to Improve Intrusion Detection,” supervised by Professor Mohsen Rezvani.

Professional Experience:

Sadjad has practical experience as a Computer Vision Software Engineer in several industries. He worked at Hookan Salt Factory in Shiraz, Iran, from November 2020 to September 2021, where he contributed to the development of a Salt Crack Sorting Machine. In this role, he employed advanced image processing techniques to detect salt impurities in real-time, utilizing tools such as OpenCV, Python, C#, and C++. Additionally, he worked at Shahaab, CO from June 2019 to December 2023 on a Plate Recognition Software project, where he contributed to a system that recognized license plates using CCTV camera data. His work involved maintaining and improving the software using C#, SQL, and other related technologies.

Research Skills:

Sadjad is highly skilled in programming languages such as Python, C++, and C#, and has a strong background in Machine Learning frameworks including PyTorch, TensorFlow, and Scikit-Learn. He is proficient in Computer Vision tools like OpenCV and has experience with databases such as Microsoft SQL Server and MySQL. His technical expertise also extends to advanced image processing, AI for medical diagnosis, and deep learning-based solutions for real-world applications.

Research Focus :

Sadjad’s research interests include Machine Learning (ML), Deep Learning (DL), Generative AI (GenAI), Medical Image Analysis, Limited Data Solutions, and Domain Adaptation. He has contributed to several journal publications, such as the development of ABANet: Attention Boundary-Aware Network for Image Segmentation (2024) and a paper on Single Image Denoising via a New Lightweight Learning-Based Model (2024), among others. His academic research also includes the application of deep learning models for lung CT image segmentation and innovations in masked face recognition using deep learning.

 

Awards :

Sadjad has received recognition for his achievements, including being a member of Iran’s National Elites Foundation in 2023 and being the third-ranked student in his Master of Science program. His certifications include AI for Medical Diagnosis from DeepLearning.AI (Coursera, 2023), Python Project for Data Science from IBM (Coursera, 2022), and specialization courses in Generative Adversarial Networks (GANs) and Machine Learning from Stanford University.

Honours and Awards

  • Member of Iran’s National Elites Foundation, 2023

  • Third-ranked student in the Master of Science in Computer Science program, 2022

 

Publication : 

 

    • Rezvani, S., Fateh, M., & Khosravi, H. (2024). ABANet: Attention Boundary-Aware Network for Image Segmentation. Expert Systems, e13625. [Published May 2024]

    • Rezvani, S., Soleymani Siahkar, F., Rezvani, Y., Alavi Gharahbagh, A., & Abolghasemi, V. (2024). Single Image Denoising via a New Lightweight Learning-Based Model. IEEE Access, August 2024.

    • Rezvani, S., Fateh, M., Fateh, A., & Jalali, Y. (2024). FusionLungNet: Multi-scale Fusion Convolution with Refinement Network for Lung CT Image Segmentation. Biomedical Signal Processing and Control, Revised Sep 2024.

conclusion:

  • Sadjad’s overall profile is well-rounded with strengths across research, academia, technical skills, and professional experience.

  • Continued focus on expanding publication reach, collaboration, and public speaking could further elevate his visibility and impact in the research community.

  • With his dedication and achievements, Sadjad is well-positioned for recognition in research excellence.

In conclusion, Sadjad is a strong candidate for the award, and with a few adjustments in outreach and collaboration, he could continue to make significant strides in the research world.