Hui Lyu | Artificial Intelligence and Machine Learning | Best Researcher Award

Published on by New Scientists

Dr. Hui Lyu | Artificial Intelligence and Machine Learning | Best Researcher Award

Student, Zibo Normal College | China

Dr. Hui Lyu is an interdisciplinary researcher specializing in complex-valued neural networks, infrared image enhancement, intelligent signal processing, data fusion, and optimization-based machine learning. Her peer-reviewed output includes 4 SCI-indexed journal articles and international conference papers, with publications in IEEE Sensors Journal, IEEE Access, Neural Processing Letters, and Signal, Image and Video Processing. Her research integrates adaptive convolutional filtering, quaternion extreme learning machines, and multisensor fault diagnosis frameworks. She is a contributor to national and municipal funded research on infrared vision systems, sensor testing, and complex neural learning algorithms, and holds 5 granted invention patents in infrared imaging and intelligent detection systems. Her work has received regional scientific achievement recognition.

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Featured Publications

Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

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Dr. Boyuan Bai | Artificial Intelligence and Machine Learning | Best Researcher Award

Doctor | Beijing University of Posts and Telecommunications | China

Dr. Boyuan Bai is an emerging researcher in advanced visual computing, with a focused contribution to 3D reconstruction, Gaussian Splatting, multi-view scene modeling, and uncertainty-aware machine learning. His work integrates computer graphics, deep learning, and computational geometry to develop intelligent systems capable of producing highly accurate and stable indoor scene reconstructions. With 83 citations, 4 Scopus-indexed publications, and an h-index of 3, he is rapidly establishing a strong research footprint. Dr. Boyuan Bai’s notable scientific contribution centers on UncertainGS, an uncertainty-aware indoor reconstruction framework published in Neurocomputing (SCI/Scopus). This research introduces a novel pipeline that integrates cross-modal uncertainty prediction to guide the optimization of Gaussian Splatting. His methodological innovation improves the fidelity of reconstructed surfaces, especially in textureless or geometrically ambiguous indoor regions. His incorporation of Manhattan-world constraints into the Gaussian Splatting process represents a significant leap forward in aligning 3D surface geometry with real-world structural patterns. His research areas broadly span multi-view 3D reconstruction, Gaussian Splatting, uncertainty modeling, scene understanding, and deep reinforcement learning for geometric perception. He actively contributes to the development of next-generation 3D vision technologies, with applications in robotics, digital twins, AR/VR environments, and autonomous spatial intelligence. His work shows strong potential for large-scale deployment in real-time virtual reconstruction and simulation systems. Dr. Boyuan Bai’s scholarly output includes peer-reviewed journal publications, research project leadership, and scientific contributions that address fundamental challenges in computational imaging. His research achievements demonstrate clear innovation, technical depth, and growing influence in the fields of computer vision and graphics. Through ongoing academic collaborations and continued focus on high-impact research problems, he is emerging as a promising researcher in intelligent 3D scene modeling and uncertainty-aware visual computing.

Profiles: Scopus | IEEE Xplore | ACM Digital Library 

Featured Publications

1. Bai, B., Qiao, X., Lu, P., Zhao, H., Shi, W., & others. (2025). Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors. Neurocomputing, 618(C). https://doi.org/10.1016/j.neucom.2024.129118

2. Huang, Y., Bai, B., Zhu, Y., Qiao, X., Su, X., Yang, L., & others. (2024). ISCom: Interest-aware semantic communication scheme for point cloud video streaming on Metaverse XR devices. IEEE Journal on Selected Areas in Communications, 42(4). https://doi.org/10.1109/JSAC.2023.3345430

3. Zhu, Y., Huang, Y., Qiao, X., Tan, Z., Bai, B., & others. (2023). A semantic-aware transmission with adaptive control scheme for volumetric video service. IEEE Transactions on Multimedia, 25. https://doi.org/10.1109/TMM.2022.3217928

4. Huang, Y., Zhu, Y., Qiao, X., Tan, Z., & Bai, B. (2021). AITransfer: Progressive AI-powered transmission for real-time point cloud video streaming. In Proceedings of the 29th ACM International Conference on Multimedia (MM ’21). https://doi.org/10.1145/3474085.3475624

Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

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Assist. Prof. Dr. Amir R. Masoodi | Artificial Intelligence and Machine Learning | Editorial Board Member

Assistant Professor | Ferdowsi University of Mashhad | Iran

Assist. Prof. Dr. Amir R. Masoodi is a highly accomplished structural engineering researcher whose work spans nonlinear mechanics, composite structures, vibration analysis, finite element modeling, and advanced material systems. With 1,789 Scopus citations, 77 publications, and an h-index of 29, he has established a strong international research footprint in computational mechanics, structural stability, soil–structure interaction, wave propagation, and multiscale modeling of advanced composites. His research contributions include developing novel finite element formulations for beams, plates, and shells, particularly for functionally graded materials (FGMs), carbon nanotube (CNT)-reinforced composites, graphene nanocomposites, and porous structural systems. Assist. Prof. Dr. Amir R. Masoodi’s work on nonlinear dynamic analysis, thermal–mechanical coupling, shell instability, and multiscale behavior of nano-engineered materials has been widely cited and influential in advancing modern structural design methodologies. He has published extensively in leading journals such as Composite Structures, Engineering Structures, Mechanics of Advanced Materials and Structures, Aerospace Science and Technology, Scientific Reports, and Applied Sciences. His publications address cutting-edge topics including vibration of hybrid nano-reinforced shells, multiscale characterization of nanocomposites, nonlinear buckling behavior of tapered beams, thermomechanical modeling of composite cables, and smart materials incorporating shape-memory alloys. Assist. Prof. Dr. Amir R. Masoodi has presented his findings at numerous international conferences and contributed several book chapters, including work on nanofillers and thermal properties in advanced materials. His research output extends to R&D projects, predictive modeling, and computational innovations in structural and nano-engineered systems. He has been recognized with multiple distinguished researcher awards, national elite recognitions, and research excellence honors. His expertise is further reflected in his editorial board memberships and contributions as a reviewer for reputable journals. Overall, Assist. Prof. Dr. Amir R. Masoodi’s research stands at the intersection of computational mechanics, smart materials, and multiscale structural engineering, offering impactful advances for next-generation civil, mechanical, and aerospace systems.

Profiles: Scopus | ORCID | Google Scholar | Sci Profiles | Web of Science

Featured Publications

1. Sobhani, E., Masoodi, A. R., & Ahmadi-Pari, A. (2021). Vibration of FG-CNT and FG-GNP sandwich composite coupled conical–cylindrical–conical shell. Composite Structures, 273, 114281.

2. Sobhani, E., Masoodi, A. R., Civalek, O., & Ahmadi-Pari, A. R. (2021). Agglomerated impact of CNT vs. GNP nanofillers on hybridization of polymer matrix for vibration of coupled hemispherical–conical–conical shells. Aerospace Science and Technology, 120, 107257.

3. Rezaiee-Pajand, M., Sobhani, E., & Masoodi, A. R. (2020). Free vibration analysis of functionally graded hybrid matrix/fiber nanocomposite conical shells using multiscale method. Aerospace Science and Technology, 105, 105998.

4. Rezaiee-Pajand, M., Arabi, E., & Masoodi, A. R. (2019). Nonlinear analysis of FG-sandwich plates and shells. Aerospace Science and Technology, 87, 178–189.

5. Rezaiee-Pajand, M., Masoodi, A. R., & Mokhtari, M. (2018). Static analysis of functionally graded non-prismatic sandwich beams. Advances in Computational Design, 3(2), 165–190.