Mr. Mingshou An | Artificial Intelligence | Excellence in Research Award
Lecturer | Xi’an Technological University | China
Mr. Mingshou An is an emerging researcher affiliated with Dong-A University, Busan, South Korea, recognized for his contributions to computer science and intelligent image processing. His research primarily focuses on deep learning, image denoising, medical and natural image enhancement, convolutional neural networks, U-Net architectures, and multi-scale attention mechanisms, with a strong emphasis on improving image quality and model efficiency. He possesses solid research skills in machine learning, deep neural network design, algorithm optimization, data preprocessing, model evaluation, and scientific computing, supported by hands-on experience in developing advanced attention-based architectures. His notable work includes an open-access publication on Multi-scale Attention Dense U-Net for image denoising, reflecting innovation in AI-driven image restoration. According to Scopus, Mr. An has 11 research documents, 15 citations , and an h-index of 3, demonstrating growing academic impact. While formal awards and honors are not yet listed, his citation growth indicates rising recognition. In conclusion, Mr. Mingshou An represents a promising researcher whose work contributes meaningfully to the advancement of intelligent imaging and deep learning applications.
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Featured Publications
A Method of Visual Positioning of Tank Truck Openings via Two-Stage Fine-Tuning
– Book Chapter, 2025 | DOI: 10.1007/978-3-031-94962-3_14
An Enhanced LSTM with Hippocampal-Inspired Episodic Memory for Urban Crowd Behavior Analysis
– Electronics (Journal), 2025 | DOI: 10.3390/electronics15010101
A Method of Image Denoising via Dense Attention DnCNN
– Book Chapter, 2024 | DOI: 10.1007/978-981-97-4182-3_43
Fusion Self-Attention Feature Clustering Mechanism Network for Person ReID
– Book Chapter, 2024 | DOI: 10.1007/978-981-99-9416-8_55
A Study on Deep Learning Algorithm for Fire Detection based on Attention BiFPN
– Journal of Korean Institute of Information Technology, 2024 | DOI: 10.14801/jkiit.2024.22.9.1