Andi Chen | Artificial Intelligence | Research Excellence Award

Dr. Andi Chen | Artificial Intelligence | Research Excellence Award

Vice President of the Student Union | Nanjing University | China

Dr. Andi Chen is an emerging researcher in computer science and artificial intelligence, with a strong focus on machine learning, deep learning architectures, and pattern recognition. His research interests center on hybrid quantum-inspired neural networks, particularly the integration of ResNet and DenseNet models to improve feature representation, classification performance, and computational efficiency in complex data environments. He demonstrates solid research skills in AI algorithm design, deep neural network modeling, pattern recognition, data analysis, and experimental evaluation, with applications relevant to intelligent systems and next-generation computing. Dr. Chen’s scholarly contributions include publications in reputable venues such as Neurocomputing, reflecting growing visibility in the AI research community. While no major awards or funded projects are currently reported, his work shows strong potential for future recognition. According to Scopus, his research profile records 3 documents, 1 citation, and an h-index of 1. In conclusion, Dr. Chen’s research trajectory highlights promising contributions to advanced AI methodologies and quantum-inspired intelligent computing.

 

Citation Metrics (Scopus)

3
2
1
0

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1

Documents

3

h-index

1

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h-index

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Featured Publications


Image Compression and Reconstruction Based on Quantum Network


– IEEE International Parallel and Distributed Processing Symposium, 2024 (Citations: 5)


Quantum Sparse Coding and Decoding Based on Quantum Network


– Applied Physics Letters, 2024 (Citations: 1)

 

Mingshou An | Artificial Intelligence | Excellence in Research Award

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.

Citation Metrics (Scopus)

15
10
5
2
0

Citations

15

Documents

11

h-index

3

Citations

Documents

h-index

View Scopus Profile
View ORCID Profile

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