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Sidan Du
Researcher at Nanjing University
Publications - 126
Citations - 2402
Sidan Du is an academic researcher from Nanjing University. The author has contributed to research in topics: Computer science & Computational complexity theory. The author has an hindex of 24, co-authored 113 publications receiving 1826 citations. Previous affiliations of Sidan Du include Northwestern University.
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Journal ArticleDOI
Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation
TL;DR: This study designed and validated a 13-layer convolutional neural network (CNN) that is effective in image-based fruit classification and observed using data augmentation can increase the overall accuracy.
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Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection
Shuihua Wang,Yudong Zhang,Zhengchao Dong,Sidan Du,Genlin Ji,Jie Yan,Jiquan Yang,Qiong Wang,Chunmei Feng,Preetha Phillips +9 more
TL;DR: A novel automatic classification system based on particle swarm optimization and artificial bee colony and three new variants of feed‐forward neural network (FNN), consisting of IABAP‐fNN, ABC‐SPSO‐FNN, and HPA‐Fnn, which was superior to existing state‐of‐the‐art methods in terms of classification accuracy.
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FUS Interacts with HSP60 to Promote Mitochondrial Damage
Jianwen Deng,Mengxue Yang,Yanbo Chen,Xiaoping Chen,Jianghong Liu,Shufeng Sun,Haipeng Cheng,Yang Li,Eileen H. Bigio,M.-Marsel Mesulam,Qi Xu,Sidan Du,Kazuo Fushimi,Li Zhu,Jane Y. Wu +14 more
TL;DR: Evidence for mitochondrial localization of FUS and its induction of mitochondrial damage is provided and mitochondrial damage may be a target in future development of diagnostic and therapeutic tools for FUS-proteinopathies, a group of devastating neurodegenerative diseases.
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Host load prediction with long short-term memory in cloud computing
TL;DR: A concise yet adaptive and powerful model called long short-term memory is applied to predict the mean load over consecutive future time intervals and actual load multi-step-ahead and achieves state-of-the-art performance with higher accuracy in both datasets.
Journal ArticleDOI
Fine-Grained Vehicle Model Recognition Using A Coarse-to-Fine Convolutional Neural Network Architecture
TL;DR: This paper demonstrates that the fine-grained vehicle model recognition problem can be addressed by locating discriminative parts, where the most significant appearance variation appears, based on the large-scale training set, and proposes a corresponding coarse-to-fine method to achieve this.