Y
Yudong Zhang
Researcher at University of Leicester
Publications - 575
Citations - 22005
Yudong Zhang is an academic researcher from University of Leicester. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 71, co-authored 437 publications receiving 15015 citations. Previous affiliations of Yudong Zhang include Nanjing Audit University & Nanjing University.
Papers
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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
TL;DR: This survey presented a comprehensive investigation of PSO, including its modifications, extensions, and applications to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology.
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Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Yudong Zhang,Lenan Wu +1 more
TL;DR: An improved bacterial chemotaxis optimization (IBCO) is proposed, which is then integrated into the back propagation (BP) artificial neural network to develop an efficient forecasting model for prediction of various stock indices.
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Binary PSO with mutation operator for feature selection using decision tree applied to spam detection
TL;DR: A novel spam detection method that focused on reducing the false positive error of mislabeling nonspam as spam, which demonstrated the MBPSO is superior to GA, RSA, PSO, and BPSO in terms of classification performance and wrappers are more effective than filters with regard to classification performance indexes.
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Preliminary Risk Assessment of Trace Metal Pollution in Surface Water from Yangtze River in Nanjing Section, China
TL;DR: In order to investigate the contamination levels of trace metals, surface water samples were collected from six regions along Yangtze River in Nanjing Section and Hazard Quotients of all metals were lower than unity, except As and Cd, suggesting that those two metals have potential adverse effects on local residents.
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A hybrid method for MRI brain image classification
TL;DR: This paper presents a neural network (NN) based method to classify a given MR brain image as normal or abnormal, which first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features.