2016 - ACM Senior Member
His primary areas of study are Artificial intelligence, Pattern recognition, Cross-validation, Principal component analysis and Support vector machine. Yudong Zhang works on Artificial intelligence which deals in particular with Artificial neural network. His work carried out in the field of Pattern recognition brings together such families of science as Contextual image classification, Entropy, Feedforward neural network and Biogeography-based optimization.
The Cross-validation study combines topics in areas such as Discrete wavelet transform, Chaotic and Wavelet. Yudong Zhang has included themes like Normalization, Confusion matrix, Wavelet transform and Feature vector in his Principal component analysis study. His studies deal with areas such as Feature extraction, Kernel and Radial basis function as well as Support vector machine.
Artificial intelligence, Pattern recognition, Convolutional neural network, Artificial neural network and Cross-validation are his primary areas of study. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision. His Pattern recognition study combines topics from a wide range of disciplines, such as Magnetic resonance imaging and Sensitivity.
Yudong Zhang focuses mostly in the field of Convolutional neural network, narrowing it down to matters related to Pooling and, in some cases, Test set. The study incorporates disciplines such as Entropy, Particle swarm optimization, Overfitting and Speech recognition in addition to Cross-validation. His Particle swarm optimization research includes themes of Genetic algorithm and Feedforward neural network.
Yudong Zhang mostly deals with Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Pooling. His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning. His Pattern recognition research incorporates themes from Extreme learning machine and Sensitivity.
His Convolutional neural network research is multidisciplinary, incorporating perspectives in Normalization, Layer, Image processing, Transfer of learning and Dropout. His research in Deep learning intersects with topics in Classifier, Image, Magnetic resonance imaging and Euclidean distance. The concepts of his Artificial neural network study are interwoven with issues in Algorithm and Particle swarm optimization.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Transfer of learning. His work on Machine learning expands to the thematically related Artificial intelligence. His biological study spans a wide range of topics, including Cancer, Artificial neural network, Extreme learning machine, Decision tree and Lung cancer.
His Convolutional neural network study combines topics in areas such as Normalization, Pooling, Classifier, Image processing and Sensitivity. His research integrates issues of Discriminant, Feature extraction, Standard error and Baseline in his study of Deep learning. His studies in Transfer of learning integrate themes in fields like Stochastic gradient descent and Component.
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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Yudong Zhang;Shuihua Wang;Shuihua Wang;Genlin Ji.
Mathematical Problems in Engineering (2015)
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Yudong Zhang;Lenan Wu.
Expert Systems With Applications (2009)
Binary PSO with mutation operator for feature selection using decision tree applied to spam detection
Yudong Zhang;Shuihua Wang;Preetha Phillips;Genlin Ji.
Knowledge Based Systems (2014)
A hybrid method for MRI brain image classification
Yudong Zhang;Zhengchao Dong;Lenan Wu;Shuihua Wang.
Expert Systems With Applications (2011)
Preliminary Risk Assessment of Trace Metal Pollution in Surface Water from Yangtze River in Nanjing Section, China
B. Wu;D. Y. Zhao;H. Y. Jia;Y. Zhang.
Bulletin of Environmental Contamination and Toxicology (2009)
An Mr Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine
Yu-Dong Zhang;Lenan Wu.
Progress in Electromagnetics Research-pier (2012)
Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach
Yudong Zhang;Lenan Wu.
Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine
Yudong Zhang;Lenan Wu.
Fruit classification using computer vision and feedforward neural network
Yudong Zhang;Shuihua Wang;Genlin Ji;Preetha Phillips.
Journal of Food Engineering (2014)
Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)
Yudong Zhang;Zhengchao Dong;Shuihua Wang;Genlin Ji.
Profile was last updated on December 6th, 2021.
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