What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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.
His most cited work include:
- A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (467 citations)
- Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network (309 citations)
- Binary PSO with mutation operator for feature selection using decision tree applied to spam detection (272 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (80.96%)
- Pattern recognition (62.41%)
- Convolutional neural network (22.65%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.96%)
- Pattern recognition (62.41%)
- Convolutional neural network (22.65%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images (66 citations)
- Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization (65 citations)
- Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling (43 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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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