What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Xun Chen mainly investigates Artificial intelligence, Image fusion, Convolutional neural network, Pattern recognition and Computer vision.
Specifically, his work in Artificial intelligence is concerned with the study of Wavelet.
As part of the same scientific family, Xun Chen usually focuses on Image fusion, concentrating on Sparse approximation and intersecting with Convolution.
Xun Chen has included themes like Pixel, Deep learning and Composite image filter in his Convolutional neural network study.
His Pattern recognition research is multidisciplinary, incorporating elements of Speech recognition and Residual.
Xun Chen has researched Speech recognition in several fields, including Eeg data, Electroencephalography, Blind signal separation, Artifact and Noise reduction.
His most cited work include:
- Multi-focus image fusion with a deep convolutional neural network (385 citations)
- Image Fusion With Convolutional Sparse Representation (275 citations)
- Deep learning for pixel-level image fusion: Recent advances and future prospects (216 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Electroencephalography, Computer vision and Convolutional neural network.
His studies in Artificial intelligence integrate themes in fields like Signal and Blind signal separation.
His study looks at the relationship between Pattern recognition and fields such as Robustness, as well as how they intersect with chemical problems.
His work carried out in the field of Computer vision brings together such families of science as Visualization and Photoplethysmogram.
His research on Convolutional neural network frequently links to adjacent areas such as Pixel.
Xun Chen has included themes like Sparse approximation and Composite image filter in his Image fusion study.
He most often published in these fields:
- Artificial intelligence (70.31%)
- Pattern recognition (46.09%)
- Electroencephalography (20.31%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (70.31%)
- Pattern recognition (46.09%)
- Feature extraction (12.50%)
In recent papers he was focusing on the following fields of study:
Xun Chen mainly focuses on Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Electromyography.
His work on Deep learning and Convolutional neural network as part of general Artificial intelligence research is often related to Field, thus linking different fields of science.
His Convolutional neural network research incorporates themes from Classifier and Feature.
His Pattern recognition study integrates concerns from other disciplines, such as Noise reduction, Feature and Electroencephalography.
His study on Pixel and Image representation is often connected to Semi blind and Display resolution as part of broader study in Computer vision.
His Image fusion research is multidisciplinary, incorporating perspectives in Phase congruency and Composite image filter.
Between 2019 and 2021, his most popular works were:
- Multi-focus image fusion: A Survey of the state of the art (24 citations)
- EEG-based Emotion Recognition via Channel-wise Attention and Self Attention (11 citations)
- ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Xun Chen spends much of his time researching Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Feature extraction.
His research in Artificial intelligence intersects with topics in Computer vision and Electroencephalography.
His Pixel study in the realm of Computer vision connects with subjects such as Semi blind and Display resolution.
The study incorporates disciplines such as Control system, Feature and Feature in addition to Convolutional neural network.
His Deep learning study combines topics from a wide range of disciplines, such as Recurrent neural network, Sparse approximation and Image.
His Feature extraction research incorporates elements of Data visualization and Human–computer interaction.
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