What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Pattern recognition
Jen Hong Tan focuses on Artificial intelligence, Convolutional neural network, Deep learning, Pattern recognition and Speech recognition.
His Myocardial infarction research extends to Artificial intelligence, which is thematically connected.
His Convolutional neural network research integrates issues from Internal medicine, Fundus and Cardiology.
His work on Artificial neural network expands to the thematically related Deep learning.
His work carried out in the field of Pattern recognition brings together such families of science as Entropy and Algorithm.
Jen Hong Tan has researched Speech recognition in several fields, including Decision tree, Decision tree learning and Mass screening.
His most cited work include:
- Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. (679 citations)
- A deep convolutional neural network model to classify heartbeats (458 citations)
- Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals (404 citations)
What are the main themes of his work throughout his whole career to date?
Jen Hong Tan mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Support vector machine and Convolutional neural network.
The study incorporates disciplines such as Speech recognition and Thermography in addition to Artificial intelligence.
His Pattern recognition research includes themes of Tsallis entropy, Sample entropy and Myocardial infarction.
His study focuses on the intersection of Computer vision and fields such as Diabetic retinopathy with connections in the field of Fundus and Macular edema.
His work on Naive Bayes classifier as part of general Support vector machine research is frequently linked to Probabilistic neural network, thereby connecting diverse disciplines of science.
The Convolutional neural network study combines topics in areas such as Deep learning and Cardiology.
He most often published in these fields:
- Artificial intelligence (64.86%)
- Pattern recognition (36.49%)
- Computer vision (25.68%)
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