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, Artificial neural network, Machine learning and Algorithm.
His Artificial intelligence research is multidisciplinary, relying on both Data mining, Computer vision and Code.
The various areas that De-Shuang Huang examines in his Pattern recognition study include Facial recognition system and Invariant.
His studies deal with areas such as Computational complexity theory and Control theory as well as Artificial neural network.
His Machine learning study combines topics from a wide range of disciplines, such as Shape matching, Dynamic programming and Key.
His study in Algorithm is interdisciplinary in nature, drawing from both Numerical analysis and Function approximation.
His most cited work include:
- An efficient local Chan-Vese model for image segmentation (429 citations)
- Robust and efficient subspace segmentation via least squares regression (358 citations)
- Palmprint Verification Based on Robust Orientation Code (354 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Artificial neural network, Machine learning and Intelligent computing.
De-Shuang Huang has included themes like Algorithm and Computer vision in his Artificial intelligence study.
Pattern recognition is frequently linked to Facial recognition system in his study.
De-Shuang Huang regularly ties together related areas like Basis in his Artificial neural network studies.
His Machine learning research incorporates elements of Embedding and Data mining.
De-Shuang Huang interconnects Engineering management, Multimedia, Software engineering, Data science and Pattern recognition in the investigation of issues within Intelligent computing.
He most often published in these fields:
- Artificial intelligence (58.54%)
- Pattern recognition (33.17%)
- Artificial neural network (22.20%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (58.54%)
- Pattern recognition (33.17%)
- Machine learning (17.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Computational biology.
His work in Artificial intelligence addresses issues such as Identification, which are connected to fields such as Re identification.
His Pattern recognition research is multidisciplinary, incorporating elements of Image and Encoding.
His research in Machine learning intersects with topics in Non-coding RNA and Key.
His Computational biology research includes elements of DNA binding site, Data mining, DNA methylation, Genomics and Gene.
His Feature extraction course of study focuses on Artificial neural network and Classifier.
Between 2016 and 2021, his most popular works were:
- iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. (183 citations)
- iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. (82 citations)
- iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC (79 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
De-Shuang Huang focuses on Artificial intelligence, Machine learning, Pattern recognition, Computational biology and Data mining.
Many of his studies involve connections with topics such as Identification and Artificial intelligence.
His Machine learning research is multidisciplinary, relying on both Non-coding RNA, Key, Task and Sparse matrix.
Many of his studies on Pattern recognition apply to Robustness as well.
His study looks at the intersection of Computational biology and topics like Cross-validation with Projection and Cancer detection.
His Data mining study combines topics in areas such as Function, Epistasis, DNA binding site and Ant colony optimization algorithms.
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