What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
His primary scientific interests are in Artificial intelligence, Data mining, Search engine indexing, Machine learning and Feature extraction.
The Artificial intelligence study combines topics in areas such as Computer vision and Multiple correspondence analysis.
His study in Data mining is interdisciplinary in nature, drawing from both Classifier, Cluster analysis, Multimodal data, Information extraction and Data set.
His work deals with themes such as Multimedia, Multimedia database, Mobile technology, Analytics and Multimedia big data, which intersect with Search engine indexing.
He combines subjects such as Image retrieval, Relevance feedback and TRECVID with his study of Machine learning.
His biological study spans a wide range of topics, including Contextual image classification, Semantics, Pattern analysis and Hidden Markov model.
His most cited work include:
- A progressive morphological filter for removing nonground measurements from airborne LIDAR data (633 citations)
- A Novel Anomaly Detection Scheme Based on Principal Component Classifier (344 citations)
- A Survey on Deep Learning: Algorithms, Techniques, and Applications (270 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Data mining, Multimedia, Machine learning and Information retrieval.
Shu-Ching Chen interconnects TRECVID, Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
His study in Data mining is interdisciplinary in nature, drawing from both Data set, Semantic gap, Cluster analysis and Multiple correspondence analysis.
His studies deal with areas such as World Wide Web, The Internet and Database as well as Multimedia.
The study incorporates disciplines such as Classifier and Semantics in addition to Machine learning.
Shu-Ching Chen works mostly in the field of Information retrieval, limiting it down to topics relating to Image retrieval and, in certain cases, Feature vector, as a part of the same area of interest.
He most often published in these fields:
- Artificial intelligence (31.70%)
- Data mining (22.68%)
- Multimedia (21.65%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (31.70%)
- Deep learning (7.47%)
- Machine learning (15.21%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Artificial neural network.
The various areas that he examines in his Artificial intelligence study include Data modeling, Multimedia and Task analysis.
His Deep learning study combines topics from a wide range of disciplines, such as Modality, Feature, Natural disaster and Big data.
His Machine learning research includes elements of Contextual image classification, Semantics and Social media.
His Convolutional neural network study also includes
- Feature learning, which have a strong connection to Evolutionary programming, Class and Data processing,
- Image processing and related Multiclass classification, Ensemble learning, TRECVID and Classifier.
Shu-Ching Chen works mostly in the field of Feature extraction, limiting it down to topics relating to Multiple correspondence analysis and, in certain cases, Data mining.
Between 2016 and 2021, his most popular works were:
- A Survey on Deep Learning: Algorithms, Techniques, and Applications (270 citations)
- Data-Driven Techniques in Disaster Information Management (213 citations)
- Multimedia Big Data Analytics: A Survey (78 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Shu-Ching Chen mostly deals with Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Contextual image classification.
His work deals with themes such as Information science, Node and Isomorphism, which intersect with Artificial intelligence.
His Deep learning research includes themes of Artificial neural network and Complex network.
His Machine learning research incorporates themes from Modality, Feature extraction, Residual and Natural disaster.
His Convolutional neural network research incorporates elements of Data modeling, Data mining, Image processing, Transfer of learning and Variety.
Shu-Ching Chen has researched Data mining in several fields, including Classifier, Ensemble learning, Support vector machine and TRECVID.
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