What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Mei-Ling Shyu spends much of his time researching Artificial intelligence, Data mining, Search engine indexing, Machine learning and Information retrieval.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Multiple correspondence analysis.
Mei-Ling Shyu combines subjects such as Classifier, Feature extraction, Data set and TRECVID with his study of Data mining.
In his work, Anomaly detection and Intrusion detection system is strongly intertwined with Principal component analysis, which is a subfield of Classifier.
His biological study spans a wide range of topics, including Multimedia, Multimedia database, Analytics, Data science and Multimedia big data.
His work deals with themes such as Visual Word, Image retrieval and Automatic image annotation, which intersect with Information retrieval.
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?
Mei-Ling Shyu mostly deals with Artificial intelligence, Data mining, Multimedia, Information retrieval and Machine learning.
The Artificial intelligence study combines topics in areas such as TRECVID, Computer vision and Pattern recognition.
His TRECVID study combines topics from a wide range of disciplines, such as Ranking, Semantic gap and Multiple correspondence analysis.
His Data mining research incorporates elements of Data modeling, Data set and Cluster analysis.
His research in Multimedia intersects with topics in World Wide Web, Presentation and Semantic data model, Database.
His Information retrieval study deals with Image retrieval intersecting with Feature vector.
He most often published in these fields:
- Artificial intelligence (39.41%)
- Data mining (29.32%)
- Multimedia (21.50%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (39.41%)
- Machine learning (18.24%)
- Deep learning (6.19%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Deep learning, Convolutional neural network and Big data.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Social media, Data mining, Process and Pattern recognition.
His research integrates issues of Scalability, Multimedia, Upload, Multivariate statistics and Decision model in his study of Data mining.
His work carried out in the field of Machine learning brings together such families of science as Contextual image classification, Information science, Residual and Data classification.
His Deep learning study also includes
- Data modeling which intersects with area such as Data processing and Task analysis,
- Word embedding and Missing data most often made with reference to Multimodal learning.
The study incorporates disciplines such as Information integration, Semantic gap, Data science and Search engine indexing in addition to Big data.
Between 2015 and 2020, his most popular works were:
- A Survey on Deep Learning: Algorithms, Techniques, and Applications (270 citations)
- Multimedia Big Data Analytics: A Survey (78 citations)
- Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of investigation include Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Big data.
His Artificial intelligence research integrates issues from Autism, Data mining and Task analysis.
Mei-Ling Shyu conducts interdisciplinary study in the fields of Data mining and Bootstrapping through his works.
His Deep learning research includes themes of Text processing, Word embedding, Artificial neural network, Information processing and Unsupervised learning.
Mei-Ling Shyu combines subjects such as Modality, Feature extraction and Multimedia with his study of Machine learning.
His Big data research focuses on subjects like Data science, which are linked to Search engine indexing, Multimedia big data and Mobile technology.
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