What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Keystroke dynamics and Data mining.
His research in Artificial intelligence is mostly concerned with Support vector machine.
His studies in Machine learning integrate themes in fields like Real-time computing and Statistical process control.
His research investigates the connection with Artificial neural network and areas like Data set which intersect with concerns in Class and Data imbalance.
The Keystroke dynamics study combines topics in areas such as Classifier and Password.
His research integrates issues of Word2vec, Training set and Curse of dimensionality in his study of Data mining.
His most cited work include:
- Web-Based Keystroke Dynamics Identity Verification Using Neural Network (161 citations)
- Keystroke dynamics identity verification-its problems and practical solutions (127 citations)
- System and method for performing user authentication based on user behavior patterns (119 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Machine learning, Data mining, Pattern recognition and Support vector machine.
Many of his studies on Artificial intelligence apply to Keystroke dynamics as well.
His biological study spans a wide range of topics, including Password, Keystroke logging and Biometrics.
He has included themes like Generalization and Training set in his Machine learning study.
In his research on the topic of Data mining, Time complexity is strongly related with Pattern recognition.
His work on Decision boundary and Structured support vector machine as part of general Support vector machine study is frequently linked to Pattern selection, therefore connecting diverse disciplines of science.
He most often published in these fields:
- Artificial intelligence (58.97%)
- Machine learning (34.36%)
- Data mining (27.18%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (58.97%)
- Pattern recognition (23.59%)
- Convolutional neural network (3.08%)
In recent papers he was focusing on the following fields of study:
Sungzoon Cho mainly focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Data mining and Class.
His Artificial intelligence study typically links adjacent topics like Machine learning.
His study in the field of Feature and Computational intelligence also crosses realms of Training and Project management.
The concepts of his Pattern recognition study are interwoven with issues in Autoencoder, Cluster analysis and Time series.
His studies deal with areas such as Ontology, Pattern recognition, Process and Product as well as Data mining.
His Class study combines topics from a wide range of disciplines, such as Document classification, Interpretability and Discriminative model.
Between 2017 and 2021, his most popular works were:
- Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning (16 citations)
- Fault Detection and Diagnosis Using Self-Attentive Convolutional Neural Networks for Variable-Length Sensor Data in Semiconductor Manufacturing (12 citations)
- Squeezed Convolutional Variational AutoEncoder for unsupervised anomaly detection in edge device industrial Internet of Things (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Sungzoon Cho mostly deals with Artificial intelligence, Pattern recognition, Convolutional neural network, Data mining and Cluster analysis.
His Artificial intelligence research is multidisciplinary, relying on both Auxiliary memory and Relation.
His Pattern recognition research is multidisciplinary, incorporating elements of Matrix, Word, Interpretation and Document clustering.
His Convolutional neural network study necessitates a more in-depth grasp of Machine learning.
While the research belongs to areas of Data mining, Sungzoon Cho spends his time largely on the problem of Ontology, intersecting his research to questions surrounding Process.
His Cluster analysis research integrates issues from Centroid and Projection.
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