What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
Artificial intelligence, Data mining, World Wide Web, Machine learning and Computer security are his primary areas of study.
His Pattern recognition research extends to Artificial intelligence, which is thematically connected.
His Data mining research integrates issues from Consistency and Scalability.
As part of the same scientific family, Yanchun Zhang usually focuses on Machine learning, concentrating on Survivability and intersecting with AdaBoost, Predictive modelling, Outlier and Random forest.
His work on Access control and Data security is typically connected to Reuse as part of general Computer security study, connecting several disciplines of science.
His research integrates issues of Web intelligence and Information retrieval in his study of Web mapping.
His most cited work include:
- Achieving convergence, causality preservation, and intention preservation in real-time cooperative editing systems (554 citations)
- Algorithms and Techniques (218 citations)
- Frontiers of WWW Research and Development - APWeb 2006 (127 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Data mining, Pattern recognition, World Wide Web and Information retrieval.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
His research on Data mining frequently links to adjacent areas such as Cluster analysis.
Web modeling, Web intelligence, Web development, The Internet and Web navigation are subfields of World Wide Web in which his conducts study.
His study in Web modeling is interdisciplinary in nature, drawing from both Web standards, Web design and Data Web.
He has researched Data Web in several fields, including Social Semantic Web, Web mining and Semantic Web Stack.
He most often published in these fields:
- Artificial intelligence (39.53%)
- Data mining (18.92%)
- Pattern recognition (19.26%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (39.53%)
- Pattern recognition (19.26%)
- Deep learning (7.26%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Feature extraction.
His work deals with themes such as Identification and Natural language processing, which intersect with Artificial intelligence.
The study incorporates disciplines such as Heartbeat, Frequency domain, Feature and Complex network in addition to Pattern recognition.
His work carried out in the field of Deep learning brings together such families of science as Social media and Fundus.
His Social media research includes elements of Information retrieval and Data science.
His research combines Graph and Machine learning.
Between 2018 and 2021, his most popular works were:
- Exploring Tourist Dining Preferences Based on Restaurant Reviews (37 citations)
- Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise (25 citations)
- Dynamic Connection-Based Social Group Recommendation (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Feature extraction, Support vector machine and Natural language processing.
His Artificial intelligence research incorporates themes from Machine learning and Identification.
His study in the field of Convolutional neural network and Classifier is also linked to topics like Signal processing and Hilbert–Huang transform.
His studies examine the connections between Classifier and genetics, as well as such issues in Arrhythmia detection, with regards to Data mining.
His Feature extraction study deals with Feature intersecting with Scheme, Brain–computer interface and Linear discriminant analysis.
The Deep learning study combines topics in areas such as Social media and Information retrieval.
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