What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Plamen Angelov mostly deals with Artificial intelligence, Data mining, Machine learning, Fuzzy logic and Cluster analysis.
Plamen Angelov interconnects Identification, Data science and Pattern recognition in the investigation of issues within Artificial intelligence.
His research in Data mining intersects with topics in Theoretical computer science, Classifier, Fuzzy rule, Outlier and Data stream.
His study in the fields of Activity recognition under the domain of Machine learning overlaps with other disciplines such as Streaming data.
His research investigates the connection between Fuzzy logic and topics such as Artificial neural network that intersect with problems in Feature.
His research integrates issues of Algorithm, Data stream mining, Analytics and Data analysis in his study of Cluster analysis.
His most cited work include:
- An approach to online identification of Takagi-Sugeno fuzzy models (798 citations)
- Evolving Fuzzy-Rule-Based Classifiers From Data Streams (278 citations)
- Evolving Intelligent Systems: Methodology and Applications (242 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Machine learning, Fuzzy logic, Data mining and Fuzzy rule.
Artificial intelligence is closely attributed to Pattern recognition in his research.
The concepts of his Machine learning study are interwoven with issues in Contextual image classification, Structure and Identification.
Within one scientific family, Plamen Angelov focuses on topics pertaining to Control theory under Fuzzy logic, and may sometimes address concerns connected to Process control.
His Data mining research focuses on Cluster analysis and how it relates to Algorithm.
His research investigates the connection between Fuzzy set operations and topics such as Neuro-fuzzy that intersect with issues in Fuzzy classification, Fuzzy number and Defuzzification.
He most often published in these fields:
- Artificial intelligence (54.19%)
- Machine learning (28.14%)
- Fuzzy logic (26.05%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (54.19%)
- Machine learning (28.14%)
- Classifier (14.42%)
In recent papers he was focusing on the following fields of study:
Plamen Angelov focuses on Artificial intelligence, Machine learning, Classifier, Fuzzy logic and Data mining.
His research on Artificial intelligence often connects related areas such as Pattern recognition.
His Machine learning research incorporates elements of Teamwork and Domain.
His studies examine the connections between Classifier and genetics, as well as such issues in Rule-based system, with regards to Feature descriptor, Remote sensing and Set.
His Fuzzy logic research is multidisciplinary, incorporating elements of Massively parallel, Convergence, Control theory, Ranking and Rule based classifier.
His studies deal with areas such as Computational intelligence and Cluster analysis as well as Data mining.
Between 2016 and 2021, his most popular works were:
- Fully online clustering of evolving data streams into arbitrarily shaped clusters (64 citations)
- A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition (58 citations)
- Human action recognition using transfer learning with deep representations (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Classifier, Cluster analysis and Algorithm.
His Artificial intelligence research includes themes of Analytics and Pattern recognition.
The study incorporates disciplines such as Image processing, Probabilistic logic, Class and Data space in addition to Machine learning.
His biological study spans a wide range of topics, including Object detection, Fuzzy rule, Fuzzy logic and Rule-based system.
His study looks at the relationship between Cluster analysis and topics such as Data mining, which overlap with Evolving intelligent system.
His Algorithm research integrates issues from Nonparametric statistics, Cosine similarity, Metric and Benchmark.
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