What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Fuzzy logic, Fuzzy classification, Fuzzy set operations and Information retrieval.
His Artificial intelligence study combines topics in areas such as Machine learning, Pattern recognition and Natural language processing.
His studies in Fuzzy logic integrate themes in fields like Mathematical optimization and Cluster analysis.
His study in Neuro-fuzzy extends to Fuzzy classification with its themes.
His work focuses on many connections between Fuzzy set operations and other disciplines, such as Defuzzification, that overlap with his field of interest in Fuzzy associative matrix.
His Fuzzy set study combines topics from a wide range of disciplines, such as Data mining, Feature selection and Fuzzy clustering.
His most cited work include:
- Similarity measures in fuzzy rule base simplification (446 citations)
- Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete (173 citations)
- Exploiting emoticons in sentiment analysis (145 citations)
What are the main themes of his work throughout his whole career to date?
Uzay Kaymak mostly deals with Fuzzy logic, Artificial intelligence, Data mining, Fuzzy set and Mathematical optimization.
His research links Cluster analysis with Fuzzy logic.
His Artificial intelligence study deals with Natural language processing intersecting with Information retrieval.
His Data mining study incorporates themes from Fuzzy rule and Selection.
Uzay Kaymak works mostly in the field of Fuzzy set operations, limiting it down to topics relating to Fuzzy number and, in certain cases, Algorithm, as a part of the same area of interest.
His Fuzzy classification research is multidisciplinary, incorporating elements of Defuzzification, Neuro-fuzzy and Membership function.
He most often published in these fields:
- Fuzzy logic (32.84%)
- Artificial intelligence (31.62%)
- Data mining (19.61%)
What were the highlights of his more recent work (between 2015-2021)?
- Fuzzy logic (32.84%)
- Artificial intelligence (31.62%)
- Data mining (19.61%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Fuzzy logic, Artificial intelligence, Data mining, Machine learning and Mathematical optimization.
While the research belongs to areas of Fuzzy logic, he spends his time largely on the problem of Python, intersecting his research to questions surrounding Software.
He has included themes like Structure and Task in his Machine learning study.
When carried out as part of a general Mathematical optimization research project, his work on Heuristic and Local search is frequently linked to work in Schedule and Train, therefore connecting diverse disciplines of study.
His studies deal with areas such as Soft computing, Decision support system, Probabilistic logic, Process and Flexibility as well as Fuzzy set.
His work carried out in the field of Fuzzy inference brings together such families of science as Fuzzy rule and Takagi sugeno.
Between 2015 and 2021, his most popular works were:
- A Survey of event extraction methods from text for decision support systems (58 citations)
- Remaining useful lifetime prediction via deep domain adaptation (25 citations)
- Dynamic Facet Ordering for Faceted Product Search Engines (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Uzay Kaymak mainly investigates Artificial intelligence, Fuzzy logic, Data mining, Interpretability and Information retrieval.
Uzay Kaymak has researched Artificial intelligence in several fields, including Base and Machine learning.
His Mathematical optimization research extends to the thematically linked field of Fuzzy logic.
His study in Data mining is interdisciplinary in nature, drawing from both Domain, Source data, Parsing, Task and Semantics.
Uzay Kaymak combines subjects such as Multivariate mutual information, Statistics and Feature with his study of Interpretability.
The Information retrieval study combines topics in areas such as User interface, Natural language processing and Presentation.
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