What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Fuzzy logic, Fuzzy set, Pattern recognition and Computer vision.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Machine learning, that overlap with his field of interest in Knowledge base.
The concepts of his Fuzzy set study are interwoven with issues in Digital image, Algorithm, Data mining and Pattern recognition.
His Algorithm study which covers Class that intersects with Partition and Minification.
The study incorporates disciplines such as Image processing, Fractal dimension, Feature and Maxima and minima in addition to Pattern recognition.
His work in Computer vision addresses subjects such as Spatial relation, which are connected to disciplines such as Rule-based system, Connected-component labeling, Mobile robot navigation, Position and Image texture.
His most cited work include:
- A possibilistic approach to clustering (2015 citations)
- A fuzzy K-nearest neighbor algorithm (1676 citations)
- Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (1033 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Fuzzy logic, Computer vision, Pattern recognition and Fuzzy set.
His Artificial intelligence study frequently links to adjacent areas such as Machine learning.
James M. Keller regularly ties together related areas like Data mining in his Fuzzy logic studies.
His Pattern recognition study incorporates themes from Object, Synthetic aperture sonar, Image and Feature.
His Cluster analysis research integrates issues from Algorithm, Relational database and Outlier.
His research in Fuzzy classification intersects with topics in Defuzzification and Membership function.
He most often published in these fields:
- Artificial intelligence (66.79%)
- Fuzzy logic (33.58%)
- Computer vision (28.14%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (66.79%)
- Pattern recognition (24.77%)
- Fuzzy logic (33.58%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Fuzzy logic, Cluster analysis and Data mining.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision.
His work deals with themes such as Constant false alarm rate and Hazard, which intersect with Computer vision.
His Image research extends to the thematically linked field of Pattern recognition.
His work on Fuzzy logic is being expanded to include thematically relevant topics such as k-nearest neighbors algorithm.
His Cluster analysis research incorporates elements of Context, Anomaly detection, Outlier and Big data.
Between 2013 and 2021, his most popular works were:
- Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders (44 citations)
- Day or Night Activity Recognition From Video Using Fuzzy Clustering Techniques (41 citations)
- Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
James M. Keller mostly deals with Artificial intelligence, Fuzzy logic, Cluster analysis, Pattern recognition and Data mining.
Artificial intelligence is closely attributed to Computer vision in his study.
His study in Fuzzy logic is interdisciplinary in nature, drawing from both Classifier, Set and Monotonic function.
James M. Keller usually deals with Cluster analysis and limits it to topics linked to Outlier and Parameterized complexity and Context.
The Anomaly detection, Big data and Measure research James M. Keller does as part of his general Data mining study is frequently linked to other disciplines of science, such as Continuous sensing, therefore creating a link between diverse domains of science.
James M. Keller works mostly in the field of Gradient descent, limiting it down to concerns involving Pattern recognition and, occasionally, Algorithm.
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