What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Fakhri Karray spends much of his time researching Artificial intelligence, Fuzzy logic, Computer vision, Fuzzy control system and Control system.
The study incorporates disciplines such as Machine learning, Pattern recognition, Speech recognition and Natural language processing in addition to Artificial intelligence.
His Natural language processing study incorporates themes from Emotion recognition, Emotion classification and Information retrieval.
His Fuzzy logic research includes themes of Stochastic programming and Stochastic optimization.
His work in the fields of Computer vision, such as Thresholding, overlaps with other areas such as Microaneurysm.
Fakhri Karray works mostly in the field of Fuzzy control system, limiting it down to topics relating to Intelligent control and, in certain cases, PID controller, Gain scheduling and Industrial control system, as a part of the same area of interest.
His most cited work include:
- Survey on speech emotion recognition: Features, classification schemes, and databases (1074 citations)
- Retinal vessel extraction by matched filter with first-order derivative of Gaussian (366 citations)
- Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs (347 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Fuzzy logic, Machine learning and Data mining.
Fakhri Karray has included themes like Speech recognition, Computer vision and Natural language processing in his Artificial intelligence study.
His research in Natural language processing intersects with topics in Semantics and Information retrieval.
Fakhri Karray interconnects Subspace topology and Embedding in the investigation of issues within Pattern recognition.
His Data mining research is multidisciplinary, incorporating perspectives in CURE data clustering algorithm and Cluster analysis.
Fakhri Karray works mostly in the field of Fuzzy control system, limiting it down to topics relating to Intelligent control and, in certain cases, Control system and Control theory.
He most often published in these fields:
- Artificial intelligence (48.97%)
- Pattern recognition (19.76%)
- Fuzzy logic (13.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (48.97%)
- Pattern recognition (19.76%)
- Deep learning (7.37%)
In recent papers he was focusing on the following fields of study:
Fakhri Karray mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Subspace topology and Embedding.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Mean squared error and Machine learning.
Fakhri Karray combines subjects such as Contextual image classification, Population decline and Creative visualization with his study of Machine learning.
His research investigates the connection with Pattern recognition and areas like MNIST database which intersect with concerns in Metric, Discrete cosine transform, Face and Information theory.
The concepts of his Deep learning study are interwoven with issues in Recurrent neural network and Statistics, Missing data, Imputation, Time series.
His work carried out in the field of Embedding brings together such families of science as Sampling, Manifold, Data mining and Dimensionality reduction.
Between 2018 and 2021, his most popular works were:
- Eigenvalue and Generalized Eigenvalue Problems: Tutorial (30 citations)
- Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review. (22 citations)
- Fisher and Kernel Fisher Discriminant Analysis: Tutorial (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Pattern recognition, Deep learning, Linear discriminant analysis and Kernel are his primary areas of study.
His study deals with a combination of Artificial intelligence and Pandemic.
His Pattern recognition research is multidisciplinary, relying on both Subspace topology, Autoencoder and Metric.
He has researched Deep learning in several fields, including Field, Recurrent neural network and Categorization.
His research in Linear discriminant analysis tackles topics such as Principal component analysis which are related to areas like Optimization problem, Kernel principal component analysis and Kernel method.
His biological study spans a wide range of topics, including Pattern analysis, Process, Feature selection and Cluster analysis.
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