What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Cluster analysis, Data mining, Pattern recognition and Machine learning.
His Artificial intelligence research incorporates elements of Computer vision and Natural language processing.
His biological study deals with issues like Algorithm, which deal with fields such as Data set, Consensus clustering and Control theory.
Mohamed S. Kamel has included themes like Wavelet, Wavelet transform and Radial basis function in his Data mining study.
The concepts of his Pattern recognition study are interwoven with issues in Pixel, Feature, Speech processing and Biometrics.
He combines subjects such as Variety and Pattern recognition with his study of Machine learning.
His most cited work include:
- Survey on speech emotion recognition: Features, classification schemes, and databases (1074 citations)
- Cost-sensitive boosting for classification of imbalanced data (919 citations)
- CLASSIFICATION OF IMBALANCED DATA: A REVIEW (683 citations)
What are the main themes of his work throughout his whole career to date?
Mohamed S. Kamel mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Data mining and Cluster analysis.
The Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing.
His Pattern recognition study frequently draws connections to adjacent fields such as Feature.
He combines topics linked to Training set with his work on Machine learning.
Data mining and Fuzzy logic are commonly linked in his work.
His study in Correlation clustering, Fuzzy clustering, CURE data clustering algorithm, Document clustering and Clustering high-dimensional data is done as part of Cluster analysis.
He most often published in these fields:
- Artificial intelligence (61.47%)
- Pattern recognition (26.32%)
- Machine learning (17.11%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (61.47%)
- Pattern recognition (26.32%)
- Machine learning (17.11%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Machine learning, Data mining and Cluster analysis are his primary areas of study.
His Artificial intelligence research integrates issues from Particle swarm optimization and Computer vision.
His study focuses on the intersection of Pattern recognition and fields such as Feature with connections in the field of Feature selection.
His study in the fields of Bayesian network under the domain of Machine learning overlaps with other disciplines such as Novelty detection.
His work deals with themes such as DBSCAN, Data modeling, Random projection and Euclidean distance, which intersect with Data mining.
He works mostly in the field of Dimensionality reduction, limiting it down to topics relating to Principal component analysis and, in certain cases, Speech recognition.
Between 2011 and 2021, his most popular works were:
- Efficient greedy feature selection for unsupervised learning (63 citations)
- Kernelized Supervised Dictionary Learning (58 citations)
- Vehicle localization in VANETs using data fusion and V2V communication (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Mohamed S. Kamel mainly investigates Artificial intelligence, Pattern recognition, Sensor fusion, Data mining and Machine learning.
His studies in Sparse approximation, K-SVD, Facial recognition system, Supervised learning and Kernel are all subfields of Artificial intelligence research.
His Pattern recognition study integrates concerns from other disciplines, such as Feature, Speech recognition and Computer vision.
His study in Sensor fusion is interdisciplinary in nature, drawing from both Computer security, Real-time computing, Simulation and Service.
His research integrates issues of Random projection, Data point, Greedy algorithm and Cluster analysis in his study of Data mining.
His work carried out in the field of Machine learning brings together such families of science as Threat assessment, Data integration, Inference and Exploit.
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