Frans Coenen focuses on Data mining, Association rule learning, Artificial intelligence, Contextual image classification and Pattern recognition. His work carried out in the field of Data mining brings together such families of science as Pattern recognition, Graph, Data structure and Graph. His Association rule learning research includes elements of Tree, Training set and Knowledge extraction.
His Artificial intelligence research includes themes of Machine learning and Collision avoidance. In his study, Fundus, Computer vision and Retinal is strongly linked to Retina, which falls under the umbrella field of Contextual image classification. His Pattern recognition study combines topics in areas such as Subspace topology, Local binary patterns and Rejection rate.
Frans Coenen mainly investigates Artificial intelligence, Data mining, Association rule learning, Pattern recognition and Context. His Artificial intelligence research focuses on Natural language processing and how it relates to Task. His work in the fields of Data mining, such as Identification, overlaps with other areas such as Cattle movement.
His Association rule learning research incorporates themes from Tree, Data structure and Data science. The Pattern recognition study combines topics in areas such as Contextual image classification, Graph and Feature. His study brings together the fields of Deep learning and Convolutional neural network.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Information retrieval, Data mining and Convolutional neural network. A large part of his Artificial intelligence studies is devoted to Deep learning. His Pattern recognition study incorporates themes from Relationship extraction, Time series classification, Time series and Phonocardiogram.
As a part of the same scientific family, Frans Coenen mostly works in the field of Information retrieval, focusing on Supervised learning and, on occasion, Relation and Knowledge base. His Data mining research integrates issues from Context, Representation, Encryption and Cluster analysis. He works mostly in the field of Convolutional neural network, limiting it down to concerns involving Feature extraction and, occasionally, Feature.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Convolutional neural network, Sentence and Context. The concepts of his Artificial intelligence study are interwoven with issues in Computer vision and Natural language processing. Frans Coenen interconnects Fitness function, Swarm behaviour, Particle swarm optimization, DBSCAN and Optimization problem in the investigation of issues within Pattern recognition.
His research in Convolutional neural network intersects with topics in Convolution and Fundus. His Feature course of study focuses on Selection and Data mining. His Data mining research is multidisciplinary, incorporating perspectives in Homomorphic encryption and Encryption.
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Convolutional Neural Networks for Diabetic Retinopathy
Harry Pratt;Frans Coenen;Deborah M. Broadbent;Simon P. Harding;Simon P. Harding.
Procedia Computer Science (2016)
A survey of frequent subgraph mining algorithms
Chuntao Jiang;Frans Coenen;Michele Zito.
Knowledge Engineering Review (2013)
Isomorphism and legal knowledge based systems
T. J. Bench-Capon;F. P. Coenen.
Artificial Intelligence and Law (1992)
Data structure for association rule mining: T-trees and P-trees
F. Coenen;P. Leng;S. Ahmed.
IEEE Transactions on Knowledge and Data Engineering (2004)
Tree Structures for Mining Association Rules
Frans Coenen;Graham Goulbourne;Paul Leng.
Data Mining and Knowledge Discovery (2004)
Text classification using graph mining-based feature extraction
Chuntao Jiang;Frans Coenen;Robert Sanderson;Michele Zito.
Knowledge Based Systems (2010)
A new method for mining Frequent Weighted Itemsets based on WIT-trees
Bay Vo;Frans Coenen;Bac Le.
Expert Systems With Applications (2013)
Driving posture recognition by convolutional neural networks
Chao Yan;Frans Coenen;Bailing Zhang.
Iet Computer Vision (2016)
One-class kernel subspace ensemble for medical image classification
Yungang Zhang;Yungang Zhang;Bailing Zhang;Frans Coenen;Jimin Xiao.
EURASIP Journal on Advances in Signal Processing (2014)
Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles
Yungang Zhang;Bailing Zhang;Frans Coenen;Wenjin Lu.
machine vision applications (2013)
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