… While neural networks and other pattern detection methods … of convolutional neural networks
in the recent past. This section covers the advantages of using CNN for image recognition. …

… model for face recognition is difficult. We present a hybrid neural-network solution which …
-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides …

… we proposed a recurrent convolutional neural network (RCNN) for (static) object recognition.
The basic idea was to add recurrent connections within every convolutional layer of the feed…

… In this article two types of convolutional neural networks— neocognitron and NEO—are
examined. The NEO is introduced as a combination of neurons based on the perceptron with the …

… propose a methodology based on efficient Convolution Neural Network (CNN) architecture in
order … and its capability to handle more layers than the previous traditional neural networks. …

… recognition methods according to these strategies. We also discuss the action recognition
… action recognition performance by presenting the recognition accuracy on these datasets. …

… convolutional neural networks (CNNs). We first present a concise description of the basic
CNN and explain how it can be used for speech recognition. … TIMIT phone recognition and the …

… the proposed Convolutional Neural Network (CNN) with three well-known image recognition
… In our experiments, the overall recognition accuracy of the PCA, LBPH, KNN and proposed …

… and a highly effective GPU application of convolution networks to make training faster. We
… discusses how a convolution neural network (CNN) functions in pattern recognition from a …

… in terms of accuracy and speed of processing in image recognition. This paper proposes
a modified Convolutional Neural Network (CNN) architecture by adding two normalization …