Convolutional Neural Network (CNN) image classifiers are traditionally designed to have sequential convolutional layers with a single output layer. This is based on the assumption that all target classes should be treated equally and exclusively. However, some classes can be more difficult to distinguish than others, and classes may be organized in a hierarchy of categories. At the same time, a...

This paper presents a novel fully automated process of features extraction and classification of Multiple sclerosis (MS) daisies from magnetic resonance images (MRI). This hybrid method uses convolution neural network (CNN) for features extraction and a multilayer neural network for classification two classes normal and MS. The convolution neural network for recognition of Multiple sclerosis is...

While recurrent neural network language models based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks, Convolutional Neural Network (CNN) language models are relatively new and have not been studied in-depth. In this paper we present an empirical comparison of LSTM and CNN language models on English broadcast news and various conversational telep...

The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms. As CNN is attributed to the strong endurance to computation errors, employing block floating point (BFP) arithmetics in CNN accelerators could save the hardware cost and data traffics efficiently, while maintaining the classification accuracy. In this paper, w...

Since its introduction to the research community in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm has become a fruitful soil for engineers and physicists, producing over 1,000 published scientific papers and books in less than 20 years (Chua & Roska, 2002), mostly related to Digital Image Processing (DIP). This Artificial Neural Network (ANN) offers a remarkable ability o...

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and large public databases. In this work, we model a deep learning architecture that can be effectively apply to recognizing Arabic handwritten characters. A Convolutional Neural Network (CNN) is a special type of feed-forward multilayer trained in supervised mode. Th...

Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need ...

In this study we propose a Convolutional Neural Network(CNN) which can classify hand drawn sketch images. Though CNN is known to be very effective on classification of realistic images, there are few studies on CNN dealing with nonphotorealistic images and even more images those types are mixing. Classifying non-photorealistic images is difficult mainly because there are no large datasets. In t...

Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization [8, 10, 13]. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers. To invert the...

In this research work, we first discuss the properties of the 3-cells CNN attractor discovered by Arena et al. (1998). Recent research has shown the importance of biological control in many biological systems appearing in nature. In computer science, machine learning and biology, cellular neural networks (CNN) are a parallel computing paradigm, similar to neural networks with the difference tha...