Image recognition tasks that involve identifying parts of an object or the contents of a vessel can be viewed as a hierarchical problem, which can be solved by initial recognition of the main object, followed by recognition of its parts or contents. To achieve such modular recognition, it is necessary to use the output of one recognition method (which identifies the general object) as the input...

This paper outlines the exploration of two methods to detect texture in a digital cryosection image from the Visible Human Project. For the purpose of this research, texture is defined as a regular or irregular placement of color in an image. A higher-level decision-making algorithm was employed to extract different body tissues: fat, muscle, and bone. This algorithm was designed on the premise...

In this paper, we present a novel neural network architecture called M-net, which exploits the don't-care information in training multilayer feedforward neural networks. Our method takes advantage of the user's prior knowledge as well as the neural network's ability to learn from examples. The user's prior knowledge is encoded in the form of don't-care inputs to reduce the number of training pa...

Adapting the profound, deep convolutional neural network models for large image classification can result in layout of architectures with a number learnable parameters and tuning those varied considerably grow complexity model. To address this problem Deep-Net Model based on extraction random patches enforcing depth-wise convolutions is proposed training widely known benchmark Breast Cancer his...

Petri net faulty models are useful for reliability analysis and fault diagnosis of discrete event systems. Such models are difficult to work out as long as they must be computed according to alarm propagation. This paper deals with Petri net models synthesis and identification based on neural network approaches, with regard to event propagation and to state propagation dataset. A learning neura...

This article presents the development of a neural network cognitive model for the classification and detection of different frequency signals. The basic structure of the implemented neural network was inspired on the perception process that humans generally make in order to visually distinguish between high and low frequency signals. It is based on the dynamic neural network concept, with delay...