We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the features and allows an automated exploration of the features combination analogous t...

Presented paper deals with images of nanotubes that provide a new way of a surface bioactivation of dental titanium implants. The evaluation of selected material parameters forms an important part of material quality assessment. The first step is an image segmentation and object detection. In the following step, object classification is a crucial point in separation object. Various methods coul...

This paper presents an approach to the well-known Traveling Salesman Problem (TSP) via competitive neural networks. The neural network model adopted in this work is the Kohonen Network or Self-Organizing Maps (SOM), which has important topological information about its neurons configuration. This paper is concerned with the initialization aspects, parameters adaptation, and the complexity analy...

In this paper, a new recurrent neural network is proposed for solving convex quadratic programming (QP) problems. Compared with existing neural networks, the proposed one features global convergence property under weak conditions, low structural complexity, and no calculation of matrix inverse. It serves as a competitive alternative in the neural network family for solving linear or quadratic p...

estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...

To improve the accuracy of remote sensing image classification based on a self-organizing competitive neural network, this paper firstly uses principal component analysis to reduce redundancy of the multi-spectral remote sensing image data, and then takes the earth surface structure information in horizontal and vertical directions of the target area as a prior knowledge. The self-organizing co...

In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. Th...