This paper presents some connectionist models that are widely used to solve NP-problems. Most well known numeric models are Neural Networks that are able to approximate any function or classify any pattern set provided numeric information is injected into the net. Neural Nets usually have a supervised or unsupervised learning stage in order to perform desired response. Concerning symbolic infor...

3D shape models that directly classify objects from 3D information have become more widely implementable. Current state of the art models rely on deep convolutional and inception models that are resource intensive. Residual neural networks have been demonstrated to be easier to optimize and do not suffer from vanishing/exploding gradients observed in deep networks. Here we implement a residual ...

Time series data mining (TSDM) techniques explores large amount of time series data in search of interesting relationships among variables. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis by adapting data mining concepts for analyzing time series data. The Feed Forward Neural Net is one of the most widely used neural ne...

The automated segmentation of brain tumors is crucial for various clinical purposes from diagnosis to treatment planning follow-up evaluations. vast majority effective models tumor are based on convolutional neural networks with millions parameters being trained. Such complex can be highly prone overfitting especially in cases where the amount training data insufficient. In this work, we devise...

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuáis to get optimal solutions. Such co-operative model differs from competitive models in the way that individuáis die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorit...

| ProjectH, a large group of international researchers, produced a huge amount of data from computer mediated discussions. The data classi ed several thousand postings frommore than thirty newsgroups. One approach to extract typical messages from this database is presented in this paper. An autoassociative neural network was trained on 3000 coded messages and then used to construct typical mess...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

Performance of neural-networks learning is known to be sensitive to the initial weight setting and architecture|number of hidden layers and neurons in these layers. This shortcoming can be alleviated if some approximation of the target concept in terms of a logical description is available. The paper reports a successful attempt to initialize neural networks by decision-tree generators. The sys...

The main problem in constructing fuzzy systems consists on finding an initial structure for it. The structure of fuzzy systems is composed of the number of fuzzy sets partitioning each variable and their distribution in the universe of discourse. This paper proposes the use of the autonomous-mountain clustering method to identify this structure for applications in fuzzy systems modeling. To inv...

Robotic manipulation with a nontrivial object providing various types of grasping points is an industrial interest. Here, efficient method simultaneous detection the proposed. Specifically, two different 3 degree-of-freedom end effectors are considered for grasping. The utilizes RGB data-driven perception system based on specifically designed fully convolutional neural network called attention ...