In this paper, a neuro-fuzzy algorithm has been implemented to improve the path planning of a mobile robot based on modification of vector field histogram (VFH) approach using neuro-fuzzy algorithm. A back propagation neural network has been used for detection of unknown obstacle to avoid collision. A neural network model is used to learn (off line) many critical status of obstacle topology. Fu...

In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based m...

A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering...

This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and prun...

A novel fuzzy rough granular neural network (NFRGNN) based on the multilayer perceptron using backpropagation algorithm is described for fuzzy classification of patterns. We provide a development strategy of knowledge extraction from data using fuzzy rough set theoretic techniques. Extracted knowledge is then encoded into the network in the form of initial weights. The granular input vector is ...

Revealing the failure of agile software projects is a great challenge faced by companies. This paper focuses on using intelligent techniques such as fuzzy logic, multiple linear regressions, support vector machine, neural network to address this challenge. also presents review some works related area interest. In paper, researchers propose an approach for revealing based two techniques: logic a...

Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patient’s life. In this survey research article we aim to portray various data mining algorithms, soft computing techniques, machine learning algorithms and bio-inspired algorithms for predicting / classifying heart disease. Several ...

Decision support systems are currently achieving higher classification accuracies by using more complex reasoning mechanisms. Examples of such mechanisms include support vector machines and neural networks. However in spite of these increases in accuracy many decision support systems are not accepted by users. In domains where there is a high cost associated with incorrect classifications, such...

Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman propo...

The aim of research is to model the dependency of enterprises on their financial ratios for predicting bankruptcy using artificial neural networks combined with fuzzy logic. A sample of companies which are financially distressed but not yet bankrupt is considered for conducting experiments. The data extracted from financial reports of financially distressed companies for past five year forms th...