This paper proposes an automatic computer aided diagnostic system (CAD) for detection of liver diseases like hepatoma and hemangioma from Abdominal Computed tomography (CT) images. Liver and Lesion is segmented using gray level methods and clustering. Histogram analyzer is used to fix the threshold and morphological operation is used for post processing. Rules are applied to remove the obstacle...

This paper provides a comparative study on the different techniques of classifying human activities that are performed using bodyworn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector ...

Live fish recognition in the open sea is a challenging multiclass classification task. We propose a novel method to recognize fish in an unrestricted natural environment recorded by underwater cameras. This method extracts 66 types of features, which are a combination of color, shape and texture properties from different parts of the fish and reduce the feature dimensions with forward sequentia...

The problem of feature selection for Support Vector Machines (SVMs) classification is investigated in the linear two classes case. We suggest a new method of feature selection based on ranking scores derived from SVMs. We analyze the retraining effects on the ranking rules based on these scores. Our features selection algorithm consists in a forward selection strategy according to the decreasin...

This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI). We state and study a novel saddle point (max-min) optimization problem to build a scoring function that is able to identify joint interactions between several features. Th...

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents thr...

considering the importance of cd and u as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the eshtehard region in iran by means of a developed artificial neural network (ann) model. the forward selection (fs) method is used to select the input variables and develop hybrid models by ann. from 45 input candidates, 13 and 14 ...

seismic facies analysis (sfa) aims to classify similar seismic traces based on amplitude, phase,frequency, and other seismic attributes. sfa has proven useful in interpreting seismic data, allowingsignificant information on subsurface geological structures to be extracted. while facies analysis hasbeen widely investigated through unsupervised-classification-based studies, there are few casesass...

abstract— due to the daily mass production and the widespread variation of medical x-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. in this paper, a medical x-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is prop...

This paper presents a new power quality event classification technique using wavelet transform and logistic model tree. The proposed method uses the samples of three cycle duration of three line voltage of power quality events. The features of these samples are obtained by using the wavelet transform and a few different feature extraction techniques. The sequential forward selection method base...