Most of the traditional approaches for medical image storage are least capable and scanning relevant matching images quite difficult. The existing content-based retrieval (C-BIR) less focused with images. available research works fuzzy logic very not efficient retrieval. Thus, there is a need work that can address both supervised unsupervised learning Hence, C-BIR technique evolved overcoming a...

Here, we develop two quantum-computational schemes for supervised and unsupervised classification tasks in a quantum world by employing the information-geometric tools of fidelity search algorithm. Presuming that pure states set given systems (or objects) belong to one known classes, objective here is decide which these classes each system belongs—without knowing its state. The binary algorithm...

A new multispectral image classification method is presented. The method is based on dividing the Near Infrared “NIR” and Visible Red “VR” scatterplot diagram into regions corresponding to their reflectance values. The best line discriminating the Soil’s components from the vegetated area is recognized by utilizing the least square fitting criterion. The vegetate line which differentiate the fu...

We introduce a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes as input a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a simple text classification algorithm to clas...

in land use planning, mapping the present land use / land cover situation is a necessary tool for determining the current condition and for identifying land use trends. in this study, in order to provide a land use/ land cover map for ilam watershed, the irs-1c image data from 25th april 2006 were used. initial qualitative evaluation on data showed no significant radiometric error. ortho-rectif...

Remote sensing has become an important source of urban land-use/cover classification, and as a result of their high spatial and spectral resolution, airborne hyperspectral images have been widely used to distinguish different urban classes. However, the previous studies into the classification of urban environments have mainly focused on a supervised scenario, which is limited by the selection ...

This paper addresses a classification problem in which class definition through training samples or otherwise is provided a priori only for a particular class of interest. Considerable time and effort may be required to label samples necessary for defining all the classes existent in a given data set by collecting ground truth or by other means. Thus, this problem is very important in practice,...

Artificial neural networks (ANNs), computer paradigms that can learn, excel in pattern recognition tasks such as disease diagnosis. Artificial neural networks operate in two different learning modes: supervised, in which a known diagnostic outcome is presented to the ANN, and unsupervised, in which the diagnostic outcome is not presented. A supervised learning ANN could emulate human expert dia...

Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was train...

Semi-supervised classification uses a large amount of unlabeled data to help a little amount of labeled data for designing classifiers, which has good potential and performance when the labeled data are difficult to obtain. This paper mainly discusses semi-supervised classification based on CPN (Counterpropagation Network). CPN and its revised models have merits such as simple structure, fast t...