Image classification is the important part of remote sensing, image analysis and pattern recognition. Digital Image Classification is the process of sorting all the pixels in an image into a finite number of individual classes. Landuse/Landcover classification of satellite images is an important activity for extracting geospatial information for military & civil purposes like inaccessible areas...

Soft corals are common marine organisms that inhabit tropical and subtropical oceans. They are shown to be rich source of secondary metabolites with biological activities. In this work, soft corals from two geographical locations were investigated using ¹H-NMR spectroscopy coupled with multivariate statistical analysis at the metabolic level. A partial least-squares discriminant analysis showed...

The extended abstract describes an onset detection algorithm that is based on a classification of spectral peaks into transient and non-transient peaks and a statistical model of the classification results to prevent detection of random transient peaks due to noise. Compared to the version used for MIREX 2010 the algorithm presented here differs with respect to the treatment of soft harmoinc on...

As a neural approach, Kohonen's Self-Organizing Map (SOM) has not been explored as thoroughly as the MLP, especially for the soft classification. In this paper, we propose two non-parametric algorithms for the SOM to provide soft classification outputs. These algorithms, which are labeling-frequency-based and are called SOM Commitment (SOMC) and SOM Typicality (SOM-T), expressing in the first c...

Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-d...

The authors analyze in this paper whether the introduction of the uncertainty associated to the classification of surface elements in the classification of landscape units can improve the results accuracy. To this end, a hybrid classification method is developed, incorporating uncertainty information in the automatic classification of very high spatial resolution multispectral satellite images ...

the concept of fuzzy soft γ-ring is introduced; and some properties of fuzzy soft γ-rings are given. then the definitions of fuzzy soft γ-ideals are proposed and some of their theories are considered.

AIM Recurrence of giant cell tumor of bone (GCTB) in the soft tissue is rarely seen in the clinical practice. This study aims to determine the prevalence of soft tissue recurrence of GCTB, and to characterize its radiographic features. METHODS A total of 291 patients treated by intralesional curettage for histologically diagnosed GCTB were reviewed. 6 patients were identified to have the recu...

Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, decision trees. In this paper, we offer new algorithm called fuzzy parameterized soft aggregation classifier (FPFS-AC) combine mod...

Post Operative patient dataset is a real world problem obtained from the UCI KDD archive which is used for our classification problem. In this paper different classification techniques such as Bayesian Classification, classification by Decision Tree Induction of data mining and also classification techniques related to fuzzy concepts of soft computing is used for implementation of our dataset. ...