background and objectives: accurate detection of type and severity of hepatitis is crucial for effective treatment of the disease. while several computational algorithms for detection of hepatitis have been proposed to date, their limited performance leaves room for further improvement. this paper proposes a novel computational method for the diagnosis of hepatitis b using pattern detection tec...

The paper deals with a recently proposed approach to combining classifiers by means of fuzzy aggregation. The approach relies on the quasi-Sugeno integral and on the t-conorm integral as a generalization of the Choquet and Sugeno integral, which have been used for combining classifiers so far. New theoretical development is presented, in particular a proposition concerning the λ measures used i...

This text studies glass bottle intelligent inspector based machine vision instead of manual inspection. The system structure is illustrated in detail in this paper. The text presents the method based on watershed transform methods to segment the possible defective regions and extract features of bottle wall by rules. Then wavelet transform are used to exact features of bottle finish from images...

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

This paper describes a novel nonlinear modeling approach by on-line clustering, fuzzy rules and support vector machine. Structure identification is realized by an on-line clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upper bounds of t...

In one-class classification, the problem is to distinguish one class of data from the rest of the feature space. It is important in many applications where one of the classes is characterized well, while no measurements are available for the other class. Schölkopf et al. first introduced a method of adapting the support vector machine (SVM) methodology to the one-class classification problem, c...

In medical applications such as recognizing the type of a tumor as Malignant or Benign, a wrong diagnosis can be devastating. Methods like Fuzzy Support Vector Machines (FSVM) try to reduce the effect of misplaced training points by assigning a lower weight to the outliers. However, there are still uncertain points which are similar to both classes and assigning a class by the given information...

At first, support vector machines (SVMs) were applied to solve binary classification problems. They can also be extended to solve multicategory problems by the combination of binary SVM classifiers. In this paper, we propose a new fuzzy model that includes the advantages of several previously published methods solving their drawbacks. For each datum, a class is rejected using information provid...