Automatic disease diagnosis systems are important for medical fields. These systems have been used to help doctors to make better diagnosis. Breast cancer is a very common class of cancers among women. In this paper, we focus on breast cancer recurrence problem, hybridizing two methodologies, Genetic Algorithm (GA) and Adaptive Neuro Fuzzy Inference System (ANFIS), to develop a good diagnosis s...

This paper proposes a novel design for a parallel nonadaptive binary pattern classifier. The structure employs randompulse (stochastic) computing elements to economically realize multimodal nonlinear discriminant functions similar in form to those used in potential function classifiers. The technique achieves efficient hardware utilization by employing a READ-ONLY memory (ROM) without addressin...

In this paper we describe a new hybrid distributed/shared memory parallel software for support vector machine learning on large data sets. The support vector machine (SVM) method is a well-known and reliable machine learning technique for classification and regression tasks. Based on a recently developed shared memory decomposition algorithm for support vector machine classifier design we incre...

Segmenting sub-cortical structures from 3D brain images is of significant practical importance. This paper presents an experimental study for caudate segmentation in MRI images using a fully automatic algorithm based on [9], which is an extension of the hybrid model approach described in [1]. The method in [9] tackles multiple sub-cortical and cortical structures. In this study (the grand chall...

In this paper, we propose a new design method for the traffic network hybrid feedback controller. In the proposed method, the PWARX classifier describes the nonlinear feedback control law of the traffic control system that the output of the previously developed controller is reproduced applying the 0-1 classifications of the PWARX systems. The proposed method is a hierarchical classification pr...

With increasing number of mobile operators, user is entitled with unlimited freedom to switch from one mobile operator to another if he is not satisfied with service or pricing. This trend is not good for operators as they lose their revenue because of customer switch. To solve it, operators are looking for machine learning tools which can predict well in advance which customer may churn, so th...

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict...

We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popular averaging techniques such as Bayesian classification, Maximum Entropy discrimination, Winnow and Bayes point machines and has implications for learning algorithm design.