Lung cancer is one of the most injurious forms of cancer, which is the leading cause of cancer death in many regions of the world. Early detection of lung nodule is essential in reducing life victim. Detection of lung cancer at early stage is not an easy task. Survival rates in lung cancer vary significantly by stage; overall, less than 15% of newly diagnosed patients will survive for 5 years. ...

This paper considers hybrid classification architectures and shows their feasibility on large data bases consisting of facial images. Our architecture, consists of an ensemble of connectionist networks radial basis functions (RBF) and decision trees (DT). This architecture enjoys robustness via (i) consensus provided by ensembles of RBF networks, and (ii) categorical classification using decisi...

A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting fea...

In this paper we describe an approach for spoken language analysis for helpdesk call routing using a combination of simple recurrent networks and support vector machines. In particular we examine this approach for its potential in a difficult spoken language classification task based on recorded operator assistance telephone utterances. We explore simple recurrent networks and support vector ma...

Classifier systems are now viewed disappointing because of their problems such as the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have developed a hybrid classifier system: GLS (Generalization Learning System). In designing GLS, we view CSs as model free learning in POMDPs and take a hybrid approach to finding the best gene...

This paper presents the results obtained when classifying a group of artificial and real world data, using a Hybrid Classifier with Genetic Weighting (HCGW). The algorithm proposed is an ensemble based system, it combines several types of classifiers: Naive Bayes, K-Means, k-Nearest Neighbours, C4.5, Decision Tables and ADTree, using a voting criterion for weighted majority to combine the indiv...

Due to the rapid increase in terrorist activities throughout the world, there is serious intention required to deal with such activities. There must be a mechanism that can predict what kind of “attack types” can happen in future and important measures can be taken out accordingly. In this paper, a hybrid classifier is proposed which consists of some existing classifiers including K Nearest Nei...

This paper introduces a hybrid model of the neuro-fuzzy classifier with an integrated prediction of pilots’ mistakes. Experiments and studies of the network were conducted on real and test samples.The upgraded hybrid neuro-fuzzy classifier structure and the learning algorithm can solve the problem of the need for multiple individual performance measurements, the dynamics of which would make it ...

The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a c...