This paper presents a systematic evaluation of Neural Network (NN) for classification of real-world data. In the field of machine learning, it is often seen that a single parameter that is ‘predictive accuracy’ is being used for evaluating the performance of a classifier model. However, this parameter might not be considered reliable given a dataset with very high level of skewness. To demonstr...

Intelligent computing methods and knowledge based systems are well known techniques used for the detection of various medical disorders. This paper is based on the review of various intelligent computing methods that are used to detect sleep disorders. The main concern is based on the detection of sleep disorders such as sleep apnea, insomnia, parasomnia and snoring. The most common diagnostic ...

Brain tumours grading is a crucial step for determining treatment planning and patient management. The grade of a tumour is defined by pathologists after reviewing biopsies under the microscope, a procedure that has been proven highly subjective. In this work, we propose a computer-based system for the automatic classification of astrocytomas that can be used as a second opinion tool for the cl...

This paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural networks. The enhanced methodology has been tried out with four filters using 18 data sets that report test error rates about 20 % or above with reference classifiers such as C4.5 or 1-NN. The proposal has also been evaluated in a liver-transplantation real-world problem with seri...

Although neural networks (NNs) have been applied for trac and congestion control in ATM networks, many implementations only consider a single service class with identical trac characteristics and requirements. In addition, the most commonly used NN is the multi-layer perceptron (MLP) networks which are known to converge slowly. We present a multi-service connection admission control (CAC) sch...

The lungs are part of complex apparatus, expanding and relaxing thousands of times each day to bring in oxygen and expel carbon dioxide. Lung disease can results from problems in any part of this system. In this paper, an automated approach for feature selection and classification of the lung diseases using CT images is presented. The lung CT image is engaged as the input. Filters are used to r...

We discuss alternative norms to train Neural Networks (NNs). We focus on the so called Multilayer Perceptrons (MLPs). To achieve this we rely on a Genetic Algorithm called an Eclectic GA (EGA). By using the EGA we avoid the drawbacks of the standard training algorithm in this sort of NNs: the backpropagation algorithm. We define four measures of distance: a) The mean exponential error (MEE), b)...

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the...

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple...

Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with high efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates the use of a predictive model in controller design. Developing a physics based model for HCCI involves significant development times and associated costs arising from devel...