Ž . A hybrid expert system prototype using artificial neural networks ANN and classical rules has been developed for predicting toxicology of compounds. Modularity was a must for the architecture of the system. The study of chemicals was approached by establishing classes. When appropriate descriptors are calculated for the molecule, the ANN classifier assigns the chemical class to the compound...

Microarrays, which allow for the measurement of thousands of gene expression levels in parallel, have created a wealth of data not previously available to biologists along with new computational challenges. Microarray studies are characterized by a low sample number and a large feature space with many features irrelevant to the problem being studied. This makes feature selection a necessary pre...

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural net...

Support vector machine (SVM) and artificial neural network (ANN) systems were applied to a drug/nondrug classification problem as an example of binary decision problems in early-phase virtual compound filtering and screening. The results indicate that solutions obtained by SVM training seem to be more robust with a smaller standard error compared to ANN training. Generally, the SVM classifier y...

Electrophysiological recordings are considered a reliable method of assessing a person’s alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. This paper deals with a novel method of analysis ...

Computer and network security has received and will still receive much attention. Any unexpected intrusion will damage the network. It is therefore imperative to detect the network intrusion to ensure the normal operation of the internet. There are many studies in the intrusion detection and intrusion patter recognition. The artificial neural network (ANN) has proven to be powerful for the intr...

Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting dependability, availability, necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) is discussed this work. Tools feature extraction selection classification are applied developed approach to monitor GCPV system under...

This retrospective and exploratory study investigated the efficiency of 3T perfusion magnetic resonance imaging (MRI) at classification MS lesion subtypes. For subtype classification, firstly, it was necessary to segment all lesions. Therefore, a Bayesian classifier based on adaptive mixture method used lesions, an artificial neural network (ANN) employed multi-layer Perceptron as classifier. T...

Road sign detection and recognition is an integral part of intelligent transportation systems. It increases protection by reminding the driver current condition route, such as notices, bans, limitations, other valuable driving information. This paper describes a novel system for automatic road signs, which achieved in two main steps. First, initial image pre-processed using DBSCAN clustering al...