An accurate fault detection, classification and direction estimation of double end fed transmission lines based on application of artificial neural networks is presented in this paper. The proposed method uses the phase voltage and current available at only the local end of line. This method is adaptive to the variation of fault inception angle, fault location and high fault resistance. The alg...

Between the various biometric methods, Face Recognition has become one of the most burning topic tasks in the pattern recognition field during the past decades. In This Work a Face Recognition System has been developed By applying different multiple classifier selection schemes on the output of three different classification methods namely Artificial Neural Network, Genetic Algorithm And Euclid...

The study aims to develop a neural network classification model predict machining failures during wire electric discharge machining. Also, process control algorithm retunes the parameters based on remaining useful time before failure. In proposed methodology, an artificial (ANN) classifier receives four in-process characteristics as input. These extracted features are energy, spark frequency, o...

A principal direction linear oracle (PDLO) ensemble classifier for DNA microarray gene expression data is proposed. The common fusion-selection ensemble based on weighted trust for a specifier classifier was replaced with pairs of subclassifiers of the same type using PDLO to perform a linear hyperplane split of training and testing samples. The hyperplane split forming the oracle was based on ...

In this paper we present a neural network based spectrum classifier (NSC) and its application to ultrasonic resonance spectroscopy. The use of an Artificial Neural Network (ANN) is proposed to meet the requirements of high sensitivity for small but relevant changes in the spectra, and simultaneous robustness against measurement noise. Provided with enough training examples, the ANNs are known t...

Background. Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs...

Brain-computer interface (BCI) has extensively been used for rehabilitation purposes. Being in the research phase, brainwave based wheelchair controlled systems suffer from several limitations, e.g., lack of focus on mental activity, complexity neural behavior different conditions, and lower accuracy. sensitive to color stimuli, EEG signal changes promises a better detection. Utilizing Electroe...

Web attackers aim to propagate malicious links using various techniques deceive users. They attempt control victims’ devices or obtain their passwords remotely, thereby acquiring access bank accounts, financial transactions, private and sensitive information they trade via the Internet. QR codes are accessible, free, easy use, can be scanned through several free apps on smartphones. As there is...

MESSAGEPAD and EMATE. Combining an artificial neural network (ANN) as a character classifier with a context-driven search over segmentation and word-recognition hypotheses provides an effective recognition system. Long-standing issues relative to training, generalization, segmentation, models of context, probabilistic formalisms, and so on, need to be resolved, however, to achieve excellent per...

This paper extends the line of research that considers the application of Artificial Neural Networks (ANNs) as an automated system, for the assignment of tumors grade. One hundred twenty nine cases were classified according to the WHO grading system by experienced pathologists in three classes: Grade I, Grade II and Grade III. 36 morphological and textural, cell nuclei features represented each...