An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal. ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are comp...

This paper studies the effects of variable selection and class distribution on the performance of specific logit regression (i.e., a primitive classifier system) and artificial neural network (ANN; a relatively more sophisticated classifier system) implementations in a customer relationship management (CRM) setting. Finally, ensemble models are constructed by combining the predictions of multip...

Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standa...

BACKGROUND Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV. METHODS We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia...

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their EC...

This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significanc...

Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifi...

OBJECTIVES This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. MATERIALS AND METHODS To evaluate effectiveness of proposed feature selection method, we em...

This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm. A dual threshold method is applied to determine the diagnosis strategy and suppress false alarm signals. The experimenta...