The proposed technique was tested on acoustic data from industrial IC engine, with three different fault classes and one healthy class.
The proposed technique was tested on the industrial IC engine data sets, with three different fault classes and one healthy class.
These reduced dimensionality data vectors are used by softmax regression-based classifier for classification of the engine into faulty and healthy class.
In this table, the sensitivity and specificity for seizure class are 98 and 100%%, whereas these performances for healthy class are 80.0 and 85.0 % and for seizure-free class are 70.0 and 89.0 %, respectively.
Each set of A and B consists of 100 single channel EEG signals, thus the size of feature vector for the healthy class is 100 × 11 in the OAT schemes.
Categorization of the data into a healthy class and two classes of disorders, namely diffuse and nodular mass lesions of vocal folds is the task pursued in this work.
Three-fourths (76%) of Rhode Island adults are in the "healthy" class, while about one-fourth is in the "unhealthy" classes.
However, social class was still important for sleep as an interaction was found whereby those in a manual social class with high baseline GHQ scores were especially likely to be in the Chronic Mixed class (rather than the Healthy class).
In reference to the Healthy class, those from manual social classes were more likely to be in any of the symptomatic sleep classes than those from non-manual occupations, though this relationship was weak (p = 0.097) for the Episodic Maintenance class, and strongest for the Chronic Mixed class.
Class 1 grouped the cows with a value of SCC lower than or equal to 200,000 cells/ml of milk (healthy), Class 3, grouped the cows with a value of SCC higher than or equal to 400,000 cells/ml of milk and Class 2 grouped the cows with intermediate value of SCC (200,000 < cell/ml of milk < 400,000).
