Visualization of the Machine Learning Process Using J48 Decision Tree for Biometrics through ECG Signal

The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

Can We Predict the Change in Code in a Software Product Line Project?

Software programs are always prone to change for several reasons. In a software product line, the change is more often as many software units are carried from one release to another. Also, other new files are added to the reused files. In this work, we explore the possibility of building a model that can predict files with a high chance of experiencing the change from one release to another. Knowing the files that are likely to face a change is vital because it will help to improve the planning, managing resources, and reducing the cost. This also helps to improve the software process, which should lead to better software quality. Also, we explore how different learners perform in this context, and if the learning improves as the software evolved. Predicting change from a release to the next release was successful using logistic regression, J48, and random forest with accuracy and precision scored between 72% to 100%, recall scored between 74% to 100%, and F-score scored between 80% to 100%. We also found that there was no clear evidence regarding if the prediction performance will ever improve as the project evolved.