Wavelet Packet Transform-Based Image Classification for Computer-Aided Glaucoma Diagnosis Using Naïve Bayes Classifier

2020 ◽  
pp. 597-605
Author(s):  
Anisha Rebinth ◽  
S. Mohan Kumar
Keyword(s):  
Image Classification ◽  
Naive Bayes ◽  
Wavelet Packet ◽  
Naïve Bayes ◽  
Wavelet Packet Transform ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Glaucoma Diagnosis ◽  
Computer Aided

Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification

2016 ◽  
Author(s):  
Mingtao Wu ◽  
Vir V. Phoha ◽  
Young B. Moon ◽  
Amith K. Belman
Keyword(s):  
Machine Learning ◽  
3D Printing ◽  
Image Classification ◽  
Decision Trees ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Printing Process

3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image from 3D printing process. The images are captured layer by layer from the top view of software simulation preview. The data extracted from images is input to two machine learning algorithms, Naive Bayes Classifier and J48 Decision Trees. The result shows Naive Bayes Classifier has an accuracy of 85.26% and J48 Decision Trees has an accuracy of 95.51% for classification.

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