Fully convolutional neural networks (CNNs) can process input of arbitrary size by applying a combination downsampling and pooling. However, we find that fully image classifiers are not agnostic to the but rather show significant differences in performance: presenting same at different scales result outcomes. A closer look reveals there is no simple relationship between model performance (no ‘bi...

Statistical and machine learning methods have become paramount in order to handle large size claims data as part of health care fraud detection frameworks. Among these, predictive such regression classification algorithms are widely used with labeled data. However, the imbalanced nature skewness distributions result challenges practical applications. This paper presents use various pre-processi...

Two neural classifiers were developed for image recognition: PCNC (Permutation Coding Neural Classifier) and LIRA (Limited Receptive Area) neural classifiers. These neural classifiers are multipurpose neural classifiers. We applied them in micromechanics. Information about shape and texture of the micro workpiece can be used to improve precision of both assembly and manufacturing processes. The...

Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting software fault-prone modules, one of the contributing features is software metric by which one ...

Two neural classifiers were developed for image recognition: PCNC (Permutation Coding Neural Classifier) and LIRA (Limited Receptive Area) neural classifiers. These neural classifiers are multipurpose neural classifiers. We applied them in micromechanics. Information about shape and texture of the micro workpiece can be used to improve precision of both assembly and manufacturing processes. The...