Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised fraud detection has long been research focus, unsupervised rarely studied this context, and there remains insufficient evidence to guide the choice between these branches of detection. Accordingly, study evaluates using proprietary claim data. Furthermore, we conduct...

Diabetes mellitus is also called gestational diabetes when a woman has high blood sugar while pregnant. It can show up at any time during pregnancy and cause problems for the mother baby or after pregnancy. If risks are found dealt with as soon possible, there chance that they be reduced. The healthcare system one of many parts our daily lives being rethought thanks to creation intelligent syst...

Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community. In real-world applications such as medical diagnosis industrial defect detection, anomalies only present a fraction of images. To extend reconstruction-based architecture to localized anomalies, we propose self-supervised approach through random masking then ...

While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy. In this paper we present a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement ...

The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training points (x alone). For the labeled points, supervised learning techniques apply, but they cannot take advantage of the unlabeled points. On the other hand, unsupervised techniques can model the unlabeled data distribution, but do not exploit the labels. Thus, this task falls between traditional s...

Unsupervised neural learning is typically employed in dimensionality reduction, to extract relevant features for subsequent stages of supervised learning. In this paper we examine a class of unsupervised learning algorithms used for a somewhat different purpose, that of clustering input vectors into various learned stereotyped behaviours in mobile robots [1] . Unsupervised techniques have signi...