Temperature deviations are critical in a pharmaceutical supply chain (SC) due to quality
deterioration concerns and resulting health risks. The current solutions ensuring …

This research demonstrates the adverse implications of using non-robust statistical methods
for detecting anomalies in hyperspectral image data, and proposes the use of multivariate …

We introduce a novel longwave polarimetric-based approach to man-made object detection
that departs from a more traditional direct use of Stokes parameters. The approach exploits …

In statistics and machine learning, logistic regression is a widely-used supervised learning
technique primarily employed for binary classification tasks. When the number of …

Hyperspectral anomaly detection is a useful means for using hyperspectral imagery to locate
unusual objects. Current anomaly detection methods commonly use non-robust statistical …

Emerging stream mining applications require classification of large data streams generated
by single or multiple heterogeneous sources. Different classifiers can be used to produce …

Transit of wasteful materials within the European Union is highly regulated through a system
of permits. Waste processing costs vary greatly depending on the waste category of a permit …

This research extends the field of hyperspectral target detection by developing autonomous
anomaly detection and signature matching methodologies that reduce false alarms relative …

We propose and analyze a distributed learning system to classify data captured from
distributed and dynamic data streams. Our scheme consists of multiple distributed learners …

In this paper, we propose a collaborative anomaly detection system based on the Active
Learning framework. In this system, multiple Edge nodes are responsible for monitoring and …