The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

In data stream environments, drift detection methods are used to identify when the context
has changed. This paper evaluates eight different concept drift detectors (ddm, eddm, pht …

A much-needed introduction to the field of discrete-valued time series, with a focus on count-
data time series Time series analysis is an essential tool in a wide array of fields, including …

Classifying streaming data requires the development of methods which are computationally
efficient and able to cope with changes in the underlying distribution of the stream, a …

Most traditional control charts used for sequential monitoring assume that full knowledge is
available regarding the prechange distribution of the process. This assumption is unrealistic …

Abstract Intrusion Detection Systems (IDS) have become pivotal in safeguarding information
systems against evolving threats. Concurrently, Concept Drift presents a significant …

Major difficulties in the study of high‐quality processes with traditional process monitoring
techniques are a high false alarm rate and a negative lower control limit. The purpose of …

Bernoulli processes have been monitored using a wide variety of techniques in statistical
process control. The data consist of information on successive items classified as …

Control charts based on the Poisson distribution are commonly used to monitor count data in
attributes. However, the Poisson distribution is based on the underlying equidispersion …

The paper presents a novel approach for biometric continuous driver authentication (CDA)
for secure and safe transportation using wearable photoplethysmography (PPG) sensors …