At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

Data stream mining is an important research topic that has received increasing attention due
to its use in a wide range of applications, such as sensor networks, banking, and …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Abstract Concept Drift is considered a challenging problem that appears in data streaming.
The classifier's error rate and the ensemble are used in most of the previous works to …

Data stream mining is an important research topic that has received increasing attention due
to its use in a wide range of applications, such as sensor networks, banking, and …

In recent years, the prevalence of technological advances has led to an enormous and ever‐
increasing amount of data that are now commonly available in a streaming fashion. In such …

ABSTRACT DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an
Online Learning that aims to identify human activities in smart homes from distributed …

DCR is a Distributed Collaborative Reasoning multi-agent model that aims to recognize
human activities in smart homes from distributed, heterogeneous and dynamic sensor data …

This paper experiments with well known pruning approaches, iterative and one-shot, and
presents a new approach to lottery ticket pruning applied to tabular neural networks based …

1.1 Background: Healthcare services is a crucial part of modern societies, where it's main
role is to provide the necessary healthcare for the community, assisting those in need of …