While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …

This paper proposes a new hybrid stochastic training algorithm using the recently proposed
grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural …

Traditional anomaly detection procedures assume that normal observations are obtained
from a single distribution. However, due to the complexities of modern industrial processes …

The existence of both uncertainty and imprecision has detrimental impact on efficiency of
decision-making applications and some machine learning methods, in particular support …

To meet customer expectations and remain competitive, industrials try constantly to improve
their quality control systems. There is hence increasing demand for adopting automatic …

The existence of noisy samples increases the inefficiency of SVM training. In SVM training,
the classification hyperplane is determined by the support vectors, therefore, the other …

Recommender systems have been widely used in e-commerce applications. They are a
subclass of information filtering system, used to either predict whether a user will prefer an …

The filled function methods are a kind of effective method to find the global minimum for
optimization problems. However, there exist four limitations to this kind of method: 1) A large …

In data analysis, incomplete data commonly occurs and can have significant effects on the
conclusions that can be drawn from the data. Incomplete data cause another problem, so …

Self-rated health (SRH) serves as an important indicator for measuring the physical and
mental well-being of older adults, holding significance for their health management and …