Abstract Knowledge modeling is an important step in building knowledge‐based
applications. Understanding the processes of knowledge modeling and the techniques …

Detecting outliers is a widely studied problem in many disciplines, including statistics, data
mining, and machine learning. All anomaly detection activities are aimed at identifying cases …

The aim of the article is the analysis of using LOF, COF and Kmeans algorithms for outlier
detection in rule based knowledge bases. The subject of outlier mining is very important …

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory
factor analyses, cluster definition, and machine-learning algorithms is promising for …

Today's world is surrounded by advanced technologies like Artificial Intelligence (AI),
Blockchain, Internet of Things (IoT) and Cloud Computing. These technologies are …

Background and Objectives: Modern classification and categorization of individuals' health
requires personalized variables such as nutrition, physical activity, lifestyle, and medical …

The article concerns the detection of outliers in rule-based knowledge bases containing data
on Covid 19 cases. The authors move from the automatic generation of a rule-based …

The article presents both methods of clustering and outlier detection in complex data, such
as rule-based knowledge bases. What distinguishes this work from others is, first, the …

Our research deals with intelligent decision support systems based on rule-based
knowledge bases. Decision support systems use rules” If a condition, then a decision” as a …

The control of the inference process is an important, though not always noticed, problem of
using rule-based systems built with the use of knowledge-based approach in practice …