This study presents hybrid particle swarm optimization with quasi-Newton (HPSO-QN), a
hybrid optimization method for accurately identifying mechanical parameters in two-mass …

Aimed at shortcomings, such as fewer risk rules for assisting decision-making in customs
entry inspection scenarios and relying on expert experience generation, a dynamic weight …

This paper presents the operation of a remotely controlled, wheel-legged robot. The
developed Wi-Fi connection framework is established on a popular ARM microcontroller …

High-utility itemsets mining (HUIM) is an important research topic in data mining that
consists of extracting efficient itemsets that generate high profit in databases. Metaheuristic …

Frequent itemset mining (FIM) is a highly resource-demanding data-mining task
fundamental to numerous data-mining applications. Support calculation is a frequently …

Besides the need for more advanced predictive methods, there is increasing demand for
easily interpretable results. Couples of enhanced association rules (a generalization of …

In some cases, it is more important to discover rare association rules than frequent itemsets.
Unique rules represent rare cases, activities or events in real-world applications. Extracting …

There has been an improvement in recent years towards increase in volume for remarkable
data in the field of Internet and computer networks. This brings up significant security-related …

In the field of machine learning, the evaluation of models typically involves training them on
a specific dataset and assessing their performance on a separate test set. However …

Association rule hiding (ARH) is the process of modifying a transaction database to prevent
sensitive patterns (association rules) from discovery by data miners. An optimal ARH …