Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in
current research and industrial applications. Processing massive amounts of data generated …

Clustering is the process of identifying groups of similar data fulfilling certain criteria. Fuzzy c-
means clustering algorithm generates cluster centers and degree of memberships of each …

This research presents a novel approach for optimizing dataset classification through the
integration of a hybrid grid partition and rough set method for fuzzy rule generation. The …

An error, bug, flaw, failure, mistake or fault in a computer program or system that generates
inaccurate/unexpected outcome or prevents software from behaving as intended is a …

This research proposes a hybrid decision-making framework that combines grid partitioning,
rough set method, and fuzzy logic to enhance production planning decision-making. The …

Source code metrics have been proved to be reliable indicators of the vulnerability of the
source code to defects. Typically, a source code unit with high value of a certain metric is …

This research focuses on optimizing dataset classification by combining hybrid grid
partitioning and rough set-based fuzzy rule generation. Traditional classification algorithms …

Achieving a balance between accuracy and interpretability is a critical challenge in dataset
classification. Traditional methods often prioritize accuracy but lack interpretability, making it …

This research proposes a novel approach that combines hybrid grid partitioning, fuzzy rule
generation, and rough set theory to enhance the accuracy and interpretability of dataset …

This research presents a hybrid grid partition and rough set method for fuzzy rule generation
in dataset classification, aiming to enhance accuracy and interpretability. The proposed …