Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

Several RGB-D datasets have been publicized over the past few years for facilitating
research in computer vision and robotics. However, the lack of comprehensive and fine …

The challenge of develo** an Android malware detection framework that can identify
malware in real-world apps is difficult for academicians and researchers. The vulnerability …

The random forest (RF) classifier, which is a combination of tree predictors, is one of the
most powerful classification algorithms that has been recently applied for fault detection and …

The selection of software metrics for building software quality prediction models is a search‐
based software engineering problem. An exhaustive search for such metrics is usually not …

Software developers and project teams spend considerable amount of time in identifying
and fixing faults reported by testers and users. Predicting defects and identifying regions in …

Feature selection has become interest to many research areas which deal with machine
learning and data mining, because it provides the classifiers to be fast, cost-effective, and …

Access to digital music collections is nowadays facilitated by content-based methods that
allow the retrieval of music on the basis of intrinsic properties of audio, in addition to …

Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic
data, and other modalities. While reviews have been written of some of this work before, no …

Software fault prediction models are used to predict faulty modules at the very early stage of
software development life cycle. Predicting fault proneness using source code metrics is an …