In today's digital world, information is growing along with the expansion of Internet usage
worldwide. As a consequence, bulk of data is generated constantly which is known to be …

Virtual sample generation (VSG) technology aims to generate virtual samples based on real
samples, in order to expand the size of the datasets and improve model performance …

Imbalanced classification is a well-known challenge faced by many real-world applications.
This issue occurs when the distribution of the target variable is skewed, leading to a …

In recent years, imbalanced data classification has emerged as a challenging task. To
address this issue, we propose an adaptive SV-Borderline SMOTE-SVM (Synthetic Minority …

There are a plethora of invented classifiers in Machine learning literature, however, there is
no optimal classifier in terms of accuracy and time taken to build the trained model …

One of the significant purposes of building a model is to increase its accuracy within a
shorter timeframe through the feature selection process. It is carried out by determining the …

The large volumes of Internet of Things (IoT) data transmission to and from the cloud leads
to one of cloud-centric processing's major drawbacks: latency. Fog computing gives a …

Learning classifiers using skewed or imbalanced datasets can occasionally lead to
classification issues; this is a serious issue. In some cases, one class contains the majority of …

Uplift modeling is a widely recognized predictive approach used to identify individuals who
are more likely to respond positively to an intervention or treatment, such as a marketing …

The correct capture of forest operations information carried out in forest plantations can help
in the management of mechanized harvesting timber. Proper management must be able to …