For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to
boost the quality of services (QoSs) needed by many applications. However, the availability …

Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …

One of the most difficult problems analysts and decision-makers may face is how to improve
the forecasting and predicting of financial time series. However, several efforts were made to …

Unmanned-aerial-vehicle (UAV)-aided wireless communication in Internet of Things (IoT)
applications is becoming the focus of attention of researchers. This article investigates a …

With the increasing popularity of smartphones, user identification has become a critical
component to ensure security and privacy. This study looked into how smartphone sensors' …

For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor
nodes is one of the major challenges in wireless sensor network. Coverage is main concern …

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 …

There is a growing demand for the detection of endangered plant species through machine
learning approaches. Ziziphus lotus is an endangered deciduous plant species in the …