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 …

Recent advances in hardware and communication technologies have accelerated the
deployment of billions of wireless sensors. This transformation has created a wide range of …

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 …

Low-power wide area networks (LPWANs) has recently emerged as a popular long-range
and low-speed radio communication technology as a result of the important growth of the …

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 …

When resolving many-objective problems, multi-objective optimization algorithms encounter
several difficulties degrading their performances. These difficulties may concern the …

Abstract The use of Artificial Intelligence solutions keeps raising in the business domain.
However, this adoption has not brought the expected results to companies so far. There are …

ABSTRACT 3D Deployment plays a fundamental role in setting up efficient wireless sensor
networks (WSNs) and IoT networks. In general, WSN are widely utilised in a set of real …

Several difficulties are generally encountered when solving many-objective problems (fitted
with three or more conflictual objectives) by applying multi-objective algorithms (resolving …