Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …

In this work, we propose hyperparameters optimization using grid search to optimize the
parameters of eight existing models and apply the best parameters to predict the outcomes …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Modern supervised machine learning algorithms involve hyperparameters that have to be
set before running them. Options for setting hyperparameters are default values from the …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Heart disease is a severe disorder, which inflicts an adverse burden on all societies and
leads to prolonged suffering and disability. We developed a risk evaluation model based on …

Extreme weather conditions, as one of many effects of climate change, is expected to
increase the magnitude and frequency of environmental disasters. In parallel, urban centres …

This paper presents a machine learning approach involving tourists' electronic word of
mouth (eWOM) to support destination marketing campaigns. This approach enhances …

Hyperparameter tuning is a critical function necessary for the effective deployment of most
machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of …

Cancer can be considered as one of the leading causes of death widely. One of the most
effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using …