This survey is an effort to provide a research repository and a useful reference for
researchers to guide them when planning to develop new Nature-inspired Algorithms …

In the era of healthcare, and its related research fields, the dimensionality problem of high
dimensional data is a massive challenge as it contains a huge number of variables forming …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

Data normalization is one of the pre-processing approaches where the data is either scaled
or transformed to make an equal contribution of each feature. The success of machine …

Feature selection (FS) is a machine learning process commonly used to reduce the high
dimensionality problems of datasets. This task permits to extract the most representative …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale medical datasets. On the other, medical applications with high …

Feature selection has gained much consideration from scholars working in the domain of
machine learning and data mining in recent years. Feature selection is a popular problem in …

The medical image fusion is the process of coalescing multiple images from multiple
imaging modalities to obtain a fused image with a large amount of information for increasing …

Feature selection has been widely used in data mining and machine learning tasks to make
a model with a small number of features which improves the classifier's accuracy. In this …

With a global search mechanism, particle swarm optimization (PSO) has shown promise in
feature selection (FS). However, most of the current PSO-based FS methods use a fix-length …