The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-
being of patients. Human error hinders accurate diagnostics, as interpreting medical …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Despite more than two decades of progress, learning from imbalanced data is still
considered as one of the contemporary challenges in machine learning. This has been …

The growth of the Internet of things (IoT) has ushered in a new area of inter-connectivity and
innovation in the home. Many devices, once separate, can now be interacted with remotely …

Data mining methods are often impaired by datasets with desperate nature. Such real-world
datasets contain imbalanced data distri-butions among classes, which affects the learning …

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 …

In recent years, the classification of class-imbalanced data has obtained increasing attention
across different scientific areas such as fraud detection, metabolomics, Cancer diagnosis …

Oversampling is a promising preprocessing technique for imbalanced datasets which
generates new minority instances to balance the dataset. However, improper generated …

Epilepsy is one of the brightest manifestations of extreme behavior in living systems.
Extreme epileptic events are seizures, that arise suddenly and unpredictably. Usually …

The application of artificial intelligence (AI) in hospitals yields many advantages but also
confronts healthcare with ethical questions and challenges. While various disciplines have …