Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …

Feature selection is the most significant pre-processing activity, which intends to reduce the
data dimensionality for enhancing the machine learning process. The evaluation of feature …

The extensive use of plastics leads to the release and diffusion of microplastics. Household
plastic products occupy a large part and are closely related to daily life. Due to the small size …

Due to the energy transition and the distribution of electricity generation, distribution power
systems gain a lot of attention as their importance increases and new challenges in …

Landslides are among the most devastating natural disasters worldwide. Landslide
susceptibility mapping (LSM) is a scientific approach for assessing landslides-prone areas …

Predicting in-stream water quality is necessary to support the decision-making process of
protecting healthy waterbodies and restoring impaired ones. Data-driven modeling is an …

Medical data includes clinical trials and clinical data such as patient-generated health data,
laboratory results, medical imaging, and different signals coming from continuous health …

Sensitive information in images is can be leaked during attacks, resulting in the malicious
acquisition of personal information. To improve the robustness of attacking defence for video …

This study introduces a hybrid feature selection technique with a multi-objective algorithm
incorporating Information Gain, Random Forest, and Relief F-based approach. We integrate …

Detecting breast cancer in early stage is not straightforward. This happens because biopsy
test requires time to determine whether the type is benign or malignant. Data mining …