Early Warning Method of Transformer Core Clamp Disconnection Based on Random Forest Algorithm

A Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis (RESEARCH NOTE)

Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...

Diagnosis of Diabetes Using a Random Forest Algorithm

Background: Diabetes is the fourth leading cause of death in the world. And because so many people around the world have the disease, or are at risk for it, diabetes can be called the disease of the century. Diabetes has devastating effects on the health of people in the community and if diagnosed late, it can cause irreparable damage to vision, kidneys, heart, arteries and so on. Therefore, it...

Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm

Background: Multiple sclerosis (MS) is a degenerative inflammatory disease which is most commonly diagnosed by magnetic resonance imaging (MRI). But, since the MRI device uses of a magnetic field, if there are metal objects in the patient's body, it can disrupt the health of the patient, the functioning of the MRI, and distortion in the images. Due to limitations of using MRI device, screening ...

P2P Network Traffic Identification Based on Random Forest Algorithm

With the rapid development of computer technique in the past decades, the emergence of P2P techniqueprompts the network computing model evolving from centralized network to distributed network. Although P2P technique has brought tremendous changes to the network technique, P2P technique also exposes a lot of problems during its implementation. If we can manage the P2P network traffic effectivel...

An early warning on early warning systems!