On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository.

A New Wavelet-Based Privatization Mechanism for Probability Distributions.

In this paper, we propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. Wavelets are employed to extract information from a wide range of types of data, including audio signals and images often related to sensors, as unstructured data. Specifically, the cumulative wavelet integral function is defined to build the perturbation on a probability with the help of this function.

Potentiation of spontaneous and evoked cortical electrical activity after spreading depression: in vivo analysis in well-nourished and malnourished rats.

Cortical spreading depression (CSD) is influenced by brain excitability and is related to neurological diseases, such as epilepsy. In vitro evidence indicates that neuronal electrical activity is potentiated after CSD. Malnutrition can cause electrophysiological changes in the brain, both in animals and in humans.