An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers

This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the important features in low dimensional feature space. Three classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the proposed work for classifying the EEG signals. The proposed method is tested on Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.Comment: Accepted in International Conference on Smart Technologies for Sustainable Development (ICSTSD2021

Using NFriendConnector to Extend Facebook to the Real World

Université Paris IV-Sorbonne, UFR de philosophie et de sociologie. Prof. Ruedi Imbach MASTER 2 (1er et 2nd semestre 2010-2011), Séminaire de philosophie médiévale Deux théories médiévales sur les catégories et les transcendantaux: Thomas d'Aquin et Dietrich de Freiberg La doctrine des transcendantaux (un, vrai, bien) est incontestablement l'un des acquis les plus originaux de la métaphysique médiévale. Quel est le rapport de cette  théorie qui tente d’identifier les déterminations générales ..

Nonparametric Estimation of Range Value at Risk

Range value at risk (RVaR) is a quantile-based risk measure with two parameters. As special examples, the value at risk (VaR) and the expected shortfall (ES), two well-known but competing regulatory risk measures, are both members of the RVaR family. The estimation of RVaR is a critical issue in the financial sector. Several nonparametric RVaR estimators are described here. We examine these estimators’ accuracy in various scenarios using Monte Carlo simulations. Our simulations shed light on how changing p and q with respect to n affects the effectiveness of RVaR estimators that are nonparametric, with n representing the total number of samples. Finally, we perform a backtesting exercise of RVaR based on Acerbi and Szekely’s test