vzanoon
๐ปWe're the outline of the same form.๐ป ๐ Computer Engineering ๐ Msc. Student Biomedical Engineering ๐ Santa Catarina
Universidade Federal de Santa CatarinaBrasil
vzanoon's Stars
merkletreejs/merkletreejs
๐ฑ Construct Merkle Trees and verify proofs in JavaScript.
nengo/nengo
A Python library for creating and simulating large-scale brain models
markstrefford/Spiking-Neural-Network
Basic SNN propogating spikes between LIF neurons
danilosalvati/svd-js
A simple library to compute Singular Value Decomposition as explained in "Singular Value Decomposition and Least Squares Solutions. By G.H. Golub et al."
AlessiaRuggeri30/epileptic-seizure-prediction
Master Thesis: Prediction of Epileptic Seizures using Machine Learning and Deep Learning models - code repository
Abpadfoot/EpilepsyDetection
The dataset used in the proposed work is obtained from the Department of Epileptology of the University of Bonn.The accuracy achieved in one of the Multi-class classification experiment in the proposed work is 98.45% which beats the state of the art accuracy in this three-class problem. Additionally, the proposed method has achieved highest accuracy of 100% in classifying normal EEG signals(eyes closed) and seizure EEG signal and an accuracy of 100% in classifying normal EEG signals(eyes open) and seizure EEG signal which is comparable with the existing state of the art EEG signal classification techniques. Six different classification techniques have been used in each of the five experiments conducted where every classification technique has been used with 8 different Daubechies wavelets db1 to db8. The results obtained from these experiments provide valuable insights establishing that SVM performs the best in most of the experiments with the db4 wavelet among the 8 wavelets achieving the highest accuracy
elielmarcos/SCAF-UFSC
Sistema de Controle, Acesso e Frequรชncia. Trava eletrรดnica com ESP32
GabrielEstevam/compilador
GabrielEstevam/GymSchedule
Sourabh0511/SeizureDetection
ML project on seizure (epilepsy) detection from ECG data