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Full-Text Articles in Physical Sciences and Mathematics
A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson
A Performance Comparison Of Neural Network And Svm Classifiers Using Eeg Spectral Features To Predict Epileptic Seizures, Ian Thomas Tennant Watson
Dissertations
Epilepsy is one of the most common neurological disorders, and afflicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to significant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artificial neural network and sup port vector machine classifiers, using EEG spectral features to predict the onset …
Exploring The Features To Classify The Musical Period Of Western Classical Music, Arturo Martínez Gallardo
Exploring The Features To Classify The Musical Period Of Western Classical Music, Arturo Martínez Gallardo
Dissertations
Music Information Retrieval (MIR) focuses on extracting meaningful information from music content. MIR is a growing field of research with many applications such as music recommendation systems, fingerprinting, query-by-humming or music genre classification. This study aims to classify the styles of Western classical music, as this has not been explored to a great extent by MIR. In particular, this research will evaluate the impact of different music characteristics on identifying the musical period of Baroque, Classical, Romantic and Modern. In order to easily extract features related to music theory, symbolic representation or music scores were used, instead of audio format. …