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Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar
Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar
Master's Theses
Classifying the electrocardiogram is of clinical importance because classification can be used to diagnose patients with cardiac arrhythmias. Many industries utilize machine learning techniques that consist of feature extraction methods followed by Naive- Bayesian classification in order to detect faults within machinery. Machine learning techniques that analyze vibrational machine data in a mechanical application may be used to analyze electrical data in a physiological application. Three of the most common feature extraction methods used to prepare machine vibration data for Naive-Bayesian classification are the Fourier transform, the Hilbert transform, and the Wavelet Packet transform. Each machine learning technique consists of …
Suspicion Encoded: Women Of Color And Biometric Technology In The United States, Lilith A. Saylor
Suspicion Encoded: Women Of Color And Biometric Technology In The United States, Lilith A. Saylor
sprinkle: an undergraduate journal of feminist and queer studies
This paper explores the commodification of privacy through biometric technology in the United States. It examines the impact of this technology on poor women of color (WOC), arguing that poor WOC face intersectional discrimination based on the convergence of sex, race, and class in their identities. I highlight the unique and powerful intrusion of biometric technology into the lives of poor WOC, and argue that the connection between data and the physical body created through biometric data has formed an environment in which the state wields unrestricted control in all spheres over the privacy of poor WOC.