However, an inherent dilemma with the traditional cryptographic strategy is the observation that passwords can be forgotten, and cards or tokens can be lost. Traditional strategies for authenticating a person’s identity incorporate cryptographic techniques, such as through the use of a secret string of characters representing a password and/or a possession of a card or token. The technology domain of biometrics displays trends of rampant interest with substantial impact on both government and private industry sectors.
#Decision tree python visualization portable
The ECG signal is also a candidate for the domain of biometrics that characterizes the electrical attributes of the heart that infers the heart’s functional status with augmented relevance through the evolution to portable and wearable systems. More sophisticated biometric techniques involve identification of the voice, face, and aspects of the eye, such as the retina and iris. For example, a standard biometric application pertains to a person’s fingerprint.
#Decision tree python visualization verification
The domain of biometrics is a rapidly advancing field that provides verification of a person based on the individual’s uniquely intrinsic physiological characteristics. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. With the application of machine learning the subject specific ECG signal can be differentiated. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity.
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