ABSTRACT

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This paper focuses on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining in order to understand and predict the learning behavior of students. These models were evaluated on the publicly available xAPI (Experience API or Experience Application Programming Interface) dataset, which is an education dataset intended for tracking students’ classroom activities, participation in online communities, and performance. Experimental results with several performance measures show the effectiveness of the developed process models in helping experts to better understand students’ learning behavioral patterns.

In this paper, we study the probability of using heart sound as a biometric for human authentication. The most significant contribution of using heart sound as a biometric is that it cannot be easily replicated as compared to other conventional biometrics. The proposed Heart Sound Authen tication System (HSAS) consists of five main phases, namely, Heart Sound Capturing, Pre-processing, Feature Extraction, Training, and Classification and Authentication phases. The proposed biometric system comprises a digital electronic stetho scope, a computer equipped with a sound card and heart sound capturing software application. In this work, two classifiers were used, which are Mean Square Error (MSE) and K Nearest Neighbor (KNN). Results indicated that the proposed system has attained recall 82.4% and precision 80.7% for MSE classifier and has attained recall 94.5% and precision 93% for KNN classifier for a database of 400 heart sounds that were recorded from 40 participants by 10 heart sound recordings for each participant.

Abstract