The changes in Heart Rate Variability (HRV) and Heart Rate Recovery (HRR) coincide well with the changes in physical training status in patient populations. But a deep probing in this area reveals that enough attention has not been paid so far to the healthy population, especially those who undergo deliberate training. This study was conducted to quantify the effect of nine months of basic police training in HRV/HRR among a sample of women police recruits of Kerala state. Consequently, the training was found to be effective in altering the parasympathetic and nonlinear control of the cardiovascular system. The statistical analysis using dependent sample t-test showed that there was a significant alteration of linear and nonlinear HRV measures and HRR features prior to post-training. The study also investigated the discriminatory potential of five-minutes each of supine HRV and post-exercise HRR in classifying the recruits into trained and untrained status. The optimal HRV/HRR feature set that could discriminate the training status with the highest accuracy were identified by using Genetic Algorithm-Artificial Neural Network (GA-ANN) and Genetic Algorithm-Support Vector Machine (GA-SVM) based wrapper functions. A reduction in HRV feature set to 50% and HRR feature set to 68.5% was found using the GA optimization. While classifying, the SVM classifier outperformed the ANN with maximum accuracy (89.7%) using the reduced feature set of HRR. The results promise objective selection of welltrained candidates to professions which demand high physical fitness.
Program curricula in higher education institutions are generally designed to satisfy a set of national and international standards within a discipline. These standards vary from an established set of educational objectives and course learning outcomes to appropriate coverage of the required knowledge areas of the discipline. To ensure that a program satisfies requirement standards, it is imperative that a continuous cycle of review and assessment of the program takes place periodically. This cycle usually encompasses the gathering and manipulation of a large amount of data from several sources. In this poster, we present the CURONTO ontology, an OWL ontology developed to model the semantics of the curriculum domain in a way that facilitates curriculum review, assessment and development.
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