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Heart diseases are characterized as heterogeneous diseases comprising multiple subtypes. Early diagnosis and prognosis of heart disease are essential to facilitate the clinical management of patients. In this research, a new computational model for predicting early heart disease is proposed. The predictive model is embedded in a new regularization based on decaying the weights according to the weight matrices’ standard deviation and comparing the results against its parents (RSD-ANN). The performance of RSD-ANN is far better than that of the existing methods. Based on our experiments, the average validation accuracy computed was 96.30% using either the tenfold cross-validation or holdout method.

Recently, there is an enormous research on the smart campus concept due to the revolution of the IoT technologies. The motivation of this paper is to: reinforce the safety on campus, reduce the cost, and take one step forward toward a University smart campus. In this paper, we are not only proposing a framework that would act as an instantaneous responder, but we also provide a glimpse of the evolving research on smart campus. In addition, we explore the challenges, and highlight the future work regarding this on-the-spot responder system.

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