In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in...
详细信息
In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced Artificial Bee Colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stackedautoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.
A two-step optimization strategy for the inductance control of through-silicon via (TSV)-based 3-dimensional (3-D) inductor is developed based on the stacked autoencoder model and multi-level particle swarm optimizati...
详细信息
A two-step optimization strategy for the inductance control of through-silicon via (TSV)-based 3-dimensional (3-D) inductor is developed based on the stacked autoencoder model and multi-level particle swarm optimization algorithm. The design parameters are divided into two categories, including structural and geometrical parameters. The non-linear relationship among the inductance value, structural and geometrical parameters is described by stacked autoencoder model. Based on the multi-level particle swarm optimization algorithm, the two-step optimization strategy is developed to optimize the structural and geometrical parameters to control the inductance value of TSV-based 3-D inductor. The effectiveness of the developed two-step optimization strategy is verified by two cases. According to the Q3D extractor, the simulated inductance value (1240.55 pH) well agrees with the desired value (1200 pH). Therefore, the inductance value of TSV-based 3-D inductor can be controlled by the developed two-step optimization strategy.
暂无评论