کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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4960484 | 1446499 | 2017 | 9 صفحه PDF | دانلود رایگان |
The study examines the use of quantization to be applied to Bi-directional Long Short-Term Memory (Bi-LSTM), a combination of the two called qBi-LSTM. Quantization used comes from Deep Belief Networks (DBN). It selected DBN for its superiority as a generative model of Deep Learning in producing an optimal artificial feature. Development of qBi-LSTM is expected to improve the performance of Bi-LSTM and also provide efficient time. The qBi-LSTM test is applied for sleep stage classification on St. Vincent's University Hospital / University College Dublin's Sleep Apnea Database. The result shows that qBi-LSTM has the highest performance compared to Bi-LSTM and DBN with precision, recall and F-measure values of 86.00%, 72.10%, and 75.27%. The best qBi-LSTM performance is to classify Stage 2 but still fails to classify the stage of REM (Rapid Eye Movement).
Journal: Procedia Computer Science - Volume 116, 2017, Pages 530-538