April 17, 2024, 4:43 a.m. | Md Zobaer Islam, Sabit Ekin, John F. O'Hara, Gary Yen

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.13035v2 Announce Type: replace-cross
Abstract: In this study, we present a deep learning-based approach for time-series respiration data classification. The dataset contains regular breathing patterns as well as various forms of abnormal breathing, obtained through non-contact incoherent light-wave sensing (LWS) technology. Given the one-dimensional (1D) nature of the data, we employed a 1D convolutional neural network (1D-CNN) for classification purposes. Genetic algorithm was employed to optimize the 1D-CNN architecture to maximize classification accuracy. Addressing the computational complexity associated with training …

abstract arxiv classification cnn cs.lg data data classification dataset deep learning eess.sp forms light nature optimization patterns sensing series study technology through type

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