@InProceedings{10.1007/978-3-031-21101-0_14,
author="Sathish, Vidya Rohini Konanur
and Woo, Wai Lok
and Ho, Edmond S. L.",
editor="Abd El-Latif, Ahmed A.
and Maleh, Yassine
and Mazurczyk, Wojciech
and ELAffendi, Mohammed
and I. Alkanhal, Mohamed",
title="Predicting Sleeping Quality Using Convolutional Neural Networks",
booktitle="Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="175--184",
abstract="Identifying sleep stages and patterns is an essential part of diagnosing and treating sleep disorders. With the advancement of smart technologies, sensor data related to sleeping patterns can be captured easily. In this paper, we propose a Convolution Neural Network (CNN) architecture that improves the classification performance. In particular, we benchmark the classification performance from different methods, including traditional machine learning methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbour (k-NN), Na{\"i}ve Bayes (NB) and Support Vector Machine (SVM), on 3 publicly available sleep datasets. The accuracy, sensitivity, specificity, precision, recall, and F-score are reported and will serve as a baseline to simulate the research in this direction in the future.",
isbn="978-3-031-21101-0"
}