@article{ref1,
title="Transportation mode detection using an optimized long short-term memory model on multimodal sensor data",
journal="Entropy (Basel, Switzerland)",
year="2021",
author="Drosouli, Ifigenia and Voulodimos, Athanasios and Miaoulis, Georgios and Mastorocostas, Paris and Ghazanfarpour, Djamchid",
volume="23",
number="11",
pages="e1457-e1457",
abstract="The advancement of sensing technologies coupled with the rapid progress in big data analysis has ushered in a new era in intelligent transport and smart city applications. In this context, transportation mode detection (TMD) of mobile users is a field that has gained significant traction in recent years. In this paper, we present a deep learning approach for transportation mode detection using multimodal sensor data elicited from user smartphones. The approach is based on long short-term Memory networks and Bayesian optimization of their parameters. We conducted an extensive experimental evaluation of the proposed approach, which attains very high recognition rates, against a multitude of machine learning approaches, including state-of-the-art methods. We also discuss issues regarding feature correlation and the impact of dimensionality reduction.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e23111457",
url="http://dx.doi.org/10.3390/e23111457"
}