POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection-Reference-Cited by-同舟云学术

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POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection

Published:2024-05-15 Issue:10 Volume:11 Page:17170-17183
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Cardoso-Pereira Isadora¹^ORCID,Borges João B.²^ORCID,Viana Aline C.³^ORCID,Loureiro Antonio A. F.¹^ORCID,Ramos Heitor S.¹^ORCID

Affiliation:

1. Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Brazil

2. Department of Computing and Technology, Federal University of Rio Grande do Norte, Natal, Brazil

3. Inria, Paris, France

Funder

Fundação de Amparo a Pesquisa do Estado de Minas Gerais

São Paulo Research Foundation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), INRIA

STIC AmSud LINT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10528217/10413209.pdf?arnumber=10413209

Reference43 articles.

1. Understanding mobility based on GPS data

2. The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection

3. Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

4. Transportation Mode Recognition With Deep Forest Based on GPS Data

5. Inferring transportation modes from GPS trajectories using a convolutional neural network

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