A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories
Indoor location-based services have been widely investigated to take advantage of semantic trajectories for providing user oriented services in indoor environments. Although indoor semantic trajectories can provide seamless understanding to users regarding the provided location-based services, studi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9431193/ |
| _version_ | 1818647817338486784 |
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| author | Yoga Yustiawan Hani Ramadhan Joonho Kwon |
| author_facet | Yoga Yustiawan Hani Ramadhan Joonho Kwon |
| author_sort | Yoga Yustiawan |
| collection | DOAJ |
| description | Indoor location-based services have been widely investigated to take advantage of semantic trajectories for providing user oriented services in indoor environments. Although indoor semantic trajectories can provide seamless understanding to users regarding the provided location-based services, studies on the application of deep learning approaches for robust and valid semantic indoor localization are lacking. In this study, we combined a stacked denoising autoencoder and long short term memory technique with a rule-based refinement method applying a rule-based hidden Markov model (HMM) to perform robust and valid semantic trajectory extraction. In particular, our rule-based HMM approach incorporates a direct set of rules into HMM to resolve invalid movements of the extracted semantic trajectories and is extensible to various deep learning techniques. We compared the performance of our proposed approach with that of other cutting-edge deep learning approaches on two different real-world data sets. The experimental results demonstrate the feasibility of our proposed approach to produce more robust and valid semantic trajectories. |
| first_indexed | 2024-12-17T01:08:33Z |
| format | Article |
| id | doaj.art-024d9d3520ac4718aa9f3da53de813dd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T01:08:33Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-024d9d3520ac4718aa9f3da53de813dd2022-12-21T22:09:12ZengIEEEIEEE Access2169-35362021-01-019731527316810.1109/ACCESS.2021.30802889431193A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic TrajectoriesYoga Yustiawan0https://orcid.org/0000-0003-2196-6536Hani Ramadhan1https://orcid.org/0000-0003-1357-7469Joonho Kwon2https://orcid.org/0000-0002-8207-9415Department of Big Data, Pusan National University, Busan, South KoreaDepartment of Big Data, Pusan National University, Busan, South KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, South KoreaIndoor location-based services have been widely investigated to take advantage of semantic trajectories for providing user oriented services in indoor environments. Although indoor semantic trajectories can provide seamless understanding to users regarding the provided location-based services, studies on the application of deep learning approaches for robust and valid semantic indoor localization are lacking. In this study, we combined a stacked denoising autoencoder and long short term memory technique with a rule-based refinement method applying a rule-based hidden Markov model (HMM) to perform robust and valid semantic trajectory extraction. In particular, our rule-based HMM approach incorporates a direct set of rules into HMM to resolve invalid movements of the extracted semantic trajectories and is extensible to various deep learning techniques. We compared the performance of our proposed approach with that of other cutting-edge deep learning approaches on two different real-world data sets. The experimental results demonstrate the feasibility of our proposed approach to produce more robust and valid semantic trajectories.https://ieeexplore.ieee.org/document/9431193/Deep learningindoor localizationthe Internet of Thingsrule-based refinementsemantic trajectories |
| spellingShingle | Yoga Yustiawan Hani Ramadhan Joonho Kwon A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories IEEE Access Deep learning indoor localization the Internet of Things rule-based refinement semantic trajectories |
| title | A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories |
| title_full | A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories |
| title_fullStr | A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories |
| title_full_unstemmed | A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories |
| title_short | A Stacked Denoising Autoencoder and Long Short-Term Memory Approach With Rule-Based Refinement to Extract Valid Semantic Trajectories |
| title_sort | stacked denoising autoencoder and long short term memory approach with rule based refinement to extract valid semantic trajectories |
| topic | Deep learning indoor localization the Internet of Things rule-based refinement semantic trajectories |
| url | https://ieeexplore.ieee.org/document/9431193/ |
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