Research Article
Year 2022,
Volume: 2 Issue: 2, 53 - 62, 26.12.2022
Abstract
Most transportation mode (i.e., train, car) identification methods can only recognize the activities that were previously seen in the training data. However, they cannot be able to detect an unseen activity without having any corresponding training sample. In this study, we propose a k-shot learning algorithm. When k is set to zero, named zero-shot learning, it can recognize a previously unseen new transportation mode (i.e., bus) even when there are no training samples of that mode in the dataset. The experiments carried out on a real-world dataset showed that the accuracy rates from 89.46% to 93.94% were achieved by the proposed method with different values of parameter k. The results also showed that our method outperformed the state-of-the-art methods in terms of classification accuracy.
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Year 2022,
Volume: 2 Issue: 2, 53 - 62, 26.12.2022
Abstract
References
- I. Akdag, "Estimation of scattering parameters of U-slotted rectangular RFID patch antenna with machine learning models," Journal of Artificial Intelligence and Data Science, vol. 1, no. 1, pp. 63-70, 2021.
- X. Liu, "GLMLP-TRANS: A transportation mode detection model using lightweight sensors integrated in smartphones," Computer Communications, vol. 194, pp. 156-166, 2022.
- P. Wang and Y. Jiang, "Transportation mode detection using temporal convolutional networks based on sensors integrated into smartphones," Sensors, vol. 22, no. 1, pp. 1-20, 2022.
- Y. Zheng, L. Zhang, X. Xie, and W. Ma, "Mining interesting locations and travel sequences from GPS trajectories," In Proceedings of International Conference on World Wild Web (WWW 2019), ACM Press, Madrid, Spain, pp. 791-800, 2009.
- H. Gjoreski, M. Ciliberto, L. Wang, F. J. O Morales, S. Mekki, S. Valentin, and D. Roggen, "The University of Sussex-Huawei locomotion and transportation dataset for multimodal analytics with mobile devices", IEEE Access, vol. 6, pp. 42592-42604, 2018.
- M-C. Yu, T. Yu, S-C. Wang, C-J. Lin, and E. Y. Chang, "Big data small footprint: The design of a low-power classifier for detecting transportation modes," In Proceedings of the VLDB Endowment, vol. 7, no. 13, pp. 1429-1440, 2014.
- C. Carpineti, V. Lomonaco, L. Bedogni, M. D. Felice, and L. Bononi, "Custom dual transportation mode detection by smartphonedevices exploiting sensor diversity," In Proceedings of International Conference on Pervasive Computing and Communications Workshops, Athens, Greece, pp. 1-6, March 2018.
- S. Roy, Y. P. Singh, U. Biswas, D. S. Gurjar and T. Goel, "Machine Learning in Smart Transportation Systems for Mode Detection," In 18th India Council International Conference, pp. 1-6, 2021.
- J. Johansson and M. J. Ewerbring, "Användning av sensordata för att detektera smartphoneanvändares transportmedel," Thesis, KTH Royal Institute of Technology, 2019.
- R. A. Hasan, H. Irshaid, F. Alhomaidat, S. Lee, and J-S. Oh, "Transportation mode detection by using smartphones and smartwatches with machine learning," KSCE Journal of Civil Engineering, vol. 26, pp. 3578–3589, 2022.
- Y. Tian, D. Hettiarachchi, and S. Kamijo, "Transportation mode detection combining CNN and vision transformer with sensors recalibration using smartphone built-in sensors." Sensors, vol. 22, pp. 1-15, 2022.
- M. Hamidi and A. Osmani, "Human activity recognition: A dynamic inductive bias selection perspective," Sensors, vol. 21, no. 21, pp. 1-42, 2021.
- R. Mishra, A. Gupta, H. Gupta, and T. Dutta, "A sensors based deep learning model for unseen locomotion mode identification using multiple semantic matrices," IEEE Transactions on Mobile Computing, vol. 21, no. 3, pp. 799-810, 2022.
- I. H. Witten, E. Frank, M. A. Hall, C. J. Pal, "Data Mining: Practical Machine Learning Tools and Techniques," 4th ed., Cambridge, MA, USA: Morgan Kaufmann, 2016.
- O. Aktas, B. Coskuner, and I. Soner, "Turkish sentiment analysis using machine learning methods: application on online food order site reviews", Journal of Artificial Intelligence and Data Science, vol. 1, pp. 1-10, 2021.
- H. Massinissa, and O. Aomar, "Description of Structural Biases and Associated Data in Sensor-Rich Environments," ArXiv, pp. 1-57, 2021.
There are 16 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 26, 2022 |
| Submission Date | October 30, 2022 |
| Published in Issue | Year 2022 Volume: 2 Issue: 2 |
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