Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study
H. Attaullah, S. Sanaullah, T. Jungeblut, Applied Sciences 14 (2024).
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Applied Sciences
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14
Zeitschriftennummer
19
Artikelnummer
9047
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Attaullah, Hasina ; Sanaullah, Sanaullah ; Jungeblut, Thorsten: Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study. In: Applied Sciences Bd. 14, MDPI AG (2024), Nr. 19
Attaullah H, Sanaullah S, Jungeblut T. Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study. Applied Sciences. 2024;14(19). doi:10.3390/app14199047
Attaullah, H., Sanaullah, S., & Jungeblut, T. (2024). Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study. Applied Sciences, 14(19). https://doi.org/10.3390/app14199047
@article{Attaullah_Sanaullah_Jungeblut_2024, title={Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study}, volume={14}, DOI={10.3390/app14199047}, number={199047}, journal={Applied Sciences}, publisher={MDPI AG}, author={Attaullah, Hasina and Sanaullah, Sanaullah and Jungeblut, Thorsten}, year={2024} }
Attaullah, Hasina, Sanaullah Sanaullah, and Thorsten Jungeblut. “Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study.” Applied Sciences 14, no. 19 (2024). https://doi.org/10.3390/app14199047.
H. Attaullah, S. Sanaullah, and T. Jungeblut, “Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study,” Applied Sciences, vol. 14, no. 19, 2024.
Attaullah, Hasina, et al. “Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study.” Applied Sciences, vol. 14, no. 19, 9047, MDPI AG, 2024, doi:10.3390/app14199047.
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