A deep learning approach to predict the number of k-barriers
- Singh, A., Nagar, J., Sharma, S., & Kotiyal, V. (2021). A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Systems with Applications, 172, 114603. https://doi.org/10.1016/j.eswa.2021.114603
- Singh, A., Amutha, J., Nagar, J., Sharma, S., & Lee, C. C. (2022). Lt-fs-id: Log-transformed feature learning and feature-scaling-based machine learning algorithms to predict the k-barriers for intrusion detection using wireless sensor network. Sensors, 22(3), 1070. https://doi.org/10.3390/s22031070
- Singh, A., Amutha, J., Nagar, J., Sharma, S., & Lee, C. C. (2022). AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network. Scientific Reports, 12(1), 1-14. https://www.nature.com/articles/s41598-022-13061-z
Citar como
ABHILASH SINGH (2024). A deep learning approach to predict the number of k-barriers (https://github.com/abhilash12iec002/intrusion_detection/releases/tag/v1.0.2), GitHub. Recuperado .
Singh, A., Amutha, J., Nagar, J., & Sharma, S. (2022). A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks. Expert Systems with Applications, 118588.
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Inspiración para: ALE: Support Vector Regression using different kernels
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Versión | Publicado | Notas de la versión | |
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1.0.2 | See release notes for this release on GitHub: https://github.com/abhilash12iec002/intrusion_detection/releases/tag/v1.0.2 |
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1.0.0 |