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
Zitieren als
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. Abgerufen .
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.
Kompatibilität der MATLAB-Version
Plattform-Kompatibilität
Windows macOS LinuxTags
Quellenangaben
Inspiriert: ALE: Support Vector Regression using different kernels
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Live Editor erkunden
Erstellen Sie Skripte mit Code, Ausgabe und formatiertem Text in einem einzigen ausführbaren Dokument.
Version | Veröffentlicht | Versionshinweise | |
---|---|---|---|
1.0.2 | See release notes for this release on GitHub: https://github.com/abhilash12iec002/intrusion_detection/releases/tag/v1.0.2 |
||
1.0.0 |