Active Mine Detection Using Hybrid LSTM With Extreme Gradient Boosting Algorithm

DOI

10.2991/978-94-6463-866-0_34How to use a DOI?

Keywords

Google Collab; Extreme Gradient Boosting; Hybrid LSTM; Land Mine Detection

Abstract

The presented project uses the XGBoost algorithm along with a Hybrid Long Short-Term Memory network to develop an intricate system of land mine detection. The system aims to enhance active mine identification accuracy via simulated data analysis. Hybrid uses both strong classification performance of XGBoost and sequential data processing ability of LSTM. The suggested model can be applied for real-time purposes as it is able to increase detection accuracy and decrease the number of false positives.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - M. Mahaboob
AU  - A. Shaik Ahamed
AU  - R. Sathish
AU  - V. Somasekar
AU  - S. Sanjay
PY  - 2025
DA  - 2025/10/31
TI  - Active Mine Detection Using Hybrid LSTM With Extreme Gradient Boosting Algorithm
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 409
EP  - 419
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_34
DO  - 10.2991/978-94-6463-866-0_34
ID  - Mahaboob2025
ER  -