@article{article_1161994, title={Mine Detection Through Ground Penetrating Radar Data Utilizing Artificial Neural Networks}, journal={Veri Bilimi}, volume={5}, pages={26–33}, year={2022}, author={Kocer, H. Erdinç and Kılıç, Hayri}, keywords={Mayın tarama, Yere nüfuz eden radar, Yapay sinir ağları}, abstract={Landmine detection has been a tremendous and, in fact, growing issue due to the concern of land mines’ adverse effect on people’s lives on the ground of economic growth and development. In this article, some of the artificial neural network methods which are commonly used to tackle the afore-mentioned problem have been explored. First of all, data that have been obtained on ground penetrating radars have been processed so as to decrease the misleading ground effect and noise. Adaline and Madaline Artificial Neural Network architectures regarding single-layer and multi-layer perceptrons have been implemented on the pre-processed data. According to the result of the implementation, for each input pattern that consists of 208 components, 60 data have been processed and, prior to processing step, forward-propagation, followed by, back-propagation have been leveraged. Single-layer Perceptron Artificial Neural Network method have yielded the best results with the success rate of 98.112%. Furthermore, the overall system has been tested with different architecture of the Artificial Neural Network based on different learning coefficients, iteration numbers and momentum constants. The proposed methodology to tackle this problem has resulted in obtaining high accuracy rates on buried objects and soil type detection.}, number={1}, publisher={Murat GÖK}