An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems
Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives...
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| Format: | Article |
| Language: | English |
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Firat University
2024-10-01
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| Series: | Firat University Journal of Experimental and Computational Engineering |
| Online Access: | https://dergipark.org.tr/tr/doi/10.62520/fujece.1476716 |
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| _version_ | 1841545192154333184 |
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| author | Ebru Efeoğlu |
| author_facet | Ebru Efeoğlu |
| author_sort | Ebru Efeoğlu |
| collection | DOAJ |
| description |
Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and image processing techniques were applied to these images. Classification algorithms were used to detect the type of bruied object from processed images. To increase the success of the algorithms, correlation-based attribute selection (CFS) and Principal Component Analysis (PCA) were used as attribute selection techniques. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. The performance of the algorithms was analyzed using the 10-fold cross-validation method. As a result, it was understood that the use of the PCA algorithm in attribute selection increases the classification success more than metaheuristic algorithms. The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system. |
| format | Article |
| id | doaj-art-43347f51d8a2448bbdb3d0ba220030ce |
| institution | Kabale University |
| issn | 2822-2881 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Firat University |
| record_format | Article |
| series | Firat University Journal of Experimental and Computational Engineering |
| spelling | doaj-art-43347f51d8a2448bbdb3d0ba220030ce2025-01-12T08:01:35ZengFirat UniversityFirat University Journal of Experimental and Computational Engineering2822-28812024-10-013336237610.62520/fujece.1476716 An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems Ebru Efeoğlu0https://orcid.org/0000-0001-5444-6647KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and image processing techniques were applied to these images. Classification algorithms were used to detect the type of bruied object from processed images. To increase the success of the algorithms, correlation-based attribute selection (CFS) and Principal Component Analysis (PCA) were used as attribute selection techniques. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. The performance of the algorithms was analyzed using the 10-fold cross-validation method. As a result, it was understood that the use of the PCA algorithm in attribute selection increases the classification success more than metaheuristic algorithms. The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system.https://dergipark.org.tr/tr/doi/10.62520/fujece.1476716 |
| spellingShingle | Ebru Efeoğlu An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems Firat University Journal of Experimental and Computational Engineering |
| title |
An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems
|
| title_full |
An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems
|
| title_fullStr |
An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems
|
| title_full_unstemmed |
An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems
|
| title_short |
An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems
|
| title_sort | artificial intelligence based hybrid approach to detect the type of buried objects with broad frequency band antenna systems |
| url | https://dergipark.org.tr/tr/doi/10.62520/fujece.1476716 |
| work_keys_str_mv | AT ebruefeoglu anartificialintelligencebasedhybridapproachtodetectthetypeofburiedobjectswithbroadfrequencybandantennasystems AT ebruefeoglu artificialintelligencebasedhybridapproachtodetectthetypeofburiedobjectswithbroadfrequencybandantennasystems |