Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques
In this paper, computation of very high frequency (VHF) signal strength for point to area network was carried out using machine learning modeling techniques. Seven different machine learning models were adopted: Decision Tree, Random Forest, AdaBoost, k-Nearest Neighbor, Support Vector Machine, Arti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penteract Technology
2023-09-01
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| Series: | Malaysian Journal of Science and Advanced Technology |
| Subjects: | |
| Online Access: | http://www.mjsat.com.my/index.php/mjsat/article/view/163 |
| _version_ | 1827820772403445760 |
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| author | Kingsley Igwe Nurudeen Olawale Adeyemi Lukman Folorunso Onadiran |
| author_facet | Kingsley Igwe Nurudeen Olawale Adeyemi Lukman Folorunso Onadiran |
| author_sort | Kingsley Igwe |
| collection | DOAJ |
| description | In this paper, computation of very high frequency (VHF) signal strength for point to area network was carried out using machine learning modeling techniques. Seven different machine learning models were adopted: Decision Tree, Random Forest, AdaBoost, k-Nearest Neighbor, Support Vector Machine, Artificial Neural Network and Linear Regression. A total of 120 data points was used in computing the signal strength. 72 data points (60%) was used to train the model, while the remaining 48 data points (40%) were used as test data to determine the accuracy of the computation for all the models. From the results, it was observed that the accuracy of the computations was greatly influenced by the amount of training data that was used. Also, from the results, in highest order of accuracy, AdaBoost was adjudged the best model. This was followed by the Artificial Neural Network model. Generally, the error margin of computation obtained for these two models were low, hence indicating that the models can be effectively relied on for computation of signal strength in the study area.
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| first_indexed | 2024-03-12T01:32:52Z |
| format | Article |
| id | doaj.art-0c3bb3b5fdd6458da3ef1160ed6e3c0e |
| institution | Directory Open Access Journal |
| issn | 2785-8901 |
| language | English |
| last_indexed | 2024-03-12T01:32:52Z |
| publishDate | 2023-09-01 |
| publisher | Penteract Technology |
| record_format | Article |
| series | Malaysian Journal of Science and Advanced Technology |
| spelling | doaj.art-0c3bb3b5fdd6458da3ef1160ed6e3c0e2023-09-11T15:31:28ZengPenteract TechnologyMalaysian Journal of Science and Advanced Technology2785-89012023-09-013310.56532/mjsat.v3i3.163Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling TechniquesKingsley Igwe0Nurudeen Olawale Adeyemi 1Lukman Folorunso Onadiran 2Department of Physics, Federal University of Technology, Minna, Nigeria.Department of Physics, Federal University of Technology, Minna, Nigeria.Department of Physics, Federal University of Technology, Minna, Nigeria.In this paper, computation of very high frequency (VHF) signal strength for point to area network was carried out using machine learning modeling techniques. Seven different machine learning models were adopted: Decision Tree, Random Forest, AdaBoost, k-Nearest Neighbor, Support Vector Machine, Artificial Neural Network and Linear Regression. A total of 120 data points was used in computing the signal strength. 72 data points (60%) was used to train the model, while the remaining 48 data points (40%) were used as test data to determine the accuracy of the computation for all the models. From the results, it was observed that the accuracy of the computations was greatly influenced by the amount of training data that was used. Also, from the results, in highest order of accuracy, AdaBoost was adjudged the best model. This was followed by the Artificial Neural Network model. Generally, the error margin of computation obtained for these two models were low, hence indicating that the models can be effectively relied on for computation of signal strength in the study area. http://www.mjsat.com.my/index.php/mjsat/article/view/163Machine Learning ModelsOrange 3.22.0Point to AreaSignal StrengthVHF |
| spellingShingle | Kingsley Igwe Nurudeen Olawale Adeyemi Lukman Folorunso Onadiran Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques Malaysian Journal of Science and Advanced Technology Machine Learning Models Orange 3.22.0 Point to Area Signal Strength VHF |
| title | Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques |
| title_full | Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques |
| title_fullStr | Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques |
| title_full_unstemmed | Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques |
| title_short | Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques |
| title_sort | computation of vhf signal strength for point to area network using machine learning modeling techniques |
| topic | Machine Learning Models Orange 3.22.0 Point to Area Signal Strength VHF |
| url | http://www.mjsat.com.my/index.php/mjsat/article/view/163 |
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