| Autor: |
Reddy, D. Tharun Kumar, Ramesh, S. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3193 Issue 1, p1-8, 8p |
| Abstrakt: |
The primary objective of this research piece is to assess the effectiveness of Random Forest and support vector machine techniques for the identification of pests. The Methodology and the Elements: During the course of this investigation, the Random Forest and Support Vector Machine techniques were utilised. Twenty-eight iterations are included in both the SVM algorithm and the RF sample size each. For the purpose of determining the percentage of accuracy, the outcomes of the simulation were examined and analysed on many occasions. According to the findings, the Random Forest Algorithm is able to attain a higher level of accuracy than the Support Vector Machine (SVM), which is 59.3 percent (79.66 percent). Based on the findings, Random Forest was found to be much superior to Support Vector Machine. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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