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- W4360994489 abstract "Detection of forest fire should be quick and accurate as forests are the important sources to lead a vital life on earth. Detection of fire can be extremely difficult using existing methods of smoke sensors installed and they are slow and cost inefficient, so in order to avoid large scale fires, detection from visual scenes is required. In this work detection of fire in an image is done by extracting features using Deep learning algorithm and with those features as input to machine learning algorithm, a model is build with the help of different machine learning algorithms like Random Forest, Support Vector Machine, XGBoost and K-Means Clustering. Using these algorithms the data sets are classified into fire and non fire images to build the model and the test data of the data set is provided as input for getting the validation accuracy of the model. Then comparison is done among machine learning algorithms to find which algorithm provides more accuracy. To test the accuracy of the fire presence classification evaluation metrics are used in the model and find that accuracy of CNN-RF and CNN-XGBOOST are 98.53% which is greater than accuracy of CNN-SVM 97.06%." @default.
- W4360994489 created "2023-03-30" @default.
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- W4360994489 date "2023-02-02" @default.
- W4360994489 modified "2023-10-04" @default.
- W4360994489 title "Forest Fire Detection using CNN-RF and CNN-XGBOOST Machine Learning Algorithms" @default.
- W4360994489 doi "https://doi.org/10.1109/icais56108.2023.10073910" @default.
- W4360994489 hasPublicationYear "2023" @default.
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