Comparison of Cultural Attractions Classification Model Performance Nakhon Si Thammarat Province
Keywords:
Cultural attractions, Artificial Neural Networks, Deep learningAbstract
Nakhon Si Thammarat is an ancient city of historical significance in Southeast Asia, boasting diverse and unique cultural tourist attractions. This research aims to compile an image dataset of key cultural tourist sites in Nakhon Si Thammarat, including 8 noteworthy sites. The dataset is used for training and comparing the performance of various image classification models. The learning process involves two main steps: dimensionality reduction of image data through either image flattening or convolutional neural networks, and model training. Three models were evaluated: nearest neighbor, decision tree, and logistic regression. The study found that using convolutional neural networks for dimensionality reduction yielded higher performance compared to image flattening, and the logistic regression model achieved the highest accuracy at 93.75%.
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