
This research aims to enhance computational efficiency in object detection by selecting appropriate feature combinations within the GLCM framework and evaluating the performance of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) classification models.


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optimizing-object-detection-accuracy-and-computational-efficiency-using-glcm-based-feature-combinations-and-machine-learning-models


Optimizing Object Detection Accuracy and Computational Efficiency Using GLCM-Based Feature Combinations and Machine Learning Models
