EVALUATING THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS AND HYBRID CNN-SVM MODELS FOR SYMBOL RECOGNITION IN COMPLEX DATASETS
Keywords:
pattern recognition, Intelligent Data Analysis, Machine Learning Algorithms, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Feature Extraction, Deep Learning, Image Classification, Pattern Recognition, Hybrid Models, k-Nearest Neighbors (k-NN), Random Forest, Optical Character Recognition (OCR), Shape Recognition, Automated Symbol IdentificationAbstract
In this paper, we propose an algorithm for identifying symbols in various contexts using intelligent data analysis methods. With the increasing need for automated systems to process symbolic data from sources such as images, texts, or audio, we explore several state-of-the-art techniques, including machine learning models, pattern recognition, and feature extraction. The proposed method improves symbol identification accuracy by integrating supervised learning with advanced feature engineering. Our results show a significant enhancement in symbol recognition rates compared to traditional approaches.
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