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Artificial intelligence has many branches of image processing-based applications in terms of classification and identification, error back-propagation neural network is a great match for such applications as long as linear vector quantization (LVQ) and pattern recognition is another great match for recognition of digital images based on their features. The dataset used in this paper are gel electrophoresis images where 6 features had been extracted from the images and used as input to a neural network for learning and then checked for recognition purposed and the system managed to recognize all the 6 images. Six features had been used: average, standard deviation, smoothness, skewness, uniformity, and entropy. A tiny error rate where allowed in the recognition program to cover the variation of the dataset and the test data (gel-electrophoresis images). The proposed system had successfully managed to identify all of the learned data in both LVQ and error-back-propagation. Error-back-propagation proved itself as a great tool in terms of learning time compared with LVQ, which was very slow in terms of learning time and recognition.
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