{'fraction_non_alphanumeric': 0.025240384615384616, 'fraction_numerical': 0.0, 'mean_word_length': 5.5078125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Comparison of neural network algorithms in image compression technique

Image compression technique is very useful for reducing memory of storage devices and to increase efficient utilization of bandwidth during transmission of data. So, we are making a comparison within different algorithm belonging to the same family of neural networking. To evaluate the best suited algorithm for medical image compression among neural networking algorithms. In this paper we consider Support Vector Machines (SVM) algorithm, Radial Basis Function (RBF) algorithm and Back Propagation (BP). We evaluate the result of those algorithms by utilizing the parameters Such as Compression Ratio (CR), Execution Time, Signal to Noise Ratio (PSNR), Memory. Based on obtained values we will justify the best algorithm among the selected three algorithms.