3 research outputs found

    Accuracy comparison of different batch size for a supervised machine learning task with image classification

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    Machine learning is a type of artificial intelligence where computers solve issues by considering examples of real-world data. Within machine learning, there are various types of techniques or tasks such as supervised, unsupervised, reinforcement, and many hyperparameters have to be tuned to have high accuracy especially in image classification. The batch size refers to the total number of images required to train a single reverse and forward pass. It is one of the most essential hyperparameters. In our paper, we have studied the supervised task with image classification by changing batch size with epoch. The characterization effect of increasing the batch size on training time and how this relationship varies with the training model have been studied, which leads to extremely large variation between them. According to our results, a larger batch size does not always result in high accuracy

    Quad-color image encryption based on Chaos and Fibonacci Q-matrix

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    The Information technology requires the transmission of daily-life images that may reach to millions or even more. Thus, securing them becomes an urgent matter using the encryption technique. Where, a secret key is used for converting the original image into a noisy one and restoring it back using the same key. Confusion and Diffusion are the wildly used steps in such a technique. Therefore, a new algorithm is presented in this work that uses a fusion, segmentation, random assembling, hyperchaotic and Fibonacci Q-matrix (FQ-matrix). A novel fusion method is designed for fusing four color images into four different sequences according to their contained information. Then the resulted four images are each divided into four segments to be assembled randomly into one image using a random-key; which confused later using a six-dimensional hyperchaotic system and diffused using the FQ-matrix. The performance and robustness of the proposed algorithm have been computed based on different tests; where it proved its powerful capability in securing the transmitted images

    Enhanced image classification using edge CNN (E-CNN)

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    Recently, deep learning has become a hot topic in wide fields, especially in the computer vision that proved its efficiency in processing images. However, it tends to overfit or consumes a long learning time in many platforms. The causes behind these issues return to the huge number of learning parameters and lack or incorrect training samples. In this work, two levels of deep convolutional neural network (DCNN) are proposed for classifying the images. The first one is enhancing the training images with removing unnecessary details, and the second one is detecting the edges of the processed images for further reduction of learning time in the DCNN. The proposed work is inspired by the human eye's way in recognizing an object, where a piece of object can be helpful in the recognition and not necessarily the whole object or full colors. The goal is to speed up the learning process of CNN based on the preprocessed training samples that are precise and lighter to work well in real-time applications. The obtained results proved to be more significant for real-time classification as it reduced the learning process by (94%) in Animals10 dataset with a validation accuracy of (99.2%) in accordance with the classical DCNNs
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