The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. The major advantages of super-resolution methods are that they are economical, independent of the image capture devices, and can be statically used. In this paper, a single-image super-resolution network model based on convolutional neural networks is proposed by combining conventional autoencoder and residual neural network approaches. A convolutional neural network-based dictionary method is used to train low-resolution input images for high-resolution images. In addition, a linear refined unit thresholds the convolutional neural network output to provide a better low-resolution image dictionary. Autoencoders aid in the removal of noise from images and the enhancement of their quality. Secondly, the residual neural network model processes it further to create a high-resolution image. The experimental results demonstrate the outstanding performance of our proposed method compared to other traditional methods. The proposed method produces clearer and more detailed high-resolution images, as they are important in real-life applications. Moreover, it has the advantage of combining convolutional neural network-based dictionary learning, autoencoder image enhancement, and noise removal. Furthermore, residual neural network training with improved preprocessing creates an efficient and versatile single-image super-resolution network.
Among various diseases one of the hazardous diseases that takes uncountable lives every year is heart disease. In todays world, it is believed that one person dies from heart disease every minute. By WHO report number of deaths that take place due to heart disease is 1.75 million, with the number expected to rise to 75 million by 2030. As a result, an accurate and fast heart disease prognosis is a critical necessity for lowering heart disease-related death rates. The Angiography is the easiest and effective clinical test but it has drawback of expensive in cost and many side effects. So, in order to deal with this difficult issue, a significant role in various disease prognosis is shown by ICT. Machine learning is an emerging technology that performs automation from previous available data and shows promising results, it is a subset of artificial intelligence. In this work different literatures related to heart disease and machine learning techniques used in those literatures are critically analyzed and a review of them is summarized in a systematic manner.
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