The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python’s to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy.
Studies carried out by researchers show that data growth can be exploited in such a way that the use of deep learning algorithms allow predictions with a high level of precision based on the data, which is why the latest studies are focused on the use of convolutional neural networks as the optimal algorithm for image classification. The present research work has focused on making the diagnosis of a disease that affects the cornea called keratoconus through the use of deep learning algorithms to detect patterns that will later be used to carry out preventive detections. The algorithm used to perform the classifications has been convolutional neural networks as well as image preprocessing to remove noise that can limit neural network learning, resulting in more than 1900 classified images out of a total of >2000 images distributed between normal eyes and those with keratoconus, which is equivalent to 92%.
In this paper, two pairs of embedded Runge-Kutta (RK) type techniques for straightforwardly tackling third-order ordinary differential equations (ODEs) of the form v″′ = f(x, v, v′) signified as RKTGD strategies were proposed and explored. Relying on the order conditions, the primary pair with mathematical order 4 and 3 was called RKTGD4(3), while different has order 5 and 4, and was named RKTGD5(4). The new strategies were determined so that the higher-order techniques were exact and the lower order techniques would bring about the best error estimates. At that point, variables step-size codes were created to support the methods and utilized in solving a lot of third-order problems. Comparisons were made between mathematical results and existing embedded RK pairs within the scientific literature, that require the problems to be reduced into a system of first-order ODEs, and the effectiveness of the new RKTGD pairs have appeared.
The Conjugate gradient method used to estimate the parameter of Marshall-Olkin Exponentiated Burr Type X distribution (MOEBX). The proposed distribution MOEBX based on the work by (Marshall-Olkin 1997). Several properties of the MOEBX distribution were investigated and studied such as quantile function, moments, moment generation function and order statistics. The estimation process by maximum likelihood estimation maybe an obstacle for statisticians, so used Conjugate Gradient method in unconstrained optimization to estimate parameters. It was employed for estimating the three parameters of the new distribution. The flexibility of the MOEBX was illustrated by using two real data sets. We compared with nested and no nested distributions and encouraging results were obtained using a real data set.
The current paper modified method of conjugate gradient for solving problems of unconstrained optimization. The modified method convergence is achieved by assuming some hypotheses. The statistical results demonstrate that the modified method is efficient for solving problems of Unconstrained Nonlinear Optimization in comparison with methods FR and HS.
http://dx.doi.org/10.25130/tjps.24.2019.095
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