ABSTRACT
Everything is moving to online platforms in this digital age. The frauds connected to this are likewise rising quickly. After COVID, the amount of fraudulent transactions increased, making this a very essential area of research. This study intends to develop a fraud detection model using machine learning's semi-supervised approach. It combines supervised and unsupervised learning methods and is far more practical than the other two. A bank fraud detection model utilizing the Laplacian model of semi-supervised learning is created. To determine the optimal model, the parameters were adjusted over a wide range of values. This model's strength is that it can handle a big volume of unlabeled data with ease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics. © Published under licence by IOP Publishing Ltd.
ABSTRACT
This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average ARIMA (1, 1, 2), and hence, a forecast about the evolution of the COVID-19 vaccination in 60 days is presented. The theoretical aspect provides equilibrium points, reproduction number R0, and biologically feasible region of the proposed model. Also, we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform. On the numerical aspect, we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model. Moreover, numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model. Finally, the concerned numerical simulations are compared with the exact real available date given in the statistical aspect. © 2023 Authors. All rights reserved.
ABSTRACT
An epidemic Susceptible-Infected-Removal (SIR) model with vital dynamics of birth and death rates is presented on a network by using graph Laplacian diffusion. Migration parameter has been introduced for controlling the population mobility between different regions. The subsequent waves for the infected occur under some restrictions on the migration parameter. Isolation strategies are investigated for different types of networks. Finally, we estimate important model parameters using the Least-Square method. © 2022 IEEE.
ABSTRACT
An epidemic Susceptible-Infected-Removal (SIR) model with vital dynamics of birth and death rates is presented on a network by using graph Laplacian diffusion. Migration parameter has been introduced for controlling the population mobility between different regions. The subsequent waves for the infected occur under some restrictions on the migration parameter. Isolation strategies are investigated for different types of networks. Finally, we estimate important model parameters using the Least-Square method. © 2022 IEEE.
ABSTRACT
This study aims to structure and evaluate a new COVID-19 model which predicts vaccination effect in the Kingdom of Saudi Arabia (KSA) under Atangana-Baleanu-Caputo (ABC) fractional derivatives. On the statistical aspect, we analyze the collected statistical data of fully vaccinated people from June 01, 2021, to February 15, 2022. Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic, based on daily series data from February 16, 2022, to April 15, 2022. The results of data analysis show that the appropriate model is autoregressive integrated moving average ARIMA (1, 1, 2), and hence, a forecast about the evolution of the COVID-19 vaccination in 60 days is presented. The theoretical aspect provides equilibrium points, reproduction number R0, and biologically feasible region of the proposed model. Also, we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform. On the numerical aspect, we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model. Moreover, numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model. Finally, the concerned numerical simulations are compared with the exact real available date given in the statistical aspect. © 2023 Authors. All rights reserved.
ABSTRACT
The first one studies three procedures of inverse Laplace Transforms: A Sinc–Thiele approximation, a Sinc and a Sinc–Gaussian (SG) method. Classical Iterated Function Systems are composed of a set of Banach contractions giving rise to a fractal attractor in a metric space E. In the reference [3], the authors extend this concept in different ways. Additionally, in the last part of the paper, they consider an infinite collection of maps and multivalued mappings wn:E→K(E), where K(E) is the Hausdorff space of compact subsets of E. The authors prove that under certain conditions, these IFSs own an attractor.
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This study presents a novel numerical method to solve PDEs with the fractional Caputo operator. In this method, we apply the Newton interpolation numerical scheme in Laplace space, and then, the solution is returned to real space through the inverse Laplace transform. The Newton polynomial provides good results as compared to the Lagrangian polynomial, which is used to construct the Adams–Bashforth method. This procedure is used to solve fractional Buckmaster and diffusion equations. Finally, a few numerical simulations are presented, ensuring that this strategy is highly stable and quickly converges to an exact solution.
ABSTRACT
Purpose: The purpose of this study is to obtain an analytical solution for a nonlinear system of the COVID-19 model for susceptible, exposed, infected, isolated and recovered. Design/methodology/approach: The Laplace decomposition method and the differential transformation method are used. Findings: The obtained analytical results are useful on two fronts: first, they would contribute to a better understanding of the dynamic spread of the COVID-19 disease and help prepare effective measures for prevention and control. Second, researchers would benefit from these results in modifying the model to study the effect of other parameters such as partial closure, awareness and vaccination of isolated groups on controlling the pandemic. Originality/value: The approach presented is novel in its implementation of the nonlinear system of the COVID-19 model © 2022, Emerald Publishing Limited.
ABSTRACT
Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals. IEEE
ABSTRACT
In this paper, we consider the Prabhakar fractional logistic differential equation. By using appropriate limit relations, we recover some other logistic differential equations, giving representations of each solution in terms of a formal power series. Some numerical approximations are implemented by using truncated series.