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Computation, Volume 9, Issue 1 (January 2021) – 7 articles

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Open AccessEditorial
Acknowledgment to Reviewers of Computation in 2020
Computation 2021, 9(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010007 - 18 Jan 2021
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Abstract
Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Computation maintains its standards for the high quality of its published papers [...] Full article
Open AccessArticle
PPDM-TAN: A Privacy-Preserving Multi-Party Classifier
Computation 2021, 9(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010006 - 16 Jan 2021
Viewed by 379
Abstract
Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while [...] Read more.
Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types. Full article
(This article belongs to the Special Issue Recent Advances in Computation Engineering)
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Open AccessArticle
Finite Element Simulation of Thermo-Mechanical Model with Phase Change
Computation 2021, 9(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010005 - 15 Jan 2021
Viewed by 328
Abstract
In this work, we consider a mathematical model and finite element implementation of heat transfer and mechanics of soils with phase change. We present the construction of the simplified mathematical model based on the definition of water and ice fraction volumes as functions [...] Read more.
In this work, we consider a mathematical model and finite element implementation of heat transfer and mechanics of soils with phase change. We present the construction of the simplified mathematical model based on the definition of water and ice fraction volumes as functions of temperature. In the presented mathematical model, the soil deformations occur due to the porosity growth followed by the difference between ice and water density. We consider a finite element discretization of the presented thermoelastic model with implicit time approximation. Implementation of the presented basic mathematical model is performed using FEniCS finite element library and openly available to download. The results of the numerical investigation are presented for the two-dimensional and three-dimensional model problems for two test cases in three different geometries. We consider algorithms with linearization from the previous time layer (one Picard iteration) and the Picard iterative method. Computational time is presented with the total number of nonlinear iterations. A numerical investigation with results of the convergence of the nonlinear iteration is presented for different time step sizes, where we calculate relative errors for temperature and displacements between current solution and reference solution with the largest number of the time layers. Numerical results illustrate the influence of the porosity change due to the phase-change of pore water into ice on the deformation of the soils. We observed a good numerical convergence of the presented implementation with the small number of nonlinear iterations, that depends on time step size. Full article
(This article belongs to the Section Computational Engineering)
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Open AccessArticle
On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19
Computation 2021, 9(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010004 - 07 Jan 2021
Viewed by 820
Abstract
The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical [...] Read more.
The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the epidemic, one problem is to determine which factors play an important role in a patient’s progression of the disease. Here, we construct an enhanced COVID-19 structured dataset from more than one source, using natural language processing to add local weather conditions and country-specific research sentiment. The enhanced structured dataset contains 301,363 samples and 43 features, and we applied both machine learning algorithms and deep learning algorithms on it so as to forecast patient’s survival probability. In addition, we import alignment sequence data to improve the performance of the model. Application of Extreme Gradient Boosting (XGBoost) on the enhanced structured dataset achieves 97% accuracy in predicting patient’s survival; with climatic factors, and then age, showing the most importance. Similarly, the application of a Multi-Layer Perceptron (MLP) achieves 98% accuracy. This work suggests that enhancing the available data, mostly basic information on patients, so as to include additional, potentially important features, such as weather conditions, is useful. The explored models suggest that textual weather descriptions can improve outcome forecast. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease
Computation 2021, 9(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010003 - 06 Jan 2021
Viewed by 557
Abstract
In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study [...] Read more.
In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%. Full article
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
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Open AccessArticle
Accurate Spectral Collocation Computation of High Order Eigenvalues for Singular Schrödinger Equations
Computation 2021, 9(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010002 - 29 Dec 2020
Viewed by 414
Abstract
We are concerned with the study of some classical spectral collocation methods, mainly Chebyshev and sinc as well as with the new software system Chebfun in computing high order eigenpairs of singular and regular Schrödinger eigenproblems. We want to highlight both the qualities [...] Read more.
We are concerned with the study of some classical spectral collocation methods, mainly Chebyshev and sinc as well as with the new software system Chebfun in computing high order eigenpairs of singular and regular Schrödinger eigenproblems. We want to highlight both the qualities as well as the shortcomings of these methods and evaluate them in conjunction with the usual ones. In order to resolve a boundary singularity, we use Chebfun with domain truncation. Although it is applicable with spectral collocation, a special technique to introduce boundary conditions as well as a coordinate transform, which maps an unbounded domain to a finite one, are the special ingredients. A challenging set of “hard”benchmark problems, for which usual numerical methods (f. d., f. e. m., shooting, etc.) fail, were analyzed. In order to separate “good”and “bad”eigenvalues, we have estimated the drift of the set of eigenvalues of interest with respect to the order of approximation and/or scaling of domain parameter. It automatically provides us with a measure of the error within which the eigenvalues are computed and a hint on numerical stability. We pay a particular attention to problems with almost multiple eigenvalues as well as to problems with a mixed spectrum. Full article
(This article belongs to the Section Computational Engineering)
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Open AccessArticle
Comparative Computational Study of L-Amino Acids as Green Corrosion Inhibitors for Mild Steel
Computation 2021, 9(1), 1; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9010001 - 25 Dec 2020
Viewed by 494
Abstract
This research evaluates the inhibitory effect of L-amino acids (AAs) with different side chain lengths on Fe (100) surfaces implementing Monte Carlo (MC) simulation. A quantitative and qualitative description of the adsorption behavior of AAs on the iron surface has been carried out. [...] Read more.
This research evaluates the inhibitory effect of L-amino acids (AAs) with different side chain lengths on Fe (100) surfaces implementing Monte Carlo (MC) simulation. A quantitative and qualitative description of the adsorption behavior of AAs on the iron surface has been carried out. Calculations have shown that the absolute values of the adsorption energy of L-amino acids increase with side chain prolongation; they are also determined by the presence of heteroatoms. The maximum absolute value of the adsorption energy AAs on the iron surface in accordance with the side chain classification increases in the following sequence: Glu (acidic) < Gln (polar) < Trp (nonpolar) < Arg (basic). AAs from nonpolar and basic groups have the best adsorption ability to the iron surface, which indicates their highest inhibitory efficiency according to the results of the MC simulation. The calculation results agree with the experimental data. Full article
(This article belongs to the Section Computational Chemistry)
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