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Journal articles on the topic 'Numerical data analysis'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Song, Daegene. "Data Analysis in an Entanglement Network Using Numerical Methods." NeuroQuantology 20, no. 2 (2022): 158–64. http://dx.doi.org/10.14704/nq.2022.20.2.nq22084.

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While the foundation of quantum theory has been debated, its pragmatism has made it enormously productive. Establishing secret keys over long distances has been realized in the real world. Once considered only a hype, quantum computers have also been implemented in laboratories and are performing computations that are superior to their classical counterparts. In this paper, building on previous work, three 2-level entangled states are studied. In particular, the extensive range of states that yield the near-optimal result when entanglement swapping is applied at joints is numerically examined.

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2

Boros, Endre, Peter L. Hammer, Toshihide Ibaraki, and Alexander Kogan. "Logical analysis of numerical data." Mathematical Programming 79, no. 1-3 (1997): 163–90. http://dx.doi.org/10.1007/bf02614316.

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3

Scott, Larkin B., and L. Ridgway Scott. "Efficient Methods for Data Smoothing." SIAM Journal on Numerical Analysis 26, no. 3 (1989): 681–92. http://dx.doi.org/10.1137/0726040.

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4

Eldén, Lars. "Numerical linear algebra in data mining." Acta Numerica 15 (May 2006): 327–84. http://dx.doi.org/10.1017/s0962492906240017.

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Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

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5

Romanov, L. N. "Risk minimization for atmospheric data restoration." Numerical Analysis and Applications 2, no. 2 (2009): 173–78. http://dx.doi.org/10.1134/s1995423909020086.

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6

Hassouba, M. A., H. I. Al-Naggar, N. M. Al-Naggar, and C. Wilke. "Numerical Analysis and Simulation Analysis for Space-Time Data." Contributions to Plasma Physics 46, no. 10 (2006): 798–806. http://dx.doi.org/10.1002/ctpp.200610080.

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7

Wang, Y. B., and T. Wei. "Numerical differentiation for two-dimensional scattered data." Journal of Mathematical Analysis and Applications 312, no. 1 (2005): 121–37. http://dx.doi.org/10.1016/j.jmaa.2005.03.025.

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8

Mohammadi, Ali, Javad Shohani, and Rajabali Borzooei. "Numerical Taxonomy Analysis With Trapezoidal Fuzzy Data." Journal of Mathematics and Computer Science 02, no. 01 (2011): 100–110. http://dx.doi.org/10.22436/jmcs.002.01.11.

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9

Hare, Jonathan. "Simple numerical analysis of longboard speedometer data." Physics Education 48, no. 6 (2013): 723–30. http://dx.doi.org/10.1088/0031-9120/48/6/723.

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10

Smith, Dan S. P., Bruce M. Law, Martin Smock, and David P. Landau. "Numerical analysis of ellipsometric critical adsorption data." Physical Review E 55, no. 1 (1997): 620–36. http://dx.doi.org/10.1103/physreve.55.620.

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11

Hypki, Arkadiusz, Mirek Giersz, Abbas Askar, Diogo Belloni, and Agostino Leveque. "BEANS – distributed data analysis for numerical simulations." Proceedings of the International Astronomical Union 14, S351 (2019): 460–63. http://dx.doi.org/10.1017/s1743921319007452.

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Abstractbeans is a tool for distributed data analysis. It provides web and command line interface for data analysis and plotting for huge datasets. beans is written in a general form and can be used in any field of research to analyze the data. The main purpose of beans is to provide to the community a versatile tool to store, analyze and then visualize any amount of scientific data (e.g. numerical simulation, observations).

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12

Sawitzki, Günther. "Testing numerical reliability of data analysis systems." Computational Statistics & Data Analysis 18, no. 2 (1994): 269–86. http://dx.doi.org/10.1016/0167-9473(94)90176-7.

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13

Costantini, Paolo, and Francesca Pelosi. "Data Approximation Using Shape-Preserving Parametric Surfaces." SIAM Journal on Numerical Analysis 47, no. 1 (2009): 20–47. http://dx.doi.org/10.1137/070694843.

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14

Harten, Ami. "Multiresolution Representation of Data: A General Framework." SIAM Journal on Numerical Analysis 33, no. 3 (1996): 1205–56. http://dx.doi.org/10.1137/0733060.

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15

Al-Maskari, Mariam, and Samir Karaa. "Numerical Approximation of Semilinear Subdiffusion Equations with Nonsmooth Initial Data." SIAM Journal on Numerical Analysis 57, no. 3 (2019): 1524–44. http://dx.doi.org/10.1137/18m1189750.

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16

Bomba, A. Ya, and M. V. Boichura. "Numerical Complex Analysis Method for Parameters Identification of Anisotropic Media using Applied Quasipotential Tomographic Data." Mathematical and computer modelling. Series: Technical sciences, no. 19 (June 20, 2019): 11–17. http://dx.doi.org/10.32626/2308-5916.2019-19.11-17.

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17

Chatelin, F., and D. Belaid. "Numerical analysis for factorial data analysis. Part I: Numerical software — the package INDA for microcomputers." Applied Stochastic Models and Data Analysis 3, no. 4 (1987): 193–206. http://dx.doi.org/10.1002/asm.3150030402.

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18

Chukanova, Svitlana. "The Notion of "Research Data": Types and Kinds of Research Data in the Context of Data Management Practice." Ukrainian Journal on Library and Information Science, no. 8 (December 20, 2021): 128–38. https://doi.org/10.31866/2616-7654.8.2021.247590.

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With the rapid development of the concept of Open Science, the quantitative growth of data obtained during the research, scientific attention to the practice of research data management (research data management) increases, which actualizes the definition of “research data” and identifying types of research data within the practice of their management, justification and coverage of the specifics of such data. The methodological tools of the study are based on the terminological method, the use of which was due to the need to identify relevant interpretations of the concept of &ldqu

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19

Lai, Ming-Jun, and Larry L. Schumaker. "Scattered Data Interpolation Using C2 Supersplines of Degree Six." SIAM Journal on Numerical Analysis 34, no. 3 (1997): 905–21. http://dx.doi.org/10.1137/s0036142994273173.

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20

Gelb, Anne, and Eitan Tadmor. "Detection of Edges in Spectral Data II. Nonlinear Enhancement." SIAM Journal on Numerical Analysis 38, no. 4 (2000): 1389–408. http://dx.doi.org/10.1137/s0036142999359153.

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21

Cohen, Albert, Wolfgang Dahmen, Ronald DeVore, Jalal Fadili, Olga Mula, and James Nichols. "Optimal Reduced Model Algorithms for Data-Based State Estimation." SIAM Journal on Numerical Analysis 58, no. 6 (2020): 3355–81. http://dx.doi.org/10.1137/19m1255185.

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22

Després, Bruno, and Maxime Herda. "Computation of Sum of Squares Polynomials from Data Points." SIAM Journal on Numerical Analysis 58, no. 3 (2020): 1719–43. http://dx.doi.org/10.1137/19m1273955.

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23

Gniazdowski, Zenon. "On the Analysis of Correlation Between Nominal Data and Numerical Data." Zeszyty Naukowe WWSI 16, no. 27 (2022): 57——82. https://doi.org/10.26348/znwwsi.27.57.

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The article investigates the possibility of measuring the strength of a linear correlation relationship between nominal data and numerical data. Correlation coefficients for variables coded with real numbers as well as for variables coded with complex numbers were studied. For variables coded with real numbers, unambiguous measures of real linear correlation were obtained. In the case of complex coding, it has been observed that the obtained complex correlation coefficients change with the permutation of the phases in the complex numbers used to code classes of elements with equal cardinalitie

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24

G.V., Suresh, and Srinivasa Reddy E.V. "Uncertain Data Analysis with Regularized XGBoost." Webology 19, no. 1 (2022): 3722–40. http://dx.doi.org/10.14704/web/v19i1/web19245.

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Uncertainty is a ubiquitous element in available knowledge about the real world. Data sampling error, obsolete sources, network latency, and transmission error are all factors that contribute to the uncertainty. These kinds of uncertainty have to be handled cautiously, or else the classification results could be unreliable or even erroneous. There are numerous methodologies developed to comprehend and control uncertainty in data. There are many faces for uncertainty i.e., inconsistency, imprecision, ambiguity, incompleteness, vagueness, unpredictability, noise, and unreliability. Missing infor

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25

Durán, Ricardo, Lucia Gastaldi, and Ariel Lombardi. "Analysis of Finite Element Approximations of Stokes Equations with NonSmooth Data." SIAM Journal on Numerical Analysis 58, no. 6 (2020): 3309–31. http://dx.doi.org/10.1137/19m1305872.

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26

Mukherjee, Indranil, Himadri Ghosh, and Aditya Chatterjee. "A Numerical Bayesian Approach to Spatial Data Analysis." Calcutta Statistical Association Bulletin 47, no. 3-4 (1997): 181–98. http://dx.doi.org/10.1177/0008068319970305.

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Investigators in diverse fields of scientific investigations (specially the geo-statisticians) have long been concerned with the so called Regionalised Variable (ReV) or variables whose values are in some way related to their positions in space. In such situations it becomes sometimes necessary to estimate the values for certain measures based on the ReV over some suitable blocking or redivision of the space. In thls paper we deliniate a numerical Bayesian method based on the Gibbs sampling approach towards solving this class of problems whlch is capable of being tuned to various situations ar

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27

Loewenstein, Walter. "Nuclear Engineering Data Bases, Standards and Numerical Analysis." Nuclear Technology 73, no. 1 (1986): 130–31. http://dx.doi.org/10.13182/nt86-a16215.

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28

Wyeth, N. Convers. "Simple numerical analysis of null‐point calorimeter data." Review of Scientific Instruments 56, no. 7 (1985): 1385–89. http://dx.doi.org/10.1063/1.1138489.

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29

Rusling, James F. "Minimizing errors in numerical analysis of chemical data." Journal of Chemical Education 65, no. 10 (1988): 863. http://dx.doi.org/10.1021/ed065p863.

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30

Dwivedi, K. K., A. Kulshreshtha, and J. Raju. "Computer oriented numerical analysis of nuclear track data." Radiation Measurements 29, no. 2 (1998): 165–71. http://dx.doi.org/10.1016/s1350-4487(98)00011-0.

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31

Kearsley, Anthony J., William E. Wallace, Javier Bernal, and Charles M. Guttman. "A numerical method for mass spectral data analysis." Applied Mathematics Letters 18, no. 12 (2005): 1412–17. http://dx.doi.org/10.1016/j.aml.2005.02.033.

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32

., Terttiaavini, Fakhry Zamzam, Mustafa Ramadhan, et al. "Clustering Analysis of Premier Research Fields." International Journal of Engineering & Technology 7, no. 4.44 (2018): 43. http://dx.doi.org/10.14419/ijet.v7i4.44.26860.

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The clusterization is one of methods which utilized to grouping a dataset which has a specific characteristics value. The processed data can be numerical or non-numerical data. Non-numeric data must be transformed first into numerical data. The case study in this study was to group research from six fields of science. The research data is non-numerical data is converted into the research contributions percentage in the science field. Utilized the c-means algorithm, the data was successfully grouped into three excellent research fields. The aim of the clustering is to know how many researchers

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33

Han, Lu, and Larry L. Schumaker. "Fitting Monotone Surfaces to Scattered Data Using C1 Piecewise Cubics." SIAM Journal on Numerical Analysis 34, no. 2 (1997): 569–85. http://dx.doi.org/10.1137/s0036142994268582.

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34

Ragozin, David L. "The Discrete k-Functional and Spline Smoothing of Noisy Data." SIAM Journal on Numerical Analysis 22, no. 6 (1985): 1243–54. http://dx.doi.org/10.1137/0722077.

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35

Locke, Sarah, and John Singler. "New Proper Orthogonal Decomposition Approximation Theory for PDE Solution Data." SIAM Journal on Numerical Analysis 58, no. 6 (2020): 3251–85. http://dx.doi.org/10.1137/19m1297002.

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36

Fornasier, Massimo, and Holger Rauhut. "Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints." SIAM Journal on Numerical Analysis 46, no. 2 (2008): 577–613. http://dx.doi.org/10.1137/0606668909.

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37

Klimova, E. G. "A Local Ensemble Data Assimilation Algorithm for Nonlinear Geophysical Models." Numerical Analysis and Applications 16, no. 1 (2023): 22–33. http://dx.doi.org/10.1134/s1995423923010032.

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38

Ling, Leevan. "Finding Numerical Derivatives for Unstructured and Noisy Data by Multiscale Kernels." SIAM Journal on Numerical Analysis 44, no. 4 (2006): 1780–800. http://dx.doi.org/10.1137/050630246.

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39

Babuška, I., B. Q. Guo, and J. E. Osborn. "Regularity and Numerical Solution of Eigenvalue Problems with Piecewise Analytic Data." SIAM Journal on Numerical Analysis 26, no. 6 (1989): 1534–60. http://dx.doi.org/10.1137/0726090.

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40

I. Dimitras, Augustinos, Stelios Papadakis, and Alexandros Garefalakis. "Evaluation of empirical attributes for credit risk forecasting from numerical data." Investment Management and Financial Innovations 14, no. 1 (2017): 9–18. http://dx.doi.org/10.21511/imfi.14(1).2017.01.

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In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric mo

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41

Atramentova, L. A. "Statistical analysis of cytogenetic data." Faktori eksperimental'noi evolucii organizmiv 28 (August 31, 2021): 146–50. http://dx.doi.org/10.7124/feeo.v28.1391.

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Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample in

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42

Bonilla, Luis L., Ana Carpio, and Carolina Trenado. "Tracking collective cell motion by topological data analysis." PLOS Computational Biology 16, no. 12 (2020): e1008407. http://dx.doi.org/10.1371/journal.pcbi.1008407.

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By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl

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43

Hengki, Tamando Sihotang, Efendi Syahril, Zarlis Muhammad, and Mawengkang Herman. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497~1504. https://doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical e

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44

Engelberg, Shlomo, and Eitan Tadmor. "Recovery of Edges from Spectral Data with Noise—A New Perspective." SIAM Journal on Numerical Analysis 46, no. 5 (2008): 2620–35. http://dx.doi.org/10.1137/070689899.

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45

Gupta, J. Sen. "A Priori Error Bounds for Parabolic Interface Problems with Measure Data." Numerical Analysis and Applications 16, no. 3 (2023): 259–75. http://dx.doi.org/10.1134/s1995423923030072.

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46

P'yanylo, Ya, H. P'yanylo, and M. Vasiunyk. "Application of orthogonal polynomials for analysis of input numerical data in the problems of mass transfer." Mathematical Modeling and Computing 2, no. 1 (2015): 88–98. http://dx.doi.org/10.23939/mmc2015.01.088.

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47

Leykekhman, Dmitriy, Boris Vexler, and Daniel Walter. "Numerical analysis of sparse initial data identification for parabolic problems." ESAIM: Mathematical Modelling and Numerical Analysis 54, no. 4 (2020): 1139–80. http://dx.doi.org/10.1051/m2an/2019083.

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In this paper we consider a problem of initial data identification from the final time observation for homogeneous parabolic problems. It is well-known that such problems are exponentially ill-posed due to the strong smoothing property of parabolic equations. We are interested in a situation when the initial data we intend to recover is known to be sparse, i.e. its support has Lebesgue measure zero. We formulate the problem as an optimal control problem and incorporate the information on the sparsity of the unknown initial data into the structure of the objective functional. In particular, we

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48

Le Roux, Marie-Noëlle, and Vidar Thomée. "Numerical Solution of Semilinear Integrodifferential Equations of Parabolic Type with Nonsmooth Data." SIAM Journal on Numerical Analysis 26, no. 6 (1989): 1291–309. http://dx.doi.org/10.1137/0726075.

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49

Xu, Wanting, and Mihai Anitescu. "A Limited-Memory Multiple Shooting Method for Weakly Constrained Variational Data Assimilation." SIAM Journal on Numerical Analysis 54, no. 6 (2016): 3300–3331. http://dx.doi.org/10.1137/15m1052706.

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50

Jovanovic, Bosko S., and Lubin G. Vulkov. "On the Convergence of Difference Schemes for Hyperbolic Problems with Concentrated Data." SIAM Journal on Numerical Analysis 41, no. 2 (2003): 516–38. http://dx.doi.org/10.1137/s0036142900382983.

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