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Journal articles on the topic 'Random information'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Telksnys, Laimutis, and Jonas Kaukėnas. "Recognition of Short-Time Specific Random Elements in Random Sequences." Informatica 22, no. 2 (2011): 279–88. http://dx.doi.org/10.15388/informatica.2011.327.

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2

Zakaria, Abdul Alif, and Norli Anida Abdullah. "Secure Information Hiding Based on Random Similar Bit Mapping." International Journal of Machine Learning and Computing 10, no. 4 (2020): 568–75. http://dx.doi.org/10.18178/ijmlc.2020.10.4.974.

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3

Tsairidis, Ch, K. Ferentinos, and T. Papaioannou. "Information and random censoring." Information Sciences 92, no. 1-4 (1996): 159–74. http://dx.doi.org/10.1016/0020-0255(96)00055-2.

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4

Lu, Jay. "Random Choice and Private Information." Econometrica 84, no. 6 (2016): 1983–2027. http://dx.doi.org/10.3982/ecta12821.

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5

Herve, T., J. M. Dolmazon, and J. Demongeot. "Random field and neural information." Proceedings of the National Academy of Sciences 87, no. 2 (1990): 806–10. http://dx.doi.org/10.1073/pnas.87.2.806.

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6

Goloboff, Pablo A. "RANDOM DATA, HOMOPLASY AND INFORMATION." Cladistics 7, no. 4 (1991): 395–406. http://dx.doi.org/10.1111/j.1096-0031.1991.tb00046.x.

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7

Sumathi, S., and R. Rajesh. "Feature-Optimized Random Forest Model for Wildfire Prediction using Weather Information." Indian Journal Of Science And Technology 18, no. 19 (2025): 1530–37. https://doi.org/10.17485/ijst/v18i19.442.

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Objectives: Forest fires present a notable danger to economies and human communities. Accurate forest fire forecasting can support timely reactions, resource allocation, and effective management strategies. Methods: The Recursive Feature Elimination with Cross-Validation approach is used to extract the key features from the dataset that was obtained from Kaggle. This technique combines feature selection and cross-validation to ensure the selected features are well-suited to new data. The Random Forest regressor, which constructs several decision trees and combines their predictions to provide

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8

Nurullaev, Mirkhon Mukhammadovich, and Rakhmatillo Djuraevich Aloev. "Working with cryptographic key information." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 911–19. https://doi.org/10.11591/ijece.v13i1.pp911-919.

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It is important to create a cryptographic system such that the encryption system does not depend on the secret storage of the algorithm that is part of it, but only on the private key that is kept secret. In practice, key management is a separate area of cryptography, which is considered a problematic area. This paper describes the main characteristics of working with cryptographic key information. In that, the formation of keys and working with cryptographic key information are stored on external media. The random-number generator for generating random numbers used for cryptographic key gener

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9

Vos, Paul W. "Random Variables Aren’t Random." Mathematics 13, no. 5 (2025): 775. https://doi.org/10.3390/math13050775.

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This paper examines the foundational concept of random variables in probability theory and statistical inference, demonstrating that their mathematical definition requires no reference to randomization or hypothetical repeated sampling. We show how measure-theoretic probability provides a framework for modeling populations through distributions, leading to three key contributions. First, we establish that random variables, properly understood as measurable functions, can be fully characterized without appealing to infinite hypothetical samples. Second, we demonstrate how this perspective enabl

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10

KIKKAWA, Sho. "Biomedical Information Engineering : Information Processing of Biomedical Random Data." Journal of the Society of Mechanical Engineers 89, no. 811 (1986): 583–89. http://dx.doi.org/10.1299/jsmemag.89.811_583.

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11

Žilinskas, Julius, and Ian David Lockhart Bogle. "Evaluation Ranges of Functions using Balanced Random Interval Arithmetic." Informatica 14, no. 3 (2003): 403–16. http://dx.doi.org/10.15388/informatica.2003.030.

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12

Tsai, Tung-Tso, Yuh-Min Tseng, and Sen-Shan Huang. "Efficient Strongly Unforgeable ID-Based Signature Without Random Oracles." Informatica 25, no. 3 (2014): 505–21. http://dx.doi.org/10.15388/informatica.2014.26.

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13

Hajek, B. "Information measures for discrete random fields." IEEE Transactions on Information Theory 45, no. 6 (1999): 2210–11. http://dx.doi.org/10.1109/tit.1999.782177.

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14

Farooq, Umer, and Stefano Mancini. "Information Dissipation in Random Quantum Networks." Open Systems & Information Dynamics 21, no. 03 (2014): 1450004. http://dx.doi.org/10.1142/s1230161214500048.

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We study the information dynamics in a network of spin-1/2 particles when edges representing XY interactions are randomly added to a disconnected graph according to a probability distribution characterized by a “weighting” parameter. In this way, we model dissipation of information initially localized in a single or in two qubits all over the network. We then show the dependence of this phenomenon on the weighting parameter and the size of the network.

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15

Argyrakis, Panos. "Information dimension in random-walk processes." Physical Review Letters 59, no. 15 (1987): 1729–32. http://dx.doi.org/10.1103/physrevlett.59.1729.

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16

Minero, Paolo, Massimo Franceschetti, and David N. C. Tse. "Random Access: An Information-Theoretic Perspective." IEEE Transactions on Information Theory 58, no. 2 (2012): 909–30. http://dx.doi.org/10.1109/tit.2011.2173711.

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17

Carvajal-Rodríguez, A. "Non-random mating and information theory." Theoretical Population Biology 120 (March 2018): 103–13. http://dx.doi.org/10.1016/j.tpb.2018.01.003.

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18

Serva, Maurizio. "Random dynamical systems, entropies and information." Physica A: Statistical Mechanics and its Applications 290, no. 1-2 (2001): 243–50. http://dx.doi.org/10.1016/s0378-4371(00)00546-x.

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19

COMETS, FRANCIS, FRANÇOIS DELARUE, and RENÉ SCHOTT. "Information Transmission under Random Emission Constraints." Combinatorics, Probability and Computing 23, no. 6 (2014): 973–1009. http://dx.doi.org/10.1017/s096354831400039x.

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We model the transmission of a message on the complete graph with n vertices and limited resources. The vertices of the graph represent servers that may broadcast the message at random. Each server has a random emission capital that decreases at each emission. Quantities of interest are the number of servers that receive the information before the capital of all the informed servers is exhausted and the exhaustion time. We establish limit theorems (law of large numbers, central limit theorem and large deviation principle), as n → ∞, for the proportion of informed vertices before exhaustion and

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20

Röver, Christian. "Random template placement and prior information." Journal of Physics: Conference Series 228 (May 1, 2010): 012008. http://dx.doi.org/10.1088/1742-6596/228/1/012008.

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21

Abdel-All, Nassar H., H. N. Abd-Ellah, and H. M. Moustafa. "Information geometry of random walk distribution." Publicationes Mathematicae Debrecen 63, no. 1-2 (2003): 51–66. http://dx.doi.org/10.5486/pmd.2003.2630.

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22

Bradley, Richard. "Proposition-valued random variables as information." Synthese 175, S1 (2010): 17–38. http://dx.doi.org/10.1007/s11229-010-9741-3.

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23

Eichenberger, Reiner, and Angel Serna. "Random errors, dirty information, and politics." Public Choice 86, no. 1-2 (1996): 137–56. http://dx.doi.org/10.1007/bf00114879.

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24

Wang, Shengbao, Peng Zeng, Kim-Kwang Raymond Choo, and Hongbing Wang. "A CCA2-Secure Multi-Decrypter Encryption Scheme Without Random Oracles." Informatica 26, no. 3 (2015): 543–56. http://dx.doi.org/10.15388/informatica.2015.63.

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25

Kraemer, Carlo, Markus Nöth, and Martin Weber. "Information aggregation with costly information and random ordering: Experimental evidence." Journal of Economic Behavior & Organization 59, no. 3 (2006): 423–32. http://dx.doi.org/10.1016/j.jebo.2004.06.026.

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26

Cawkell, A. E. "Random superimposed coding." Journal of Information Science 11, no. 2 (1985): 90–91. http://dx.doi.org/10.1177/016555158501100206.

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27

Yu, Xiuqing, and Fengsheng Xu. "Random inverse packet information and its acquisition." Applied Mathematics and Nonlinear Sciences 5, no. 2 (2020): 357–66. http://dx.doi.org/10.2478/amns.2020.2.00042.

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AbstractPacket sets and inverse packet sets are two kinds of novel mathematical tools to analyze dynamic information systems. With advances in inverse packet sets, random inverse packet information is proposed by introducing random characteristics into inverse packet sets. Hence, random inverse packet information has dynamic and random characteristics, and is an extended form of inverse packet sets. Furthermore, random feature, dynamic feature, and identification relation about the random inverse packet information are discussed. Finally, based on the above theory, an instance is used to illus

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28

Bareiša, Eduardas, Vacius Jusas, Kęstutis Motiejūnas, and Rimantas Šeinauskas. "Functional Test Generation Based on Combined Random and Deterministic Search Methods." Informatica 18, no. 1 (2007): 3–26. http://dx.doi.org/10.15388/informatica.2007.159.

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29

Francois, Michael, Thomas Grosges, Dominique Barchiesi, and Robert Erra. "A New Pseudo-Random Number Generator Based on Two Chaotic Maps." Informatica 24, no. 2 (2013): 181–97. http://dx.doi.org/10.15388/informatica.2013.391.

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30

Li, Shiyong, Wei Sun, Yaming Zhang, and Yehua Chen. "Optimal Congestion Control and Routing for Multipath Networks with Random Losses." Informatica 26, no. 2 (2015): 313–34. http://dx.doi.org/10.15388/informatica.2015.50.

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31

Abed, A. Kh. "Encryption Method to Transmit Information at Random." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 22, no. 2 (2016): 233–37. http://dx.doi.org/10.17277/vestnik.2016.02.pp.233-237.

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32

Fabian, Z. "Information and entropy of continuous random variables." IEEE Transactions on Information Theory 43, no. 3 (1997): 1080–84. http://dx.doi.org/10.1109/18.568724.

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33

Gorelov, M. A. "Games with random errors of information transmission." Automation and Remote Control 76, no. 12 (2015): 2201–15. http://dx.doi.org/10.1134/s0005117915120097.

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34

Cui, Libin, Jinwei Shi, Yanrong Wang, et al. "Retrieval of contaminated information using random lasers." Applied Physics Letters 106, no. 20 (2015): 201101. http://dx.doi.org/10.1063/1.4921327.

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35

Collins, Benoît, and Ion Nechita. "Random matrix techniques in quantum information theory." Journal of Mathematical Physics 57, no. 1 (2016): 015215. http://dx.doi.org/10.1063/1.4936880.

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36

Garetto, Michele, Alessandro Nordio, Carla-Fabiana Chiasserini, and Emilio Leonardi. "Information-Theoretic Capacity of Clustered Random Networks." IEEE Transactions on Information Theory 57, no. 11 (2011): 7578–96. http://dx.doi.org/10.1109/tit.2011.2159574.

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37

Masson, J.-B., M. Bailly Bechet, and M. Vergassola. "Chasing information to search in random environments." Journal of Physics A: Mathematical and Theoretical 42, no. 43 (2009): 434009. http://dx.doi.org/10.1088/1751-8113/42/43/434009.

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38

Lerner, Vladimir S. "Information macrodynamic modelling of a random process." International Journal of Systems Science 40, no. 7 (2009): 729–44. http://dx.doi.org/10.1080/00207720902953169.

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39

Gurung, Sandeep, Mrinaldeep Chakravorty, Abhi Agarwal, and M. K. Ghose. "Multiple Information Hiding Using Circular Random Grids." Procedia Computer Science 48 (2015): 65–72. http://dx.doi.org/10.1016/j.procs.2015.04.111.

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40

STARK, DUDLEY. "Information Loss in Top to Random Shuffling." Combinatorics, Probability and Computing 11, no. 6 (2002): 607–27. http://dx.doi.org/10.1017/s0963548302005382.

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A top to random shuffle of a deck of cards is performed by taking the top card off of the deck and replacing it in a randomly chosen position of the deck. We find approximations of the relative entropy of a deck of n cards after m successive top to random shuffles. Initially the relative entropy decays linearly and for larger m it decays geometrically at a rate that alters abruptly at m = n log n. It converges to an explicitly given expression when m = [n log n+cn] for a constant c.

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41

Pupyshev, I. D., A. B. Polevoi, and D. E. Krivobokov. "Information Content of the Random Process Signal." Fibre Chemistry 37, no. 3 (2005): 230–37. http://dx.doi.org/10.1007/s10692-005-0088-3.

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42

Kohlas, Jürg. "Uncertain information: Random variables in graded semilattices." International Journal of Approximate Reasoning 46, no. 1 (2007): 17–34. http://dx.doi.org/10.1016/j.ijar.2006.12.005.

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43

Cotter, Kevin D. "Convergence of information, random variables and noise." Journal of Mathematical Economics 16, no. 1 (1987): 39–51. http://dx.doi.org/10.1016/0304-4068(87)90020-6.

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44

Sonsino, Doron, and Radosveta Ivanova-Stenzel. "Experimental internet auctions with random information retrieval." Experimental Economics 9, no. 4 (2006): 323–41. http://dx.doi.org/10.1007/s10683-006-7050-y.

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45

Lerner, Vladimir S. "Dynamic approximation of a random information functional." Journal of Mathematical Analysis and Applications 335, no. 2 (2007): 1461–81. http://dx.doi.org/10.1016/j.jmaa.2007.02.027.

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46

Gu, Yujie, Qianyu Zhang, and Liying Yu. "Some Inequalities Combining Rough and Random Information." Entropy 20, no. 3 (2018): 211. http://dx.doi.org/10.3390/e20030211.

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47

Tajeddine, Razane, Antonia Wachter-Zeh, and Camilla Hollanti. "Private Information Retrieval Over Random Linear Networks." IEEE Transactions on Information Forensics and Security 15 (2020): 790–99. http://dx.doi.org/10.1109/tifs.2019.2928483.

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48

Ahmad, R., and S. Kubik. "Information Theory, Statistical Decision Functions, Random Processes." Journal of the Royal Statistical Society. Series A (Statistics in Society) 152, no. 2 (1989): 265. http://dx.doi.org/10.2307/2982936.

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49

Dahl, David B., Ryan Day, and Jerry W. Tsai. "Random Partition Distribution Indexed by Pairwise Information." Journal of the American Statistical Association 112, no. 518 (2017): 721–32. http://dx.doi.org/10.1080/01621459.2016.1165103.

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50

Lerner, Vladimir S. "Information macrodynamic modelling of a random process." International Journal of Systems Science 40, no. 8 (2009): 903. http://dx.doi.org/10.1080/00207720903198343.

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