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Journal articles on the topic 'Interval information'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Qian, Yuhua, Jiye Liang, and Chuangyin Dang. "Interval ordered information systems." Computers & Mathematics with Applications 56, no. 8 (2008): 1994–2009. http://dx.doi.org/10.1016/j.camwa.2008.04.021.

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2

Kahraman, Cengiz, Basar Oztaysi, and Sezi Cevik Onar. "Interval-Valued Intuitionistic Fuzzy Confidence Intervals." Journal of Intelligent Systems 28, no. 2 (2019): 307–19. http://dx.doi.org/10.1515/jisys-2017-0139.

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Abstract Confidence intervals are useful tools for statistical decision-making purposes. In case of incomplete and vague data, fuzzy confidence intervals can be used for decision making under uncertainty. In this paper, we develop interval-valued intuitionistic fuzzy (IVIF) confidence intervals for population mean, population proportion, differences in means of two populations, and differences in proportions of two populations. The developed IVIF intervals can be used in cases of both finite and infinite population sizes. The developed fuzzy confidence intervals are equivalent decision-making tools to fuzzy hypothesis tests. We apply the proposed confidence intervals to the differences in the mean lives and failure proportions of two types of radiators used in automobiles, and a sensitivity analysis is given to check the robustness of the decisions.

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3

Žilinskas, Julius. "Comparison of Packages for Interval Arithmetic." Informatica 16, no. 1 (2005): 145–54. http://dx.doi.org/10.15388/informatica.2005.090.

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Žilinskas, Antanas, and Julius Žilinskas. "Interval Arithmetic Based Optimization in Nonlinear Regression." Informatica 21, no. 1 (2010): 149–58. http://dx.doi.org/10.15388/informatica.2010.279.

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5

Jurio, A., M. Pagola, R. Mesiar, G. Beliakov, and H. Bustince. "Image Magnification Using Interval Information." IEEE Transactions on Image Processing 20, no. 11 (2011): 3112–23. http://dx.doi.org/10.1109/tip.2011.2158227.

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6

Zeng, Jiasheng, Zhaowen Li, Meng Liu, and Shimin Liao. "Information Structures in an Incomplete Interval-Valued Information System." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 809. http://dx.doi.org/10.2991/ijcis.d.190712.001.

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7

YANG, Wei-ping, and Meng-lei LIN. "Information granularity in interval-valued intuitionistic fuzzy information systems." Journal of Computer Applications 32, no. 6 (2013): 1657–61. http://dx.doi.org/10.3724/sp.j.1087.2012.01657.

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8

Freedman, Eric G. "The Role of Diatonicism in the Abstraction and Representation of Contour and Interval Information." Music Perception 16, no. 3 (1999): 365–87. http://dx.doi.org/10.2307/40285797.

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Previous research on melody recognition indicates that listeners can recognize contour information when melodies are retained for brief intervals and can recognize interval information of melodies held in longterm memory. However, past research has failed to control for the diatonicism and familiarity of the melodies. In three experiments, the relative contributions of contour and interval information during the abstraction of novel diatonic and nondiatonic sequences are examined. Listeners recognize the melodic contours of melodies held over an extended retention interval. Additionally, listeners use the diatonic context to recognize both the contour and interval information. In nondiatonic contexts, listeners rely predominantly on the contour information. In addition, musically experienced listeners can recognize both the contour and interval information, whereas musically inexperienced listeners rely predominantly on the contour information. Recognition of melodic contour remained relatively accurate during a 24-hr retention interval. Thus, the results indicate that the diatonic scale mediates the abstraction of interval information. Listeners seem to acquire a musical schema for diatonic melodies.

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9

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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10

LUZHANSKY, V., D. MAKARUSHKIN, and T. HONCHARUK. "INVESTIGATION OF CHANNEL PERFORMANCE DEPENDING ON DIFFICULTY AND INFORMATION PARAMETERS OF TIMER SIGNAL STRUCTURES." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (2020): 14–20. https://doi.org/10.31891/2307-5732-2020-289-5-14-20.

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In timer signal constructions, the information about the transmitted symbol is not laid down in the values of individual binary digits of the code number, but in the locations of significant modulation moments (ZMM) and in the interval of formation of signal constructions. The reason for the increase in channel bandwidth when using TSK is the importance of creating more signal structures at a given time interval than when using a bit-digital code. Increasing the bandwidth of the communication channel leads to an increase in the transmission rate of digital information flows at a given communication quality.In the bit-digital encoding method, the information about the transmitted bit is determined by the type of signal on a single (quest) interval. In timer signal constructions (TSCs), in contrast to the bit-digital encoding method, the information is laid down in the durations of several separate time segments of the signal on the design interval and their relative position. In order to reduce inter-character distortion, the duration of time intervals is at least the most quiz interval. The time interval shows the part of a single element and is determined by the interference in the communication channel and the allowable probability of erroneous reception of the signal structure. The reason for the increase in channel bandwidth when using TSC is the importance of creating more signal structures at a given time interval than when using bit-digit code. Increasing the bandwidth of the communication channel leads to an increase in the transmission rate of digital information flows at a given communication quality. In the scientific article the research of efficiency of use of timer signal designs for increase in throughput of the communication channel depending on noise immunity and information parameters of timer signal designs is carried out.

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11

Chen, Xiao Hui, Ren Pu Li, and Zhi Wang Zhang. "Interval Granules in Incomplete Information Systems." Advanced Materials Research 418-420 (December 2011): 1915–18. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.1915.

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Rough set theory is an efficient mathematical theory for data reduction and knowledge discovery of various fields. However, classical rough set theory is not applicable for knowledge induction of incomplete information systems. In this paper, a concept of interval granule is presented. Based on this concept, the hierachical structure of knowledge granularity and approximation of rough sets in incomplete information systems are studied, and related properties are given. An example show that the interval granule have better results than existing models for knowledge induction and approximation.

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12

Shanmugam, Ramalingam. "Kullback-leibler information and interval estimation." Communications in Statistics - Theory and Methods 28, no. 9 (1999): 2057–63. http://dx.doi.org/10.1080/03610929908832406.

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13

Golan, Amos, and Aman Ullah. "Interval estimation: An information theoretic approach." Econometric Reviews 36, no. 6-9 (2017): 781–95. http://dx.doi.org/10.1080/07474938.2017.1307573.

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14

Žilinskas, Julius. "Estimation of Functional Ranges Using Standard and Inner Interval Arithmetic." Informatica 17, no. 1 (2006): 125–36. http://dx.doi.org/10.15388/informatica.2006.128.

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15

Liu, Peide, Qaisar Khan, and Tahir Mahmood. "Application of Interval Neutrosophic Power Hamy Mean Operators in MAGDM." Informatica 30, no. 2 (2019): 293–325. http://dx.doi.org/10.15388/informatica.2019.207.

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16

Petry, Frederick. "Intuitionistic Fuzzy Sets for Spatial and Temporal Data Intervals." Information 15, no. 4 (2024): 240. http://dx.doi.org/10.3390/info15040240.

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Spatial and temporal uncertainties are found in data for many critical applications. This paper describes the use of interval-based representations of some spatial and temporal information. Uncertainties in the information can arise from multiple sources in which degrees of support and non-support occur in evaluations. This motivates the use of intuitionistic fuzzy sets to permit the use of the positive and negative memberships to capture these uncertainties. The interval representations will include both simple and complex or nested intervals. The relationships between intervals such as overlapping, containing, etc. are then developed for both the simple and complex intervals. Such relationships are required to support the aggregation approaches of the interval information. Both averaging and merging approaches to interval aggregation are then developed. Furthermore, potential techniques for the associated aggregation of the interval intuitionistic fuzzy memberships are provided. A motivating example of maritime depth data required for safe navigation is used to illustrate the approach. Finally, some potential future developments are discussed.

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17

El Obadi, Saadia, and Silvia Miquel. "Uncertainty in Information Market Games." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp01 (2020): 11–29. http://dx.doi.org/10.1142/s0218488520400024.

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A new product can be produced and sold in a market thanks to the entrance of a patent holder into the market. This market is divided into submarkets controlled by only some firms and the profit attainable in each submarket is uncertain. In this paper, this situation is studied by means of cooperative games under interval uncertainty. We consider different ways of allocating the interval profit among the firms. One of these is the interval [Formula: see text]-value, which is defined for interval games satisfying some conditions. Efficient interval solutions in terms of the market data are provided.

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18

CHIU, PETER P. K., and Y. S. CHEUNG. "INTERVAL LOGIC FOR PROCESS CONTROL SYSTEM SPECIFICATION." Journal of Circuits, Systems and Computers 01, no. 03 (1991): 303–26. http://dx.doi.org/10.1142/s0218126691000100.

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A new methodology for the specification of process control systems involving time interval information and verification of their design is proposed. The specification is based on interval logic and a time-interval operator is defined. By means of this operator, time interval information is incorporated in the specification. Thus properties that involve time intervals can be verified. Moreover, combinational and sequential processes can be specified. An application example for a process control system is demonstrated.

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19

Karasan, Ali, Edmundas Kazimieras Zavadskas, Cengiz Kahraman, and Mehdi Keshavarz-Ghorabaee. "Residential Construction Site Selection Through Interval-Valued Hesitant Fuzzy CODAS Method." Informatica 30, no. 4 (2019): 689–710. http://dx.doi.org/10.15388/informatica.2019.225.

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20

Watson, DM, and GAC Beattie. "Effect of weather station logging interval on the precision of degree-day estimates." Australian Journal of Experimental Agriculture 35, no. 6 (1995): 795. http://dx.doi.org/10.1071/ea9950795.

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The relationship between data-logging intervals and degree-day estimates was examined to determine the longest interval giving equivalent information to estimates based on 12-min intervals and, so, the most efficient interval for estimation of degree-days

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21

Jinlai, Lv, Zhang Hui, Fan Wenlei, and Du Xiaoping. "Uncertainty Measures in Interval Ordered Information Systems." Journal of Applied Sciences 13, no. 17 (2013): 3522–27. http://dx.doi.org/10.3923/jas.2013.3522.3527.

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22

Bayne, Charles K., Amy B. Dindal, Roger A. Jenkins, Deana M. Crumbling, and Eric N. Koglin. "Meeting Data Quality Objectives with Interval Information." Environmental Science & Technology 35, no. 16 (2001): 3350–55. http://dx.doi.org/10.1021/es001572m.

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23

Dai, Jianhua, Wentao Wang, and Ju-Sheng Mi. "Uncertainty measurement for interval-valued information systems." Information Sciences 251 (December 2013): 63–78. http://dx.doi.org/10.1016/j.ins.2013.06.047.

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24

Beer, Michael, and Vladik Kreinovich. "Interval or moments: which carry more information?" Soft Computing 17, no. 8 (2013): 1319–27. http://dx.doi.org/10.1007/s00500-013-1002-1.

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25

Yin, Yunfei, Guanghong Gong, and Liang Han. "A framework for interval-valued information system." International Journal of Systems Science 43, no. 9 (2012): 1603–22. http://dx.doi.org/10.1080/00207721.2010.549580.

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26

Hesamian, Gholamreza. "One-way ANOVA based on interval information." International Journal of Systems Science 47, no. 11 (2015): 2682–90. http://dx.doi.org/10.1080/00207721.2015.1014449.

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27

Geetha, Sivaraman, V. Lakshmana Gomathi Nayagam, and R. Ponalagusamy. "A complete ranking of incomplete interval information." Expert Systems with Applications 41, no. 4 (2014): 1947–54. http://dx.doi.org/10.1016/j.eswa.2013.08.090.

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28

Noghin, V. D. "Multicriteria Choice Based on Interval Fuzzy Information." Scientific and Technical Information Processing 51, no. 6 (2024): 526–34. https://doi.org/10.3103/s0147688224700461.

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29

Srisuradetchai, Patchanok, Ausaina Niyomdecha, and Wikanda Phaphan. "Wald Intervals via Profile Likelihood for the Mean of the Inverse Gaussian Distribution." Symmetry 16, no. 1 (2024): 93. http://dx.doi.org/10.3390/sym16010093.

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The inverse Gaussian distribution, known for its flexible shape, is widely used across various applications. Existing confidence intervals for the mean parameter, such as profile likelihood, reparametrized profile likelihood, and Wald-type reparametrized profile likelihood with observed Fisher information intervals, are generally effective. However, our simulation study identifies scenarios where the coverage probability falls below the nominal confidence level. Wald-type intervals are widely used in statistics and have a symmetry property. We mathematically derive the Wald-type profile likelihood (WPL) interval and the Wald-type reparametrized profile likelihood with expected Fisher information (WRPLE) interval and compare their performance to existing methods. Our results indicate that the WRPLE interval outperforms others in terms of coverage probability, while the WPL typically yields the shortest interval. Additionally, we apply these proposed intervals to a real dataset, demonstrating their potential applicability to other datasets that follow the IG distribution.

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30

Yamaka, Woraphon, and Songsak Sriboonchitta. "Forecasting Using Information and Entropy Based on Belief Functions." Complexity 2020 (September 1, 2020): 1–16. http://dx.doi.org/10.1155/2020/3269647.

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This paper introduces an entropy-based belief function to the forecasting problem. While the likelihood-based belief function needs to know the distribution of the objective function for the prediction, the entropy-based belief function does not. This is because the observed data likelihood is somewhat complex in practice. We, thus, replace the likelihood function with the entropy. That is, we propose an approach in which a belief function is built from the entropy function. As an illustration, the proposed method is compared to the likelihood-based belief function in the simulation and empirical studies. According to the results, our approach performs well under a wide array of simulated data models and distributions. There are pieces of evidence that the prediction interval obtained from the frequentist method has a much narrower prediction interval, while our entropy-based method performs the widest. However, our entropy-based belief function still produces an acceptable range for prediction intervals as the true prediction value always lay in the prediction intervals.

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31

Pinheiro, Jocivania, Regivan H. N. Santiago, Benjamin Bedregal, and Flaulles Bergamaschi. "Developing Constrained Interval Operators for Fuzzy Logic with Interval Values." Axioms 12, no. 12 (2023): 1115. http://dx.doi.org/10.3390/axioms12121115.

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A well-known problem in the interval analysis literature is the overestimation and loss of information. In this article, we define new interval operators, called constrained interval operators, that preserve information and mitigate overestimation. These operators are investigated in terms of correction, algebraic properties, and orders. It is shown that a large part of the properties studied is preserved by this operator, while others remain preserved with the condition of continuity, as is the case of the exchange principle. In addition, a comparative study is carried out between this operator g¨ and the best interval representation: g^. Although g¨⊆g^ and g¨ do not preserve the Moore correction, we do not have a loss of relevant information since everything that is lost is irrelevant, mitigating the overestimation.

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32

Dai, Jian-hua, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, and Hong Shi. "Attribute reduction in interval-valued information systems based on information entropies." Frontiers of Information Technology & Electronic Engineering 17, no. 9 (2016): 919–28. http://dx.doi.org/10.1631/fitee.1500447.

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33

Qi Yuan. "Assessment of Information Security Risk with Interval Intuitionistic Trapezoidal Fuzzy Information." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 9 (2011): 215–20. http://dx.doi.org/10.4156/aiss.vol3.issue9.29.

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34

Guo, Yingni, and Eran Shmaya. "The Interval Structure of Optimal Disclosure." Econometrica 87, no. 2 (2019): 653–75. http://dx.doi.org/10.3982/ecta15668.

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A sender persuades a receiver to accept a project by disclosing information about a payoff‐relevant quality. The receiver has private information about the quality, referred to as his type. We show that the sender‐optimal mechanism takes the form of nested intervals: each type accepts on an interval of qualities and a more optimistic type's interval contains a less optimistic type's interval. This nested‐interval structure offers a simple algorithm to solve for the optimal disclosure and connects our problem to the monopoly screening problem. The mechanism is optimal even if the sender conditions the disclosure mechanism on the receiver's reported type.

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35

Bhattacharyya, Balaram, Uddalak Mitra, and Ramkishore Bhattacharyya. "Tandem repeat interval pattern identifies animal taxa." Bioinformatics 37, no. 16 (2021): 2250–58. http://dx.doi.org/10.1093/bioinformatics/btab124.

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Abstract Motivation We discover that maximality of information content among intervals of Tandem Repeats (TRs) in animal genome segregates over taxa such that taxa identification becomes swift and accurate. Successive TRs of a motif occur at intervals over the sequence, forming a trail of TRs of the motif across the genome. We present a method, Tandem Repeat Information Mining (TRIM), that mines 4k number of TR trails of all k length motifs from a whole genome sequence and extracts the information content within intervals of the trails. TRIM vector formed from the ordered set of interval entropies becomes instrumental for genome segregation. Results Reconstruction of correct phylogeny for animals from whole genome sequences proves precision of TRIM. Identification of animal taxa by TRIM vector upon feature selection is the most significant achievement. These suggest Tandem Repeat Interval Pattern (TRIP) is a taxa-specific constitutional characteristic in animal genome. Availabilityand implementation Source and executable code of TRIM along with usage manual are made available at https://github.com/BB-BiG/TRIM. Supplementary information Supplementary data are available at Bioinformatics online.

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36

Şentürk, Sevil, and Jurgita Antucheviciene. "Interval Type-2 Fuzzy c-Control Charts: An Application in a Food Company." Informatica 28, no. 2 (2017): 269–83. http://dx.doi.org/10.15388/informatica.2017.129.

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37

Xu, Xue-Guo, Hua Shi, Feng-Bao Cui, and Mei-Yun Quan. "Green Supplier Evaluation and Selection Using Interval 2-Tuple Linguistic Hybrid Aggregation Operators." Informatica 29, no. 4 (2018): 801–24. http://dx.doi.org/10.15388/informatica.2018.193.

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38

Zhang, Shu Guang, and Su Huan Chen. "Static Interval Optimization for Structures with Interval Parameters and Interval Loading." Advanced Materials Research 443-444 (January 2012): 738–44. http://dx.doi.org/10.4028/www.scientific.net/amr.443-444.738.

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A static interval optimization method for structures was developed. The matrices of structures with interval parameters are given. Combining the interval extension of functions with the perturbation theory of static analysis, the method for interval static analysis of the structure with interval parameters and interval loading was derived. The Interval optimization problem was transformed into a corresponding deterministic one. Because the mid-values and the uncertainties of the interval parameters can be selected as the design variables, more information of the optimization results can be obtained by the present method than that obtained by the deterministic one. The numerical results show that the present method is valid.

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Zhang, Yimeng, Xiuyi Jia, Zhenmin Tang та Xianzhong Long. "Uncertainty measures for interval set information tables based on interval δ-similarity relation". Information Sciences 501 (жовтень 2019): 272–92. http://dx.doi.org/10.1016/j.ins.2019.06.014.

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40

Shan, Guogen, and Weizhen Wang. "Exact one-sided confidence limits for Cohen’s kappa as a measurement of agreement." Statistical Methods in Medical Research 26, no. 2 (2014): 615–32. http://dx.doi.org/10.1177/0962280214552881.

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Cohen’s kappa coefficient, κ, is a statistical measure of inter-rater agreement or inter-annotator agreement for qualitative items. In this paper, we focus on interval estimation of κ in the case of two raters and binary items. So far, only asymptotic and bootstrap intervals are available for κ due to its complexity. However, there is no guarantee that such intervals will capture κ with the desired nominal level 1– α. In other words, the statistical inferences based on these intervals are not reliable. We apply the Buehler method to obtain exact confidence intervals based on four widely used asymptotic intervals, three Wald-type confidence intervals and one interval constructed from a profile variance. These exact intervals are compared with regard to coverage probability and length for small to medium sample sizes. The exact intervals based on the Garner interval and the Lee and Tu interval are generally recommended for use in practice due to good performance in both coverage probability and length.

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41

Wang, Xin-Fan, Jian-Qiang Wang, and Wu-E. Yang. "A Group Decision Making Approach Based on Interval-Valued Intuitionistic Uncertain Linguistic Aggregation Operators." Informatica 26, no. 3 (2015): 523–42. http://dx.doi.org/10.15388/informatica.2015.62.

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42

Yang, Xinyu, Xiao Zhang, Jiancang Xie, Xu Zhang, and Shihui Liu. "Monthly Runoff Interval Prediction Based on Fuzzy Information Granulation and Improved Neural Network." Water 14, no. 22 (2022): 3683. http://dx.doi.org/10.3390/w14223683.

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High-precision monthly runoff prediction results are of great significance to regional water resource management. However, with the changes in human activity, climate, and underlying surface conditions, the runoff sequence presents highly nonlinear and random characteristics. In order to improve the accuracy of runoff prediction, this study proposed a runoff prediction model based on fuzzy information granulation (FIG) and back propagation neural network (BPNN) improved with genetic algorithm (FIG-GA-BP). First, FIG was used to process the original runoff data to generate three sequences of minimum, average, and maximum that can reflect the rule of runoff changes. Then, genetic algorithms (GA) were used to obtain the optimal initial weights and thresholds of the BPNN through selection, crossover, and mutation. Finally, BPNN was used to predict the generated three sequences separately to obtain the prediction interval. The proposed model was applied to the monthly runoff interval prediction of Linjiacun and Weijiabu hydrological stations in the main stream of the Wei River and Zhangjiashan hydrological station on Jing River, a tributary of the Wei River. Compared with the interval prediction model FIG-BP, FIG-WNN, and traditional BP model. The results show that the FIG-GA-BP interval prediction model had a good prediction effect, with higher prediction accuracy and a narrower range of prediction intervals. Therefore, this model has superiority and practicability in monthly runoff interval prediction.

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43

Huang, Bing, Yu-liang Zhuang, and Hua-xiong Li. "Information granulation and uncertainty measures in interval-valued intuitionistic fuzzy information systems." European Journal of Operational Research 231, no. 1 (2013): 162–70. http://dx.doi.org/10.1016/j.ejor.2013.05.006.

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44

Jin, Yan, and Jiang Hong Ma. "An Interval Slope Approach to Fuzzy C-Means Clustering Algorithm for Interval Valued Data." Advanced Materials Research 989-994 (July 2014): 1641–45. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1641.

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Interval data is a range of continuous values which can describe the uncertainty. The traditional clustering methods ignore the structure information of intervals. And some modified ones have been developed. We have already used Taylor technique to perform well in the fuzzy c-means clustering algorithm. In this paper, we propose a new way based on the mixed interval slopes technique and interval computing. Experimental results also show the effectiveness of our approach.

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45

Šaliga, Ján, Ondrej Kováč, and Imrich Andráš. "Analog-to-Information Conversion with Random Interval Integration." Sensors 21, no. 10 (2021): 3543. http://dx.doi.org/10.3390/s21103543.

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A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used components. The performance of the proposed method was evaluated using ECG signals from the MIT-BIH database, multi-sine, and own database of environmental test signals. The proposed method performance is compared to commonly used analog-to-information conversion methods: random sampling, random demodulation, and random modulation pre-integration. A comparison of the mentioned methods is performed by simulation in LabVIEW software. The achieved results suggest that the random interval integration outperforms other single-channel architectures. In certain situations, it can reach the performance of a much-more complex, but commonly used random modulation pre-integrator.

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46

Shinohara, Kazumitsu. "Resource for Temporal Information Processing in Interval Production." Perceptual and Motor Skills 88, no. 3 (1999): 917–28. http://dx.doi.org/10.2466/pms.1999.88.3.917.

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47

Jou, Jerwen, and James W. Aldridge. "Memory representation of alphabetic position and interval information." Journal of Experimental Psychology: Learning, Memory, and Cognition 25, no. 3 (1999): 680–701. http://dx.doi.org/10.1037/0278-7393.25.3.680.

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48

Huang, Bing. "Graded dominance interval-based fuzzy objective information systems." Knowledge-Based Systems 24, no. 7 (2011): 1004–12. http://dx.doi.org/10.1016/j.knosys.2011.04.012.

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49

Mu, Chunyan, and David Clark. "An Interval-based Abstraction for Quantifying Information Flow." Electronic Notes in Theoretical Computer Science 253, no. 3 (2009): 119–41. http://dx.doi.org/10.1016/j.entcs.2009.10.009.

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Gopal, Ram D., Paulo B. Goes, and Robert S. Garfinkel. "Interval Protection of Confidential Information in a Database." INFORMS Journal on Computing 10, no. 3 (1998): 309–22. http://dx.doi.org/10.1287/ijoc.10.3.309.

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