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Journal articles on the topic 'Information filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 May 2024

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1

Peyravi, Farzad, and Alimohammad Latif. "Secure Multi Agent Information Filtering." International Journal of Computer Theory and Engineering 6, no. 3 (2014): 240–46. http://dx.doi.org/10.7763/ijcte.2014.v6.869.

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2

Loeb, Shoshana, and Douglas Terry. "Information filtering." Communications of the ACM 35, no. 12 (December 1992): 26–28. http://dx.doi.org/10.1145/138859.138860.

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3

Rowe, Timothy. "Filtering Information." Journal of Obstetrics and Gynaecology Canada 32, no. 4 (April 2010): 303–4. http://dx.doi.org/10.1016/s1701-2163(16)34471-1.

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4

FITZPATRICK, TAMARA, and LYNDA FITZPATRICK. "Filtering consistent information." Nursing 26, no. 9 (September 1996): 9. http://dx.doi.org/10.1097/00152193-199609000-00003.

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5

Ciaccia, Paolo, and Marco Patella. "Metric information filtering." Information Systems 36, no. 4 (June 2011): 708–20. http://dx.doi.org/10.1016/j.is.2010.09.007.

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6

Silva, Josep. "Information Filtering and Information Retrieval with the Web Filtering Toolbar." Electronic Notes in Theoretical Computer Science 235 (April 2009): 125–36. http://dx.doi.org/10.1016/j.entcs.2009.03.008.

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7

Belkin, Nicholas J., and W. Bruce Croft. "Information filtering and information retrieval." Communications of the ACM 35, no. 12 (December 1992): 29–38. http://dx.doi.org/10.1145/138859.138861.

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8

Zhu, Li, and Lan Bai. "Web Information Filtering Technology Based on Mutual Information." Applied Mechanics and Materials 687-691 (November 2014): 2224–28. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2224.

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The popularity of Internet application makes web page illegal information filtering an important work. Based on mutual information model study of illegal information filtering in web page, this article first elaborated the theoretical basis of mutual information filtering algorithm, then designed a mutual information filtering algorithm based on improved formula, and designed and implemented illegal web page filtering system by using the algorithm and related technology and did a series of experiments, which effectively reduced the impact of negative factors on healthy development of network culture industry, and it is advantageous to the purification of social morality and social stability.

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9

Newton, Nigel J. "Information geometric nonlinear filtering." Infinite Dimensional Analysis, Quantum Probability and Related Topics 18, no. 02 (June 2015): 1550014. http://dx.doi.org/10.1142/s0219025715500149.

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This paper develops information geometric representations for nonlinear filters in continuous time. The posterior distribution associated with an abstract nonlinear filtering problem is shown to satisfy a stochastic differential equation on a Hilbert information manifold. This supports the Fisher metric as a pseudo-Riemannian metric. Flows of Shannon information are shown to be connected with the quadratic variation of the process of posterior distributions in this metric. Apart from providing a suitable setting in which to study such information-theoretic properties, the Hilbert manifold has an appropriate topology from the point of view of multi-objective filter approximations. A general class of finite-dimensional exponential filters is shown to fit within this framework, and an intrinsic evolution equation, involving Amari's -1-covariant derivative, is developed for such filters. Three example systems, one of infinite dimension, are developed in detail.

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10

Quintana, Yuri. "Intelligent medical information filtering." International Journal of Medical Informatics 51, no. 2-3 (August 1998): 197–204. http://dx.doi.org/10.1016/s1386-5056(98)00115-4.

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11

Tian, Fanjiang, Congrong Li, and Dingxing Wang. "Evolving information filtering for personalized information service." Journal of Computer Science and Technology 16, no. 2 (March 2001): 168–75. http://dx.doi.org/10.1007/bf02950421.

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12

Lvovich, Ya E., A. P. Preobrazhenskiy, Yu P. Preobrazhenskiy, and Yu A. Klimenko. "The Analysis of Software and Hardware Solutions for Blocking Prohibited Information in Information and Telecommunication Networks." Informacionnye Tehnologii 28, no. 8 (August 15, 2022): 429–37. http://dx.doi.org/10.17587/it.28.429-437.

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The paper conducts a comprehensive analysis of approaches related to the blocking of information in information and telecommunication networks. The features of technical methods of filtering network traffic are shown: blocking by IP address, DNS record distortion processes, URL blocking process, file type blocking process, https request filtering procedures, Packet filtering, HTTP proxy server-based filtering processes, search results filtering processes. The need for content filtering of access to the global network in educational institutions is shown. The mathematical models of categorization used are discussed: the simplest thematic categorization, semantic categorization. It is shown how it is possible to carry out thematic categorization on the basis of calculating the weight coefficients of terms that belong to the category. It is demonstrated how the design and mathematical formalization of the system is carried out.

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13

Yokoi, Takeru, Hidekazu Yanagimoto, and Sigeru Omatu. "Information Filtering Using Latent Semantics." IEEJ Transactions on Electronics, Information and Systems 126, no. 7 (2006): 865–70. http://dx.doi.org/10.1541/ieejeiss.126.865.

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14

Mendel, M. B., and T. B. Sheridan. "Filtering information from human experts." IEEE Transactions on Systems, Man, and Cybernetics 19, no. 1 (1989): 6–16. http://dx.doi.org/10.1109/21.24527.

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15

Baclace, Paul E. "Competitive agents for information filtering." Communications of the ACM 35, no. 12 (December 1992): 50. http://dx.doi.org/10.1145/138859.138865.

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16

Lin, Rey-Sern, and Yung-Cheh Hsueh. "Multichannel filtering by gradient information." Signal Processing 80, no. 2 (February 2000): 279–93. http://dx.doi.org/10.1016/s0165-1684(99)00128-0.

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17

Shapira, Bracha, Peretz Shoval, and Uri Hanani. "Stereotypes in information filtering systems." Information Processing & Management 33, no. 3 (May 1997): 273–87. http://dx.doi.org/10.1016/s0306-4573(97)00003-4.

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18

Blankenburg, Sven, Wei Wu, Benjamin Lindner, and Susanne Schreiber. "Information filtering in resonant neurons." Journal of Computational Neuroscience 39, no. 3 (November 6, 2015): 349–70. http://dx.doi.org/10.1007/s10827-015-0580-6.

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19

Laureti, P., L. Moret, Y. C. Zhang, and Y. K. Yu. "Information filtering via Iterative Refinement." Europhysics Letters (EPL) 75, no. 6 (September 2006): 1006–12. http://dx.doi.org/10.1209/epl/i2006-10204-8.

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20

Schweighofer, Erich. "Downloading, information filtering and copyright." Information & Communications Technology Law 6, no. 2 (June 1997): 121–35. http://dx.doi.org/10.1080/13600834.1997.9965762.

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21

Resnick, Paul. "Filtering Information on the Internet." Scientific American 276, no. 3 (March 1997): 62–64. http://dx.doi.org/10.1038/scientificamerican0397-62.

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22

NISHIDA, SHOGO, MIE NAKATANI, TAKASHI KOISO, and YUSUKE ATOJI. "INFORMATION FILTERING FOR EMERGENCY MANAGEMENT." Cybernetics and Systems 34, no. 3 (April 2003): 193–206. http://dx.doi.org/10.1080/01969720302838.

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23

Yokoi, Takeru, Hidekazu Yanagimoto, and Sigeru Omatu. "Information filtering using latent semantics." Electrical Engineering in Japan 165, no. 2 (November 15, 2008): 53–59. http://dx.doi.org/10.1002/eej.20564.

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24

You, He, Xiong Wei, and Ma Qiang. "Composite filtering with feedback information." Journal of Systems Engineering and Electronics 18, no. 1 (March 2007): 54–56. http://dx.doi.org/10.1016/s1004-4132(07)60050-4.

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25

KAJIURA, Masahiro, and Seiji MIIKE. "Information filtering system operated for NewsWatch’s Daily News Filtering Service." Journal of Information Processing and Management 47, no. 4 (2004): 267–74. http://dx.doi.org/10.1241/johokanri.47.267.

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26

Dimitriadis, Athanasios, Christos Prassas, Jose Luis Flores, Boonserm Kulvatunyou, Nenad Ivezic, Dimitris A. Gritzalis, and Ioannis K. Mavridis. "Contextualized Filtering for Shared Cyber Threat Information." Sensors 21, no. 14 (July 18, 2021): 4890. http://dx.doi.org/10.3390/s21144890.

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Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.

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27

Yu, Zhi Guo, and Feng Ming Liu. "Information Filtering Based on Immune Mechanism." Advanced Materials Research 457-458 (January 2012): 1428–32. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1428.

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Natural Immune System is a highly complicated, distributed, and auto-adaptive system. It has very strong ability of self-learning, memory and association, and then it can quickly eliminate the intruders and keep itself stability. Information filtering is to choose useful information with filtering strategies from information. So, the original information is going to be as antigen, and the filtering strategies and procedure be as antibody, based on the immune self-learning mechanism, we design a model of information filtering. Then, we illustrate and discuss the model performance by experimental evaluation results.

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28

STUCKENSCHMIDT, HEINER. "APPROXIMATE INFORMATION FILTERING WITH MULTIPLE CLASSIFICATION HIERARCHIES." International Journal of Computational Intelligence and Applications 02, no. 03 (September 2002): 295–302. http://dx.doi.org/10.1142/s1469026802000634.

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Web page categorization is an approach for improving precision and efficiency of information retrieval on the web by filtering out irrelevant pages. Current approaches to information filtering based on categorization assume the existence of a single classification hierarchy used for filtering. In this paper, we address the problem of filtering information categorized according to different classification hierarchies. We describe a method for approximating Boolean queries over class names across different class hierarchies.

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29

Zhanabaev, Z. Zh. "INFORMATION-ENTROPY METHOD FOR DETECTING GRAVITATIONAL WAVE SIGNALS." Eurasian Physical Technical Journal 20, no. 2 (44) (June 21, 2023): 79–86. http://dx.doi.org/10.31489/2023no2/79-86.

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The detection of gravitational waves came from a pair of merging black holes marked the beginning of the era of GW astronomy. Traditionally, to extract gravitational wave signals from experimental data, the scientific collaborations use the standard matched filtering technique. The matched filtering technique relies on the existing waveform templates, that makes it difficult to find gravitational wave signals that go beyond theoretical expectations. Moreover, the computational cost of matched filter is veryhigh, as it depends on the number of templates used. In this article, we propose a new information-entropy method for gravitational waves detection that does not require a theoretical bank of signal templates. To demonstrate the reliability of our method we conducted an analysis using simulated and real data. Through this study, we revealed that our measure of conditional information detects the gravitational wave signals and can be used along with the matched filtering method.

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30

Cullen, Clara. "Filtering information from the Internet: the Borges information filtering service project in Dublin City University." VINE 26, no. 3 (March 1996): 45–50. http://dx.doi.org/10.1108/eb040614.

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31

LAU, RAYMOND, ARTHUR H. M. TER HOFSTEDE, and PETER D. BRUZA. "BELIEF REVISION FOR ADAPTIVE INFORMATION FILTERING AGENTS." International Journal of Cooperative Information Systems 10, no. 01n02 (March 2001): 57–79. http://dx.doi.org/10.1142/s0218843001000230.

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Agent-based information filtering alleviates the problem of information overload on the Internet by proactively scanning through the incoming stream of information on behalf of the users. Nevertheless, users' information needs will change over time. Therefore, it is essential for the information filtering agents to learn and adapt to the users' changing information needs in order to maintain the accuracy of the filtering process. Applying logic-based representation and adaptation to adaptive information filtering agents is promising since the semantic relationships among information items can be captured and reasoned about during the agents' learning and adaptation processes. This opens the door to a more responsive reinforcement learning than can be obtained from a purely statistical approach. The AGM belief revision paradigm that models rational and minimal change of an agent's beliefs offers a sound theoretical foundation for constructing the learning components of adaptive information filtering agents. This paper describes a symbolic framework for representing domain knowledge in an adaptive information filtering agent, and illustrates how the AGM belief revision paradigm can be applied to develop the agent's learning mechanism.

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32

Weeraddana, Dilusha M., Chulantha Kulasekere, and Kirthi S. Walgama. "Dempster–Shafer Information Filtering Framework: Temporal and Spatio-Temporal Evidence Filtering." IEEE Sensors Journal 15, no. 10 (October 2015): 5576–83. http://dx.doi.org/10.1109/jsen.2015.2442153.

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33

Kuflik, Tsvi, Bracha Shapira, and Peretz Shoval. "Stereotype-based versus personal-based filtering rules in information filtering systems." Journal of the American Society for Information Science and Technology 54, no. 3 (2003): 243–50. http://dx.doi.org/10.1002/asi.10220.

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34

Yanagimoto, Hidekazu, and Sigeru Omatu. "Information Filtering Using Kullback-Leibler Divergence." IEEJ Transactions on Electronics, Information and Systems 125, no. 7 (2005): 1147–52. http://dx.doi.org/10.1541/ieejeiss.125.1147.

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35

Miyahara, K., and T. Okamoto. "Collaborative information filtering in cooperative communities." Journal of Computer Assisted Learning 14, no. 2 (June 1998): 100–109. http://dx.doi.org/10.1046/j.1365-2729.1998.1420100.x.

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36

Yu, Zhi Guo, and Feng Ming Liu. "Information Filtering Based on Immune Mechanism." Advanced Materials Research 457-458 (January 2012): 1428–32. http://dx.doi.org/10.4028/scientific5/amr.457-458.1428.

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37

Liu, Guoliang, Florentin Worgotter, and Irene Markelic. "Square-Root Sigma-Point Information Filtering." IEEE Transactions on Automatic Control 57, no. 11 (November 2012): 2945–50. http://dx.doi.org/10.1109/tac.2012.2193708.

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38

Zhang, Fuguo, and An Zeng. "Improving information filtering via network manipulation." EPL (Europhysics Letters) 100, no. 5 (December 1, 2012): 58005. http://dx.doi.org/10.1209/0295-5075/100/58005.

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39

Ren, Jie, Tao Zhou, and Yi-Cheng Zhang. "Information filtering via self-consistent refinement." EPL (Europhysics Letters) 82, no. 5 (May 30, 2008): 58007. http://dx.doi.org/10.1209/0295-5075/82/58007.

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40

Zivotofsky, Ari Z., and Naomi T. S. Zivotofsky. "Withholding or Necessary Filtering of Information?" American Journal of Bioethics 11, no. 12 (December 2011): 70–72. http://dx.doi.org/10.1080/15265161.2011.623814.

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41

Capocci, Andrea, František Slanina, and Yi-Cheng Zhang. "Filtering information in a connected network." Physica A: Statistical Mechanics and its Applications 317, no. 1-2 (January 2003): 259–69. http://dx.doi.org/10.1016/s0378-4371(02)01337-7.

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42

Tauritz, D. R., J. N. Kok, and I. G. Sprinkhuizen-Kuyper. "Adaptive Information Filtering using evolutionary computation." Information Sciences 122, no. 2-4 (February 2000): 121–40. http://dx.doi.org/10.1016/s0020-0255(99)00123-1.

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43

Ye, Zhirui, Yuan Wang, and Yuan Wen. "Effects of Information Filtering on Driving." Procedia - Social and Behavioral Sciences 96 (November 2013): 1755–63. http://dx.doi.org/10.1016/j.sbspro.2013.08.200.

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44

Deffuant, Guillaume, and Sylvie Huet. "Propagation effects of filtering incongruent information." Journal of Business Research 60, no. 8 (August 2007): 816–25. http://dx.doi.org/10.1016/j.jbusres.2007.02.001.

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45

Chen, Bo-Lun, Fen-Fen Li, Yong-Jun Zhang, and Jia-Lin Ma. "Information filtering in evolving online networks." Physics Letters A 382, no. 5 (February 2018): 265–71. http://dx.doi.org/10.1016/j.physleta.2017.11.027.

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46

Cho, Jinhyung, Kwiseok Kwon, and Yongtae Park. "Collaborative Filtering Using Dual Information Sources." IEEE Intelligent Systems 22, no. 3 (May 2007): 30–38. http://dx.doi.org/10.1109/mis.2007.48.

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47

Nie, Da-Cheng, Zi-Ke Zhang, Jun-Lin Zhou, Yan Fu, and Kui Zhang. "Information Filtering on Coupled Social Networks." PLoS ONE 9, no. 7 (July 8, 2014): e101675. http://dx.doi.org/10.1371/journal.pone.0101675.

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48

Julier, S., Y. Baillot, D. Brown, and M. Lanzagorta. "Information filtering for mobile augmented reality." IEEE Computer Graphics and Applications 22, no. 5 (September 2002): 12–15. http://dx.doi.org/10.1109/mcg.2002.1028721.

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49

Xin, Pan, Deng Gui-Shi, and Liu Jian-Guo. "Information Filtering via Improved Similarity Definition." Chinese Physics Letters 27, no. 6 (June 2010): 068903. http://dx.doi.org/10.1088/0256-307x/27/6/068903.

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50

Fotinos, A., S. Fotopoulos, and E. Zigouris. "Colour filtering using local density information." Electronics Letters 36, no. 18 (2000): 1530. http://dx.doi.org/10.1049/el:20001109.

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