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Journal articles on the topic 'Information fusion'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Zhang, Xin, Li Yang, and Yan Zhang. "Multi-Source Information Fusion Based on Data Driven." Applied Mechanics and Materials 40-41 (November 2010): 121–26. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.121.

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Take data driven method as theoretical basis, study multi-source information fusion technology. Using online and off-line data of the fusion system, does not rely on system's mathematical model, has avoided question about system modeling by mechanism. Uses principal component analysis method, rough set theory, Support Vector Machine(SVM) and so on, three method fusions and supplementary, through information processing and feature extraction to system's data-in, catches the most important information to lower dimensional space, realizes knowledge reduction. From data level, characteristic level, decision-making three levels realize information fusion. The example indicated that reduced computational complexity, reduced information loss in the fusion process, and enhanced the fusion accuracy.

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2

Yao, JingTao, Vijay V. Raghavan, and Zonghuan Wu. "Web information fusion." Information Fusion 9, no. 4 (October 2008): 444–45. http://dx.doi.org/10.1016/j.inffus.2008.05.001.

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3

Anderson, Peter Meade, Luisa-Marie Manning, Brian Rubin, Timothy A. Chan, Scott E. Kilpatrick, Rabi Hanna, and Zheng Jin Tu. "Nested set information derived from fusion genes in Ewing sarcoma and other cancers." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e23521-e23521. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e23521.

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e23521 Background: Ewing family tumors including Ewing sarcoma (ES) and desmoplastic small round cell tumor (DSRCT) are characterized by EWSR1 and ETS fusion partners including EWS-FLI1, EWS-ERG, EWS-WT1, and others. Since 2019 our Next Generation Sequencing (NGS) sarcoma panel (N = 338) identified both fusion partners and exons containing the EWS breakpoint in ES and DSRCT. Hence, it should be possible to learn more about the exact breakpoints of EWS fusion genes. The molecular diversity and functionality of these fusion transcripts, especially the exact sequence, which could be identical, similar, or unique, may have significant biological implications for diagnostics and treatment options. We have used in-frame analysis of EWS gene fusion breakpoints to identify corresponding polypeptides spanning the breakpoints of the most common EWS gene fusions to provide useful insights about ES and DSRCT. Methods: Pathology reports and EWS gene fusions were analyzed using the Cleveland Clinic NGS panel which is based on anchored multiplex polymerase chain reaction (PCR) enriched for 34 gene targets. The amplicons were subjected to massively parallel sequencing with 151x2 cycle pair-end reads. An informatics pipeline was used for read alignment (GRCh37 as reference genome), fusion identification, and annotation. The DNA sequence across fusion junction and translational amino acid sequences were extracted for comparison in a preliminary teaching set (37/338 EWS fusions). Results: EWS-FLI1 fusions at exon 7-7 (chr22:29683123-chr11:128675261) and exon 7-6 (chr22:29683123-chr11:128651853) were the most common fusion genes in ES (N = 24/32;75%). Other ES gene fusions included exons 10-6, 7-10, 9-8, 7-2 EWS-FEV, 7-8 EWS-ERG, 7-9 EWS-ERG, and 10-9 EWS-ERG. EWS-WT1 gene fusions (chr22:29683123-chr11:32414301) in DSRCT exon 7-7 occurred in 4 of 5 cases (80%). In frame analysis of the common gene fusions in 75% of ES and DSRCT (total n = 37) yielded identical corresponding polypeptides that span the breakpoint (table). Conclusions: Analysis of ES and DSRCT fusion genes provides evidence that cancer-specific fusion genes are associated with common identical breakpoints and corresponding in-frame polypeptides. Our results show that the identification of in-frame polypeptides from the fusion gene sequencing can identify potential nested sets of cancer-specific mRNAs and polypeptides. This information may become relevant for diagnostic and therapeutic targets future vaccines and diagnostics against ES and DSRCT as well as other cancers characterized by fusion genes or frameshift mutations. [Table: see text]

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4

Popovich, V. V. "Information fusion and geographic information systems." Herald of the Russian Academy of Sciences 77, no. 4 (August 2007): 429–30. http://dx.doi.org/10.1134/s1019331607040181.

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Chiu, Readman, Ka Ming Nip, and Inanc Birol. "Fusion-Bloom: fusion detection in assembled transcriptomes." Bioinformatics 36, no. 7 (December 2, 2019): 2256–57. http://dx.doi.org/10.1093/bioinformatics/btz902.

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Abstract Summary Presence or absence of gene fusions is one of the most important diagnostic markers in many cancer types. Consequently, fusion detection methods using various genomics data types, such as RNA sequencing (RNA-seq) are valuable tools for research and clinical applications. While information-rich RNA-seq data have proven to be instrumental in discovery of a number of hallmark fusion events, bioinformatics tools to detect fusions still have room for improvement. Here, we present Fusion-Bloom, a fusion detection method that leverages recent developments in de novo transcriptome assembly and assembly-based structural variant calling technologies (RNA-Bloom and PAVFinder, respectively). We benchmarked Fusion-Bloom against the performance of five other state-of-the-art fusion detection tools using multiple datasets. Overall, we observed Fusion-Bloom to display a good balance between detection sensitivity and specificity. We expect the tool to find applications in translational research and clinical genomics pipelines. Availability and implementation Fusion-Bloom is implemented as a UNIX Make utility, available at https://github.com/bcgsc/pavfinder and released under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/. Supplementary information Supplementary data are available at Bioinformatics online.

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6

Ross, Arun, and Anil Jain. "Information fusion in biometrics." Pattern Recognition Letters 24, no. 13 (September 2003): 2115–25. http://dx.doi.org/10.1016/s0167-8655(03)00079-5.

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7

Das, Subrata. "Agent-based information fusion." Information Fusion 11, no. 3 (July 2010): 216–19. http://dx.doi.org/10.1016/j.inffus.2010.01.008.

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8

Snidaro, Lauro, Jesus Garcia, and Juan Manuel Corchado. "Context-based information fusion." Information Fusion 21 (January 2015): 82–84. http://dx.doi.org/10.1016/j.inffus.2014.02.001.

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9

Willett, P. K. "FUSION 2000 third international conference on information fusion." IEEE Aerospace and Electronic Systems Magazine 16, no. 2 (February 2001): 21–25. http://dx.doi.org/10.1109/maes.2001.904240.

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10

Xue, Ying Hua, and Jing Li. "Distributed Information Fusion Structure Based on Data Fusion Tree." Advanced Materials Research 225-226 (April 2011): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.488.

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A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.

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Svensson, Per, and Johan Schubert. "FUSION 2004: The seventh international conference on information fusion." Information Fusion 7, no. 4 (December 2006): 342–45. http://dx.doi.org/10.1016/j.inffus.2006.03.004.

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12

Gong, Ting, and Hui Yan. "Multi-Sensor Information Fusion and Application." Applied Mechanics and Materials 602-605 (August 2014): 2623–26. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2623.

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Technology of multi-sensor information fusion is an emerging discipline and its theories and methods have been applied in many research areas. In this paper, the model and structure of multi-sensor information fusion, major technologies and methods of information fusion, theoretical system of information fusion as well as application of information fusion technology are introduced and summarized.

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13

Ziyang Chen, Ziyang Chen, and Yang Zhang Ziyang Chen. "Conflict Evidence Fusion Algorithm Based on Cosine Distance and Information Entropy." 電腦學刊 34, no. 3 (June 2023): 343–55. http://dx.doi.org/10.53106/199115992023063403026.

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<p>Dealing with high conflict evidence, traditional evidence theory sometimes has certain limitations, and results in fusion results contrary to common sense. In order to solve the problem of high conflict evidence fusion, this paper analyzes traditional evidence theory and proposes an evidence fusion method that combines cosine distance and information entropy. Cosine distance can measure the directionality between two vectors. The better the directionality, the more similar the two vectors are. Therefore, this article uses cosine distance to determine the similarity between evidences, and then calculates the credibility of each piece of evidence. Information entropy can calculate the amount of information for each evidence. The greater the information entropy, the greater the uncertainty of the evidence. Therefore, this article uses information entropy to measure the uncertainty of the evidence. Then, the credibility and uncertainty of the evidence are fused to calculate the weight of the evidence. Then we use d-s evidence theory for evidence fusion. The numerical example shows that the method is feasible and effective in dealing with conflict evidence. </p> <p> </p>

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14

Jaeger, Stefan, and Su-Shing Chen. "Information Fusion for Biological Prediction." Journal of Data Science 8, no. 2 (July 10, 2021): 269–88. http://dx.doi.org/10.6339/jds.2010.08(2).607.

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15

Lee, Jin-Hyung, Seong-Won Jo, Jae-Min Kim, and Sun-Tae Chung. "Object Tracking Using Information Fusion." Journal of Korean institute of intelligent systems 18, no. 5 (October 25, 2008): 666–71. http://dx.doi.org/10.5391/jkiis.2008.18.5.666.

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16

Perols, Johan L., and Uday S. Murthy. "Information Fusion in Continuous Assurance." Journal of Information Systems 26, no. 2 (November 2012): 35–52. http://dx.doi.org/10.2308/isys-50216.

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17

Kumar, A. Senthil, and K. L. Majumder. "Information fusion in tree classifiers." International Journal of Remote Sensing 22, no. 5 (January 2001): 861–69. http://dx.doi.org/10.1080/01431160051060327.

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18

FABRE, S., A. APPRIOU, and X. BRIOTTET. "SENSOR FUSION INTEGRATING CONTEXTUAL INFORMATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, no. 03 (June 2001): 369–409. http://dx.doi.org/10.1142/s0218488501000855.

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The application of multi-sensor fusion, which aims at recogizing a state among a set of hypotheses for object classification, is of major interest as regards the performance improvement brought by the sensor complementarily. Nevertheless this needs to take into account the more accurate as possible information and take advantage of the statistical learning of the previous measurements acquired by sensors. The classical probabilistic fusion methods lack of performance when the previous learning is not representative of the real measurements provided by sensors. The theory of evidence is then introduced to face this disadvantage by integrating a further information which is the context of the sensor acquisitions. In this paper, we propose a formalism of modeling of the sensor reliability to the context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learnt: the first one amounts to integrate this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as mass functions. These two methods are based on the theory of fuzzy events. Afterwards, we are interested in the evolvement of these two methods in the case where the previous learning is unavailable for a hypothesis associated to an object of the scene and compare these two methods in order to deduce a global method of contextual information integration in the fusion process.

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19

Chen, H., and P. Meer. "Robust Fusion of Uncertain Information." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 35, no. 3 (June 2005): 578–86. http://dx.doi.org/10.1109/tsmcb.2005.846659.

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20

Post-Zwicker, Andrew. "Fusion information on the web." American Journal of Physics 68, no. 9 (September 2000): 788. http://dx.doi.org/10.1119/1.1304910.

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21

Taxt, Torfinn, and Anne H. Schistad Solberg. "Information fusion in remote sensing." Vistas in Astronomy 41, no. 3 (January 1997): 337–42. http://dx.doi.org/10.1016/s0083-6656(97)00036-6.

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22

Schubert, Johan. "Counter-deception in information fusion." International Journal of Approximate Reasoning 91 (December 2017): 152–59. http://dx.doi.org/10.1016/j.ijar.2017.09.003.

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23

Sastry, SVS, and S. Santra. "Structure information from fusion barriers." Pramana 54, no. 6 (June 2000): 813–26. http://dx.doi.org/10.1007/s12043-000-0177-z.

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24

Perlovsky, Leonid I. "Cognitive high level information fusion." Information Sciences 177, no. 10 (May 2007): 2099–118. http://dx.doi.org/10.1016/j.ins.2006.12.026.

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25

Dawson, Andrew Samuel. "“Philosophical Foundations of Information Fusion." Process Studies 40, no. 1 (2011): 202. http://dx.doi.org/10.5840/process201140126.

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26

Jones, E. K., N. Denis, and D. Hunter. "Hypothesis management for information fusion." IEEE Aerospace and Electronic Systems Magazine 18, no. 6 (June 2003): 3–8. http://dx.doi.org/10.1109/maes.2003.1209583.

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27

Perols, Johan, Kaushal Chari, and Manish Agrawal. "Information Market-Based Decision Fusion." Management Science 55, no. 5 (May 2009): 827–42. http://dx.doi.org/10.1287/mnsc.1080.0977.

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28

Dasarathy, Belur V. "Information Fusion - A status update." Information Fusion 8, no. 4 (October 2007): 335–36. http://dx.doi.org/10.1016/j.inffus.2007.04.004.

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Giacinto, Giorgio, Fabio Roli, and Carlo Sansone. "Information fusion in computer security." Information Fusion 10, no. 4 (October 2009): 272–73. http://dx.doi.org/10.1016/j.inffus.2009.03.002.

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30

León, Fernando Puente, and Jürgen Beyerer. "Information fusion for cognitive automobiles." Information Fusion 12, no. 4 (October 2011): 242–43. http://dx.doi.org/10.1016/j.inffus.2011.04.001.

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31

Balasubramanian, Vineeth N., Shayok Chakraborty, and Sethuraman Panchanathan. "Conformal predictions for information fusion." Annals of Mathematics and Artificial Intelligence 74, no. 1-2 (January 28, 2014): 45–65. http://dx.doi.org/10.1007/s10472-013-9392-4.

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Li, Xinde, Xianzhong Dai, Jean Dezert, and Florentin Smarandache. "Fusion of imprecise qualitative information." Applied Intelligence 33, no. 3 (February 21, 2009): 340–51. http://dx.doi.org/10.1007/s10489-009-0170-2.

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33

Loskiewicz-Buczak, Anna, and Robert E. Uhrig. "Information Fusion by Set Operation Information Fusion by Fuzzy Set Operation and Genetic Algorithms." SIMULATION 65, no. 1 (July 1995): 52–66. http://dx.doi.org/10.1177/003754979506500106.

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34

Ma, Nan, Hong Bao, Yun Zhai, Liyi Ma, and Wenfa Li. "Curriculum Architecture Based on Generalized Multimodal Information Fusion for ITO Training." International Journal of Information and Education Technology 6, no. 6 (2016): 500–503. http://dx.doi.org/10.7763/ijiet.2016.v6.740.

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35

Willett, Peter. "Conference Report - Ninth International Conference on Information Fusion (FUSION 2006)." IEEE Aerospace and Electronic Systems Magazine 22, no. 1 (January 2007): 29–31. http://dx.doi.org/10.1109/maes.2007.327522.

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36

Ma, Zhonglin, Zhihao Yu, and Jingshan Zhang. "Sports Information Acquisition and Functional Training System Based on Multisensor Information Fusion." Journal of Sensors 2021 (September 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/6941258.

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Since entering the 21st century, multisensor information fusion technology has developed rapidly, and multisensor information fusion is a trend in the coming years and even decades. This article is aimed at studying how the traditional sports information acquisition and functional training system can be implemented under the multisensor information fusion. This article proposes the use of multisensor information fusion technology to conduct an in-depth study on the traditional ways of acquiring sports information and the cognition of sports information by different groups of people, as well as the use of multisensor information fusion technology to update the functional training system and discuss whether this new functional training system is in line with the speed and rhythm of the current era compared with the traditional training system. The results of obtaining sports information from different groups of people show that more people know about sports events at home and abroad, reaching 22.5%. Under the same circumstances, 60% of the elderly are willing to pay attention to sports information, while only 30% of the young people are willing to pay attention to sports information. And from 2016 to 2021, the number of people in functional training nationwide has increased from 200,000 to 1.5 million.

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37

Han, Binzi, and Baiqing Hu. "Research on the Relationship between Information Fusion Method and Information Failure Mode in Integrated Navigation System." Modern Electronic Technology 2, no. 2 (July 27, 2018): 53. http://dx.doi.org/10.26549/met.v2i2.853.

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Abstract：On the basis of the basic principles of weighted fusion, Kalman filtering and BP neural networks, the basic principles of information fusion methods used in integrated navigation systems are expounded. Through the analysis of the basic principles, the association of information fusion methods commonly used in integrated navigation systems and information failure modes is obtained: the information fault mode of weighted fusion method The model is closely related to the specific weight allocation method, which depends on the fault mode of the sensor or sub-system in which the weight is dominant; the information fault mode of the Kalman filtering information fusion method is a continuous mutation fault corresponding to the nonlinear time interval of the system; the information fault mode of the BP neural network method is gradual with time. The information failure mode of the BP neural network method is a slowly varying fault that gradually accumulates over time. Starting from the complexity associated with the information fusion method and the information failure mode, it is pointed out that in order to systematically express the relationship between the information fusion method and the information failure mode, further research can be carried out.

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38

Xie, Zhibing, and Ling Guan. "Multimodal Information Fusion of Audiovisual Emotion Recognition Using Novel Information Theoretic Tools." International Journal of Multimedia Data Engineering and Management 4, no. 4 (October 2013): 1–14. http://dx.doi.org/10.4018/ijmdem.2013100101.

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This paper aims at providing general theoretical analysis for the issue of multimodal information fusion and implementing novel information theoretic tools in multimedia application. The most essential issues for information fusion include feature transformation and reduction of feature dimensionality. Most previous solutions are largely based on the second order statistics, which is only optimal for Gaussian-like distribution, while in this paper we describe kernel entropy component analysis (KECA) which utilizes descriptor of information entropy and achieves improved performance by entropy estimation. The authors present a new solution based on the integration of information fusion theory and information theoretic tools in this paper. The proposed method has been applied to audiovisual emotion recognition. Information fusion has been implemented for audio and video channels at feature level and decision level. Experimental results demonstrate that the proposed algorithm achieves improved performance in comparison with the existing methods, especially when the dimension of feature space is substantially reduced.

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39

Zhang, Xiuquan, Lin Shen, and Kaiquan Shi. "(αF,αF¯)-Information Fusion Generated by Information Segmentation and Its Intelligent Retrieval." Mathematics 10, no. 5 (February 24, 2022): 713. http://dx.doi.org/10.3390/math10050713.

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Making use of the mathematical model with dynamic features and attribute disjunctive characteristics, the new concepts of αF-information segmentation, αF¯-information segmentation, (αF,αF¯)-information segmentation and their attribute characteristics are given, and the intelligent acquisition of matrix reasoning and information segmentation is given, as well as the information segmentation theorem. Moreover, the equivalence between information segmentation and information fusion is discussed, and the information fusion intelligent acquisition intelligent retrieval algorithm is given. Based on these theoretical results, the intelligent information fusion retrieval algorithm and its simple application in health big data are presented. In conclusion, the results presented in this paper are entirely based on new ideas.

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40

Becerra, M. A., Y. Uribe, D. H. Peluffo-Ordóñez, Karla C. Álvarez-Uribe, and C. Tobón. "Information fusion and information quality assessment for environmental forecasting." Urban Climate 39 (September 2021): 100960. http://dx.doi.org/10.1016/j.uclim.2021.100960.

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41

Rodríguez, R. M., Z. S. Xu, and L. Martínez. "Hesitant fuzzy information for information fusion in decision making." Information Fusion 42 (July 2018): 62–63. http://dx.doi.org/10.1016/j.inffus.2017.09.015.

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42

Garg, Adarsh. "Fusion microfinance: expansion with information technology." Emerald Emerging Markets Case Studies 11, no. 1 (May 5, 2021): 1–23. http://dx.doi.org/10.1108/eemcs-02-2020-0029.

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Learning outcomes After working through the case and assignment questions, students will be able to understand the following: the functioning of the microfinance institutions (MFIs); how the importance of using information technology (IT) can generate a competitive edge; and how emergent technologies, business analytics play a significant role in the expansion of business by helping in decision-making and meet up the corporate social responsibility by ethical disposal of electronic waste. Case overview/synopsis Fusion Microfinance Private Limited is a start-up company with a vision to build a professionally managed MFI that can achieve a healthy amalgamation of social and financial sustainability. It is operational in the less penetrated North Central part of India spread across four states (Madhya Pradesh, Uttarakhand, Uttar Pradesh and Delhi). Fusion appreciated the importance of IT and gradually leveraged IT to help the automation of various functions. Fusion wants to further optimize the organizational outreach to its rural clients by integrating its core function with IT. IT facilitates its huge client network conveniently with the usage of IT. Fusion also aims further to reduce its carbon footprint, thus moving towards the goal of achieving environmental sustainability through IT. Complexity academic level Undergraduate- or graduate-level course on management information systems, environmental sustainability or emergent technologies. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 1: Accounting and Finance.

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Jinming Qiu, Chibiao Liu, and Xiuqiong Chen. "P-Sets and P-Information Fusion." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 10 (May 31, 2013): 115–22. http://dx.doi.org/10.4156/aiss.vol5.issue10.14.

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Yager, Ronald R. "On Possibilistic and Probabilistic Information Fusion." International Journal of Fuzzy System Applications 1, no. 3 (July 2011): 1–14. http://dx.doi.org/10.4018/ijfsa.2011070101.

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This article discusses the basic features of information provided in terms of possibilistic uncertainty. It points out the entailment principle, a tool that allows one to infer less specific from a given piece of information. The problem of fusing multiple pieces of possibilistic information is and the basic features of probabilistic information are described. The authors detail a procedure for transforming information between possibilistic and probabilistic representations, and using this to form the basis for a technique for fusing multiple pieces of uncertain information, some of which is possibilistic and some probabilistic. A procedure is provided for addressing the problems that arise when the information to be fused has some conflicts.

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45

Mönks, Uwe, Helene Dörksen, Volker Lohweg, and Michael Hübner. "Information Fusion of Conflicting Input Data." Sensors 16, no. 11 (October 29, 2016): 1798. http://dx.doi.org/10.3390/s16111798.

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46

Angeli, Davide, and Jesus S. Aguilar-Ruiz. "Information Fusion in Biological Network Inference." Current Bioinformatics 13, no. 2 (March 26, 2018): 110–19. http://dx.doi.org/10.2174/1574893612666170718145952.

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47

Cao, Jie, and Yanlin Li. "Fusion information-based moving tracking algorithm." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 25, no. 3 (April 11, 2011): 211–17. http://dx.doi.org/10.3724/sp.j.1187.2011.00211.

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48

Bhatt, Sujay, and Vikram Krishnamurthy. "Incentivized Information Fusion with Social Sensors." ACM SIGMETRICS Performance Evaluation Review 45, no. 3 (March 20, 2018): 90–95. http://dx.doi.org/10.1145/3199524.3199539.

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49

Nakamura, Eduardo F., Antonio A. F. Loureiro, and Alejandro C. Frery. "Information fusion for wireless sensor networks." ACM Computing Surveys 39, no. 3 (September 3, 2007): 9. http://dx.doi.org/10.1145/1267070.1267073.

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50

Zhong, Li, Wang Ailing, and Qi Furong. "Study on Extenics Information Fusion Method." International Journal of Education and Management Engineering 1, no. 1 (July 29, 2011): 12–19. http://dx.doi.org/10.5815/ijeme.2011.01.03.

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