| 000 | 05605nam a22006375i 4500 | ||
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| 001 | 978-3-030-03643-0 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125021.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 190401s2019 sz | s |||| 0|eng d | ||
| 020 |
_a9783030036430 _9978-3-030-03643-0 |
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| 024 | 7 |
_a10.1007/978-3-030-03643-0 _2doi |
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| 050 | 4 | _aQA76.9.D343 | |
| 072 | 7 |
_aUNF _2bicssc |
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_a006.312 _223 |
| 245 | 1 | 0 |
_aInformation Quality in Information Fusion and Decision Making _h[electronic resource] / _cedited by Éloi Bossé, Galina L. Rogova. |
| 250 | _a1st ed. 2019. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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| 300 |
_aXVI, 620 p. 167 illus., 127 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aInformation Fusion and Data Science, _x2510-1536 |
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| 505 | 0 | _aPartI: Information Quality: Concepts, Models and Dimensions -- Chapter1: Information Quality in Fusion Driven Human-Machine Environments -- Chapter2: Quality of Information Sources in Information Fusion -- Chapter3: Using Quality Measures in the Intelligent Fusion of Probabilistic Information -- Chapter4: Conflict management in information fusion with belief functions -- Chapter5: Requirements for total uncertainty measures in the theory of evidence.-Chapter6: Uncertainty Characterization and Fusion of Information from Unreliable Sources -- Chapter7: Assessing the usefulness of information in the context of coalition operations -- Chapter8: Fact, Conjecture, Hearsay and Lies: Issues of Uncertainty in Natural Language Communications -- Chapter9: Fake or Fact? Theoretical and Practical Aspects of Fake News -- Chapter10: Information quality and social networks -- Chapter11: Quality, Context, and Information Fusion -- Chapter12: AnalyzingUncertain Tabular Data. Chapter13: Evaluation of information in the context of decision-making -- Chapter14: Evaluating and Improving Data Fusion Accuracy -- PartII: Aspects of Information Quality in various domains of application -- Chapter15: Decision-Aid Methods based on Belief Function Theory with Application to Torrent Protection -- Chapter16: An Epistemological Model for a Data Analysis Process in Support of Verification and Validation -- Chapter17: Data and Information Quality in Remote Sensing -- Chapter18: Reliability-Aware and Robust Multi-Sensor Fusion Towards Ego-Lane Estimation Using Artificial Neural Networks -- Chapter19: Analytics and Quality in Medical Encoding Systems -- Chapter20: Information Quality: The Nexus of Actionable Intelligence -- Chapter21: Ranking Algorithms: Application for Patent Citation Network -- Chapter22: Conflict Measures and Importance Weighting for Information Fusion applied to Industry 4.0 -- Chapter23: Quantify: An Information Fusion Model based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness -- Chapter24: Adaptive fusion. | |
| 520 | _aThis book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field. | ||
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aQuantitative research. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aOperations research. | |
| 650 | 0 | _aComputational intelligence. | |
| 650 | 0 | _aSystem theory. | |
| 650 | 1 | 4 | _aData Mining and Knowledge Discovery. |
| 650 | 2 | 4 | _aData Analysis and Big Data. |
| 650 | 2 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aOperations Research and Decision Theory. |
| 650 | 2 | 4 | _aComputational Intelligence. |
| 650 | 2 | 4 | _aComplex Systems. |
| 700 | 1 |
_aBossé, Éloi. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aRogova, Galina L. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030036423 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030036447 |
| 830 | 0 |
_aInformation Fusion and Data Science, _x2510-1536 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-03643-0 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
| 999 |
_c173288 _d173288 |
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