000 | 06417nam a2201477 i 4500 | ||
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001 | 5263228 | ||
003 | IEEE | ||
005 | 20230927112345.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 100317t20152000nyua ob 001 0 eng d | ||
020 |
_a9780470545355 _qelectronic |
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020 |
_z9780780334045 _qprint |
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020 |
_z0470545356 _qelectronic |
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024 | 7 |
_a10.1109/9780470545355 _2doi |
|
035 | _a(CaBNVSL)mat05263228 | ||
035 | _a(IDAMS)0b000064810c33d0 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
082 | 0 | 4 | _a610/.285/63 |
100 | 1 |
_aHudson, D. L., _q(Donna L.) _eauthor. |
|
245 | 1 | 0 |
_aNeural networks and artificial intelligence for biomedical engineering / _cDonna L. Hudson, Maurice E. Cohen. |
264 | 1 |
_aNew York : _bInstitute of Electrical and Electronics Engineers, _cc2000. |
|
264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1999] |
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300 |
_a1 PDF (xxiii, 306 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aIEEE press series on biomedical engineering ; _v3 |
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504 | _aIncludes bibliographical references and index. | ||
505 | 0 | _aPreface. Acknowledgments. Overview. NEURAL NETWORKS. Foundations of Neural Networks. Classes of Neural Networks. Classification Networks and Learning. Supervised Learning. Unsupervised Learning. Design Issues. Comparative Analysis. Validation and Evaluation. ARTIFICIAL INTELLIGENCE. Foundation of Computer-Assisted Decision Making. Knowledge Representation. Knowledge Acquisition. Reasoning Methodologies. Validation and Evaluation. ALTERNATIVE APPROACHES. Genetic Algorithms. Probabilistic Systems. Fuzzy Systems. Hybrid Systems. HyperMerge, a Hybird Expert System. Future Perspectives. Index. About the Authors. | |
506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
520 | _aUsing examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: . Types of neural networks and neural network algorithms. Knowledge representation, knowledge acquisition, and reasoning methodologies. Chaotic analysis of biomedical time series. Genetic algorithms. Probability-based systems and fuzzy systems. Evaluation and validation of decision support aids. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson. | ||
530 | _aAlso available in print. | ||
538 | _aMode of access: World Wide Web | ||
588 | _aDescription based on PDF viewed 12/21/2015. | ||
650 | 0 |
_aArtificial intelligence _xMedical applications. |
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650 | 0 | _aNeural networks (Computer science) | |
650 | 0 | _aExpert systems (Computer science) | |
650 | 0 |
_aBiomedical engineering _xComputer simulation. |
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655 | 0 | _aElectronic books. | |
695 | _aAccuracy | ||
695 | _aAlgorithm design and analysis | ||
695 | _aArteries | ||
695 | _aArtificial intelligence | ||
695 | _aArtificial neural networks | ||
695 | _aBayesian methods | ||
695 | _aBinary trees | ||
695 | _aBiographies | ||
695 | _aBiological cells | ||
695 | _aBiological neural networks | ||
695 | _aBiological system modeling | ||
695 | _aBiomedical imaging | ||
695 | _aBlood | ||
695 | _aBlood pressure | ||
695 | _aBrain models | ||
695 | _aChaos | ||
695 | _aClassification algorithms | ||
695 | _aClustering algorithms | ||
695 | _aCognition | ||
695 | _aComputational modeling | ||
695 | _aComputers | ||
695 | _aConvergence | ||
695 | _aData models | ||
695 | _aDatabases | ||
695 | _aDecision making | ||
695 | _aDecision trees | ||
695 | _aDesign automation | ||
695 | _aDiseases | ||
695 | _aDrugs | ||
695 | _aElectric potential | ||
695 | _aElectrocardiography | ||
695 | _aElectroencephalography | ||
695 | _aEngines | ||
695 | _aEuclidean distance | ||
695 | _aExpert systems | ||
695 | _aFeature extraction | ||
695 | _aFires | ||
695 | _aFuzzy sets | ||
695 | _aGenetics | ||
695 | _aGold | ||
695 | _aHeart | ||
695 | _aHopfield neural networks | ||
695 | _aHospitals | ||
695 | _aHumans | ||
695 | _aIndexes | ||
695 | _aInference algorithms | ||
695 | _aKnowledge acquisition | ||
695 | _aKnowledge based systems | ||
695 | _aKnowledge representation | ||
695 | _aLinear matrix inequalities | ||
695 | _aMathematical model | ||
695 | _aMeasurement | ||
695 | _aMedical diagnostic imaging | ||
695 | _aMedical services | ||
695 | _aNatural language processing | ||
695 | _aNeurons | ||
695 | _aNumerical models | ||
695 | _aObject oriented modeling | ||
695 | _aOptimization | ||
695 | _aOrganisms | ||
695 | _aPain | ||
695 | _aPartitioning algorithms | ||
695 | _aProbabilistic logic | ||
695 | _aProcess control | ||
695 | _aProduction | ||
695 | _aSearch problems | ||
695 | _aSimulated annealing | ||
695 | _aSoftware | ||
695 | _aSpectroscopy | ||
695 | _aSupervised learning | ||
695 | _aSupport vector machine classification | ||
695 | _aTesting | ||
695 | _aTiles | ||
695 | _aTime series analysis | ||
695 | _aTraining | ||
695 | _aTransforms | ||
695 | _aUnsupervised learning | ||
695 | _aVectors | ||
700 | 1 |
_aCohen, M. E. _q(Maurice E.) |
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710 | 2 |
_aJohn Wiley & Sons, _epublisher. |
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710 | 2 |
_aIEEE Xplore (Online service), _edistributor. |
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776 | 0 | 8 |
_iPrint version: _z9780780334045 |
830 | 0 |
_aIEEE Press series in biomedical engineering ; _v3 |
|
856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5263228 |
999 |
_c40093 _d40093 |