000			06502nam a2201489 i 4500
001			5263228
003			IEEE
005			20220712205629.0
006			m o d
007			cr |n|||||||||
008			100317t20152000nyua ob 001 0 eng d
020			_a9780470545355 _qelectronic
020			_z9780780334045 _qprint
020			_z0470545356 _qelectronic
024	7		_a10.1109/9780470545355 _2doi
035			_a(CaBNVSL)mat05263228
035			_a(IDAMS)0b000064810c33d0
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aR859.7.A78 _bH84 2000eb
082	0	4	_a610/.285/63 _222
100	1		_aHudson, D. L., _q(Donna L.) _eauthor. _926622
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]
300			_a1 PDF (xxiii, 306 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE press series on biomedical engineering ; _v3
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. _94809
650		0	_aNeural networks (Computer science) _93414
650		0	_aExpert systems (Computer science) _93392
650		0	_aBiomedical engineering _xComputer simulation. _926623
655		0	_aElectronic books. _93294
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.) _926624
710	2		_aJohn Wiley & Sons, _epublisher. _96902
710	2		_aIEEE Xplore (Online service), _edistributor. _926625
776	0	8	_iPrint version: _z9780780334045
830		0	_aIEEE Press series in biomedical engineering ; _v3 _926626
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5263228
942			_cEBK
999			_c73830 _d73830