Get Advanced Computational Intelligence Paradigms in Healthcare PDF

By M. Sordo, S. Vaidya, L. C. Jain (auth.), Dr. Margarita Sordo, Dr. Sachin Vaidya, Prof. Lakhmi C. Jain (eds.)

ISBN-10: 3540776613

ISBN-13: 9783540776611

ISBN-10: 3540776621

ISBN-13: 9783540776628

Advanced Computational Intelligence (CI) paradigms are more and more used for enforcing powerful machine functions to foster protection, caliber and efficacy in all points of healthcare. This study ebook covers an plentiful spectrum of the main complex functions of CI in healthcare.

The first bankruptcy introduces the reader to the sphere of computational intelligence and its purposes in healthcare. within the following chapters, readers will achieve an realizing of potent CI methodologies in different very important themes together with scientific choice help, selection making in medication effectiveness, cognitive categorizing in scientific info approach in addition to clever pervasive healthcare platforms, and agent middleware for ubiquitous computing. chapters are dedicated to imaging functions: detection and category of microcalcifications in mammograms utilizing evolutionary neural networks, and Bayesian equipment for segmentation of clinical photos. the ultimate chapters conceal key points of healthcare, together with computational intelligence in tune processing for blind humans and moral healthcare agents.

This publication could be of curiosity to postgraduate scholars, professors and practitioners within the parts of clever structures and healthcare.

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Breast Cancer Prognosis using EMN Architecture. Proceedings of the IEE International Conference on Neural Networks. June 1994. 33. B. Kovalerchuk, E. F. Ruiz. The reliability issue of computeraided breast cancer diagnosis. Comput Biomed Res, 33, 296–313. 2000. 34. H. Burke, D. Rosen, P. Goodman. Comparing the Prediction Accuracy of Artificial Neural Networks and Other Statistical Models for Breast Cancer Survival. , & Leen, T. ), Advances in Neural Information Processing Systems, Vol. 7, pp. 1063–1067.

W. Kan et al. and traces three-dimensional random walks of photons in a medium. The two key parameters in Monte Carlo modeling are the scattering angle and the mean free path for a photon-tissue interaction. The mean free path is determined by the probability that a photon is scattered or absorbed after a given step size. This probability is determined by local optical properties: µs , the scattering coefficient, and µa , the absorption coefficient. Similarly, the scattering angle is determined by the anisotropy factor g.

On crossover as an evolutionary viable strategy. K. B. ), Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 61–68 San Mateo, California. Morgan Kaufmann Publishers, 1991. 22. D. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Learning. IEEE Press, 1995. 23. T. B¨ ack, Evolutionary Algorithms in Theory and Practice. The Clarendon Press, Oxford University Press, New York, 1996. 24. G. Syswerda. Uniform crossover in genetic algorithms. D. ), Proceedings of the Third International Conference on Genetic Algorithms, pp 2–9 George Mason University.

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Advanced Computational Intelligence Paradigms in Healthcare - 3 by M. Sordo, S. Vaidya, L. C. Jain (auth.), Dr. Margarita Sordo, Dr. Sachin Vaidya, Prof. Lakhmi C. Jain (eds.)

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