Introduction - Computational Intelligence in Biomedical Engineering - page 13

machine-learning community as these are regarded as the core technologies
that drive these techniques. Finally, we discuss the future developments in
biomedical engineering and how CI could play a role in them. Some examples
include the development of wireless healthcare systems and the use of sensor
networks and intelligent information techniques.
1.6 Book Usage
This topic can be used in a variety of ways. Chapters 2 and 3 contain back-
ground material that would be useful for comprehending the following chap-
ters. Selected topics in these chapters may be used together with material
from one or more other chapters for undergraduate and postgraduate courses
in biomedical engineering. For biomedical researchers, we hope that this topic
provides a review of the advances and remaining challenges in the application
of CI to problems in the biomedical field. We have tried to present the mate-
rial such that each chapter can be independently read and we hope that the
readers will find it useful and enjoy the material to read as much as we have
enjoyed presenting it.
References
Barnett, G., N. Justice, M. Somand, J. Adams, B. Waxman, P. Beaman,
M. Parent, F. Vandeusen, and J. Greenlie (1979). COSTAR—a computer-
based medical information system for ambulatory care. Proceedings of the
IEEE 67(9) , 1226-1237.
Barricelli, N. A. (1954). Esempi numerici di processi di evoluzione. Methodos ,
45-68.
Fogel, D. (1995). Evolutionary Computation: Toward a New Philosophy of
Machine Intelligence . New York: IEEE Press.
Fraser, A. (1957). Simulation of genetic systems by automatic digital comput-
ers I. Introduction. Australian Journal of Biological Sciences 10 , 484-491.
Hudson, D. L. and M. E. Cohen (1999). Neural Networks and Artificial Intel-
ligence for Biomedical Engineering . IEEE Press Series in Biomedical Engi-
neering. New York: Institute of Electrical and Electronics Engineers.
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information
storage and organization in the brain. Psychological Review 65 , 386-408.
Sarker, R., M. Mohammadian, and X. Yao (Eds) (2002). Evolutionary Opti-
mization . Boston, MA: Kluwer Academic.
Shavlik, J. and T. Dietterich (1990). Readings in Machine Learning . The
Morgan Kaufmann series in machine learning. San Mateo, CA: Morgan
Kaufmann Publishers.