The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Introduction to artificial neural networks
Abstract
The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy.
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Cites
Self-organization of orientation sensitive cells in the striate cortex
von der Malsburg, C. (1973) · Kybernetik · doi:10.1007/bf00288907 · Cited by 1,461
The ART of adaptive pattern recognition by a self-organizing neural network
Carpenter, G. A., & Grossberg, S. (1988) · Computer · doi:10.1109/2.33 · Cited by 1,287
An introduction to neural computing
Kohonen, T. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90020-2 · Cited by 1,202
Generalization and network design strategies
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Collective computational properties of neural networks: New learning mechanisms
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Is artificial intelligence an intelligent choice for gastroenterologists?
Hollander, D. (2003) · Digestive and Liver Disease · doi:10.1016/s1590-8658(03)00056-2 · Cited by 5
Cited by
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Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.030 · Cited by 696
Sousa, W. G. D., Melo, E. R. P. D., Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2019.07.004 · Cited by 501
Hirose, H., Takayama, T., Hozawa, S., Hibi, T., & Saito, I. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.09.005 · Cited by 72
Pneumonia Detection from Chest X-rays Using the CheXNet Deep Learning Algorithm
Bairwa, H., & `Jangid, R. (2024) · doi:10.20944/preprints202407.0104.v1 · Cited by 8
Metadata
- Authors
- Grossi, E., & Buscema, M.
- Venue
- European Journal of Gastroenterology & Hepatology
- Published
- 2007-12
- Volume
- 19
- Issue
- 12
- Pages
- 1046-1054
- ISSN
- 0954-691X (ISSN-L)
- OpenAlex
- W2043585054
- Cited by
- 342
- References
- 7
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- closed
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