The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease
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Citations
Cites
Advances in knowledge discovery and data mining
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90007-3 · Cited by 2,971
Knowledge Discovery in Databases
Piateski, G., & Frawley, W. (1991) · The MIT Press eBooks · Cited by 1,749
A tutorial on Principal Components Analysis
Smith, L. I. (2002) · Otago University Research Archive (University of Otago) · Cited by 1,636
Knowledge discovery in databases
FrawleyWilliam, J., Piatetsky-ShapiroGregory, & MatheusChristopher, J. (1992) · AI Magazine · doi:10.5555/140629.140633 · Cited by 1,055
Self-learning fuzzy controllers based on temporal backpropagation
Jang, J. R. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.159060 · Cited by 904
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
Güler, İ., & Übeyli, E. D. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.04.013 · Cited by 548
Guest Editors' Introduction: On Applied Research in Machine Learning
Provost, F., & Kohavi, R. (1998) · Machine Learning · doi:10.1023/a:1007442505281 · Cited by 378
Wang, X., & Paliwal, K. K. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00044-x · Cited by 260
Methodologies from Machine Learning in Data Analysis and Software
Michie, D. (1991) · The Computer Journal · doi:10.1093/comjnl/34.6.559 · Cited by 39
Cited by
COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach
Pinter, G., Felde, I., Mosavi, A., Ghamisi, P., & Gloaguen, R. (2020) · Mathematics · doi:10.3390/math8060890 · Cited by 328
Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm
Maniruzzaman, M., Kumar, N., Menhazul Abedin, M., Shaykhul Islam, M., Suri, H. S., El-Baz, A. S., & Suri, J. S. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.09.004 · Cited by 270
Qawqzeh, Y. K., Bajahzar, A. S., Jemmali, M., Otoom, M. M., & Thaljaoui, A. (2020) · BioMed Research International · doi:10.1155/2020/3764653 · Cited by 71
RetractedIntelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey
Jiang, Q., Zhou, X., Wang, R., Ding, W., Chu, Y., Tang, S., Jia, X., & Xu, X. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108835 · Cited by 40
Metadata
- Authors
- Polat, K., & Güneş, S.
- Venue
- Digital Signal Processing
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 17
- Issue
- 4
- Pages
- 702-710
- ISSN
- 1051-2004 (ISSN-L)
- OpenAlex
- W2016979930
- Cited by
- 377
- References
- 9
- FWCI
- 15.04
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
