A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems

Gülbağ, Ali; Temurtaş, Feyzullah

doi:10.1016/j.snb.2005.09.009

Cited by 82 publications

(33 citation statements)

References 27 publications

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“…Due to this fact, a few first PCs are usually enough to represent the data variance well. It was reported that the Levenberg-Marquardt (LM) al- gorithm [36] provides generally faster convergence and better estimation results than other training algorithms [37]. However, this method can cause a memorization effect when overtraining occurs.…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

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The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

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Section: Back-propagation Neural Networkmentioning

confidence: 99%

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya¹,

Odedra²,

Samanta³

et al. 2010

Rev Diabet Stud

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show abstract

“…So, this type trained neural network gives similar result for untrained test sets also. But, if a neural network starts to memorize the training set, its generalisation starts to decrease and it's performance may not be improved for untrained test sets (Gulbag & Temurtas, 2006). The k-fold cross-validation method shows how good generalisation can be made using neural network structures (Ozyılmaz & Yıldırım, 2002).…”

Section: K-fold Cross-validationmentioning

confidence: 99%

A comparative study on thyroid disease diagnosis using neural networks

Temurtaş

2009

Expert Systems with Applications

148

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“…By utilising the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human process information, ANFIS harnesses the power of the two paradigms: ANNs and fuzzy logic, and overcomes their own shortcomings simultaneously. Successful implementations of ANFIS have been reported in the medical field (Gü ler & Ü beyli, 2005;Ü beyli & Gü ler, 2005), chemical field (Gulbag & Temurtas, 2006;Lo & Lin, 2005) and fault diagnosis (Lou & Loparo, 2004;Ye, Sadeghian, & Wu, 2006). Generally, statistical characteristics in different domains are extracted, respectively, to acquire rich faulty information and enhance the competence of the diagnosis systems, but a large feature set contains irrelevant or redundant features as well as superior features.…”

Section: Introductionmentioning

confidence: 99%