Relational Demonic Fuzzy Refinement

Tchier, Fairouz

doi:10.1155/2014/140707

Cited by 2 publications

(2 citation statements)

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“…Our future work will involve applying our approach to other complex real-world diagnosis problems. We will also try alternative fuzzy logic approaches such as Neuro-Fuzzy networks or Fuzzy Petri with demonic relational methods [19,20]. Since neural networks has shown capabilities in rule and feature extracting, we can combine adaptive fuzzy inference systems and principle component analysis neural networks, to introduce a new computerized diagnostic tool and give similar accurate diagnostic results.…”

Section: Resultsmentioning

confidence: 99%

“…Fuzzy logic was invented by Zadeh [25] in 1965 for handling uncertain and imprecise knowledge in real world applications. It has proved to be a powerful tool for decision-making, and to handle and manipulate imprecise and noisy data [19][20][21][22][23][24][25]. A fuzzy system is characterized by a set of linguistic statements based on expert knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy relational model and genetic algorithms for early detection and diagnosis of breast cancer in Saudi Arabia

Tchier

Alharbi

2016

Filomat

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Breast Cancer (BC) is considered as the most implacable malignancy and the leading cause of mortality among women in general and in Saudi Arabia specially. Most of the previous work in Saudi Arabia on this subject was on epidemiology, knowledge of (BC) and practice of breast self-examination (BSE), etiological factors, metastases and rate of survival. Early detection and diagnosis of Breast Cancer (BC) is an important, real-world medical problem. In this paper, we propose a soft computing methodology to build a Breast Cancer (BC) diagnosis system with high capabilities as described by Andres et al. [4] but on the Saudi Arabian breast cancer dataset and using a simplified fitness function. We focus on combining fuzzy concepts and genetic algorithms so as to automatically produce diagnostic systems to support and assist the expert to understand and evaluate its results with high classification performance.

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Section: Resultsmentioning

confidence: 99%