A fuzzy-based methodology for the analysis of diabetic neuropathy
References (27)
- et al.
A commutative L-monoid for classifications with fuzzy attributes
Int. J. Approximate Reasoning
(2001) An algebraic fuzzy structure for the approximate reasoning
Fuzzy Sets and Systems
(1992)- et al.
Classifying through a fuzzy algebraic structure
Fuzzy Sets and Systems
(1996) - et al.
A complete, flexible fuzzy-based approach to the classification problem
Int. J. Approximate Reasoning
(1995) - et al.
An algebraic approximation to the classification with fuzzy attributes
Int. J. Approximate Reasoning
(1993) Why triangular membership function?
Fuzzy Sets and Systems
(1994)Fuzzy sets
Inform. Control
(1965)Fuzzy set theory in medical diagnosis
IEEE Trans. System, Man, Cybernet.
(1986)- K.S. Collazos, J.M. Barreto, S.M. Nassar, Fuzzy analogical reasoning for medical diagnosis,...
- et al.
Neurophysiological study of the effect of combined kidney and pancreas transplantation on diabetic neuropathy: a 2-year follow-up evaluation
Diabetologia
(1991)
A fuzzy-based approach to stereotype selection in hypermedia
User Modeling User-Adapted Interaction
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Study and prediction of prostate cancer using fuzzy inference system
2022, Materials Today: ProceedingsAnalysis and investigation of fuzzy expert system for predicting the child anaemia
2022, Materials Today: ProceedingsCitation Excerpt :For this purpose, masses of layout strategies had been derived currently [4,8]. Fuzzy common sense performs an essential function in medicinal drug areas [9–11], to be expecting the forecast of quality with wireless systems [12] to examine diabetic neuropathy [13], to calculate volumes of mind tissue for MRI closer to examine the MRI data [14], and to examine the blood stress through fuzzy common sense. To assist the docs to determine speedy and powerful approximately the dose of medication to deal with the 2 hundred dialysis patients [15] had been used to broaden a fuzzy rule to primarily based totally automated machine.
A methodology to prioritize spatio-temporal monitoring of drinking water quality considering population vulnerability
2020, Journal of Environmental ManagementCitation Excerpt :There are many multiple criteria analysis methods. Among them, fuzzy logic was proposed by Zadeh (1965) and has been used in many different applications: medical diagnosis (Lascio et al., 2002), information technology (Lee, 1996), inventory classification (Kabir and Hasin, 2013), source water quality assessment (Chang et al., 2001; Lu et al., 1999) and drinking water quality assessment (Francisque et al., 2009). Fuzzy logic allows the combination of variables of different forms (quantitative, qualitative) from different subjects related to water and its consumption (water quality, infrastructure, population sensitivity and sociodemographics, etc.).
Risk assessment of particulate matters in a dentistry school using fuzzy inference systems
2018, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :Fuzzy logic is a mathematical concept that uses an experience of expert to solve problems by tolerant of imprecise data to arrive at the accurate conclusion possible. Unlike the classic mathematical theory, which explain certain crisp events (i.e., occurrences that either happen or do not happen), fuzzy logic uses probability theory to describe how an incident will happen [21,22]. Also, this theory uses linguistic variables (e.g. very low, low, moderate, high and very high) for indicating how uncertain events can occur [23].
Optimization of conditions (pH and temperature) for Lemna gibba production using fuzzy model coupled with Mamdani's method
2015, Ecological EngineeringCitation Excerpt :Fuzzy logic and fuzzy sets are effective tools for modeling complex mathematical problems with parameters that demonstrate uncertainty (Scannapieco et al., 2012; Kotti et al., 2013). This approach has proved very useful in medical diagnosis (Lascio et al., 2002), information technology (Lee, 1996), water quality (Lu et al., 1999), reliability analysis (Sadiq et al., 2004) and many other industrial applications (Lawry, 2001), where reported data are either qualitative and decision-making is performed based upon expert opinions. Fuzzy sets are the sets with boundaries that are not precise and the usage is not a matter of affirmation or denial, but rather a matter of degree.
Risk assessment for transboundary rivers using fuzzy synthetic evaluation technique
2014, Journal of HydrologyCitation Excerpt :Linguistic reasoning is gaining significance in many emergent fields including applied sciences and engineering. Fuzzy logic approach has proved its utility in autonomous robot navigation (Saffiotti, 1997), medical diagnosis (Di Lascio et al., 2002), information technology (Lee, 1996), data mining (Mitra et al., 2002), water quality assessment (Lu and Lo, 2002; Lu et al., 1999; Sadiq et al., 2004), hydrology (Lohani et al., 2006, 2012) reservoir operation (Raman and Chandramouli, 1996), real time flood forecasting (Lohani et al., 2005, 2014), water resource sharing and allocation (Kucukmehmetoglu et al., 2010) and in many other industrial, scientific, policy making, economic sectors. Fuzzy approach finds greater appeal among all sections including engineers, regulators, decision-makers, policy makers, managers, expert panels and other stake-holders.