Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases

First Order Linear Homogeneous Ordinary Differential Equation in Fuzzy Environment Based On Laplace Transform

In this paper the First Order Linear Ordinary Differential Equations (FOLODE) are described in fuzzy environment. Here coefficients and /or initial condition of FOLODE are taken as Generalized Triangular Fuzzy Numbers (GTFNs).The solution procedure of the FOLODE is developed by Laplace transform. It is illustrated by numerical examples. Finally imprecise bank account problem and concentration of drug in blood problem are described

First Order Linear Non Homogeneous Ordinary Differential Equation in Fuzzy Environment

In this paper, the solution procedure of a first order linear non homogeneous ordinary differential equation in fuzzy environment is described. It is discussed for three different cases. They are i) Ordinary Differential Equation with initial value as a fuzzy number, ii) Ordinary Differential Equation with coefficient as a fuzzy number and iii) Ordinary Differential Equation with initial value and coefficient are fuzzy numbers. Here fuzzy numbers are taken as Generalized Triangular Fuzzy Numbers (GTFNs). An elementary application of population dynamics model is illustrated with numerical example. Keywords: Fuzzy Ordinary Differential Equation (FODE), Generalized Triangular fuzzy number (GTFN), strong solution

Generalized intuitionistic fuzzy laplace transform and its application in electrical circuit

In this paper we describe the generalized intuitionistic fuzzy laplace transform method for solving first order generalized intutionistic fuzzy differential equation. The procedure is applied in imprecise electrical circuit theory problem. Here the initial condition of those applications is taken as Generalized Intuitionistic triangular fuzzy numbers (GITFNs).Publisher's Versio

A consistent neuro-fuzzy inference system

Велики број аутора сматра да велике могућности експертских система леже у хибридним моделима, што су ови системи и доказали у пракси. Мотивисан тиме, предложени модел система у основи представља интеграцију неуронских мрежа и фази система, чиме се боље користе добре стране оба приступа. Полазна основа овог рада је да понашање система, кроз скуп лингвистичких правила, треба да описују управо они који систем највише познају и разумеју (насупрот аутоматски генерисаним правилима која су најчешће рогобатна и неразумљива). Знање експерата из било које области лако се може формулисати вербалним исказима, а теорија фази скупова и фази логике омогућава превођење оваквих исказа у одговарaјуће математичке изразе. Класична теорија фази скупова не задовољава све Булове аксиоме. Из овог разлога у раду је примењена конзистентна реално-вредносна [0,1] логика, која се заснива на интерполативној Буловој алгебри (ИБА). Свака логичка функција може се једнозначно трансформисати у одговарајући генерализовани Булов полином (ГБП) коришћењем ИБА при чему се чувају сви Булови закони. Оправданост коришћења конзистентног приступа најпре је илустрована на примеру конзистентног фази система закључивања (КФИС). Сврха приказаног КФИС-а је да процени могућност да је пацијент на дијализи трбушне марамице (лат. peritoneum) оболео од перитонитиса. Добијени резултати указују на чињеницу да класичан ФИС и конзистентан приступ не воде увек ка истим резултатима, а разлика је најуочљивија када правила укључују негацију. Како би се КФИС даље унапредио, коришћена је неуронска мрежа, тј. њен алгоритам учења, који, на основу скупа улазно-излазних података, подешава параметре тако да више одговарају реалном систему. На тај начин, предложени конзистентан неуро-фази систем (КНФИС) користи знање садржано у подацима и унапређује закључивање. Такође, елиминише се субјективност коју експерти у некој мери изражавају приликом дефинисања параметара система...A number of authors find that the greatest potential of expert systems lies in hybrid models, and such models have proven this viewpoint in practice.Therein lies the motivation for introducing a new system model, integrating neural networks and fuzzy systems, thus building on the best features of each of these approaches. The main premise of this thesis is that the behavior of a system should be described, through a set of linguistic rules, by those who know and understand the system the best (as opposed to the automatic generation of rules that are often cumbersome and incomprehensible). Expert knowledge in any domain can be easily expressed in the form of verbal statements, and fuzzy set theory and fuzzy logic enable the transformation of such verbal statements into mathematical expressions. Conventional fuzzy set theory does not satisfy all Boolean axioms. For this reason, the consistent real-valued [0,1] logic, based on the Interpolative realization of Boolean algebra (IBA), is applied in this thesis. Any logical function can be uniquely transformed into a corresponding generalized Boolean polynomial (GBP) using IBA thereby preserving all Boolean laws. The justification for using a consistent approach is first illustrated on an example of a consistent fuzzy inference system (CFIS). The purpose of the described CFIS is to estimate the likelihood that a patient undergoing peritoneal dialysis, has peritonitis. The obtained results demonstrate that conventional FIS and the Boolean consistent approach do not always lead to the same results, and this discrepancy is most pronounced when the established rules include negations. In order to further enhance CFIS a neural network, or, more precisely, its learning algorithm, is used to fine-tune the parameters, in accordance with a set of input-output data, so that the parameters better suit the real system. Consequently, the proposed consistent neuro-fuzzy system (CNFIS) uses the knowledge contained in the data to improve the inference process. In addition, it eliminates the subjectivity incorporated into the system by experts when defining the parameters of the system..

Fuzzy Modeling and Control of HIV Infection

The present study proposes a fuzzy mathematical model of HIV infection consisting of a linear fuzzy differential equations (FDEs) system describing the ambiguous immune cells level and the viral load which are due to the intrinsic fuzziness of the immune system's strength in HIV-infected patients. The immune cells in question are considered CD4+ T-cells and cytotoxic T-lymphocytes (CTLs). The dynamic behavior of the immune cells level and the viral load within the three groups of patients with weak, moderate, and strong immune systems are analyzed and compared. Moreover, the approximate explicit solutions of the proposed model are derived using a fitting-based method. In particular, a fuzzy control function indicating the drug dosage is incorporated into the proposed model and a fuzzy optimal control problem (FOCP) minimizing both the viral load and the drug costs is constructed. An optimality condition is achieved as a fuzzy boundary value problem (FBVP). In addition, the optimal fuzzy control function is completely characterized and a numerical solution for the optimality system is computed