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\title{Fuzzy Reinforcement Learning using Neural Network: An Application to Medical Diagnosis and Business Intelligence}
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             \author[1]{Poli Venkata Subba  Reddy}

             \affil[1]{  Sri Venkateswara University, Tirupati}

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\date{\small \em Received: 11 June 2021 Accepted: 1 July 2021 Published: 15 July 2021}

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\begin{abstract}
        


The information available to the system is incomplete in many applications, particularly in Decision Support Systems. The fuzzy logic deals incomplete information with belief rather than likelihood (probability). Sometimes the decision has to be taken with fuzzy information. In this paper, fuzzy machine learning is studied for decision support systems. The fuzzy Decision set is defined with two-fold fuzzy set. The fuzzy inference is studied with fuzzy neural network for fuzzy Decision sets. Business application is given as application.

\end{abstract}


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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
nformation available to many applications like Business, Medical, Geological, Control Systems, etc is incomplete or uncertain. The fuzzy logic will deal with incomplete information with belief rather than likelihood (probable). Zadeh formulated uncertain information as fuzzy set with a single membership functions. The fuzzy set with two membership functions will give more evidence than a single membership function. The two-fold fuzzy set is with fuzzy membership functions "Belief" and "Disbelief". Usually, in Medical and Business applications, there are two opinions like "Belief" and "Disbelief" about the information and decision has to be taken under risk. For instance, in Mycin \hyperref[b0]{[1]}, the medical information is defined with belief and disbelief i.e. 
\section[{/, CF[h,e]=MB[h,e] -MD[h.e],}]{/, CF[h,e]=MB[h,e] -MD[h.e],}\par
where "e" is the evidence for given hypothesis "h". The fuzzy set is used instead of Probability to define fuzzy certainty factor.\par
The fuzzy neural networks are one of the learning techniques to study fuzzy problems. In the following, some methods of fuzzy conditional inference are studied through fuzzy neural network and before that preliminaries of fuzzy logic and neural network are discussed.\par
In the following fuzzy logic \hyperref[b9]{[10]} and Generalized fuzzy logic \hyperref[b8]{[9]} are studied briefly. The fuzzy Certainty Factor is studied and fuzzy Decision set is proposed. The fuzzy inference and fuzzy reasoning are studied for fuzzy Decision set. The Business applications are studied as applications of fuzzy Decision set.\par
Author: e-mail: pvsreddy@hotmail.co.in II. 
\section[{Fuzzy Logic}]{Fuzzy Logic}\par
Various theories are studied to deal with imprecise, inconsistent and inexact information and these theories deal with likelihood whereas fuzzy logic with belief. Zadeh \hyperref[b9]{[10]} has introduced fuzzy set as a model to deal with uncertain information as single membership functions. The fuzzy set is a class of objects with a continuum of grades of membership. The set A of X is characterized by its membership function µA(x) and ranging values in the unit interval  For instance "Rama has mild headache" with Fuzziness 0.4[0, 1]. µA(x): X ?[0, 1], x ? X,\par
The fuzzy logic is defined as combination of fuzzy sets using logical operators  {\ref [21]}. Some of the logical operations are given below Let A, B and C are fuzzy sets. The operations on fuzzy sets areNegation If x is not A A'=1-µ A (x)/x 
\section[{Conjunction}]{Conjunction}\par
x is A and y is B? (x, y) is A x B A x B=min(µ A (x)), µ B (y)\}(x,y) 
\section[{If x=y}]{If x=y}\par
x is A and y is B? (x, y) is A?BA?B=min(µ A (x)), µ B (y)\}/x x is A or y is B? (x, y) is A' x B' A' x B' =max(µ A (x)), µ B (y)\}(x,y) If x=y x is A and x is B? (x, x) is A V B AVB=max(µ A (x)), µ B (y)\}/x Disjunction Implication If x is A then y is B =A?B = min\{1, 1-µ A (x)) +µ B (y)\}/(x,y) if x= y A?B= min \{1, 1-µ A (x)) +µ B (y)\}/x If x is A then y is B else y is C= A x B + A' x C\par
The fuzzy proposition "If x is A then y is B else y is C" may be divided into two clause "If x is A then y is B " and "If x is not A then y is C"[15] If x is A then y is B else y is C =A?B= min \{1, 1-µA(x)+ µB(y)\}/(x,y) If x is not A then y is B else y is C =A'? C = min \{1, 1-µ A (x)) +µ C (y)\}/(x,y) Composition A o B= A x B=min\{ µ A (x)), µ B (y)\}/(x,y) If x = y A o B==min\{ µ A (x)), µ B (y)\}/x Composition\par
The fuzzy propositions may contain quantifiers like "Very", "More or Less". These fuzzy quantifiers may be eliminated asConcentration x is very A µ very A (x)), =µ A (x) ² 
\section[{Diffusion}]{Diffusion}\par
x is very A µ more or less A (x) =µ A (x) 0.5 
\section[{III. Generalized Fuzzy Logic with two-Fold Fuzzy Set}]{III. Generalized Fuzzy Logic with two-Fold Fuzzy Set}\par
Since formation of the generalized fuzzy set simply as two-fold fuzzy set and is extension Zadeh fuzzy logic. The fuzzy logic is defined as combination of fuzzy sets using logical operators. Some of the logical operations are given below Suppose A, B and C are fuzzy sets. The operations on fuzzy sets are given below for two-fold fuzzy sets.\par
Since formation of the generalized fuzzy set simply as two-fold fuzzy set, Zadeh fuzzy logic is extended to these generalized fuzzy sets.Negation A?= \{1-µ A Belief (x), 1-µ A Disbelief (x) \}/x Disjunction AVB=\{ max(µ A Belief (x) , µ A Belief (y)), max(µ B Disbelief (x) , µ B Disbelief (y))\}(x,y) Conjunction A?B=\{ min(µ A Belief (x) ,µ A Belief (y)), min(µ B Disbelief (x) , µ B Disbelief (y)) \}/(x,y) Implication A?B= \{min (1, 1-µ A Belief (x) + µ B Belief (y) , min ( 1, 1-µ A Disbelief (x) + µ B Disbelief (y)\}(x,y) If x is A then y is B else y is C = A x B + A' x C If x is A then y is B else y is C =A?B = \{min (1, 1-µ A Belief (x) + µ B Belief (y) , min ( 1, 1-µ A Disbelief (x) + µ B Disbelief (y)\}/(x,y) if `A If x is not A then y is B else y is C =A??C = min (1, µ A Belief (x) + µ C Belief (y) , min (1,µ A Disbelief (x) + µ C Disbelief (y)\}(x,y) Composition A o R= \{min x ( µ A Belief (x), µ A Belief (x) ), min x ( µ R Disbelief (x), µ R Disbelief (x) )\}/y\par
The fuzzy propositions may contain quantifiers like "very", "more or less". These fuzzy quantifiers may be eliminated asConcentration "x is very A µ very A (x) = \{ µ A Belief (x) 2 , µ A Disbelief (x)µ A (x) 2 \}\par
Diffusion "x is more or less A" µ more or less A (x) = ( µ A Belief (x) 0.5 , µ A Disbelief (x)µ A (x) 0.5\par
For instance, Let A, B and C are A = \{ 0.8/x 1 + 0.9/x 2 + 0.7/x 3 + 0.6/x 4 +0.5/x 5 , 0.4/x 1 + 0.3/x 2 + 0.4/x 3 + 0.7/x 4 +0.6/x 5 \} B = \{ 0.9/x 1 + 0.7/x 2 + 0.8/x 3 + 0..5/x 4 +0.6/x 5 , 0.4/x 1 + 0.5/x 2 + 0.6/x 3 + 0.5/x 4 +0.7/x 5 \} A V B = \{ 0.9/x 1 + 0.9/x 2 + 0.  
\section[{Fuzzy Neural Network}]{Fuzzy Neural Network}\par
The neural network concept is taken from the Biological activity of nervous system. The neurons passes information to other neurons. There are many models described for neural networks. The McCulloch-Pitts model contributed in understanding neural network and Zedeh explain that activity of neuron is fuzzy process \hyperref[b12]{[13]}.\par
The McCulloch and Pitt's model consist of set of inputs, processing unit and output and it is shown in Fig.    
\section[{Fuzzy Decision Set}]{Fuzzy Decision Set}\par
Zadeh \hyperref[b9]{[10]} proposed fuzzy set to deal with incomplete information. Generalized fuzzy set with two-fold membership function µ A (x ) = \{ µ A Belief (x ) , µ A Disbelief (x ) \} is studied [18] The fuzzy Certainty Factor may be defined as (FCF) The Generalized fuzzy set for Demand for the Items and fuzzy certainty factor is shown in Fig5. Decision may be taken under Decision shown in Fig.  {\ref 6}.µ A FCF (x ) = µ A Belief (x ) -µ A Disbelief (x ) , where µ A FCF (x) = µ A Belief (x) -µ A Disbelief (x) µ A Belief (x) ?µ A Disbelief (x) = 0 µ A Belief (x) <µ A Disbelief (x) 
\section[{Fig. 6: Fuzzy Decision set}]{Fig. 6: Fuzzy Decision set}\par
The fuzzy logic is combination of logical operators. Consider the logical operations on fuzzy Decision sets r1, R2 and R3Negation If x is not R1 ( ) D Year 2021 R1'=1-µ R1 (x)/x Conjunction x is R1 and y is R2? (x, y) is R1 x R2 R1 x R2=min(µ R1 (x)), µ R2 (y)\}(x,y) If x=y x is R1 and y is R2? (x, y) is R1?R2 R1?R2=min(µ R1 (x)), µ R2 (y)\}/x x is R1 or y is R2? (x, y) is R1' x R2' R1' x R2' =max(µ R1 (x)), µ R2 (y)\}(x,y) If x=y x is R1 and x is R2? (x, x) is R1 V R2 R1VR2=max(µ R1 (x)), µ R2 (y)\}/x Disjunction Implication if x is R1 then y is R2 =R1?R2 = min\{1, 1-µ R1 (x)) +µ R2 (y)\}/(x,y) if x= y R1?R2= min \{1, 1-µ R1 (x)) +µ R2 (y)\}/x Composition R1 o R2= R1 x R2=min\{ µ R1 (x)), µ R2 (y)\}/(x,y) If x = y R1 o R2==min\{ µ R1 (x)), µ R2 (y)\}/x\par
The fuzzy propositions may contain quantifiers like "Very", "More or Less". These fuzzy quantifiers may be eliminated as  
\section[{Concentration}]{Concentration} 
\section[{Fuzzy Conditional Infrence in Decision Making}]{Fuzzy Conditional Infrence in Decision Making}\par
Decision management is usually happens in Decision Support Systems.  
\section[{Conclusion}]{Conclusion}\par
The decision has to be taken under incomplete information in many applications like Business, Medicine etc. The fuzzy logic is used to deal with incomplete information The fuzzy Decision set is defined with twofold fuzzy set. The fuzzy logic is discussed with two-fold fuzzy set. The fuzzy Decision set, inference and reasoning are studied. The Business applications is discussed for fuzzy Decision set.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_2}2}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Fig. 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{34}\includegraphics[]{image-5.png}
\caption{\label{fig_4}Fig. 3 : 4 Fig}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Fuzzy}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Fig. 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{778899}\includegraphics[]{image-8.png}
\caption{\label{fig_7}x 7 Fig. 7 : 8 Fig. 8 : 9 Fig. 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Young=\{.95/10+0.9/20+0.8/30+0.6/40+0.4/50+0.3/6\\
0+0.2/70+0.15/80+0.1/90\}\\
Not young=\{ 0.05/10 + 0.1/20 + 0.2/30+0.4/40\\
+0.6/50 + 0.8/60+0.7/70 +0.95/80+0.9/90 \}\\
For instance "Rama is young" and the fuzziness\\
of "young" is 0.8 The Graphical representation of young\\
and not young is shown in fig.1\end{longtable} \par
  {\small\itshape [Note: A = µA(x1)/x1 + µA(x2)/x2 + ? + µA(xn)/xn, "+" is unionFor example, the fuzzy proposition "x is young"]} 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.7133286318758816\textwidth}P{0.08032440056417489\textwidth}P{0.023977433004231313\textwidth}P{0.03236953455571227\textwidth}}
\tabcellsep \multicolumn{3}{l}{0.16/x 1 + 0.09/x 2 + 0.16/x 3 + 0.49/x 4 +0.36/x 5}\\
µ More or Less A (x)\tabcellsep = ( µ A\tabcellsep Belief (x) 1/2 , µ A\tabcellsep Disbelief (x)µ A (x) 1/2 \}\\
\tabcellsep \multicolumn{2}{l}{= \{ 0IV.}\tabcellsep \\
Year 2021\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{8/x 3 + 0.6/x 4 +0.6/x 5 , 0.4/x 1 + 0.5/x 2 + 0.6/x 3 + 0.7/x 4 +0.7/x 5 \} A ? B = \{ 0.8/x 1 + 0.7/x 2 + 0.7/x 3 + 0.5/x 4 +0.5/x 5 , 0.4/x 1 + 0.3/x 2 + 0.4/x 3 + 0.5/x 4 +0.6/x 5 \} A' = not A= \{ 0.2/x 1 + 0.1/x 2 + 0.3/x 3 + 0.4/x 4 +0.5/x 5 , 0.6/x 1 + 0.7/x 2 + 0.6/x 3 + 0.3/x 4 +0.4/x 5 \} A? B = \{ 1/x 1 + 0.8/x 2 + /x 3 + 0.9/x 4 +1/x 5 , 1/x 1 + 1/x 2 + 1/x 3 + 0.8/x 4 +1/x 5 \} A o B = \{ 0.8/x 1 + 0.7/x 2 + 0.7/x 3 + 0.5/x 4 +0.5/x 5 , 0.4/x 1 + 0.3/x 2 + 0.4/x 3 + 0.5/x 4 +0.6/x 5 \} = \{ µ A Belief (x) 2 , µ A Disbelief (x)µ ( ) µ Very A (x) D}\end{longtable} \par
  {\small\itshape [Note: A (x) 2 \} = \{ 0.64/x 1 + 0.81/x 2 + 0.49/x 3 + 0.36/x 4 +0.25/x 5 ,]} 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.006217345872518287\textwidth}P{0.3157523510971787\textwidth}P{0.1381138975966562\textwidth}P{0.18252351097178685\textwidth}P{0.2025078369905956\textwidth}P{0.004885057471264368\textwidth}}
\tabcellsep \multicolumn{3}{l}{Zadeh inference is given as A?B= min\{1, 1-µ A (x) + µ B (x)\}}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{µ Demand ? High Price}\tabcellsep FCF (x ) = 0.9/x1+1/x2+0.5/x3+1/x4+0.52/x5\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{µ Demand ? High Price}\tabcellsep FCF (x ) ?0.6 = 1/x1+1/x2+0/x3+1/x4+0/x5\\
\tabcellsep \multicolumn{3}{l}{Mamdani inference is given as A?B= min\{µ A (x) , µ B (x)\}}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{µ Demand ? High Price}\tabcellsep FCF (x ) = 0.4/x1+.44/x2+0.29/x3+.33/x4+0.2/x5\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{µ Demand ? High Price}\tabcellsep FCF (x ) ?0.6 = 1/x1+1/x2+0/x3+1/x4+0/x5\\
\tabcellsep \multicolumn{3}{l}{Mamdani inference is given as A?B= min\{µ A (x) \}}\\
Year 2021\tabcellsep Example 2\tabcellsep \multicolumn{2}{l}{µ Demand ? High Price µ Demand ? High Price FCF (x ) = 0.4/x1+.44/x2+0.29/x3+.33/x4+0.2/x5 FCF (x ) ?0.6 = 1/x1+1/x2+0/x3+1/x4+0/x5}\\
\tabcellsep \multicolumn{2}{l}{Consider Medical Diagnosis}\\
\tabcellsep \multicolumn{3}{l}{If x has infection in the leg then surgery}\\
\tabcellsep \multicolumn{3}{l}{Let x1, x2, x3, x4, x5 are the Patients.}\\
\tabcellsep The fuzzy set\tabcellsep \\
\tabcellsep \tabcellsep µ Infection\tabcellsep FCF (x ) = 0.76/x1+0.78/x2+0.46/x3+0.86/x4+0.58/x5,\\
\tabcellsep \tabcellsep \tabcellsep 0.16/x1+0.12/x2+0.06/x3+0.14/x4+0.05/x5\}\\
\tabcellsep \tabcellsep \tabcellsep = 0.6/x1+0.64/x2+0.4/x3+0.72/x4+0.53/x5\\
\tabcellsep \tabcellsep µ Surgery\tabcellsep FCF (x ) = 0.59/x1+0.26/x2+0.55/x3+0.24/x4+0.35/x5,\\
\tabcellsep \tabcellsep \tabcellsep 0.09/x1+0.06/x2+0.05/x3+0.04/x4+0.03/x5 \}\\
\tabcellsep \tabcellsep \tabcellsep = 0.5/x1+0.2/x2+0.5/x3+0.2/x4+0.32/x5\\
( ) D\tabcellsep \multicolumn{3}{l}{Using inference rule A?B= min\{1, 1-µ A (x) + µ B (x)\}}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{µ Infection ? Surgery}\tabcellsep FCF (x ) = 0.9/x1+0.56/x2+0.9/x3+1/x4+1/x5\\
\tabcellsep \tabcellsep \tabcellsep µ Infection ? Surgery\tabcellsep R (x ) = 1 µ Infection ? Surgery\tabcellsep FCF (x ) ?6\\
\tabcellsep \tabcellsep \tabcellsep 0 µ Infection ? Surgery\tabcellsep FCF (x ) <6\\
\tabcellsep The fuzzy risk set R is\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep 1/x1+0/x2+1/x3+1/x4+1/x5\\
\tabcellsep Example 1\tabcellsep \multicolumn{2}{l}{µ Infection ? Surgery}\tabcellsep FCF (x ) = 0.9/x1+0.56/x2+0.9/x3+1/x4+1/x5\\
\tabcellsep Consider Business rule\tabcellsep \multicolumn{2}{l}{µ very Surgery}\tabcellsep FCF (x ) = 0.25/x1+0.2\textbackslash 04/x2+0.25/x3+0.04/x4+0.1/x5\\
\tabcellsep \multicolumn{3}{l}{If x is Demand of the product then x is High Price Let x1, x2, x3, x4, x5 be the Items. x is very Demand o Demand?Increase Price}\\
\tabcellsep The Generalized fuzzy set\tabcellsep \tabcellsep =0.35/x1+0.66/x2+0.35/x3+0.04/x4 + 0.1/x5\\
\tabcellsep \multicolumn{3}{l}{Demand =\{ 0.56/x1+0.48/x2+0.86/x3+0.36/x4+0.88/x5, 0.06/x1+0.04/x2+0.07/x3+0.03/x4+0.2/x5 \}}\\
\tabcellsep VII.\tabcellsep \multicolumn{2}{l}{µ Demand}\tabcellsep FCF (x ) = 0.5/x1+0.44/x2+0.79/x3+0.33/x4+0.68/x5\\
\tabcellsep \multicolumn{3}{l}{High Price = 0.49/x1+0.52/x2+0.35/x3+0.4/x4+0.3/x5,}\\
\tabcellsep \tabcellsep \tabcellsep 0.09/x1+0.02/x2+0.06/x3+0.02/x4+0.1/x5 \}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{µ High Price}\tabcellsep FCF (x ) = 0.4/x1+0.5/x2+0.29/x3+0.38/x4+0.2/x5\end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
 			\footnote{© 2021 Global Journals} 			\footnote{Fuzzy Reinforcement Learning using Neural Network: An Application to Medical Diagnosis and Business} 			\footnote{Fuzzy Reinforcement Learning using Neural Network: An Application to Medical Diagnosis and Business Intelligence} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
