A membership function describes the degree of membership of a value in a fuzzy set. Fuzzy logic in our Current Work Fuzzification Retrieve the matched cases from the case base.Convert the case weight numerical value into the crisp value.This phase generates a fuzzy input set. Build the Fuzzy Rules (Inference) Assign zero value to the unused requirements in the retrieved case with respect to given input requirements.Adjust the given input to the value 100 % by reducing the requirement value with equal priority.Generating the fuzzy input requirement value [0,1] Defuzzification Fuzzy output set in given as input to this phase. i.e., the best case from retrieved cases.Converting the fuzzy output to the Boolean value for the acceptance of cases. 1. Input: Given the input requirements as {r1, r2,} 2. Retrieve all the matched cases from the case base with respect to the given input. 3. Finding out the value for each matched case with respect to the given input requirements 4. Example: matched Case = {r1, r2, r3…} 5. Assigning zero value to the unnecessary requirement {r1, r2, 0 ...} (rule1) 6. Identifying the r1, r2 … values in each matched retrieved case 7. Adjust the given input to the value 100 % by reducing the requirement value with equal priority. 8. Adding all case requirement values to generate a case value between 0.0 to 1.0 ranges 9. Member function = 10. Retrieving all the matched case between the given ranges low to high. Sample Scenario: Input=

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