Effectiveness of Logarithmic Entropy Measures for Pythagorean Fuzzy Sets in diseases related to Post COVID Implications under TOPSIS Approach

Abstract

Following the second wave of Covid-19 infections in India, individuals are now arriving to hospitals with a variety of symptoms, not simply for mucormycosis, a fungal infection. The most common symptoms are extreme tiredness, drowsiness, body and joint pain, mental fog, and fever, but pneumonia, collapsed lungs, heart attacks, and strokes have all been reported. Pythagorean fuzzy sets (PFSs) proposed by Yager [42] offers a novel technique to characterize uncertainty and ambiguity with greater precision and accuracy. The idea was developed specifically to describe uncertainty and ambiguity mathematically and to provide a codified tool for dealing with imprecision in real-world circumstances. This article addresses novel logarithmic entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and validity of the proposed entropy measures. Application of the measures is used in detecting diseases related to Post COVID 19 implications through TOPSIS method. Comparison of the suggested measures with the existing ones is also demonstrated.