Despite the fact that several technologies have been developed to assist healthcare workers in reducing errors and improving accuracy in illness diagnosis, there is still substantial ambiguity regarding the accurate disease diagnosis based on symptoms. The goal of this work is to establish a group decision-making problem in an uncertain situation to assist medical practitioners in generating accurate illness predictions based on symptoms. This study proposes a novel distance measure for Pythagorean fuzzy sets that incorporates the inherent uncertainty of complex, uncertain data by incorporating indeterminacy in the computation. First, we establish the proposed Mabala distance measure by describing it’s properties. Then, the suggested distance measure is applied to solve group decision-making problems in uncertain situations. A case study of disease analysis based on symptoms is presented to illustrate the decision-making procedure involving four medical professionals, five symptoms, and five probable diseases. Furthermore, We have presented two cases of disease analysis using non-standard and standard Pythagorean fuzzy soft matrices. The results suggest that the proposed Mabala’s distance measure has great potential for improving disease analysis. The proposed Mabala distance measure is compared to five existing distance measures using an identical data set of prospective disease symptoms. The comparative analysis indicates that the suggested Mabala distance measure’s result almost coincides with the results of the other distance measurements. A set of sensitivity analysis is provided to analyze the durability and consistency of the proposed distance measurements across different input scenarios.

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.