Covid-19 is a dangerous communicable virus which lets down the world economy. Severe respiratory syndrome SARS-COV-2 leads to Corona Virus Disease (COVID-19) and has the capability of transmission through human-to-human and surface-to-human transmission leads the world to catastrophic phase. Computational system based biological signal analysis helps medical officers in handling COVID-19 tasks like ECG monitoring at Intensive care, fatal ventricular fibrillation, etc., This paper is on diagnosing heart dysfunctions such as tachycardia, bradycardia, ventricular fibrillation, cardiac arrhythmia using fuzzy relations and artificial intelligence algorithm. In this study, the heart pulse base signal and features like spectral entropy, largest lyapunov exponent, Poincare plot and detrended fluctuation analysis are extracted and presented for classification purpose. The RR intervals of Poincare plot summarize RR time series obtained from an ECG in one picture, and a time interval quantities derives information duration of HRV. This analysis eases the prediction of heart rate fluctuation due to Covid or other heart disorders. The better accuracy level in diagnosing heart pulse irregularity using Artificial Neural network(ANN) is an integer value (0 to 4)but for Fuzzy Classifier, it is 0.8 to 0.9.The processing time for analyzing heart dysfunctionalties is 0.05s using ANN which is far better than Fuzzy classifier.

The notion called lattice ordered neutrosophic soft set is initiated with some properties. Using this theory, an application is developed to assist the decision makers in choosing an NGO to utilize the Covid-19 fund of a large scale company.

Digital transformation is the significant phenomena in contemporary global environment. All the conventional fuzzy sets are extended by the Linear Diophantine Fuzzy Set (LDFS). LDFS is the most viable adaptable method for dcision makers to choose their grade values as it includes reference parameters. The foremost vision is to promote the resilient integration of Linear Diophantine Multi-Fuzzy Set (LDMFS) as a model for constructing decisions in order to identify the appropriate standards for digital transformation. Aggregation Operators are crucial in fuzzy systems for fusing information. To aggregate the LDMF, a number of operators have been devised, such as the Linear Diophantine Multi-Fuzzy Weighted Geometric Operator (LDMFWGO), Linear Diophantine Multi-Fuzzy Ordered Weighted Geometric Operator (LDMFOWGO), Linear Diophantine Multi-Fuzzy Weighted Averaging Operator (LDMFWGO) and Linear Diophantine Multi-Fuzzy Ordered Weighted Averaging Operator (LDMFOWAO). By integrating preferred aggregating operations, a novel method for MCDM with LDMF data is studied. The best option from the current alternatives can be determined using these operators. Moreover, a comparison of LDMF operators is made. Additionally, the idea of a scoring function for LDF is designed to examine the rank of viable alternaties. Additionally, a novel approach to solving LDMF sets is suggested. The annals on organisational digital transformation is presented as the final section to test the supremacy of the theory. Accurate rankings for digital transformation are provided by the outcome. To exhibit the robustness of the MCDM methodology, a prompt comparative analysis is established between the suggested concept and the currently used approaches.

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