Decision-making to limit epidemics spread based on fuzzy-soft and topological spaces

Abstract

Real-world applications process enormous amounts of data, especially in the area of large-dimension features. This work aims to present new classes of functions based on SM*-open sets that are a modification of simply open sets; namely, SM*-continuous, SM*-irresolute, proper continuous, SM*-open, SM*-closed, strongly SM*-irresolute, Pre SM*-irresolute, Pre SM*-open, super SM*-open and completely irresolute. The idea of fuzzy soft multifunction between fuzzy soft topological spaces, developed by Metin, is frequently used. Our current research project uses our suggested idea to introduce coronavirus application and infer the most important causal symptoms of coronavirus patients. Furthermore, we created an algorithm to demonstrate the applicability of our proposed technique based on the presented concept. Additionally, the results obtained using MATLAB programming. Finally, realistic WHO-compliant results were achieved for the most serious symptoms of coronavirus patients, as well as a suggested strategy that is competitive. Therefore, decision-making in the future needs to consider our suggestion. In order to promote the long-term wellbeing of both nature and humanity. Our proposed approach is reasonable and effective. The results showed that the methodology we used was reliable as it was consistent with World Health Organization publications.

Keywords

• Simply open and closed
• Types of continuous functions
• A fuzzy soft multifunction's upper inverse
• Coronavirus
• Discovering intelligence
• Making decisions