This paper presents a model based on mediative fuzzy logic in this COVID-19 pandemic. COVID-19 (novel coronavirus respiratory disease) has become a pandemic now and the whole world has been affected by this disease. Different methodologies and many prediction techniques based on various models have been developed so far. In the present article, we have developed a mediative fuzzy correlation technique based on the parameters for COVID-19 patients from different parts of India. The proposed mediative fuzzy correlation technique provides the relation between the increments of COVID-19 positive patients in terms of the passage of increment with respect to time. The peaks of infected cases in connection with the other condition are estimated from the available data. The mediative fuzzy logic mathematical model can be utilized to find a good fit or a contradictory model for any pandemic model. The proposed approach to the prediction in COVID-19 based on mediative fuzzy logic has produced promising results for the continuous contradictory prediction in India.

Now a days Novel Coronavirus named COVID-19 becomes major health concern causing severe health issue in human beings and it becomes a pandemic. It’s a kind of zoonotic that means it can transmit animals to humans. It may spread via polluted hands or metals. No specific treatment is available so far for COVID-19, so initial identification and preventions for COVID-19 will be crucial to control or to break down the chain of COVID-19. For this purpose, we have proposed a fuzzy inference system to diagnose the COVID-19 disease by taking six input factor like as; Ethanol, Atmospheric Temperature (AT), Body Temperature (BT), Breath Shortness (BS), Cough and Cold and the output factor has divided into three linguistic categories which denotes the severity level of the infected patients.

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Fuzzy logic is an approach which deals with the incomplete information to handle the imperfect knowledge. In the present research paper we have proposed a new approach that can handle the imperfect knowledge, in a broader way that we will consider the unfavourable case also as the intuitionistic fuzzy logic does. The mediative fuzzy logic is an extensive approach of intuitionistic fuzzy logic, which provides a solution, when there is a contradiction in the expert knowledge for favourable as well as unfavourable cases. The purpose of the present paper is to design a mediative fuzzy inference system based Sugeno-TSK model for the diagnosis of heart disease. Our proposed method is the extension of Sugeno-TSK fuzzy logic controller in the form of Sugeno-TSK mediative fuzzy logic controller.

The notion of fuzzy set theory has not so far been districted over medical diagnosis. There are some added applications, for example, in image processing, pattern identification, and many medical devices. In this research article, we introduced a new mediative fuzzy ranking technique as the fuzzy extension in decision making. The proposed mediative fuzzy logic-based technique is more relevant and applicable to incomplete and doubtful situations or some contradictions present in the expert knowledge. The value of the contradictory degree for mediative fuzzy sets used in the extension principle is defined. The proposed mediative fuzzy ranking method is easily implemented in the medical field, and the proposed mediative fuzzy extension-based measured technique is useful to medical experts and doctors in many decision-making situations; the entire work is illustrated with numerical examples. We have also given some future aspects of mediative fuzzy extension in the interpretation of type-2 intuitionistic fuzzy sets.