The ongoing epidemic SARS-CoV-2 named Corona Virus Disease (COVID-19) is highly infectious and subsequently spread all over the world affecting millions of people. Humans have never seen such a deadly disease so far, and as there is no specific drug or vaccination, the mortality rate of the disease has been increasing exponentially. This current situation exacerbated people’s restlessness and fear. Because of this pandemic, the world is travelling on a different path. This world has recovered from many disasters, but this is entirely a different situation. Today’s world is struggling in many ways to get rid of this disease. On the other hand, the number of people recovering from this disease gives us comfort. Yet, we have to take urgent precautionary measures to control this disease in all possible ways. Therefore, forecasting is one of the ways to take the necessary precautionary measures. In this paper, using fuzzy–grey–Markov model, we predict the number of affected and recovered patient count, death count using real-time data in different approaches and compared with the real data. The study concludes with important recommendations for the Indian government to manage the COVID 19 critical situation in advance.
This paper presents a method to analyze the traffic flow pattern at a crowded junction in Chennai, one of the metropolitan cities in India, using waiting time in the signal in different time intervals with the help of Fuzzy Cognitive Map and Induced Fuzzy Cognitive Map.
Markov chain is a stochastic model for estimating the equilibrium of any system. It is a unique mathematical model in which the future behavior of the system depends only on the present. Often biased possibilities can be used over biased probabilities, for handling uncertain information to define Markov chain using fuzzy environment. Indeterminacy is different from randomness due to its construction type where the items involved in the space are true and false in the same time. In this context as an extension of conventional and fuzzy probabilities neutrosophic probability (NP) was introduced. These neutrosophic probabilities can be captured as neutrosophic numbers. In this paper, Markov chain based on neutrosophic numbers is introduced and a new approach to the ergoticity for the traffic states in the neutrosophic Markov chain based on neutrosophic numbers is verified. The proposed approach is applied to decision-making in the prediction of traffic volume.
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