Normalized input matrix beneficial criteria are created as the degree of truthness [T.sub.L](x), the degree of indeterminacy and degree of falsehood are considered as [I.sub.L](x) = [F.sub.L](x) = 1 - [T.sub.L](x) respectively.
Normalized input matrix non-beneficial criteria are created as the degree of indeterminacy and falsehood as [I.sub.L](x) = [F.sub.L](x), the degree of truthness is considered as [T.sub.L](x) = 1 - [I.sub.L](x) = 1 - [F.sub.L](x).
After that, the normalized decision matrix was transformed into the SVNS decision matrix comprised of the degree of truthness [T.sub.L](x), indeterminacy [I.sub.L](x), and falsehood [F.sub.L](x) using the conversion rule for beneficial and non-beneficial criteria.
Pickles said tweets containing false information might be removed if they also violate rules against hate speech, but he said the company will not take down tweets based solely on "the truthness
of a piece of information."
In a related way, Burms concludes that strong embodied significance has this character of indisputability (of 'truthness
') and the impossibility to be rationally grounded.
This is what actually happened It is not late even now for both of them sware to the truthness
of the statement.
The greater value of [[micro].sub.A](x), indicate the greater truthness of the statement that element x belongs to set A.
Let us suppose that the data collected from 100 people for an attribute [x.sub.i] reveals that more or less 70 people are in support of the truthness of the attribute and the rest 30 are in support of falseness.
It has been suggested on the lines of fuzzy logic but instead of giving one defuzzified value, output value in neutrosophic classifier takes the neutrosophic format of the type: output (truthness
, indeterminacy, falsity) .