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Table 4 Input data in the form of NIVIFNs

From: New ranking method for normal intuitionistic sets under crisp, interval environments and its applications to multiple attribute decision making process

	\({\mathfrak {B}}_1\)	\({\mathfrak {B}}_2\)	\({\mathfrak {B}}_3\)
\({\mathcal {V}}_{1}\)	\(\left( (3, 0.4),[0.4, 0.5], [ 0.3, 0.4] \right) \)	\(\left( (5, 0.6 ),[0.4, 0.6],[ 0.2, 0.4] \right) \)	\(\left( (7,0.6),[0.1, 0.3],[ 0.5 ,0.6]\right) \)
\({\mathcal {V}}_{2}\)	\(\left( (4, 0.2) ,[ 0.6, 0.7],[ 0.2, 0.3] \right) \)	\(\left( (6,0.4),[ 0.6, 0.7],[ 0.2, 0.3] \right) \)	\(\left( (5,0.3),[0.4 ,0.8],[ 0.1 ,0.2]\right) \)
\({\mathcal {V}}_{3}\)	\(\left( (6,0.3) ,[ 0.3 ,0.6],[ 0.3 ,0.4 ] \right) \)	\(\left( (5,0.2),[0.5 ,0.6],[ 0.3, 0.4] \right) \)	\(\left( (6,0.4), [0.4 ,0.5],[ 0.1 ,0.3]\right) \)
\({\mathcal {V}}_{4}\)	\(\left( (5,0.5) ,[ 0.7 ,0.8],[ 0.1 ,0.2] \right) \)	\(\left( (7,0.5), [0.6, 0.7 ],[0.1, 0.3] \right) \)	\(\left( (3,0.2), [0.3, 0.4 ],[0.1, 0.2]\right) \)

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