Dear S. M. Aqil Burney,

- Fuzzy logic is to define the truth value (T) of an affirmation by a number which varies between 0 and 1 progressively, space 1D.

- Intuitionist fuzzy logic is to characterize each logical assertion in a 2D space, where each dimension of the space represents, respectively, the truth (T) and the false (F) of the assertion under consideration, where T, F are the real subsets.

- Neutrosophic logic is to characterize each logical assertion in a 3D space, where each dimension of the space represents, respectively, the truth (T), the false (F) and the indeterminacy (I) of the assertion under consideration , where T, I, F are the real standard or non-standard subsets of ]-0, 1+[. T, I, F are independent components.

For more information about this subject i suggest you to see links and attached file on topic.

Article A Novel Approach to Neutrosophic Soft Rough Set under Uncertainty

https://www.hindawi.com/journals/ijcm/2015/232919/

http://fs.unm.edu/neut/NeutrosophicSoftRoughTopology.pdf

http://fs.unm.edu/NSS/RoughNeutrosophicMultisets.pdf

Article Interval neutrosophic covering rough sets based on neighborhoods

Best regards

Things should start like this .What is set what is fuzzy set,what is rough set ,what is fuzzy rough set ,what is ANN ,.Why ANN incorporated.What is NFRST.Then comes Neutrosophic sets and statistics .Eventually we can learn the differences or comparison amomg these paradigm of soft computing .

Your answer is nice and I am thankful.

http://fuzzy.cs.ovgu.de/nfdef.html#:~:text=A%20neuro-fuzzy%20system%20is,rules)%20by%20processing%20data%20samples.&text=A%20neuro-fuzzy%20system%

Article Applications of Rough Sets in Health Sciences and Disease Diagnosis

Read the paper for rough sets.

Fuzzy sets,rough sets ,now Neutrosophic sets ,Neuro fuzzy sets ,Neuro fuzzy rough sets and so all are used for vagueness and discerniblity and while used in soft and intelligent Computing .In Classification and regressions also apply them .Some our work and references can also be given .We can also use Neuro fuzzy support vector machines for learning and doing classification and discrimination .All such sets have membership functions as they are not crisp sets.