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MAGDM strategy using similarity measure based on binary logarithm function under single valued neutrosophic environment is yet to appear.
* Is it possible to define a new similarity measure between single valued neutrosophic sets using binary logarithm function?
* To define binary logarithm similarity measures for SVNS environment and prove the basic properties.
Having motivated from the above researches on neutrosophic similarity measures, we introduce the concept of binary logarithm similarity measures for SVNS environment.
Section 3 proposes binary logarithm similarity measures and weighted binary logarithm similarity measures, hybrid binary logarithm similarity measure (HBLSM), weighted hybrid binary logarithm similarity measure (WHBLSM) in SVNSs environment.
In this section, the concepts of NSs, SVNSs, operations on NSs and SVNSs and binary logarithm function are outlined.
In mathematics, the logarithm of the form [log.sub.2.sup.x], x > 0 is called binary logarithm function .
In this section, we define two types of binary logarithm similarity measures and their hybrid and weighted hybrid similarity measures.
The binary logarithm similarity measure (type-I) between SVNSs A and B are defined as follows:
Of course, at the output of the second stage the latched data are in carry-save form ([A.sub.1], [A.sub.2], [B.sub.1], [B.sub.2]) and the binary logarithms
will never be produced explicitly among the pipelined structure.
As the suggested design uses LNS the survey on log based hardware were made, most of the binary logarithm
model utilizes Mitchell (1962) approximation because of its easiness but it lacks in accuracy of about 3.5 bits, various works were employed to reduce this error most notably LUT based method of Brubaker and Becker (1975), which was further enhanced and modified by Paul et al.