Table 4 Expert information.

From: A novel decision-making approach for the selection of best deep learning techniques under logarithmic fractional fuzzy set information

Alternatives

\(k_{1}\)

\(k_{2}\)

\(k_{3}\)

\(k_{4}\)

\(k_{5}\)

\(h_{1}\)

\(\left\langle 0.513,0.449\right\rangle\)

\(\left\langle 0.475,0.403\right\rangle\)

\(\left\langle 0.522,0.447\right\rangle\)

\(\left\langle 0.428,0.505\right\rangle\)

\(\left\langle 0.492,0.432\right\rangle\)

\(h_{2}\)

\(\left\langle 0.529,0.456\right\rangle\)

\(\left\langle 0.513,0.445\right\rangle\)

\(\left\langle 0.467,0.472\right\rangle\)

\(\left\langle 0.489,0.450\right\rangle\)

\(\left\langle 0.403,0.406\right\rangle\)

\(h_{3}\)

\(\left\langle 0.475,0.423\right\rangle\)

\(\left\langle 0.436,0.424\right\rangle\)

\(\left\langle 0.529,0.431\right\rangle\)

\(\left\langle 0.467,0.403\right\rangle\)

\(\left\langle 0.551,0.422\right\rangle\)

\(h_{4}\)

\(\left\langle 0.445,0.375\right\rangle\)

\(\left\langle 0.561,0.403\right\rangle\)

\(\left\langle 0.490,0.488\right\rangle\)

\(\left\langle 0.505,0.400\right\rangle\)

\(\left\langle 0.498,0.487\right\rangle\)

\(h_{5}\)

\(\left\langle 0.439,0.431\right\rangle\)

\(\left\langle 0.501,0.450\right\rangle\)

\(\left\langle 0.528,0.488\right\rangle\)

\(\left\langle 0.490,0.447\right\rangle\)

\(\left\langle 0.433,0.408\right\rangle\)