Enhancing the Accuracy of Fingerprint Image Using Novel Fuzzy Logic System in Comparison with Random Forest

For enhancing fingerprint image accuracy using Random Forest algorithms and a new fuzzy logic framework. A novel fuzzy logic system (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N = 10$</tex>) performs classification over Random Forest (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N = 10$</tex>). Using GPower, the sample size is determined with pretest power set to 0.8 and alpha set to 0.05. The new fuzzy logic system's mean accuracy (98%) is higher than Random Forest's (95%). For accuracy and loss, the significance value is 0.065 (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p &gt; 0.05$</tex>). Compared to Random Forest, the fingerprint image enhancement system employing innovative fuzzy logic has a higher mean accuracy. The findings suggest that while both the fuzzy logic system and Random Forest algorithm are effective in enhancing fingerprint image accuracy, the innovative fuzzy logic framework has demonstrated a higher mean accuracy in our tests. This suggests that the novel fuzzy logic system might offer a more precise enhancement of fingerprint images compared to the traditional Random Forest approach, though the observed difference in accuracy is not statistically significant.