Human Fingerprint Reputation Gadget in Cybercrime Analysis using Naive Bayes in Evaluation Over Stepwise Polynomial Regression

The research project aims to identify human fingerprints accurately in cybercrime analysis by employing the Naive Bayes (NB) algorithm instead of Stepwise Polynomial Regression (SR). For the recognition of human fingerprints, accuracy is projected. A Naive Bayes fingerprint identification model with a sample size of 23 is used, and its <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex>-power value is set to 80 %. The anticipated accuracy for Naive Bayes is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 4. 3 \%}$</tex>, which is somewhat higher than the accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 1. 1 \%}$</tex> for Stepwise Polynomial Regression (SR). The significance value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p}=\mathbf{0. 9 8 2}\left(\mathbf{p}_{\mathbf{i} \mathbf{0} \mathbf{0} \mathbf{0} \mathbf{5}}\right)$</tex> for the results indicates that two groups are not statistically significant. Comparing Naive Bayes to Stepwise Polynomial Regression, the former performs better in terms of discovering