Optimizing Web Interfaces with AI-Generated CSS Through Machine Learning and Deep Learning

This paper draws an intense assessment of five models-VGG, AlexNet, GOOGLENET, Resnet, and a Proposed Model-in the formulation of the problems associated with binary classification for the determination of intrinsic performance criteria such as ROC curve analysis, F1 score, recall, accuracy, and precision in the bid of trying to find the most efficient model by evaluating its capacity to differentiate between both constructive and destructive courses to minimize errors. The performance of the Resnet is excellent, with a highly accurate AUC of 0.96, which offers excellent discrimination. Comparatively, equal performance is observed in AlexNet and GOOGLENET models. AUCs are equal to 0.93 and 0.94, respectively. For instance, it achieved a peak F1 score of 97%, a peak accuracy of 97%, and a maximum AUC value of 0.97; therefore, the predictive performance was excellent. In the experiments, the proposed model demonstrated good classification efficiency by being among the most trustworthy and accurate models that had been tested.