Time Complexity and Runtime Comparison of the Proposed Hybrid Model and other Models. This table contrasts the computational characteristics of the evaluated models. The proposed hybrid model’s complexity, O(p × i × n² × d), is dominated by PSO-based SVM optimization, where p is the particles, i is the iterations, n is the samples, d is the features. While the experimental runtime of the proposed hybrid model, 55 seconds, is higher than that of simpler models (such as logistic regression and KNN), it is significantly lower than that of complex deep learning models. The results demonstrate that the hybrid model is competitive in terms of computational efficiency, in addition to being more accurate.

Time Complexity and Runtime Comparison of the Proposed Hybrid Model and other Models. This table contrasts the computational characteristics of the evaluated models. The proposed hybrid model’s complexity, O(p × i × n² × d), is dominated by PSO-based SVM optimization, where p is the particles, i is the iterations, n is the samples, d is the features. While the experimental runtime of the proposed hybrid model, 55 seconds, is higher than that of simpler models (such as logistic regression and KNN), it is significantly lower than that of complex deep learning models. The results demonstrate that the hybrid model is competitive in terms of computational efficiency, in addition to being more accurate.