Table 6.

Training configuration summary.

Model	Key hyperparameters	Search space	Selected configuration
CNN	Optimizer; learning rate; batch size; epochs; regularization	Adamax, LR = 1 × 10−4 (fixed strategy); batch = 16; ≤100 epochs; dropout = 0.4; He-normal init.	Fixed configuration—ReduceLROnPlateau (factor 0.5, patience 7) + EarlyStopping (patience 15, restore best weights)
KNN	Number of neighbors (k); distance metric; weighting	k ∈ {3, 5, 7, 9, 11}; Euclidean distance; uniform weighting	k = 5 (5-fold grid search on training partition)
SVM	Kernel; regularization C; kernel coefficient γ; feature scaling	RBF kernel; C ∈ {0.1, 1, 10, 100}; γ ∈ {‘scale’, 0.01, 0.001}; z-score standardization	C = 10, γ = ‘scale’; one-vs-rest decision function
RF	Number of trees; max depth; min samples/leaf; split criterion	n_estimators ∈ {100, 200, 300, 500}; max_depth ∈ {10, 20, None}; min_samples_leaf ∈ {1, 2, 4}; Gini impurity	n_estimators = 300, max_depth = 20, min_samples_leaf = 2; bootstrap sampling