## 3 Classification of \"No-Stress\" and \"Stress\" States Using Machine Learning  
The following machine learning models were used to classify non-stress and stress states based on input vectors containing significant features: k-Nearest Neighbors (kNN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). These models were primarily chosen for their ease of use and low computational requirements and were built using the open-source Python library Scikit-learn [41]. The kNN algorithm [42] is a fundamental nonparametric method in machine learning that is widely used for classification problems. The advantages of this algorithm include its simplicity in implementation, flexibility in accommodating new data, and minimal need for hyperparameter tuning. The Minkowski metric was chosen as the distance metric. The internal parameters of the model are algorithm: auto, leaf_size: 30, metric: minkowski, metric_params: None, n_jobs: None, n_neighbors: 5, p: 2, weights: uniform. Naive Bayes [43] is a probabilistic classification method grounded in Bayes' theorem that assumes independence among features. This approach is particularly effective for smaller datasets and demonstrates efficient data handling capabilities. The advantages of Naive Bayes include its simplicity of implementation, high-speed performance, and efficiency with large datasets. Settings used are priors: None, var_smoothing: \(1\times 10^{9}\). Random Forest [44] is an ensemble learning method that combines multiple decision trees to enhance prediction accuracy and mitigate overfitting. Its advantages include a low risk of overfitting, the ability to handle both numerical and categorical data, and the capacity to identify feature importance. Settings used are bootstrap: True, ccp_alpha: 0.0, class_weight: None, criterion: gini, max_depth: None, max_features: sqrt, max_leaf_nodes: None, max_samples: None, min_impurity_decrease: 0.0, min_samples_leaf: 1, min_samples_split: 2, min_weight_fraction_leaf: 0.0, monotonic_cst: None, n_estimators: 100, n_jobs: None, oob_score: False, random_state: 24, verbose: 0, warm_start: False. Support Vector Machine [45] is a nonparametric method that utilizes a set of learning algorithms for classification. It is grounded in statistical learning theory, which helps reduce uncertainty in the model's structure. SVM classifiers are known for their strong performance in high-dimensional spaces and their excellent accuracy. They are memory-efficient, as they only utilize a subset of the training data. The assumption that the obtained data are nonlinear led to the selection of the SVM model with a radial basis function (RBF) kernel. For linear classification tasks, a linear kernel may be appropriate, as it effectively separates linearly separable data. However, for nonlinear classification problems, it is essential to consider the use of nonlinear kernels, such as the RBF. Alternatively, polynomial or sigmoid kernels may also be employed to handle more complex decision boundaries. The settings applied in the experiment were C: 1.0, break_ties: False, cache_size: 200, class_weight: None, coef0: 0.0, decision_function_shape: ovr, degree: 3, gamma: scale, kernel: rbf, max_iter: \(-1\), probability: False, random_state: 24, shrinking: True, tol: 0.001, verbose: False. It should also be noted that the aim of the study was to test the hypothesis about the possibility of classifying stress conditions caused by sirens and not to conduct extensive ex