Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain’s electrical activity through electrodes placed on the scalp’s surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was “subject-dependent”. In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

Intrusion Detection Systems (IDS) figure as one of the leading solutions adopted in the network security area to prevent intrusions and ensure data and services security. However, this issue requires IDS to be assertive and efficient processing time. Undersampling techniques allow classifiers to be evaluated from smaller subsets in a representative manner, aiming high assertive metrics in less processing time. There are several solutions in literature for IDS projects, but some criteria are not respected, such as the adoption of a replicable methodology. In this work, we selected three undersampling methodologies: random, Cluster centroids, and NearMiss in two novel unbalanced datasets (CIC2017 and CIC2018) for comparison between five classifiers using cross-validation and Wilcoxon statistical test. Our main contribution is a systematic and replicable methodology for using subsampling techniques to balance the data sets adopted in the IDS project. We choose three metrics for classifier's choice in an IDS design: accuracy, f1-measure, and processing time. The results indicate that the under-sampling by Cluster centroids presents the best performance when applied to distance-based classifiers. Moreover, under-sampling techniques influence the process of choosing the best classifier in the design of an IDS.

Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.

Intrusion Detection Systems (IDS) are responsible for monitoring and detecting anomalies in computer networks. Currently, machine learning techniques have been employed in the development of IDs and the classifiers performance evaluation in intrusion detection systems is a critical task. Designers should evaluate a variety of classifiers in a timely manner and thus an approach that leverage the evaluation process and maintains performance and generalization capability is required. In this work, we use a data-driven approach based on an under- sampling technique to evaluate the performance of classifiers in the detection of network intrusion. We applied an under-sampling technique in two IDS datasets and evaluated the performance of 5 (five) classifiers in a 5% dataset portion followed by a validation step in a 2% portion of the same dataset without overlaps or repetitions. The results indicate that the use of a stratified train/test into under-sampled datasets show stability in the results in relation to the validation subsampling. The use of this approach allows us to evaluate the classifiers in reduced time, including those considered computationally costly.