Stroke is a leading cause of death globally, with higher chances of recovery when prompt and accurate diagnosis is followed by appropriate treatment. Various neuroimaging techniques, including computed tomography (CT), are used for stroke detection. Computer-aided diagnosis (CAD) systems can capture information imperceptible to the human eye, making them valuable tools in stroke diagnosis. This study proposes a novel approach for segmenting hemorrhagic stroke in CT scans using Deep Learning. Specifically, we evaluate the effectiveness of SSD, YOLO-v4, and YOLACT as prompts for the Segment Anything Model (SAM) in hemorrhagic stroke segmentation. Additionally, we compare YOLACT and SAM for segmentation performance. The methods showed promising results, with the proposed SAM and MobileSAM achieving an accuracy of 99.82%, while YOLACT attained 99.74%. The use of zeroshot and one-stage models demonstrated exceptional efficiency in addressing the segmentation challenges in medical images.

Traditional retrieval systems are hardly adequate for Legal Research, mainly because only returning the documents related to a given query is usually insufficient. Legal documents are extensive, and we posit that generating questions about them and detecting the answers provided by these documents help the Legal Research journey. This paper presents a pipeline that relates Legal Questions with documents answering them. We align features generated by Large Language Models with traditional clustering methods to find convergent and divergent answers to the same legal matter. We performed a case study with 50 legal documents on the Brazilian judiciary system. Our pipeline found convergent and divergent answers to 23 major legal questions regarding the case law for daily fines in Civil Procedural Law. The pipeline manual evaluation shows it managed to group diverse similar answers to the same question with an average precision of 0.85. It also managed to detect two divergent legal matters with an average F1 Score of 0.94.

Among illnesses, heart diseases are accounted for as one of the most responsible for deaths. Precise and fast diagnoses increase the patient's chances to receive treatment time. A non-invasive and low-cost way to diagnose it is by using Electrocardiogram (ECG). In this paper, we propose a way to diagnosis two types of heart arrhythmia, by using the ECG record as an image. To access the performance of our system, five feature extraction methods well-known in literature are used along with five different classifiers are tested. We were able to identify heart disorders with over 96.00% of accuracy, using a vanilla neural-network, Multilayer Perceptron (MLP), and Local Binary Patterns (LBP) from ECG images. This investigation has shown promising results from a medical point-of-view.

In this paper, we propose a new localization and navigation approach for mobile robots using topological maps and classification with reject option applying convolutional neural networks (CNN) for feature extraction in omnidirectional images. The use of CNN as feature extractor is based on the concept of Transfer Learning. Reject option is used to improve the task of the classifiers, querying information from the topological map. With the objective of evidencing the high performance of the technique considered, an analysis is made between several feature extractors and classifiers, established in the literature. Parameters such as processing time and accuracy are calculated to prove the credibility and effectiveness of the approach, since these properties are fundamental in the analysis of embedded systems. Considering the proposed approach, CNN stands out among the other feature extractors, as it generated the best results in extraction time and accuracy. It obtained an average accuracy of 99.86% and an extraction time of 0.1517s, proving to be a relevant method for the localization and navigation activities.