Maritime transportation is central to the global economy, and analyzing its large-scale behavioral data is critical for operational planning, environmental stewardship, and governance. This work presents a spatio-temporal analytical framework based on discrete-time Markov chains to model vessel movement patterns in the Gulf of St. Lawrence, with particular emphasis on disruptions induced by the COVID-19 pandemic. We discretize the maritime domain into hexagonal cells and construct mobility signatures for distinct vessel types using cell transition frequencies and dwell times. These features are used to build origin-destination matrices and spatial transition probability models that characterize maritime dynamics across multiple temporal resolutions. Focusing on commercial, fishing, and passenger vessels, we analyze the temporal evolution of mobility behaviors during the pandemic, highlighting significant yet transient disruptions to recurring transport patterns. The methodology we contribute to this paper allows for an extensive behavioral analytics key for transportation planning. Accordingly, our findings reveal vessel-specific mobility signatures that persist across spatially disjoint regions, suggesting behaviors invariant to time. In contrast, we observe temporal deviations among passenger and fishing vessels during the pandemic, reflecting the influence of social isolation measures and operational constraints on non-essential maritime transport in this region.

Routing vessels through narrow and dynamic waterways is challenging due to changing environmental conditions and operational constraints. Existing vessel-routing studies typically fail to generalize across multiple origin-destination pairs and do not exploit large-scale, data-driven traffic graphs. In this paper, we propose a reinforcement learning solution for big maritime data that can learn to find a route across multiple origin-destination pairs while adapting to different hexagonal grid resolutions. Agents learn to select direction and speed under continuous observations in a multi-discrete action space. A reward function balances fuel efficiency, travel time, wind resistance, and route diversity, using an Automatic Identification System (AIS)-derived traffic graph with ERA5 wind fields. The approach is demonstrated in the Gulf of St. Lawrence, one of the largest estuaries in the world. We evaluate configurations that combine Proximal Policy Optimization with recurrent networks, invalid-action masking, and exploration strategies. Our experiments demonstrate that action masking yields a clear improvement in policy performance and that supplementing penalty-only feedback with positive shaping rewards produces additional gains.

Urban vulnerability assessment is crucial for understanding the spatial distribution of deprived areas and associated risks. Slum residents face significantly worse health outcomes than non-slum urban populations, with neighborhood effects being critical in social epidemiology. Identifying such areas is vital because they present public health challenges that climate change and increased air pollution can exacerbate. Accordingly, this study proposed an AI-driven methodology that integrates remote sensing data, socioeconomic indicators, and machine learning algorithms to identify and analyze vulnerable areas in Brazil. To create a vulnerability index, we incorporate multiple data sources, including Sentinel-2 and Sentinel-5P imagery, Brazilian socioeconomic indicators, and OpenStreetMap. Hence, we predicted pollution indicators using regression algorithms such as Random Forest, XGBoost, and Linear Regression. Our findings demonstrate that integrating multi-source data is a promising approach for better understanding deprived areas, indicating that slums (called “favelas” in Brazil) exhibit an intense concentration of the sociocconomic vulnerability index, a key determinant of deprivation. However, non-slum areas may present heterogeneous conditions, with some regions showing vulnerability levels comparable to those of slums while others show better conditions. Our results highlight the potential of AIdriven approaches for urban vulnerability assessment, offering insights for policymakers and researchers.

Accurate vessel trajectory prediction is crucial for navigational safety, route optimization, traffic management, search and rescue operations, and autonomous navigation.Traditional data-driven models lack real-world physical constraints, leading to forecasts that violate vessel motion dynamics, such as in scenarios with limited or noisy data where sudden course changes or speed variations occur due to external factors.To address this limitation, we propose a Physics-Informed Neural Network (PINN) approach for trajectory prediction that integrates a streamlined kinematic model for vessel motion into the neural network training process via firstand second-order, finite-difference physics-based loss functions.These loss functions, discretized using the first-order forward Euler method, Heun's second-order approximation, and refined with a midpoint approximation based on Taylor series expansion, enforce fidelity to fundamental physical principles by penalizing deviations from expected kinematic behavior.We evaluated PINN using real-world AIS datasets that cover diverse maritime conditions and compared it with state-of-the-art models.Our results demonstrate that the proposed method reduces average displacement errors by up to 32% across models and datasets while maintaining physical consistency.These results enhance model reliability and adherence to mission-critical maritime activities, where precision translates into better situational awareness in the oceans.

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies.In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them.To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports.Through this representation, we utilize machine learning to assess the relative significance of various port features.Our model examined such features and revealed that geographical characteristics and the port's depth are indicators of a port's importance to the Ports Network.Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to the extent of ports.Our work aims to inform decision-making processes related to port development, resource allocation, and infrastructure planning within the industry.

Time-series forecasting is one of the most active research topics in artificial intelligence. It has the power to bring light to problems in several areas of knowledge, such as epidemiological studies, healthcare inference, and climate change analysis. Applications in real-world time series should consider two factors for achieving reliable predictions: modeling dynamic dependencies among multiple variables and adjusting the model's intrinsic hyperparameters. An open gap in the literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. The existing applications consistently disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions, named after Recurrent Graph Evolution Neural Network ( ReGENN ). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing ReGENN with tens of ensemble methods and classical statistical ones. The results outperformed both statistical and ensemble-learning approaches, showing an improvement of 64.87 percent over the competing algorithms on the SARS-CoV-2 dataset of the renowned John Hopkins University for 188 countries simultaneously. For further validation, we tested our architecture in two other public datasets of different domains, the PhysioNet Computing in Cardiology Challenge 2012 and Brazilian Weather datasets. We also analyzed the Evolution Weights arising from the hidden layers of ReGENN to describe how the variables of the dataset interact with each other; and, as a result of looking at inter and intra-temporal relationships simultaneously, we concluded that time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.

Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, can predict probable future conditions. Previous works have tackled the problem by using artificial neural network architectures that do not benefit from bi-directional temporal processing, or by utilizing non-generalizable inference approaches. Differently, we introduce a Deep Learning architecture whose design results from an intensive experimental process; our final architecture is based on two parallel Minimal Gated Recurrent Unit networks working in bi-directional manner, which was extensively tested with two real-world datasets. Our results demonstrate significant improvements in automated medical prognosis, as measured with metrics Precision@, Recall@, F1-Score, and AUC-ROC. We contribute with an architecture and with insights for the design of Deep Learning architectures.

Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician's perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.

This Research Full Paper introduces a machine learning methodology to automatically identify the learning style of students interacting with a Learning Management System. Studies in Cognitive Psychology and Pedagogy have already reported that each individual has a specific Learning Style, which describes her/his best means of perceiving and acquiring knowledge. The detection of the personal Learning Style of each student has long been made by using questionnaires; an analysis that demands too much effort, mainly in courses with hundreds of students. Therefore, the automatic modeling of learning styles has gained attention in the computing and education areas. This study compares different Machine Learning algorithms for the detection of students' Learning Styles. As such, a dataset is extracted from a real course in the Moodle learning platform. This course had 105 students interacting with 252 learning objects during 12 months. The learning styles were described using the classic model of Felder-Silverman. According to the experimental results using these data, a single machine learning algorithm was not able to induce models with predictive accuracy comparable to those from existing alternatives. However, when models from different algorithms were combined, it was possible to obtain a predictive accuracy superior to those reported in the related literature.

Pantanal is one of the most important biomes of the world, with a large number of wild animal species, some of them are in extinction. The automatic identification of wild animals is extremely important for the estimation of the species' population within Pantanal. However, digital processing techniques for the identification and tracking of species have faced great challenges due to clumsy light and pose conditions present in images taken in the wild. To overcome such problems, we propose a methodology that, by combining regular RGB images and thermal images, improves the identilication of species even in images taken in rough circumstances. We use the SLIC segmentation algorithm to identify the regions of the images where animals are present; after that, we apply convolutional neural networks to classify the identified regions according to eight possible animal species. We experiment on a real-world dataset composed of 1,600 images. Our results showed an average gain between 6% and 10% when compared to the method Fast R-CNN.

Software Quality Assurance is a sub-process that ensures that developed software meets and complies with defined or standardized quality specifications. Focusing on source code, there are characteristics that can be used to evaluate the quality. Introductory courses must encourage freshmen students to improve internal quality of their source code, but only as sophomore they have contact with Software Engineering concepts, including Quality Assurance. In this paper we present a tool to source code quality evaluation aimed at supporting students to improve their source code and, consequently, their programming skills. The proposed tool uses quality reports (available to professional environment integrate with software repositories) to analyze students' source code and provide a feedback about the student coding. The proposed tool run locally, with few computational resources. In addition, we proposed the methodology to use the proposed tool: it consists of challenging students to perform a set of maintenance tasks in a controlled environment. We prepared a source code by introducing common defects, what decreases the quality of source code, and ask to students to perform maintenance tasks in order to both eliminate the introduced defects and introduce new features. After each modification, the students must evaluate their code using the proposed tool to obtain a feedback about quality of source code. To evaluate the approach and the tool, we created a survey and applied to students and the teacher. As a result, we show the benefits of using the proposed tool to both teachers and students perspectives. The results are positive to enhance the teaching-learning Software Quality Assurance to Software Engineering students.

Theoretical Computer Science area (TCS) stands out by being an important study field, and it is composed by Formal Languages and Automata Theory (FLA), Computer Science Theory (CST), and Theory of Compilers (TC). This area is responsible for introducing the beginnings of the Computer Science through formalisms - which represent a set of methods, techniques, or rules that describe the solution to a problem with restrictions - and it has a substantial impact on the student's knowledge. Computer science theory is based on the understanding of computability and techniques to solve challenges, and to improve the teaching-learning process used to introduce these concepts we proposed a Combined Methodology for Theoretical Computing (CMTC). Our methodology is based on formalism development to ground the knowledge acquired during classes of FLA, CST, and TC, where students are introduced to Theoretical Computing during one year and a half. In each course, we applied the same methodology where each student used data structures, computer graphics, and algorithms to solve problems. We address this methodology to understand how much the incomprehension of formalisms is influenced by new concepts and its abstractions. Against this background, we demonstrate that the that CMTC has the aim to build knowledge and make the new concepts and formalisms concrete. Our results are based on statistical analysis from students' grades, where we could observe among other results, the correlation between the practical activities and the conceptual knowledge.

Formal languages and automata (FLA) theory have fundamental relevance to the base of knowledge in the computer science area, especially focusing on scientific education. Usually presented by a discipline, the teaching-learning process of FLA is characterized by the high level of abstraction, and it is considered difficult due to the complexity of language formalisms. As support for the learning process, tools have been used to simulate language formalisms. However, the simulation is not enough to reinforce the construction of an abstract concept. In this paper, we present an FLA teaching-learning methodology based on the development of simulators as an approach to clarify the formalism for the students. Through developing their simulators, students are exposed to the data structure and algorithms to handle the formalism. Consequently, students have the opportunity to make the concept concrete.