This work presents a comparison between techniques for recognizing inorganic molecular structures from hand-drawn sketches. The approach used combines Convolutional Neural Networks (CNN) and the Histogram of Oriented Gradients (HOG) method to classify and recognize these sketches. For the tests, we used images drawn by high school students. The comparison of the results obtained with the CNN and HOG techniques was carried out in detail. Additionally, we developed an application capable of generating three-dimensional visualizations of the molecular structures, allowing their representation in a virtual environment. This 3D visualization provides a more intuitive understanding of the molecular structures. This work highlights the effectiveness of machine learning technologies in education, by offering an accessible and interactive educational tool that complements traditional chemistry teaching. In this way, it provides students with a deeper understanding of the physical and chemical properties of inorganic molecules, enriching the learning process and making it more engaging.

Neurodegenerative diseases (NDDs) cause, among other symptoms, motor impairment. Given the incurable nature of the NDDs, several studies have investigated gait using artificial intelligent models as non-invasive alternative methods to assist in the diagnosis of these diseases. This work proposes a novel method using an Encoder-Only Transformer to detect NDDs, a multi-classification task, through gait signal analysis. The approach comprises data preprocessing, windowing technique, a modified transformer architecture and cross-validation evaluation. The results indicate the transformer-based architecture can be a promising alternative to accomplish this goal.

This paper presents the "Letter Explorers" platform, an educational tool designed to support preschool children in their writing acquisition process. The platform was designed based on recognized pedagogical guidelines, structuring its activities into progressive levels of difficulty. The main focus is on learning letters and encouraging independent writing, respecting the motor and cognitive development of young children. Its features include real-time visual and auditory feedback, which helps correct and reinforce learning, as well as the possibility of personalizing activities based on the child’s name, a factor that contributes to motivation and engagement. The proposal aims to serve, especially, educational contexts in the interior of emerging countries, where access to quality digital solutions may be limited. Preliminary results indicate the potential of the tool to promote an interactive, inclusive and contextualized environment for learning to write in preschool education.

Tuberculosis (TB) remains a major global health crisis, disproportionately affecting vulnerable populations. Despite advances in artificial intelligence (AI) for chest X-ray (CXR) analysis, these tools have limited impact in low-resource regions due to inadequate infrastructure, specialist shortages, and high equipment costs. This study proposes a practical AI-based approach using optimized binary phase pattern congruence (BPPC) feature selection to distinguish between TB cases and healthy individuals. Its lower computational requirements and costs make it particularly suitable for vulnerable regions. We experimented with multiple CXR databases and segmentation scenarios using optimized feature selection. Results outperform existing literature, achieving a minimum area under the curve (AUC) of 97.64%, showing potential to enhance CXR analysis and assist specialists in TB diagnosis.

Assistive technologies enable individuals with special needs to access resources that would otherwise be inaccessible due to their condition. In this context, people who are blind or visually impaired face daily challenges related to mobility, communication, and, most notably, access to education in specialized fields. In this work, we perform a systematic literature review that aims to identify and map assistive technologies designed to support programming education for individuals with visual impairments. The review was conducted across three academic databases and resulted in the selection of 22 articles that address the research questions posed in this study.

This paper presents a computational approach for the machine translation of comic book texts, aiming to promote linguistic accessibility, particularly in emerging countries. The proposed system performs end-to-end processing: it detects text regions, extracts content using Optical Character Recognition (OCR), translates the text into the target language, and reinserts the translated content into the original image, preserving its visual structure. Built with open-source libraries, the system is lightweight and suitable for low-resource computational environments. The methodology was validated through an experiment involving 1,000 pages of comics in four languages—English, French, Spanish, and Japanese. Results demonstrated high accuracy in the OCR and translation stages for Latin-based languages, and satisfactory performance for Japanese, despite its right-to-left reading layout and ideographic characters. The tool also showed potential as an assistive reading solution, with applications in educational and inclusive contexts. This work contributes to the development of accessible technologies aligned with the United Nations Sustainable Development Goals (SDGs), particularly in promoting quality education and reducing inequalities.

This work presents a method for interacting with video players through real-time hand gesture recognition, implemented using the MediaPipe library and the Random Forest algorithm. The main goal was to develop a lightweight and accessible system capable of controlling playback, time navigation, and volume adjustment without requiring physical contact with the screen. The prototype was developed for Android devices and evaluated through an experiment conducted under real usage conditions, where users interacted with the system on their own mobile devices. Usability was assessed using the System Usability Scale (SUS), which resulted in an average score of 83.38, classified as "Good." The user experience was further evaluated using the short version of the User Experience Questionnaire (UEQ-S), which yielded "Good" results in the pragmatic dimension and "Excellent" in the hedonic dimension. The results indicate that the system is intuitive, functional, and pleasant to use, even among users with little prior experience with gesture-based interaction. The model demonstrated good generalization capabilities, even with a reduced training dataset. The proposed approach proved to be viable and promising for future applications in various contexts, such as entertainment, education, and accessibility.

Predicting liver and/or lung metastases in colorectal cancer (CRC) patients remains a critical challenge, especially due to the strong class imbalance commonly found in clinical datasets such as SEER (Surveillance, Epidemiology, and End Results Program). To address this issue, this study presents a comparative analysis of eight data balancing techniques combined with seven machine learning algorithms for metastasis prediction using SEER data. The evaluated techniques include traditional SMOTE, ADASYN, Borderline-SMOTE, SMOTE-LOF, Radius-SMOTE, RDSMOTE, and CUSS, along with a baseline configuration without any balancing. A total of 53,463 CRC patients were analyzed, and model performance was assessed using F1-score and AUC metrics under five-fold stratified cross-validation. Among the techniques, SMOTE-LOF achieved the best overall results, particularly when combined with XGBoost, reaching an F1-score of 0.6642 and AUC of 0.8988. The results indicate that combining oversampling with noise filtering or structural awareness significantly enhances model sensitivity. This study highlights the importance of selecting appropriate resampling techniques for clinical prediction tasks and offers insights for improving the robustness and fairness of machine learning applications in oncology.

The solution space of an optimization problem consists of all its feasible solutions. In this work, we present a systematic literature review on the application of Information Visualization (IV) techniques for understanding and exploring such solution spaces. The review was conducted on several search databases, and we identified 264 papers that satisfied our inclusion criteria. A performance filter was applied to these papers, and we further analyzed and extracted data from 65 of them. Our analysis shows that there are a variety of solution space visualization approaches and provides useful references to support further studies on the subject.

Since vaccination started, the COVID-19 scenario has improved. On the other hand, although the number of deaths has significantly dropped, the number of new cases is still a concern. Thus, patient tracking and follow-up are essential tasks, and chest X-ray examination is the first-order tool. While several studies using CXR and computing have been developed, they did not translate into clinical applications yet. One of the reasons is the computational effort required to run huge deep learning models and its high cost to be adopted in community clinics. Therefore, this work proposes a lightweight (few computational resources needed), fast (training and inference time), and reasoned solution for automatic COVID-19 detection and assessment of its severity. Our method is based on extracting features by Binary Pattern of Phase Congruency (BPPC) in segmented CXR images. Radiomic features are extracted from the segmented CXR image, and an SVM-based selection process is used to build two models of a shallow Feed-Forward network. The results surpass previous studies, with an average accuracy for COVID-19 detection of 98.71%. For images without evidence of infection but with a positive PCR test, an accuracy of 94.74% is reached. In a second task, the severity level of COVID 19 is estimated with an AUC of 98.92%. This high performance helps improve the speed and accuracy of diagnosis and severity assessment of COVID19 infection, proving to be a viable option in transitioning from a research field to a clinical environment.

Amyotrophic Lateral Sclerosis (ALS) is a neurode-generative disease characterized by the progressive and specific loss of motor neurons in the brain, causing a variety of symptoms, including weakness of muscles and changes in gait. Currently, there is no cure for ALS, nor there is a definitive diagnostic test that can detect whether someone has ALS. Therefore, there is still a need for alternative and non-invasive methods to aid the diagnosis of ALS. This article proposes an automatic method to aid the diagnosis of ALS. A feature extraction technique based on metrics of fluctuation magnitude and fluctuation dynamics, followed by a machine learning algorithm to separate subjects with ALS from healthy ones, was used. The results showed that the proposed approach is comparable to others in the literature even though a simpler and smaller feature set was considered. Five different machine learning classifiers were compared and evaluated using the leave-one-out cross-validation method. A comparison and discussion of the results based on the foot from which the data were extracted and the phases of the gait were also carried out.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that affects the nervous system responsible for muscle movement and eventually compromising one's ability to walk. Diagnosing ALS is a difficult task since no test can provide a definite diagnosis. In this sense, automatic methods that aid the diagnosis of ALS have an essential role in helping to reach a diagnose. However, most of the existing approaches that use gait dynamics are based on a 5-minute observation, which can be exhausting and demanding for a patient with ALS seeking the diagnosis. This paper proposes an automated method to aid the diagnosis of ALS using information obtained from one minute gait observation. The GaitNDD database, which provides gait data recorded for 5 minutes from people with ALS and from healthy subjects, was used to support and validate this study. Results are reported and evaluated for different machine learning classifiers. Features extracted from either 1-min or 5-min observations are evaluated. Our results show that 96.6% of accuracy was achieved for data derived from either the first or the 5-minute walking, with excellent sensitivity and specificity, thus showing that our method can help aid the diagnosis of ALS while reducing the time required for the walking experiment.

This work aims to investigate, by means of a Systematic Literature Review, to evaluate the current state of the use of artificial intelligence in automated pupillometric technology and its application in helping to diagnose diseases, to identify the methods and equipment used and propose case new equipment based on computer vision is feasible. We also investigated the accuracy of methodologies and equipment that use computerized pupilometry to identify pathologies or disorders, as well as the viability and usability of existing pupilometers. In this sense, creating a pupilometer capable of stimulating and varying wavelengths, providing an interface to preview the exam, and embedding the classification algorithms is a great challenge. In this systematic review of the literature, we consider publications from the last ten years (2010 - 2020) indexed by seven solid scientific databases. The review identified a vast amount of work on pupillometry; however, a small amount related to the construction and viability of a pupilometer with an embedded system, easy to use and with a preview interface. Having identified this, we propose a new methodology for the construction of the pupilometer as well as the algorithm for extracting the characteristics through pupilometry.

Chest radiography (CXR) is one of the first choices in epidemiological analyses such as tuberculosis, cancer, pneumonia, and, recently, COVID-19. It provides crucial information for decision making, treatment, and monitoring the evolution of clinical cases from small to high complexity. Thus, it is a valuable source of information for the study, training, research, and development of computational support to medical diagnoses. In this work, we introduce a new method for chest X-ray adjustment to identifying and correcting radiographic images orientation. So, they can be automatically rotated to a standard position. Our proposal uses structural characteristics and statistics of pixel intensity patterns of CXR images. Divided into three steps, our method begins with the preparation of the photos, followed by a feature extraction strategy, and it ends with the X-ray image orientation identification. We use three different databases that include pediatric and adult radiographic imaging. A result showed 99.4% accuracy in the databases in our experiments. The code prepared by the authors is publicly available.

Remote sensing techniques by satellite imagery have been widely applied in various fields of agrarian sciences due to allowing real-time information, allowing data retention in a given region without the need for displacement, avoiding costs, and also enabling the creation of more efficient methods for the task of monitoring crops. In special to remote sensing applied to sugarcane varietal identification, the possibility of discrimination among the varieties is important due to allows the monitoring of the crop growth concerning characteristics by plants, measures controls, and the preservation of copyright of developed varieties. Among the researches involving studies with sugar cane regarding varietal identification, the purpose of the paper implies to present a review of the literature, conferring methods, and checking state of the art about the subject of discrimination of sugarcane varieties by remote sensing.

Assessment of children's spelling development stages is an activity frequent in literacy classrooms. Usually, teachers adopt dictation sessions, using a paper-and-pencil based method, which has to be conducted individually with each child. As there are many students in a class, the activity becomes laborious to be offered frequently, pushing teachers to opt to a sub-optimal amount of tests. Moreover, the number of tests to be performed in person is now limited. In this context, we aim to develop a method for conducting automated word dictation sessions for an in-person and remote assessment. Besides, our proposal aims to support teachers, parents, and other literacy professionals in identifying children's spelling development stages. In this paper, we report the conception of our computational artifact through the Design Science Research Methodology. After reviewing the existing studies, we developed a high fidelity prototype and evaluated the concept during a focus group discussion with literacy teachers. The collected qualitative result indicates the feasibility and utility of our approach, and evident limitations in existing apps to assess children's spelling, contributing to future research in this area.

This work proposes the development of a method that allows the use of smartwatch as a control for interactive films using continuous recognition of gestures and conducts a case study with the film "Black Mirror: Bandersnatch". We developed two prototypes to interact with interactive movies. The first one uses gesture recognition on the smartwatch screen. Thus, we created a set of gestures composed of straight lines that represent the actions in the interactive movie. In this prototype, the recognition of gestures is performed by the algorithm of continuous recognition of gestures, in this way, a gesture can be recognized before the user finalize, and action is sent quickly to the movie. The second prototype uses a touch of pressure in the smartwatch to control the film. Thus, the actions to be performed in the smartwatch that represents the actions in the interactive film is proposed, the recognition of the gestures is determined by the change of the values obtained by the accelerometer. The prototypes communicate with a simulation service responsible for running the movie. The prototypes were used in a study with users, as well as in usability and experience tests, and the results show that the method has the potential to be used in everyday users to control interactive films efficiently and effectively.

Huntington's Disease (HD) is a genetic disorder that causes the progressive breakdown of nerve cells in the brain, reducing an individual's ability to reason, walk, and speak. Due to its severity, new approaches are important for the development of methods that contribute to the correct classification of this disease. In this paper, we propose an automatic method for diagnosing Huntington's Disease using gait dynamics information. Our approach is divided into a four-stage pipeline: preprocessing, feature extraction, classification, and diagnosis output. We evaluate the performance of our proposed method through well-known classifiers that are commonly used in machine learning problems. A publicly available database on Gait Dynamics in Neuro-Degenerative Disease is used, and the experimental results show that both Support Vector Machines (SVM) and Decision Tree (DT) were able to achieve an average accuracy of 100:0%, representing an improvement in the field.

In this paper, we propose to apply an Evolutionary Programming (EP) heuristic to solve the resource allocation problem in wireless networks, aiming to maximize the total data rate and attain certain QoS (Quality of Service) parameters. The performance of the resource allocation algorithm is verified and compared to others in the literature using computational simulations. In these simulations, we also consider 5G techniques such as f-OFDM (filtered-Orthogonal Frequency Division Multiplexing) and carrier aggregation in order to show that the EP based scheduling can provide higher data rate than other algorithms in the literature with the same scenario.