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.

The Brazilian capital market presents unique challenges for portfolio optimization due to its volatility and information asymmetries. This paper proposes a Deep Reinforcement Learning (DRL) framework that integrates fundamental indicators, Portuguese-language news sentiment (via Gemini Pro), and market data (prices, volume). Five DRL algorithms (A2C, PPO, DDPG, TD3, SAC) were trained and evaluated across three feature scenarios using performance metrics such as Sharpe Ratio, Annual Return, and Maximum Drawdown. News sentiment classification and entity extraction were performed using Gemini Pro LLM. Statistical validation over 44 executions, including Shapiro-Wilk and Kruskal-Wallis tests, showed no significant differences among DRL methods. However, all DRL approaches outperformed the Ibovespa index and uniform Buy-and-Hold benchmark, highlighting the value of combining DRL with localized and diverse data sources for portfolio optimization in emerging markets.

Many investors still rely on their emotions as their primary guide for asset allocation, blindly following one or two news sources that may or may not be an actual synthesis of the market, without any mathematical formulation to support them, even if slightly. This work aims to conduct a Systematic Review using Kitchenham’s protocol to understand the state of the art regarding the combination of Portfolio Optimization techniques and Artificial Intelligence in the context of trading assets, with a special focus on Sentiment Analysis techniques. This was achieved through the definition of search keywords used in 4 different major research databases and selection through inclusion and exclusion criteria. A total of 384 articles published between 2019–2025 were identified, among which 27 articles were selected that fit all the selection criteria. The research questions address the specific techniques employed for optimization, with the most proliferated being Deep Reinforcement Learning.

Traditional paper-based children’s spelling assessments were hampered due to Covid-19 because existing technologies did not provide strategic signals to teachers, such as the child’s handwriting direction and how they read what they write. Our project emerged as a novel method to assess children’s spelling by touchscreens in this context. Hence, this paper aims to extend community knowledge concerning children’s experience and perception of handwriting spelling on tablet devices. The experiment consisted in presenting three handwriting methods (paper and pencil, finger and pen writing) and was conducted with eight Brazilian children between 4.5 and 7 years old. In addition to observation, in our experimental protocol we adopted the Fun Sorter, Again-Again Table, and the Smileyometer as evaluation tools. Our results show children were excited about handwriting using a touch pen on the tablet. Most of them even revealed they prefer the pen tablet mode to the traditional paper and pencil mode. However, the majority of children did not feel comfortable writing by finger, and it required more time than other methods. Furthermore, we observed child’s handwriting using finger looks different when compared to paper and pencil, while the tracing using a touch pen is similar to the registration produced on paper.

Tuberculosis is a severe and contagious lung dis-ease that kills about 1.5 million people worldwide. One of the ways to combat this disease is by tracking, detecting, and iso-lating the infected. In this sense, chest radiography (CXR) is an effective alternative for this task, given its high availability, low charge, and quick response. Thus, considering the importance of this topic, our work proposal is a machine learning method for tuberculosis detection in CRXs. Our method combines local binary patterns (LBP) feature extraction and a feature selection wrapper algorithm by Monarch Butterfly Optimization (MBO) with an evaluation KNN classifier. The results are compared to a reference work on various metrics and show 90.33 % and 92.41 % accuracy and the area under the ROC curve, respectively. Our proposal is a solution that combines performance, reduced computational cost, and simplicity of implementation, composing a viable and aligned alternative to the Internet of Things (IoT) solutions.

In this work, we explore machine learning to ob-tain sentiment indicators from financial market text messages collected from Twitter and news in Portuguese. A statistical analysis was carried out with Sperman’s correlation coefficient between sentiment indicators and actual variables in the Brazilian financial market. For sentiment analysis, the MaxEnt and CNN models provided F1-scores of 85% and 96%, for tweets and news, respectively. A result shown a moderate correlation (Cohen’s scale) between some variables such as, amount of tweets and sentiment of the news; market variation and tweet sentiment; amount of retweets published and sentiment of the news; trading volume and tweet sentiment. Moreover, a very large correlation was identified between the amount of negative tweets and retweets, leading to the belief that pessimism is often propagated.

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.

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.

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.

Medical X-ray images are an important and valuable source of studies and diagnoses for diseases with low cost besides its high availability. However, radiological images are subject to degradations related to low contrast and presence of noise. Based on this finding, this article presents a simple but efficient enhancement method for these images with the objective of contrast gain and noise removal. The proposed method (MP) consists of a sequence of interactive steps. Start from the step of double precision conversion and end with removing impulsive noises. An evaluation with the PSNR, Entropy, AMBE, and IQR indicators was performed, besides gain check on the thresholding process and the histogram characterization. The evaluation was conducted on three different datasets in a total of 1409 images chest X-rays. The results compared to others known in the literature proved to be promising and put it as an interesting alternative in the process of enhancement medical X-ray images.

Since 1946 methods for sensory substitution of vision has been studied; however, half a century after the beginning of this line of research, this keep been a massive problem in a world with about 50.6 million people with irreversible blindness. This research presents how self-help devices for visually impaired are approach in recent years and proposes a new approach based on object recognition with deep learning. Through it, it is possible to perceive the trends in this line of research, how devices obtain information from the environment, how they interact with users, and other aspects - pointing essential factors to all those who research or wish to study this area.

Microblogs have increasingly been used by the crowd to post their thoughts and speeches about everything. Thus, one of the themes is the stock market that is exploited by many researchers. Although obtaining indicators of stock market dynamics through online social networking has been gaining the attention of academia and the business world, there are many questions to be analyzed about their effectiveness. This work presents the development of a simulator for buying and selling stocks based on microblog data. Therefore, we collected tweets about the Brazilian stock exchange market, produced indicators using sentiment analysis and performed a set of heuristics for decision making. The first technique is the composition of the decision-making strategy for buying and selling stocks composed of pure logic, tweets volume thresholds, profit objective and technical analysis in the stock exchange. The contribution of this work is a decision-making architecture using Twitter's data as an index of future expectation about the social mood that may change the market behavior. As a result, it has pointed to attractive profits for Brazilian market actions and many issues that can be analyzed and improved. Our study showed that it is possible to obtain market dynamics information on the Twitter social network and this information could be used to compose stock buying and selling strategies.