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.

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.

Recommendation systems (RS) are important applications to help consumers to find interesting products in large databases available on line. A wide range of RS can be easily found such as Netflix and Amazon. In this paper, we propose a novel content-based approach for clothing recommendation, termed CRESA, which combines textual attributes, visual features and human visual attention to compose the clothes profile. Traditionally, the RS uses textual and/or visual features to derive the similarity measure between two products. However some parts of the image may call much more attention of users than others. We believe that the characterization of this phenomenon can improve the quality of the recommendation systems, especially when applied to the clothing recommendation problem. With this purpose, in this work we propose weighting the similarity of visual attention between corresponding parts of products with the measures conventionally used in content-based image recommendation systems. The experimental results showed that our approach reached the best accuracy rates when compared to the baseline approaches.