Leprosy, or Hansen's disease, is a Neglected Tropical Disease (NTD) caused by Mycobacterium leprae that mainly affects the skin and peripheral nerves, causing neuropathy to varying degrees. It can result in physical disabilities and functional loss and is particularly prevalent amongst the most vulnerable populations in tropical and subtropical regions worldwide. The persistent stigma and social exclusion associated with leprosy complicate eradication efforts exacerbate the wider challenges faced by NTDs in sourcing the necessary resources and attention for control and elimination. The introduction of Multidrug Therapy (MDT) significantly lowers the global disease burden. Despite this breakthrough in the treatment of leprosy, over 200,000 new leprosy cases are reported annually across more than 120 countries, emphasizing the need for ongoing detection and management efforts. Artificial Intelligence (AI) has the potential to transform leprosy care by accelerating early detection, improving accurate diagnosis, and enabling predictive modeling to improve the quality for those affected. The potential of AI to provide information to assist healthcare professionals in interventions that reduce the risk of disability, and consequently stigma, particularly in endemic regions, presents a promising path to reducing the incidence of leprosy and improving integration social status of patients. This systematic literature review (SLR) examines the state of the art in research on the use of AI for leprosy care. From an initial 657 works from six scientific databases (ACM Digital Library, IEEE Xplore, PubMed, Scopus, Science Direct and Springer), only 30 relevant works were identified, after analysis of three independent reviewers. We have excluded works due duplication, couldn't be retrieved and quality assessment. Results show that current research is focused primarily on the identification of symptoms using image based classification using three main techniques, neural networks, convolutional neural networks, and support vector machines; a small number of studies focus on other thematic areas of leprosy care. A comprehensive systematic approach to research on the application of AI to leprosy care can make a meaningful contribution to a leprosy-free world and help deliver on the promise of the Sustainable Development Goals (SDG).

Augmented Reality (AR) Systems have been largely adopted as a useful technology. They are excellent candidates for AR application execution platforms due to their recent popularity. Nonetheless, the limited processing power and battery autonomy restrict the possibilities of more extensive use of smartphones as a broadly adopted AR platform. This work explores the use of Edge Computing and Code Offloading in a collaborative AR system. We present the proposed application architecture, which includes several interconnected components divided into three primary blocks. The benefits of AR are discussed, including its use in education, medical training, and industry. The central objective of this work is to provide an in-depth and structured analysis of the proposed architecture, paving the way for extensive studies that address various dependability issues. We implemented an AR system to validate the proposed architecture. We collected the data related to the execution times of the algorithms allocated to the Edge, where it was possible to characterize a probability distribution suitable for characterizing these times. The proposed architecture was validated, and the results obtained open possibilities for future developments and better adequation of the applications implemented according to the proposed architecture.

Abstract Travel delays and bus overcrowding are some of the daily dissatisfactions of public transportation users. These problems may be caused by bus bunching, an event that occurs when two or more buses are running the same route together, i.e. out of schedule. Due to the stochastic nature of the traffic, a static schedule is not effective to avoid the occurrence of these events; thus, preventive actions are necessary to improve the reliability of the public transportation system. In this context, we propose a decision tree ensemble model to predict bus bunching. We use an ensemble of Random Forest, eXtreme Gradient Boosting and Categorical Boosting models applied to Global Positioning System, General Transit Feed Specification, weather and traffic situation data. The efficacy of the proposed model has been demonstrated using real data sets and has been compared with four baselines: Linear Regression, Logistic Regression, Support Vector Machine and Relevance Vector Machine. According to the results, the proposed model can achieve an efficacy between 74 and 80% and can be used to predict bus bunching in real time up to 10 stops before its occurrence.

Summary Several methods of entity resolution (ER) have been developed in academia and industry over the years, with the intention to identify duplicate entities (eg, records) in datasets. To evaluate the efficacy of such methods, it is necessary to compare their results with a ground‐truth, which consists of a document containing all known duplicate record pairs in a dataset. In general, the generation of ground‐truths for real datasets is performed manually from the inspection of all combinations of pairs of records in a dataset. This is subject to error and presents quadratic complexity, with respect to the size(s) of the dataset(s), requiring a long time to be performed. In this context, some works present (semi)automatic approaches for the generation of ground‐truths for the ER task. However, such approaches are either not applicable to several domains or still present a considerable manual effort. In this work, we propose GTGenERAL, a semiautomatic approach that combines results from multiple algorithms of ER together with active learning to generate accurate ground‐truths employing reduced manual effort. Experiments using real datasets show that, with great manual effort reduction, GTGenERAL is able to generate ground‐truths close to those generated by the state‐of‐the‐art approach.