This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.

Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

The growth of the world population forces a more outstanding and more efficient food production, forcing agribusiness into the race for greater productivity. Thus, agrochemicals as a tool for increasing and defending production have become more critical with each harvest. This work presents an ontology that describes the knowledge involved in the need for Agrochemical Pervasive Traceability Model (APTM). This proposed ontology is called ontology for pervasive traceability of agrochemicals (OntoPTA). We present classes and their relationships in a hierarchical way and a visualization from the OWL ontology language. This ontology fills the gap in the understanding and modeling of this type of agribusiness process. This modeling helps farm administrators and software developers to perform better analysis for the development, use and maintenance of systems in agribusiness.

Este artigo apresenta um mapeamento sistemático de trabalhos relacionados à aplicação de Internet das Coisas e Aprendizado de Máquina para realização de auscultação, com escopo na aquisição, processamento, análise da qualidade do sinal e apoio ao diagnóstico de disfunções cardiovasculares. Esta pesquisa abrange buscas de 2010 até julho de 2021 nas bases IEEE Xplore, PubMed Central, ACM Digital Library, JMIR - Journal of Medical Internet Research, Springer Library e Sciencedirect. A busca inicial resultou em 4.372 artigos e após aplicação dos critérios de inclusão e exclusão foram selecionados 58 artigos para leitura completa com o intuito de responder as questões de pesquisa. Os principais resultados são: dos 58 artigos selecionados foi constatado que 79,31% (46) citam métodos de observação de batimentos cardíacos com sensores vestíveis e estetoscópios digitais e 58,62% (34) fazem menção aos cuidados utilizando algoritmos de aprendizado de máquina. A análise do artigos demonstrou a tendência do uso de serviços inteligentes no diagnóstico de disjunções cardiovasculares.