APRENDIZADO DE MÁQUINA PARA ROTULAÇÃO AUTOMÁTICA DE USUÁRIOS DE UMA REDE SOCIAL ACADÊMICA

Social networks have become relevant in the Internet due to the great variety of Web sites that use the concept. Its users form databases that provide an important way of sharing, organizing, finding content and making contacts. Thus, Scientia.Net is a social networking site that integrates information from various Internet services (forums, article repositories, websites, blogs and other social networks). Besides, the tool provides the user interaction (students, teachers and researchers) for academic purposes, based on their common interests. This paper presents an application developed to automatically group Scientia.Net users, showing the performance of various machine learning algorithms, offering to Scientia.Net a sorting mechanism that presents a list of other researchers to each user of the network, based on their common interests. With this, we intend to contribute to the interaction among users with similar profiles, allowing an improvement in the productivity of their research efforts. Furthermore, this paper proposes a model that uses a combination of supervised and unsupervised learning algorithms to create groups and identify users based on their relevant attributes.Redes sociais tornaram-se especialmente relevantes na Internet devido à grande variedade de sites Web que utilizam o conceito. Seus usuários formam bases de dados que proveem um importante meio de compartilhar, organizar e encontrar conteúdo, estabelecer contatos com base em interesses comuns. Dessa forma, o Scientia.Net é um site de rede social que integra informações contidas em diversos serviços da Internet (fóruns, repositórios de artigos, sites, blogs e demais redes sociais). Além disso, a ferramenta provê a interação de seus usuários (estudantes, professores e pesquisadores) para fins acadêmicos, com base nos seus interesses em comum. Este artigo apresenta uma aplicação desenvolvida para agrupar de forma automática os usuários do Scientia.Net, mostrando o desempenho de vários algoritmos de aprendizagem de máquina, visando a oferecer ao Scientia.Net um mecanismo de classificação que apresente a cada usuário da rede, uma relação de outros pesquisadores com base nos seus interesses em comum. Com isso, pretende-se contribuir para a interação entre usuários de perfis semelhantes e assim possibilitar que estes melhorem a produtividade de suas pesquisas, ao aumentar sua capacidade de troca de conhecimento. Além disso, o presente artigo propõe um modelo que utiliza uma combinação entre algoritmos com aprendizagem supervisionada e não-supervisionada com o objetivo de criar grupos e identificar quais atributos podem defini-los

Abordagem Semissupervisionada usando Deep Learning Aplicada á Rotulação e Classificação de Dados

Large-scale data generation has brought the need for the developmentof intelligent techniques capable of analyzing this data automatically. In thissense, this paper proposes a semisupervisioned classification model capable oflabeling unlabeled data from a few labeled examples. For this, a deep neuralnetwork was trained with labeled and unlabeled examples, simutaneally. Theexperiments performed show that the model is efficient in labeling data andpredicting new examples

Group Labeling Methodology Using Distance-based Data Grouping Algorithms

Clustering algorithms are often used to form groups based on the similarity of their members. In this context, understanding a group is just as important as its composition. Identifying, or labeling groups can assist with their interpretation and, consequently, guide decision-making efforts by taking into account the features from each group. Interpreting groups can be beneficial when it is necessary to know what makes an element a part of a given group, what are the main features of a group, and what are the differences and similarities among them. This work describes a method for finding relevant features and generate labels for the elements of each group, uniquely identifying them. This way, our approach solves the problem of finding relevant definitions that can identify groups. The proposed method transforms the standard output of an unsupervised distance-based clustering algorithm into a Pertinence Degree (GP), where each element of the database receives a GP concerning each formed group. The elements with their GPs are used to formulate ranges of values for their attributes. Such ranges can identify the groups uniquely. The labels produced by this approach averaged 94.83% of correct answers for the analyzed databases, allowing a natural interpretation of the generated definitions

Task Force of the Latin American Society of Hypertension

ABI, ankle-brachial index; ABPM, ambulatory blood pressure monitoring; ACCORD, Action to Control Cardiovascular Risk in Diabetes; ACE-I, angiotensin-converting-enzyme-inhibitors; ARB, AT1 blockers; BP, blood pressure; CARMELA, Cardiovascular Risk Factor Multiple Evaluation in Latin America; CARMEN, Community Actions for Multifactorial Reduction of Non- Communicable Diseases; CCB, calcium channel blocker; CCM, Wagner’s Chronic Care Model; CDC, Chronic Disease Center; CTA, computed tomography angiography; CV, cardiovascular; DALY, disability-adjusted life year; DPP- 4, dipeptidyl-peptidase-4; GLP-1, glucagon-like peptide 1; HBPM, home blood pressure monitoring; HOPE, Heart Outcomes Prevention Evaluation; HOT, Hypertension Optimal Treatment; HT, hypertension; LA, Latin America; LASH, Latin American Society of Hypertension; MRA, magnetic resonance angiography; NCD, noncommunicable disease; OSAS, obstructive apnea–hypopnea syndrome; PAD, peripheral artery disease; PAHO, Pan American Health Organization; RAAS, renin–angiotensin–aldosterone system; RISS, Redes Integradas de Servicios de Salud; SGLUT2, sodium–glucose cotransporter-2; SPRINT, SBP Intervention Trial; UKPDS, United Kingdom Prospective Diabetes Study; VIDA, Veracruz Initiative for Diabetes Awarenes