This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor. The outcomes show that this approach presents better accuracy according to the progress of students, mainly when the JRip and PART algorithms are used. Furthermore, the use of this method enabled the generation of rules that stress the factors that mainly affect the dropout phenomenon at different degree moments.
Home-based health monitoring systems are currently being used to support early detection of abnormal conditions and prevention of its serious consequences. Many patients can benefit from continuous ambulatory monitoring as a part of a diagnostic procedure, optimal maintenance of a chronic condition or during supervised recovery from an acute event or surgical procedure. An evolution of this approach is the use of the mobile infrastructure and wearable technology, which mainly provides more freedom to their users. While these approaches use mobile communication devices just as a router of health information, we argue that such devices can make use of reasoning mechanisms so that they can take decisions and provide a better health care support to their users. This paper discusses the specification of a deductive health monitoring system, where its components are represented by assistant agents running in mobile devices and using a low cost wireless communication protocol (SMS) to exchange knowledge with a central root. Requirements for communication protocol and agent reasoning, based on a production system, are shown in details together with some practical experiments.
Abstract. Due to the need to improve access to knowledge and the establishment of means for sharing and organizing data in the health area, this research proposes an architecture based on the paradigm of Knowledge-as-a-Service (KaaS). This can be used in the medical field and can offer centralized access to ontologies and other means of knowledge representation. In this paper, a detailed description of each part of the architecture and its implementation was made, highlighting its main features and interfaces. In addition, a communication protocol was specified and used between the knowledge consumer and the knowledge service provider. Thus, the development of this research contributed to the creation of a new architecture, called H-KaaS, which established itself as a platform capable of managing multiple data sources and knowledge models, centralizing access through an easily adaptable API.
No contexto de Cidades Inteligentes, uma das preocupações é com relação a Mobilidade Urbana, que consiste em identificar alternativas para a diminuição do tráfego de veículos individuais, melhor ocupação do espaço urbano, entre outros aspectos. Uma alternativa é a adoção de trens elétricos. No entanto, surge um problema com relação ao consumo energético. Dessa forma, esse trabalho tem como objetivo propor um framework baseado em Algoritmos Genéticos (AGs), denominado SmartSubway, para auxiliar especialistas na inserção de informações do domínio no problema da eficiência energética em trens elétricos a fim de identificar perfis de condução energeticamente eficientes. Como prova de conceito foi implementado um sistema inspirado nos AGs. Para validar o sistema foram inseridas as informações do domínio de um cenário real, onde foi possível realizar seis experimentos e identificar os que obtiveram melhores resultados.
O presente artigo descreve uma extensão do middleware Ginga, desenvolvida no contexto da plataforma Knowledge TV (KTV), que propõe uma camada semântica ao ambiente da TV Digital. É descrito o Semantic Integration, API da plataforma Knowledge TV, integrada ao middleware, responsável pela comunicação entre o servidor KTV e o middleware Ginga. Esta é responsável pela coleta de metadados da programação e o envio via canal de retorno para o servidor KTV.
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