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.

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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.

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The use of mobile healthcare systems is an option to provide health-monitoring services without restrictions of time and location. Rather than just storing and transmitting health signals, the current stage of the mobile technology enables the development of intelligent modules, so that mobile devices can carry out an initial analysis of the collected data, taking important decisions and acting as a personal health assistant. This work summarises the current state of the art in healthcare monitoring and proposes an extension of this technology via the use of a rule-based module for deduction. The principal advantage of this approach is the easy way to codify the knowledge of domain experts via logic sentences. As example, we discuss a cardiovascular monitoring system that was developed to run in mobile phones

For developing applications and services in the Knowledge TV platform there is a need in structuring, organising and standardising data according to consolidating academic and industry models, however inserted in the context of interactive Digital TV (iDTV). This work presents research carried on the implementation of the Conceptual Framework MDM-KTV, which means Multi Data Models KTV, considering operational, analytical, semantic and Linked Data based data, in the context of the Knowledge TV project.