Falls in the elderly and disabled people represent a major health problem in terms of primary care costs facing the public and private systems. This paper presents a multi-agent system capable of detecting falls through sensors in a mobile device and act accordingly at runtime. The new system incorporates a fall detection algorithm based on machine learning and data classification using decision trees. The base of the system are three types of interrelated agents that coordinate to know the position of a user from data obtained through a mobile terminal, and GPS position, which in case of fall may be sent via SMS or by an automatic call. The proposed system is self-adaptive, since as new fall date is incorporated, the decision mechanisms are automatically updated and personalized taking into account the user profile.
This paper presents a multiagent system developed to predict the behaviour of the Atlantic Ocean in relation to the sinks/sources of CO2. The heart of the multiagent system is an intelligent agent capable of automatically making predictions about the flux of CO2 in the North Atlantic ocean. The multiagent system has been tested in simulation conditions and this work presents the preliminary obtained results.
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