Abstract
Simulation is a very useful tool to gather new information about an implemented model, because it can run artificial environments instead of putting in risk some entities that are influenced in the real process. The simulation of physical, chemical and biological processes in coastal ecosystems is used as a way to understand the system internal dynamics and to predict its evolution over time, in order to promote behaviors environmentally friendly and to induce effective and efficient management of the ecosystem as a whole. However, there are several ways of translating and interpreting the data provided by the simulation such as applying appropriate data mining models. This paper describes an approach that uses a Decision Tree model to produce intuitive information about the influence of several environmental variables on the growth conditions of bivalve species within an aquaculture exploration in a coastal ecosystem. This information is captured by relating the values of simulated variables, like water temperature or organic matter, with the length of the bivalve's shell, extrapolating information about the organic or physical conditions that increase or decrease the growth of the bivalve species, and guiding the stakeholders to locations for the best practice of the seeding process.

Abstract
Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using low-resolution images like the ones obtained with the camera in the current work.

Abstract
Affective computing has increased its significance both in terms of academic and industry attention and investment. Alongside, immersive digital environments have settled as a reliable domain, with progressively inexpensive hardware solutions. Having this in mind, the authors envisioned the automatic real-time user emotion extraction through biometric readings in an immersive digital environment. In the running example, the environment consisted in an aeronautical simulation, and biometric readings were based mainly on galvanic skin response, respiration rate and amplitude, and phalanx temperature. The assessed emotional states were also used to modify some simulation context variables, such as flight path, weather conditions and maneuver smoothness level. The results were consistent with the emotional states as stated by the users, achieving a success rate of 77%, considering single emotions and 86% considering a quadrant-based analysis. © Springer Science & Business Media BV 2009.

Abstract

Abstract

Abstract
This work presents a methodology for modelling and simulating aquatic ecological systems and an associated methodology and software for creating Decision Support Systems (DSS) for this kind of environment. Both are based on object oriented programming (OOP) and, in the case of the DSS, on Autonomous Intelligent Agents. The modelling software (EcoDynamo) includes several object dynamic link libraries to simulate different physical and biogeochemical ecosystem processes. These libraries were designed to be possible their linkage with different model shells, for the sake of portability and usability. A high level communication language (ECOLANG) was developed to allow the communication between EcoDynamo and the agents. The DSS uses one methodology of discrete multi-criteria, where given a users preference structure (elicited with the Analytic Hierarchy Process methodology - AHP), a priority ranking of the preprocessed scenarios is achieved. To validate the approach, EcoDynamo was used with the DSS to simulate different management scenarios in a coastal lagoon at the South of Portugal - Ria Formosa. The experiments performed showed that these tools may be widely used by people involved in the management of coastal areas to integrate environmental, economic and social issues in the decision process, without an in-depth knowledge of modelling methodologies.