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BackgroundThis paper presents a system for classification of asthma and chronic obstructive pulmonary disease (COPD) based on fuzzy rules and the trained neural network.MethodsFuzzy rules and neural network parameters are defined according to Global Initiative for Asthma (GINA) and Global Initiative for chronic Obstructive Lung Disease (GOLD) guidelines. For neural network training more than one thousand medical reports obtained from database of the company CareFusion were used. Afterwards the system was validated on 455 patients by physicians from the Clinical Centre University of Sarajevo.ResultsOut of 170 patients with asthma, 99.41% of patients were correctly classified. In addition, 99.19% of the 248 COPD patients were correctly classified. The system was 100% successful on 37 patients with normal lung function. Sensitivity of 99.28% and specificity of 100% in asthma and COPD classification were obtained.ConclusionOur neuro-fuzzy system for classification of asthma and COPD uses a combination of spirometry and Impulse Oscillometry System (IOS) test results, which in the very beginning enables more accurate classification.Additionally, using bronchodilatation and bronhoprovocation tests we get a complete patient's dynamic assessment, as opposed to the solution that provides a static assessment of the patient.

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In this paper, we present an algorithm for fully autonomous exploration and mapping of an unknown indoor robot environment. This algorithm is based on the active SLAM (simultaneous localization and mapping) approach. The mobile robot equipped with laser sensor builds a map of an environment, while keeping track of its current location. Autonomy is introduced to this system by automatically setting goal points so that either previously unknown space is mapped, or known landmarks are revisited in order to increase map accuracy. Final aim is to maximize both map coverage and accuracy. The proposed procedure is experimentally verified on Pioneer 3-DX mobile robot in real environment, using ROS framework for implementation.

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