Ibermática (Spain)

Using implicit responses to determine consumers’ response to different stimuli is becoming a popular approach, but research is still needed to understand the outputs of the different technologies used to collect data. During the present research, electroencephalography (EEG) responses and self-reported liking and emotions were collected on different stimuli (odor, taste, flavor samples) to better understand sweetness perception. Artificial intelligence analytics were used to classify the implicit responses, identifying decision trees to discriminate the stimuli by activated sensory system (odor/taste/flavor) and by nature of the stimuli (‘sweet’ vs. ‘non-sweet’ odors; ‘sweet-taste’, ‘sweet-flavor’, and ‘non-sweet flavor’; and ‘sweet stimuli’ vs. ‘non-sweet stimuli’). Significant differences were found among self-reported-liking of the stimuli and the emotions elicited by the stimuli, but no clear relationship was identified between explicit and implicit data. The present research sums interesting data for the EEG-linked research as well as for EEG data analysis, although much is still unknown about how to properly exploit implicit measurement technologies and their data.

Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.

<b>Introduction:</b> Thermography has been implemented in different fields of medicine, but its use in sleep medicine has been limited. The objective of this study was to validate the use of a thermographic camera, using an artificial intelligence algorithm, to differentiate wakefulness and sleep in comparison with the conventional polysomnographic (PSG) results. <b>Methods:</b> Staging of wakefulness and sleep was compared between the EEG of PSG recordings and the facial thermographic images obtained by means of a thermographic camera, treated with an artificial intelligence system. The periods of sleep and wakefulness were estimated second by second. Videos of a duration greater than 2.7 hours were considered as valid (61 videos). <b>Results:</b> Characteristics of subjet analized were: age 48±10 years, 73 % men; body mass index 27.0±3.8 kg/m2 and Epworth sleepness scale of 9.0±4.0 points). Of the 943 seconds detected as sleep by thermography, 940 seconds corresponded to sleep phases in PSG (99.7% correct), and of the 867 seconds classified as wakefulness, 766 corresponded to wakefulness in the PSG (88.4% of these). Table I presents the validity results. <b>Conclusions:</b> Treatment of thermographic images, through an artificial intelligence algorithm, is a valid and non-invasive system for determining sleep and wakefulness. Development of these systems could facilitate the implementation of “no contact” diagnosis methods for sleep analysis.

INTRODUCTION: We present early results of an ongoing investigation into the use of sound for real-time monitoring of anomalous behaviour in digital and digital/physical systems. OBJECTIVES: We aim to define design guidelines to both support authors in the process of creating sonifications that a

This issue’s column reports on presentations at the 11th International Symposium on Empirical Software Engineering and Measurement, 13th International Conference on Predictive Models and Data Analytics in Software Engineering, and 21st International Systems and Software Product Line Conference.

In this paper we present the technical approach followed in the InfoCitizen project. Its novel approximation to Public Administration necessities lies in the combination of Web Services as providers and Intelligent Agents as consumers of public services. The distributed nature of a Web Service network requires a similarly distributed architecture, that in turn requires an assurance of availability and transparency achieved with the use of Intelligent Agents.

In this paper we present the technical approach followed in the InfoCitizen project. Its novel approximation to Public Administration necessities lies in the combination of Web Services as providers and Intelligent Agents as consumers of public services. The distributed nature of a Web Service network requires a similarly distributed architecture, that in turn requires an assurance of availability and transparency achieved with the use of Intelligent Agents.KeywordsLegacy SystemPersonal AgentIntelligent AgentService NodeSimple Object Access ProtocolThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.