Group decision making and quality-of-information in e-Health systems

Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One`s knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSS), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision making process. In this way it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services

Using computer peripheral devices to measure attentiveness

Attention is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner’s attention affects learning results and can define the success or failure of a student. The negative effects are especially significant when carrying out long or demanding tasks, as often happens in an assessment. This paper presents a monitoring system using computer peripheral devices. Two classes were monitored, a regular one and an assessment one. Results show that it is possible to measure attentiveness in a non-intrusive way.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A different approach in an AAL ecosystem: a mobile assistant for the caregiver

Currently the Ambient Assisted Living and the Ambient Intelligence areas are very prolific. There is a demand of security and comfort that should be ensured at people’s homes. The AAL4ALL (ambient assisted living for all) pro-ject aims to develop a unified ecosystem and a certification process, allowing the development of fully compatible devices and services. The UserAccess emerges from the AAL4ALL project, being a demonstration of its validity. The UserAc-cess architecture, implementation, interfaces and test scenario are presented, along with the sensor platform specially developed for the AAL4ALL project.Project "AAL4ALL", co-financed by the European Community Fund FEDER, through COMPETE - Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013 and the project PEst-OE/EEI/UI0752/2014. Project CAMCoF - Context-aware Multimodal Communication Framework funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980

Self-organizing maps versus growing neural Gas in detecting anomalies in data centers

Reliability is one of the key performance factors in data centres. The out-of-scale energy costs of these facilities lead data centre operators to increase the ambient temperature of the data room to decrease cooling costs. However, increasing ambient temperature reduces the safety margins and can result in a higher number of anomalous events. Anomalies in the data centre need to be detected as soon as possible to optimize cooling efficiency and mitigate the harmful effects over servers. This article proposes the usage of clustering-based outlier detection techniques coupled with a trust and reputation system engine to detect anomalies in data centres. We show how self-organizing maps or growing neural gas can be applied to detect cooling and workload anomalies, respectively, in a real data centre scenario with very good detection and isolation rates, in a way that is robust to the malfunction of the sensors that gather server and environmental information

Treating colon cancer survivability prediction as a classification problem

This work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the sixfeature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/85291/ 2012.info:eu-repo/semantics/publishedVersio

Treating Colon Cancer Survivability Prediction as a Classification Problem

This work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the six-feature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance

A caregiver support platform within the scope of an ambient assisted living ecosystem

The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.Project “AAL4ALL”, co-financed by the European Community Fund FEDER, through COMPETE—Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013. Project CAMCoF—Context-Aware Multimodal Communication Framework funded by ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT—Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980. This work is part-funded by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/201

Soft computing models to analyze atmospheric pollution issues

Multidisciplinary research into statistical and soft computing models is detailed that analyses data on emissions of atmospheric pollution in urban areas. The research analyses the impact on atmospheric pollution of an extended bank holiday weekend in Spain. Levels of atmospheric pollution are classified in relation to the days of the week, seeking to differentiate between working days and non-working days by taking account of such aspects as industrial activity and traffic levels. The case study is based on data collected by a station at the city of Burgos, which forms part of the pollution measurement station network within the Spanish Autonomous Region of Castile-Leon

A machine learning approach to environmental sustainability

Dissertação de mestrado em Systems EngineeringEnvironmental sustainability is one of the biggest concerns nowadays. With environmental increasingly latent negative impacts, it is substantiated that future generations may be compromised. Thus, this research addresses this topic, in particular, the air quality and atmospheric pollution, as well as water issues regarding a wastewater treatment plant. This study comes from a combination of Machine Learning supervised models to predict multiple parameters regarding environmental sustainability. Through the application of regression and classification models, the study target involves the air and the water quality in Guimarães city. Therefore, the key research goals are to predict attributes such as the Ultraviolet index, Carbon Monoxide air concentration, and water pH. Using Decision Trees, Random forest, Multilayer Perceptron, and Long Short-Term Memory, these parameters were forecasted. In this way, this study describes these models’ implementation and optimization processes, as well as the results generated. Predicting parameters of this nature will allow the anticipation of problematic situations, enabling preventive actions. Further, it grants the optimization and reallocation of resources, promoting the best for the population and the common good. After the entire implementation process, several conclusions arose from this research. First, from the Ultraviolet index levels (defined by the World Health Organization) prediction, was achieved a maximum accuracy of approximately 93%. Moreover, regarding this parameter prediction using regression models, the best result showed a Mean Absolute Error of 0.36. Besides, this index was further predicted based on a time series, resulting in a Mean Absolute Error of about 0.15. Additionally, also using a time series approach, the Carbon Monoxide air concentration was forecasted, achieving a Mean Absolute Error of 1.345 × 10−7. Finally, considering the water pH problem was reached, as the lowest Mean Absolute Error, a value equal to 0.11.A sustentabilidade ambiental é uma das maiores preocupações da atualidade. Com impactos negativos ambientais cada vez mais latentes, está provado que as gerações futuras podem estar comprometidas. Assim, esta pesquisa vem abordar este tópico, em particular, a qualidade do ar e a poluição atmosférica, bem como as questões hídricas no contexto de uma estação de tratamento de águas residuais. Este estudo advém de uma combinação de modelos de aprendizagem supervisionada com o objetivo de prever vários parâmetros referentes à sustentabilidade ambiental. Através da aplicação de modelos de regressão e classificação, o alvo da investigação envolve a qualidade do ar e da água na cidade de Guimarães. Por conseguinte, os principais objetivos da pesquisa passam por prever atributos como o índice de radiação ultravioleta, a concentração de monóxido de carbono no ar e o pH da água. Usando Árvores de Decisão, Random Forest, Perceptron Multicamadas e Long Short-Term Memory, esses parâmetros foram alvo de previsão. Deste modo, este estudo descreve os processos de implementação e otimização desses modelos, bem como os resultados gerados. A previsão de parâmetros desta natureza permitirá a antecipação de situações problemáticas, possibilitando ações preventivas. Ademais, permite a otimização e realocação de recursos, promovendo o melhor para a população e o bem comum. Após todo o processo de implementação, várias conclusões surgiram desta pesquisa. Em primeiro lugar, da previsão dos níveis do índice ultravioleta (definidos pela Organização Mundial da Saúde), foi alcançada uma precisão máxima de, aproximadamente, 93 %. Além disso, em relação à previsão deste parâmetro por meio de modelos de regressão, o melhor resultado apresentou um Erro Médio Absoluto de 0,36. Além do mais, esse índice foi alvo de previsão com base em uma série temporal, resultando em um Erro Médio Absoluto de cerca de 0,15. Ainda, também utilizando uma abordagem de série temporal, a concentração de monóxido de carbono no ar foi prevista, atingindo um Erro Médio Absoluto de 1,345 ×10−7. Por fim, considerando o problema do pH da água foi atingido, como o menor Erro Médio Absoluto, um valor igual a 0,11.This work was partially supported by National Funds through the Portuguese funding agency, FCT - Foundation for Science and Technology, within the project DSAIPA/AI/0099/201