Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Clustering of variables with a three-way approach for health sciences

TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.

Extracção de regras de associação com itens raros e frequentes

Ao longo dos últimos anos, as regras de associação têm assumido um papel relevante na extracção de informação e de conhecimento em base de dados e vêm com isso auxiliar o processo de tomada de decisão. A maioria dos trabalhos de investigação desenvolvidos sobre regras de associação têm por base o modelo de suporte e confiança. Este modelo permite obter regras de associação que envolvem particularmente conjuntos de itens frequentes. Contudo, nos últimos anos, tem-se explorado conjuntos de itens que surgem com menor frequência, designados de regras de associação raras ou infrequentes. Muitas das regras com base nestes itens têm particular interesse para o utilizador. Actualmente a investigação sobre regras de associação procuram incidir na geração do maior número possível de regras com interesse aglomerando itens raros e frequentes. Assim, este estudo foca, inicialmente, uma pesquisa sobre os principais algoritmos de data mining que abordam as regras de associação. A finalidade deste trabalho é examinar as técnicas e algoritmos de extracção de regras de associação já existentes, verificar as principais vantagens e desvantagens dos algoritmos na extracção de regras de associação e, por fim, desenvolver um algoritmo cujo objectivo é gerar regras de associação que envolvem itens raros e frequentes.

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Mining social individuals movements and friends based on mobile devices

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Modelação de um data warehouse para a Direcção Geral do Tesouro e Finanças e implementação de um data mart para o processo de gestão patrimonial.

Dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Estatística e Gestão da Informação

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

Diagnóstico cardíaco a partir de dados acústicos e clínicos

Este documento foi redigido no âmbito da dissertação do Mestrado em Engenharia Informática na área de Arquiteturas, Sistemas e Redes, do Departamento de Engenharia Informática, do ISEP, cujo tema é diagnóstico cardíaco a partir de dados acústicos e clínicos. O objetivo deste trabalho é produzir um método que permita diagnosticar automaticamente patologias cardíacas utilizando técnicas de classificação de data mining. Foram utilizados dois tipos de dados: sons cardíacos gravados em ambiente hospitalar e dados clínicos. Numa primeira fase, exploraram-se os sons cardíacos usando uma abordagem baseada em motifs. Numa segunda fase, utilizamos os dados clínicos anotados dos pacientes. Numa terceira fase, avaliamos a combinação das duas abordagens. Na avaliação experimental os modelos baseados em motifs obtiveram melhores resultados do que os construídos a partir dos dados clínicos. A combinação das abordagens mostrou poder ser vantajosa em situações pontuais.

Text Mining: análise de sentimentos na classificação de notícias

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e Computadores

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Modelos de negócios para a internet das coisas baseados em big data : Muzzley, um estudo de caso

A Internet das Coisas tal como o Big Data e a análise dos dados são dos temas mais discutidos ao querermos observar ou prever as tendências do mercado para as próximas décadas, como o volume económico, financeiro e social, pelo que será relevante perceber a importância destes temas na atualidade. Nesta dissertação será descrita a origem da Internet das Coisas, a sua definição (por vezes confundida com o termo Machine to Machine, redes interligadas de máquinas controladas e monitorizadas remotamente e que possibilitam a troca de dados (Bahga e Madisetti 2014)), o seu ecossistema que envolve a tecnologia, software, dispositivos, aplicações, a infra-estrutura envolvente, e ainda os aspetos relacionados com a segurança, privacidade e modelos de negócios da Internet das Coisas. Pretende-se igualmente explicar cada um dos “Vs” associados ao Big Data: Velocidade, Volume, Variedade e Veracidade, a importância da Business Inteligence e do Data Mining, destacando-se algumas técnicas utilizadas de modo a transformar o volume dos dados em conhecimento para as empresas. Um dos objetivos deste trabalho é a análise das áreas de IoT, modelos de negócio e as implicações do Big Data e da análise de dados como elementos chave para a dinamização do negócio de uma empresa nesta área. O mercado da Internet of Things tem vindo a ganhar dimensão, fruto da Internet e da tecnologia. Devido à importância destes dois recursos e á falta de estudos em Portugal neste campo, com esta dissertação, sustentada na metodologia do “Estudo do Caso”, pretende-se dar a conhecer a experiência portuguesa no mercado da Internet das Coisas. Visa-se assim perceber quais os mecanismos utilizados para trabalhar os dados, a metodologia, sua importância, que consequências trazem para o modelo de negócio e quais as decisões tomadas com base nesses mesmos dados. Este estudo tem ainda como objetivo incentivar empresas portuguesas que estejam neste mercado ou que nele pretendam aceder, a adoptarem estratégias, mecanismos e ferramentas concretas no que diz respeito ao Big Data e análise dos dados.

Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

A bayesian approach for NDT data fusion: The Saint Torcato Church case study

This paper presents a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions. The aim of the methodology is to reduce the uncertainties of the parameter estimation. The Young's modulus of granite stones was chosen as an example for the present paper. The methodology considers several levels of uncertainty since the parameters of interest are considered random variables with random moments. A new concept of Trust Factor was introduced to affect the uncertainty related to each test results, translated by their standard deviation, depending on the higher or lower reliability of each test to predict a certain parameter.