Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.

NovaIntell - projecto de text-Mining para a língua portuguesa numa empresa de Gestão de Informação e Conhecimento

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

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

TURTLE: Systems and technologies for deep ocean long term presence

This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

SEROPREVALENCE OF T. cruzi INFECTION IN BLOOD DONORS AND CHAGAS CARDIOMYOPATHY IN PATIENTS FROM THE COAL MINING REGION OF COAHUILA, MEXICO

Context and Objective: Chagas disease is considered a worldwide emerging disease; it is endemic in Mexico and the state of Coahuila and is considered of little relevance. The objective of this study was to determine the seroprevalence of T. cruzi infection in blood donors and Chagas cardiomyopathy in patients from the coal mining region of Coahuila, Mexico.Design and Setting: Epidemiological, exploratory and prospective study in a general hospital during the period January to June 2011.Methods: We performed laboratory tests ELISA and indirect hemagglutination in three groups of individuals: 1) asymptomatic voluntary blood donors, 2) patients hospitalized in the cardiology department and 3) patients with dilated cardiomyopathy.Results: There were three levels of seroprevalence: 0.31% in asymptomatic individuals, 1.25% in cardiac patients and in patients with dilated cardiomyopathy in 21.14%.Conclusions: In spite of having detected autochthonous cases of Chagas disease, its importance to local public health remains to be established as well as the details of the dynamics of transmission so that the study is still in progress.

Building clusters for CRM strategies by mining airlines customer data

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

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

ArcelorMittal: Metals & Mining: Equity Investment Report

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

ArcelorMittal: Metals & Mining – Europe: Equity Investment Report

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Environmental remediation project of Cunha Baixa mining area cost benefit analysis

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Automatic information retrieval through text-mining

The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.

Effects of ocean acidification on the swimming ability, development and biochemical responses of sand smelt larvae

Ocean acidification, recognized as a major threat to marine ecosystems, has developed into one of the fastest growing fields of research in marine sciences. Several studies on fish larval stages point to abnormal behaviours, malformations and increased mortality rates as a result of exposure to increased levels of CO2. However, other studies fail to recognize any consequence, suggesting species-specific sensitivity to increased levels of CO2, highlighting the need of further research. In this study we investigated the effects of exposure to elevated pCO2 on behaviour, development, oxidative stress and energy metabolism of sand smelt larvae, Atherina presbyter. Larvae were caught at Arrábida Marine Park (Portugal) and exposed to different pCO2 levels (control: ~600μatm, pH=8.03; medium: ~1000μatm, pH=7.85; high: ~1800μatm, pH=7.64) up to 15days, after which critical swimming speed (Ucrit), morphometric traits and biochemical biomarkers were determined. Measured biomarkers were related with: 1) oxidative stress - superoxide dismutase and catalase enzyme activities, levels of lipid peroxidation and DNA damage, and levels of superoxide anion production; 2) energy metabolism - total carbohydrate levels, electron transport system activity, lactate dehydrogenase and isocitrate dehydrogenase enzyme activities. Swimming speed was not affected by treatment, but exposure to increasing levels of pCO2 leads to higher energetic costs and morphometric changes, with larger larvae in high pCO2 treatment and smaller larvae in medium pCO2 treatment. The efficient antioxidant response capacity and increase in energetic metabolism only registered at the medium pCO2 treatment may indicate that at higher pCO2 levels the capacity of larvae to restore their internal balance can be impaired. Our findings illustrate the need of using multiple approaches to explore the consequences of future pCO2 levels on organisms.