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

Software for pattern recognition of the larvae of Aedes aegypti and Aedes albopictus

Software for pattern recognition of the larvae of mosquitoes Aedes aegypti and Aedes albopictus, biological vectors of dengue and yellow fever, has been developed. Rapid field identification of larva using a digital camera linked to a laptop computer equipped with this software may greatly help prevention campaigns.

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.

Epidemiological pattern of leprosy in an endemic area of North-East Brazil, 1996-2005: the supporting role of a Nongovernmental Organization

In an endemic area of North-East Brazil (the town of Picos, State of Piauí), a nongovernmental organization (NGO) supported the activity against leprosy in connection with governmental health organizations and local agents. The indicators of leprosy elimination were compared over time (within Picos) and across space (Picos versus Piauí). The case detection rate, above 8 per 10,000 people in the last two years of observation, increased over time in Picos (p=0.010). This finding could be due to active detection activities rather than expanding endemicity, as suggested by the reduction in leprosy in children (p=0.053) and the decrease in the proportion of new cases with grade 2 disability (p<0.001). These indicators showed a more favorable time trend in the city than in the State, suggesting that NGO activity was supportive in the battle towards leprosy control.

Semantic relations extraction from unstructured information for domain ontologies enrichment

Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.

Breaking the Pattern and Creating New Paths – Feminist Mizrahi Women Artists in Israel

This article addresses the work of Mizrahi women artists, i.e., Israeli-Jewish women of Asian or African ethnic origin, using the artist Vered Nissim as a case study. Nissim seeks to affirm the politics of identity and recognition, as well as feminism in order to create a paradigm shift with regards to the local regime of cultural representations in the Israeli art scene. Endeavouring to find ways of undermining the rigid imbalances between different social groups, she calls for a comprehensive reform of the status quo through artistic activism. Nissim employs a style, content, and medium that disrupts the accepted social order, using humour and irony as unique weapons with which she takes liberties with conventional moral, social, and economic values. Placing issues of race, class and gender at the centre of her work, she seeks to undermine and problematize essentialist attitudes, highlighting the political intersections of different identity categories as the critical analysis of intersectionality unfolds.

Text mining e twitter : o poder das redes sociais num mercado competitivo

Actualmente, com a massificação da utilização das redes sociais, as empresas passam a sua mensagem nos seus canais de comunicação, mas os consumidores dão a sua opinião sobre ela. Argumentam, opinam, criticam (Nardi, Schiano, Gumbrecht, & Swartz, 2004). Positiva ou negativamente. Neste contexto o Text Mining surge como uma abordagem interessante para a resposta à necessidade de obter conhecimento a partir dos dados existentes. Neste trabalho utilizámos um algoritmo de Clustering hierárquico com o objectivo de descobrir temas distintos num conjunto de tweets obtidos ao longo de um determinado período de tempo para as empresas Burger King e McDonald’s. Com o intuito de compreender o sentimento associado a estes temas foi feita uma análise de sentimentos a cada tema encontrado, utilizando um algoritmo Bag-of-Words. Concluiu-se que o algoritmo de Clustering foi capaz de encontrar temas através do tweets obtidos, essencialmente ligados a produtos e serviços comercializados pelas empresas. O algoritmo de Sentiment Analysis atribuiu um sentimento a esses temas, permitindo compreender de entre os produtos/serviços identificados quais os que obtiveram uma polaridade positiva ou negativa, e deste modo sinalizar potencias situações problemáticas na estratégia das empresas, e situações positivas passíveis de identificação de decisões operacionais bem-sucedidas.

Saúde em Portugal : estudo das urgências hospitalares através do Data Mining

Qualquer assunto relacionado com a saúde é sempre um tema sensível, pela importância que tem junto da população, já que interage diretamente com o bem-estar das pessoas e, essencialmente, com a sensação de segurança que as estas pretendem ter na prestação dos cuidados básicos de saúde. Dados estatísticos mostram que a população está cada vez mais envelhecida, reforçando a importância da existência de bons centros hospitalares e de um bom Sistema Nacional de Saúde (SNS) (Plano Nacional de Saúde, 2010). Em Portugal, caso os pacientes necessitem de cuidados mais urgentes, podem recorrer ao Serviço de Urgências disponibilizado para toda a população através do SNS. No entanto, a gestão e planeamento deste serviço é complexa, dado este serviço ser frequentemente utilizado por pacientes que não necessitam de cuidados urgentes, levando a que os hospitais deixem de conseguir dar a resposta esperada, implicando a prestação por vezes um serviço de menor qualidade. Neste sentido, analisaram-se dados de um hospital do norte do país com o intuito de perceber o ponto de situação das urgências, de forma a encontrar padrões relevantes através da análise de clusters e de regras de associação. Começando pela análise de clusters, utilizaram-se apenas as variáveis que foram consideradas importantes para o problema, resultando da análise final 3 clusters. O primeiro cluster é constituído por elementos do sexo masculino de todas as idades, o segundo cluster por elementos do sexo masculino mais jovens e por elementos do sexo feminino até aos 60 anos e o terceiro cluster apenas por elementos do sexo feminino a partir dos 40 anos. No final verificaram-se muitas semelhanças entre os clusters 1 e 3, pois ambos continham os pacientes mais idosos, havendo um padrão comum no seu comportamento. No ano 2012 não houve registo de nenhuma epidemia, não havendo por isso nenhuma doença que se destacasse comparativamente às restantes. Concluiu-se também que na maior parte dos casos houve a necessidade de uma intervenção urgente (pulseira de cor Amarela), no entanto a maioria dos pacientes observados conseguiu regressar às suas habitações após as consultas nas Urgências Hospitalares, sem intervenções médicas adicionais. Relativamente às regras de associação, houve a necessidade de transformar e eliminar algumas variáveis que enviesassem o estudo. Após o processo da criação das regras de associação, percebeu-se que as regras eram muito similares entre si, apresentando uma maior confiança nas variáveis que apareceram em maior número (“Pacientes com pulseira de cor Amarela”, “distrito do Porto” ou “Alta Médica para a Residência”).

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Task clustering on ETL systems – A pattern-oriented approach

Usually, data warehousing populating processes are data-oriented workflows composed by dozens of granular tasks that are responsible for the integration of data coming from different data sources. Specific subset of these tasks can be grouped on a collection together with their relationships in order to form higher- level constructs. Increasing task granularity allows for the generalization of processes, simplifying their views and providing methods to carry out expertise to new applications. Well-proven practices can be used to describe general solutions that use basic skeletons configured and instantiated according to a set of specific integration requirements. Patterns can be applied to ETL processes aiming to simplify not only a possible conceptual representation but also to reduce the gap that often exists between two design perspectives. In this paper, we demonstrate the feasibility and effectiveness of an ETL pattern-based approach using task clustering, analyzing a real world ETL scenario through the definitions of two commonly used clusters of tasks: a data lookup cluster and a data conciliation and integration cluster.

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273