This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize. Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.

In this study, three different algorithms are implemented to solve thecapacitated vehicle routing problem with and without time windows:ant colony optimization, a genetic algorithm and a genetic algorithmwith self-organizing map. For the capacitated vehicle routing problemthe Augerat et al’s benchmark problems were used and for the capaci-tated vehicle routing problem with time windows the Solomon’sbenchmark problems. All three algorithms were tuned over thirtyinstances per problem with the tuners SPOT and ParamILS. The tuningresults from all instances were combined to the final parameter valuesand tested on a larger set of instances. The test results were used tocompare the algorithms and tuners against each other. The ant colonyoptimization algorithm outperformed the other algorithms on bothproblems when considering all instances. The genetic algorithm withself-organizing map found more best known solutions than any otheralgorithm when using parameters, on the capacitated vehicle routingproblem. The algorithms performed well and several new best knownresults were discovered for the capacitated vehicle routing problem andnew best solutions found by heuristics were discovered for the 100customer Solomon problems. When comparing the tuners they bothworked well and no clear winner emerged.

Le but de cette thèse est de documenter la variabilité des teneurs en aérosols minéraux en Afrique de l’Ouest et de comprendre les mécanismes qui la contrôlent. L’analyse des concentrations mesurées au sol au Sahel montre que ce sont les transports d’aérosols sahariens qui sont responsables des maxima de concentration observés en saison sèche Marticorena et al. (2010). Ces évènements présentent une très forte variabilité à l’échelle intra-saisonnière et interannuelle mais une certaine persistance à l’échelle régionale. Quelles sont les conditions météorologiques qui expliquent cette variabilité ? La réponse à cette question ne peut être obtenue qu’au travers d’une analyse systématique et conjointe des conditions météorologiques régionales et des mesures d’aérosol désertiques sur le continent (concentration de surface, épaisseurs optiques en aérosols) et au large de l’Afrique de l’Ouest. Les mesures d’épaisseur optique en aérosols (AOT) permettent la surveillance globale du contenu atmosphérique en particules. Cependant la relation permettant de retrouver les concentrations massiques en (PM10) à partir des mesures d’épaisseur optique n’est pas toujours simple. Ainsi dans un premier temps l’analyse de la variabilité de la concentration en PM10 et des conditions météorologiques locales mesurées au sol, a permis de confirmer un cycle saisonnier régional présenté par Marticorena et al. (2010), caractérisé par un maximum de concentration en saison sèche et un minimum pendant la saison des pluies sur l’ensemble de trois stations. L’analyse du cycle diurne des concentrations a permis de voir qu’il est diffèrent selon les saisons. En saison sèche, le jet de basses couches (NLLJ en anglais) saharien semble moduler le cycle diurne des concentrations en particules mesurées au Sahel, tandis que l’évolution diurne des concentrations de poussière en saison des pluies apparaît être modulé par l’évolution des systèmes convectifs a l’échelle régionale.Dans un deuxième temps, une méthodologie basée sur une classification en types de temps a été proposée. Elle permet de mettre en évidence des situations météorologiques typiques pour lesquelles la relation AOT-PM10 est simplifiée, puis une modélisation de PM10 à partir de l’épaisseur optique. Pour cela deux approches ont été adoptées. D’abord une famille composée de six régimes de temps bruts a été définie décrivant la saisonnalité climatique. La mise en relation AOT-PM10 permet de voir que les régimes de temps caractéristiques du flux d’harmattan présentent une relation meilleure en terme de corrélation par rapport à la relation avant la séparation en type de temps. Ensuite six régimes de temps ayant un impact sur la variabilité interannuelle des aérosols ont été définis. Les régimes de temps obtenus sont caractérisés par une forte variabilité interannuelle et moins persistante que pour les régimes de temps bruts. Les régimes de temps sont caractérisés par des anomalies de circulation atmosphérique, soit cyclonique, soit anticyclonique, d’échelle synoptique et centrée sur le nord du domaine. L’intégration de l’ensemble de cette approche en régimes de temps permet d’obtenir des résultats satisfaisants dans la prévision des valeurs de PM10, ouvrant des perspectives en terme de système d’alerte précoce. / The aim of this thesis is to document the variability of the atmospheric dust content in West Africa and to understand the mechanisms that control it. The analysis of the concentrations measured ground in the Sahel shows that it is the transport of Saharan aerosols that are responsible for the maximum concentration observed in the dry season. These events have a very high variability at intra-seasonal and interannual scale with some persistence on a regional scale. What are the weather conditions that explain this variability? The answer to this question can be obtained only through a systematic analysis of regional weather and desert aerosol measurements on the continent (surface concentration, aerosol optical thickness) and of the West Africa. The aerosol optical thickness measurements (AOT) allow global monitoring of atmospheric particulate content. However the relationship allowing to find the concentrations (PM10) from the optical thickness measurement is not always simple. So at first the analysis of the variability of the PM10 concentration and local meteorological conditions measured on the ground, confirmed a regional seasonal cycle presented by Marticorena et al. (2010), characterized by a maximum concentration during the dry season and a minimum during the rainy season on all three stations. The analysis of the diurnal cycle concentrations allowed seeing that it differ depending on the season. In the dry season, the Saharian low level jet (NLLJ) appears to modulate the diurnal cycle of particulate concentrations in the Sahel, while the diurnal evolution of concentrations of the rainy season appears to be modulated by changes in convective systems has regionally. Secondly, a methodology based on a classification into weather types was proposed. It allows to highlight typical meteorological situations in which the AOT- PM10 relationship is simplified and modeling PM10 from the Aerosol Optical Thickness. For this, two approaches have been adopted. At first a family of six weather types has been defined describing the climate seasonality. The link between AOT and PM10 can allowed to see that the weather types characterise to harmattan flow have a better relationship in terms of correlation to the relationship before separating in weather type. Then six weather types affecting the interannual variability of aerosols were defined. The resulting weather patterns are characterized by high interannual variability and less persistent than for rough weather regimes. The weather patterns are characterized by atmospheric circulation anomalies, either cyclonic or anticyclonic at synoptic scale and centered on the northern area. The integration of all of this approach in weather types provides satisfactory results in forecasting PM10 values, opening perspectives in terms of early warning system.

Abstract A future vision of enabling technologies for the needs of energy conservation as well as energy efficiency based on the most important megatrends identified, namely climate change, urbanization, and digitalization. In the United States and in the European Union, about 40% of total energy consumption goes into energy use by buildings. Moreover, indoor climate quality is recognized as a distinct health hazard. On account of these two factors, energy efficiency and healthy housing are active topics in international research. The main aims of this thesis are to study which elements affect indoor climate quality, how energy consumption describes building energy efficiency and to analyse the measured data using intelligent computational methods. The data acquisition technology used in the studies relies heavily on smart metering technologies based on Building Automation Systems (BAS), big data and the Internet of Things (IoT). The data refining process presented and used is called Knowledge Discovery in Databases (KDD). It contains methods for data acquisition, pre-processing, data mining, visualisation and interpretation of results, and transformation into knowledge and new information for end users. In this thesis, four examples of data analysis and knowledge deployment concerning small houses and school buildings are presented. The results of the case studies show that the data mining methods used in building energy efficiency and indoor climate quality analysis have a great potential for processing a large amount of multivariate data effectively. An innovative use of computational methods provides a good basis for researching and developing new information services. In the KDD process, researchers should co-operate with end users, such as building management and maintenance personnel as well as residents, to achieve better analysis results, easier interpretation and correct conclusions for exploiting the knowledge. / Tiivistelmä Tulevaisuuden visio energiansäästön sekä energiatehokkuuden mahdollistavista teknologioista pohjautuu tärkeimpiin tunnistettuihin megatrendeihin, ilmastonmuutokseen, kaupungistumiseen ja digitalisoitumiseen. Yhdysvalloissa ja Euroopan unionissa käytetään noin 40 % kokonaisenergiankulutuksesta rakennusten käytön energiatarpeeseen. Myös rakennusten sisäilmaston on havaittu olevan ilmeinen terveysriski. Perustuen kahteen edellä mainittuun tekijään, energiatehokkuus ja asumisterveys ovat aktiivisia tutkimusaiheita kansainvälisessä tutkimuksessa. Tämän väitöskirjan päätavoitteena on ollut tutkia, mitkä elementit vaikuttavat sisäilmastoon ja rakennusten energiatehokkuuteen pääasiassa analysoimalla mittausdataa käyttäen älykkäitä laskennallisia menetelmiä. Tutkimuksissa käytetyt tiedonkeruuteknologiat perustuvat etäluentaan ja rakennusautomaatioon, big datan hyödyntämiseen ja esineiden internetiin (IoT). Väitöskirjassa esiteltävä tietämyksen muodostusprosessi (KDD) koostuu tiedonkeruusta,datan esikäsittelystä, tiedonlouhinnasta, visualisoinnista ja tutkimustulosten tulkinnasta sekä tietämyksen muodostamisesta ja oleellisen informaation esittämisestä loppukäyttäjille. Tässä väitöstutkimuksessa esitellään neljän data-analyysin ja niiden pohjalta muodostetun tietämyksen hyödyntämisen esimerkkiä, jotka liittyvät pientaloihin ja koulurakennuksiin. Esimerkkitapausten tulokset osoittavat, että käytetyillä tiedonlouhinnan menetelmillä sovellettuna rakennusten energiatehokkuus- ja sisäilmastoanalyyseihin on mahdollista jalostaa suuria monimuuttuja-aineistoja tehokkaasti. Laskennallisten menetelmien innovatiivinen käyttö antaa hyvät perusteet tutkia ja kehittää uusia informaatiopalveluja. Tutkijoiden tulee tehdä yhteistyötä loppukäyttäjinä toimivien kiinteistöhallinnan ja -ylläpidon henkilöstön sekä asukkaiden kanssa saavuttaakseen parempia analyysituloksia, helpompaa tulosten tulkintaa ja oikeita johtopäätöksiä tietämyksen hyödyntämiseksi.

Abstract The effect of alcohol intake on the pathophysiology of atherosclerotic cardiovascular disease is controversial, especially with respect to heavy alcohol intake. The pathobiology behind atherosclerosis is a complex and multiparametric phenomenon, therefore a self-organizing map (SOM), an unsupervised learning based artificial neural network technique, was applied in the present work. This study was carried out to investigate the effect of heavy alcohol intake on the pathophysiology of atherosclerosis, including several lipoproteins and adiponectin, an adipocyte-derived cytokine that may ameliorate atherosclerosis. Firstly, the effect of heavy alcohol intake on the capacity of HDL and its subclasses (HDL2 and HDL3) to mediate cholesterol efflux from macrophages was studied. Secondly, data of ultracentrifugally isolated lipoproteins were fed into SOM analysis to investigate whether this method can find diverse lipoprotein phenotypes from the heterogeneous lipoprotein data. Thirdly, the aforementioned method was applied to multivariate data of alcohol drinkers to study whether distinct metabolic profiles are associated to heavy alcohol consumption. The results revealed that HDL2, not HDL3, of heavy alcohol drinkers had an enhanced capacity to remove cholesterol from macrophages when compared with control persons. SOM analysis enhanced the ultracentrifugally based lipoprotein data and depicted several novel lipoprotein phenotypes. In addition, lipoprotein-based SOM analysis found two distinct metabolic profiles in heavy alcohol drinkers: an anti-atherogenic and a metabolic syndrome-like profile with opposite metabolic features, such as characteristics of lipoproteins, plasma concentration of adiponectin and prevalence of metabolic syndrome. These profiles also tended to differ in their CV risk. In conclusion, the enhanced cholesterol efflux capacity of HDL2 in heavy drinkers is an anti-atherogenic change linked to alcohol drinking. However, clinically it may be important to be aware that although heavy alcohol drinkers have a low LDL-C level, they differ in their other lipoprotein measures, forming distinct phenotypes with potentially different CV risks. Finally, SOM analysis of ultracentrifugally based lipoprotein data generates in silico classification of lipoprotein particles and thereby offers a new tool for lipoprotein research. / Tiivistelmä Alkoholinkäytön vaikutus ateroskleroottisen sydän- ja verisuonitaudin patofysiologiaan on kiistanalainen, etenkin runsaan alkoholinkäytön kohdalla. Koska patobiologia ateroskleroosin taustalla on monimutkainen ilmiö, tässä työssä sovellettiin menetelmänä itseorganisoituvaa karttaa, joka on ohjaamattomaan oppimiseen perustuva neuroverkkomalli. Tutkimuksen tavoitteena oli selvittää runsaan alkoholinkäytön vaikutusta ateroskleroosin patofysiologisiin merkkiaineisiin, mukaan lukien useita lipoproteiineja sekä adiponektiini, rasvasoluperäinen sytokiini, joka voi lievittää ateroskleroosia. Ensimmäisessä osatyössä tutkittiin runsaan alkoholinkäytön vaikutusta HDL:n ja sen alafraktioiden (HDL2 ja HDL3) kykyyn poistaa kolesterolia makrofageista. Toisessa osatyössä ultrasentrifuugaukseen perustuva lipoproteiiniaineisto syötettiin itseorganisoituvaan karttaan. Työssä selvitettiin löytäisikö menetelmä erilaisia lipoproteiinifenotyyppejä heterogeenisestä aineistosta. Kolmannessa osatyössä em. menetelmää sovellettiin monimuuttuja-aineistoon, joka koostui runsaasti alkoholia käyttävistä ja verrokeista. Tutkittiin, liittyykö runsaaseen alkoholinkäyttöön erilaisia metabolisia profiileja. Tulokset osoittivat, että suurkuluttajien HDL2-hiukkasen kolesterolinpoistokyky makrofageista oli suurempi kuin verrokeilla. Itseorganisoituvaan karttaan perustuva lipoproteiinien luokittelumenetelmä löysi useita uusia lipoproteiinifenotyyppejä. Lisäksi, em. menetelmä löysi suurkuluttajilta kaksi erilaista metabolista profiilia: anti-aterogeeninen ja metabolisen syndrooman kaltainen. Näillä oli vastakkaiset metaboliset piirteet, kuten lipoproteiinien ominaisuudet, adiponektiinin pitoisuus plasmassa ja metabolisen syndrooman esiintyvyys. Profiileihin liittyi mahdollisesti myös erilainen sydän- ja verisuonitautiriski. Tutkimus osoittaa, että alkoholin suurkuluttajilla havaittu parempi HDL2:n kyky poistaa kolesterolia soluista on anti-aterogeeninen muutos, joka liittyy alkoholin käyttöön. Kliinisesti voi olla merkittävää, että vaikka alkoholin suurkuluttajilla oli pieni LDL-C pitoisuus, he jakaantuivat muiden lipoproteiiniperäisten muuttujien perusteella kahteen eri fenotyyppiryhmään, joihin liittyi erilainen sydäntautiriski. Lisäksi itseorganisoituva kartta loi ultrasentrifugoinnilla eristetyille lipoproteiineille in silico -luokittelun, joten se tarjoaa uuden työkalun lipoproteiinitutkimukseen.

Abstract The amount of information available has increased due to the development of our modern digital society. This has caused an information overflow, meaning that there is lot of data available but the meaningful information or knowledge is hidden inside the overwhelming data smog. Nevertheless, the large amount of data together with the increased capabilities of computers provides a great opportunity to learn the behaviour of different kinds of phenomena at a more detailed level. The quality of life, well-being and a healthy living environment, for example, are fields where new information services can assist the creation of proactive decisions to avoid environmental problems caused by industrial activity, traffic, or extraordinary weather conditions. The combination of data coming from different sources such as public registers, companies’ operational information systems, online sensors and process monitoring systems provides a fruitful basis for creating new valuable information for citizens, decision makers or other end users. The aim of this thesis is to present the concept of intelligent information services and a methodological background in order to add intelligence using computational methods for the enrichment of multidimensional data. Moreover, novel examples are presented where new significant information is created and then provided for end users. The data refining process used is called data mining and contains methods for data collection, pre-processing, modelling, visualizing and interpreting the results and sharing the new information thus created. Information systems are a base for the creation of information services, meaning that stakeholder groups have access only to information but they do not own the whole information system that contains measurement systems, data collecting, and a technological platform. Intelligence in information services comes from the use of computational intelligent methods in data processing, modelling and visualization. In this thesis the general concept of such services is presented and concretized using five cases that focus on environmental and industrial examples. The results of these case studies show that the combination of different data sources provides fertile ground for developing new information services. The data mining methods used such as clustering and predictive modelling together with effective pre-processing methods have great potential to handle the large amount of multivariate data in this environmental context also. A self-organizing map combined with k-means clustering is useful for creating more detailed information about personal energy use. Predictive modelling using a multilayer perceptron (MLP) is well suited for estimating the number of tourists visiting a leisure centre and to find the correspondence between pulp process characteristics and the chemicals used. These results have many indirect effects on reducing negative concerns regarding our surroundings and maintaining a healthy living environment. The innovative use of stored data is one of the main elements in the creation of future information services. Thus, more emphasis should be placed on the development of data integration and effective data processing methods. Furthermore, it is noted that final end users, such as citizens or decision makers, should be involved in the data refining process at the very first stage. In this way, the approach is truly customer-oriented and the results fulfil the concrete need of specific end users. / Tiivistelmä Informaation määrä on kasvanut merkittävästi tietoyhteiskunnan kehittymisen myötä. Käytössämme onkin huomattava määrä erimuotoista tietoa, josta voimme hyödyntää kuitenkin vain osan. Jatkuvasti mitattavan datan suuri määrä ja sijoittuminen hajalleen asettavat osaltaan haasteita tiedon hyödyntämiselle. Tietoyhteiskunnassa hyvinvointi ja terveellisen elinympäristön säilyminen koetaan aiempaa tärkeämmäksi. Toisaalta yritysten toiminnan tehostaminen ja kestävän kehityksen edistäminen vaativat jatkuvaa parantamista. Informaatioteknologian avulla moniulotteista mittaus- ja rekisteritietoa voidaan hyödyntää esimerkiksi ennakoivaan päätöksentekoon jolla voidaan edistää edellä mainittuja tavoitteita. Tässä työssä on esitetty ympäristöalan älykkäiden informaatiopalveluiden konsepti, jossa oleellista on loppukäyttäjien tarpeiden tunnistaminen ja ongelmien ratkaiseminen jalostetun informaation avulla. Älykkäiden informaatiopalvelujen taustalla on yhtenäinen tiedonlouhintaan perustuva tiedonjalostusprosessi, jossa raakatieto jalostetaan loppukäyttäjille soveltuvaan muotoon. Tiedonjalostusprosessi koostuu datan keräämisestä ja esikäsittelystä, mallintamisesta, tiedon visualisoinnista, tulosten tulkitsemisesta sekä oleellisen tiedon jakamisesta loppukäyttäjäryhmille. Datan käsittelyyn ja analysointiin on käytetty laskennallisesti älykkäitä menetelmiä, josta juontuu työn otsikko; älykkäät informaatiopalvelut. Väitöskirja pohjautuu viiteen artikkeliin, joissa osoitetaan tiedonjalostusprosessin toimivuus erilaisissa tapauksissa ja esitetään esimerkkejä kuhunkin prosessin vaiheeseen soveltuvista laskennallisista menetelmistä. Artikkeleissa on kuvattu matkailualueen kävijämäärien ennakointiin ja kotitalouksien sähköenergian kulutuksen pienentämiseen liittyvät informaatiopalvelut sekä analyysi selluprosessissa käytettävien kemikaalien määrän pienentämiseksi. Näistä saadut kokemukset ja tulokset on yleistetty älykkään informaatiopalvelun konseptiksi. Väitöskirjan toisena tavoitteena on rohkaista organisaatioita hyödyntämään tietovarantoja aiempaa tehokkaammin ja monipuolisemmin sekä rohkaista tarkastelemaan myös oman organisaation ulkopuolelta saatavien tietolähteiden käyttämistä. Toisaalta, uudenlaisten informaatiopalvelujen ja liiketoimintojen kehittämistä tukisi julkisilla varoilla kerättyjen, ja osin yritysten hallussa olevien, tietovarantojen julkaiseminen avoimiksi.

For scientific or socio-economic reasons it is often necessary or desirable that biological material be identified. Given that there are an estimated 10 million living organisms on Earth, the identification of biological material can be problematic. Consequently the services of taxonomist specialists are often required. However, if such expertise is not readily available it is necessary to attempt an identification using an alternative method. Some of these alternative methods are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This thesis explores the use of SOMs as a technique for the identification of indigenous trees of the Acacia species in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is its usefulness for visualizing the results of the analysis of numerical, multivariate botanical data sets. The SOM’s ability to investigate, discover and interpret relationships within these data sets is examined, and the technique’s ability to identify tree species successfully is tested. These data sets are also tested using the C5 and CN2 classification techniques. Results from both these techniques are compared with the results obtained by using a SOM commercial package. These results indicate that the application of the SOM to the problem of biological identification could provide the start of the long-awaited breakthrough in computerized identification that biologists have eagerly been seeking. / Dissertation (MSc)--University of Pretoria, 2011. / Computer Science / unrestricted

Les diatomées sont des algues microscopiques qui sont largement utilisées pour évaluer la qualité écologique des cours d'eau.Les méthodes utilisées se basent sur des modèles simplifiés de biologie des communautés, dans lesquels seules les réponses individuelles des espèces à l'environnement sont prises en compte.Le test de l'importance de processus complémentaires a montré un impact fort des dynamiques de colonisation des espèces, mais un impact négligeable des phénomènes de compétition ou de facilitation.Ces processus impliquent une structure des assemblages bien plus complexe que celle habituellement assumée par les méthodologies de bioindication.L'adaptation et la mise en oeuvre de méthodes de réseaux de neurones et de logique floue nous ont permis de redéfinir des éco-régions françaises et de décrire des relations générales entre les traits biologiques des espèces et l'environnement, tout en prenant mieux en compte cette complexité. / Diatoms are microscopic algae which are widely used to monitor the ecological quality of strems and rivers. The regular methodologies are based on simpllified community models. In these models, only the invidual species responses to environment are accounted for.Testing the importance of complementary processes showed a significant effect of colonization dynamics, but only a slight effect of biotic relationships. These processes led us to considerate a more complex assemblage structure than the one usually assumed by the biomonitoring methodologies.Therefore we implemented both neural networks models and fuzzy logic methodologies, in order to refine French ecoregions and to describe relationships between species traits and environment.

[pt] A maioria dos laboratórios não conta com um sistema informatizado para gestão dos procedimentos pertinentes a cada caso. A administração e controle das amostras é feito manualmente, através de diversas fichas que são preenchidas desde o colhimento do material biológico, no hospital, até a identificação final da bactéria no laboratório. Dessa forma, a organização das informações fica limitada, uma vez que, estando as informações escritas à mão e guardadas em livros, é quase impossível a extração de conhecimento útil que possa servir não só no apoio à decisão, como também, na formulação de simples estatísticas. Esta dissertação teve dois objetivos principais. O desenvolvimento de um sistema Web, intitulado BCIWeb (Bacterial Classification and Identification for Web), que fosse capaz de auxiliar na identificação bacteriológica e prover a tecnologia necessária para a administração e controle de amostras clínicas oriundas de hospitais. E a descoberta de conhecimento na base de dados do sistema, através da mineração de dados utilizando os métodos de Mapas Auto-Organizáveis (SOM: Self-Organizing Maps) e Redes Multilayer Perceptrons (MLP) para classificação e identificação de bactérias. A partir do desenvolvimento desta ferramenta amigável, no estudo de caso, os dados históricos do LDCIC (Laboratório de Difteria e Corinebactérias de Importância Clínica) do Departamento de Biologia da UERJ foram inseridos no sistema. Os métodos inteligentes propostos para classificação e identificação de bactérias foram analisados e apresentaram resultados promissores na área. / [en] Most laboratories do not have a computerized system for management procedures. The administration and control of the samples are made manualy through many forms of data sheets which are filled from the beginning, when the samples of biological materials are gathered at the hospital, up to the final identification at the laboratory. In this context, the organization of the information become very limited, while the information writting by hands and stored in books, its almost impossible to extract useful knowledge, which could help not only supporting decisions but also in the formulations of simples statistics. This thesis had two objectives. The development of a web system called BCIWeb (Bacterial Classifiation and Identification for Web) that could assist in bacterial identification and provide the technology necessary for the administration and control of clinical specimen coming from the hospitals and the discovery of knowledge in database system, through data mining methods using SOM (Self Organizing Maps) and Multilayer Perceptron Neural Networks (MLP) for classification and identificatin of bactéria. From the development of this friendly tool, in the case study, the historical data from LDCIC (Laboratório de Difteria e Corinebactérias de Importância Clínica) of UERJ Biology Department were entered into the system. The proposed intelligent methods for classification and identification of bacteria were analysed and showed promising results.

In today's society, companies are trying to find ways to utilize all the data they have, which considers valuable information and insights to make better decisions. This includes data used to keeping track of timber that flows between forest and industry. The growth of Artificial Intelligence (AI) and Machine Learning (ML) has enabled the development of ML modes to automate the measurements of timber on timber trucks, based on images. However, to improve the results there is a need to be able to get information from unlabeled images in order to decide weather and lighting conditions. The objective of this study is to perform an extensive for classifying unlabeled images in the categories, daylight, darkness, and snow on the load. A comparative study between partitional clustering and competitive learning is conducted to investigate which method gives the best results in terms of different clustering performance metrics. It also examines how dimensionality reduction affects the outcome. The algorithms K-means and Kohonen Self-Organizing Map (SOM) are selected for the clustering. Each model is investigated according to the number of clusters, size of dataset, clustering time, clustering performance, and manual samples from each cluster. The results indicate a noticeable clustering performance discrepancy between the algorithms concerning the number of clusters, dataset size, and manual samples. The use of dimensionality reduction led to shorter clustering time but slightly worse clustering performance. The evaluation results further show that the clustering time of Kohonen SOM is significantly higher than that of K-means.