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We report our investigation of how news stories influence the behaviour of tradable financial assets, in particular, equities. We consider the established methods of turning news events into a quantifiable measure and explore the models which connect these measures to financial decision making and risk control. The study of our thesis is built around two practical, as well as, research problems which are determining trading strategies and quantifying trading risk. We have constructed a new measure which takes into consideration (i) the volume of news and (ii) the decaying effect of news sentiment. In this way we derive the impact of aggregated news events for a given asset; we have defined this as the impact score. We also characterise the behaviour of assets using three parameters, which are return, volatility and liquidity, and construct predictive models which incorporate impact scores. The derivation of the impact measure and the characterisation of asset behaviour by introducing liquidity are two innovations reported in this thesis and are claimed to be contributions to knowledge. The impact of news on asset behaviour is explored using two sets of predictive models: the univariate models and the multivariate models. In our univariate predictive models, a universe of 53 assets were considered in order to justify the relationship of news and assets across 9 different sectors. For the multivariate case, we have selected 5 stocks from the financial sector only as this is relevant for the purpose of constructing trading strategies. We have analysed the celebrated Black-Litterman model (1991) and constructed our Bayesian multivariate predictive models such that we can incorporate domain expertise to improve the predictions. Not only does this suggest one of the best ways to choose priors in Bayesian inference for financial models using news sentiment, but it also allows the use of current and synchronised data with market information. This is also a novel aspect of our work and a further contribution to knowledge.

As abelhas e plantas apresentam diferentes graus de especialização em suas interações. Parceiros mais especialistas frequentemente apresentam uma história evolutiva mútua e sobreposição nas áreas de ocorrência. No entanto, a estrutura espacial dos ambientes nos quais esses grupos se distribuem é caracterizada por padrões complexos e dinâmicos. Para analisar a influência dos fatores que atuam na distribuição de espécies de abelhas e plantas associadas foram utilizadas a modelagem de distribuição de espécies, análise multivariada e ferramentas de sistemas de informações geográficas. Os resultados indicaram que a distribuição de gêneros estritamente associados, como é o caso de Peponapis e Cucúrbita, é influenciada pelo clima das áreas de ocupação, bem como provavelmente, por sua história evolutiva e pela domesticação das abóboras (Cucúrbita). Já os gêneros Krameria e Centris apresentam um padrão mais intrincado de distribuição, uma vez que a interação entre ambos é mais complexa. Centris é um grupo diverso que utiliza recursos florais de outras famílias botânicas além de Krameriaceae, o que provavelmente influencia seus padrões de distribuição. No entanto, os resultados obtidos para Krameria demonstraram de maneira geral, a influência de características climáticas na distribuição do grupo e uma provável dependência maior de Centris em alguns casos. Finalmente, foi também demonstrada a importância da inclusão de dados bióticos no processo da modelagem de distribuição, resultando no aumento da acurácia dos modelos e na alteração da projeção da distribuição para o futuro, considerando-se um cenário de mudança climática. Os resultados foram mais conspícuos quando foram consideradas interações mais estreitas entre espécies de abelhas parasitas e hospedeiras do gênero Bombus, do que entre Centris e Krameria. As técnicas utilizadas, em especial a modelagem de distribuição, representaram uma importante contribuição para a análise efetuada. No entanto, embora crescentemente utilizada, a modelagem de distribuição de espécies demanda técnicas e testes mais robustos para avaliar a acurácia dos modelos gerados. Além disso, um desafio adicional a ser vencido consiste no aumento e melhoria da qualidade dos pontos de ocorrência das espécies, principalmente no Brasil. Faz-se necessário um esforço adicional de coleta, especialmente em algumas áreas específicas, bem como, a conservação e digitalização dos dados das coleções biológicas. Porém, as técnicas utilizadas mostraram um grande potencial a ser explorado em outras análises, envolvendo questões biológicas diferentes, ou outros grupos taxonômicos e camadas de dados. / Bees and plants present different degrees of specialism in their interactions. More specialized partners generally present a mutual evolutionary history and overlap with their occurrence areas. Nevertheless, the spatial structure of environments occupied by them is characterized by complex and dynamic patterns. Species distribution modelling, multivariate analyses and geographical system information tools were used in order to analyze the influence of different factors that act in the geographical distribution of associated bees and plants. Results showed that the geographical distribution of close associated genera, such as Peponapis and Cucúrbita are influenced by the clime of occurrence areas, and also, by their evolutionary history and cucurbits domestication (squashes and pumpkins). On the other hand, Centris and Krameria genera presented a more intricate distribution pattern, since their interaction is more complex. Centris is a diverse group that uses other floral resources than those provided by the Krameriacea family, which has probably influenced its distribution, also. However, the results obtained for Krameria showed the influence of clime in its distribution and a stronger relationship with Centris in some cases. Finally, the importance of including biotic data in the species distribution modelling process was also demonstrated, resulting in a general increase in the models accuracy and also altering future scenarios projection, considering climate changes. Stronger interaction, such as the host-parasite bee species of Bombus showed more conspicuous results than those found for Krameria and Centris. The techniques, especially distribution modelling, made an important contribution to the analyses. However, in spite of being increasingly used, distribution modelling demands more robust tests and techniques to evaluate the accuracy of final models. Besides, an additional challenge to be achieved consists in the increase and improvement of species occurrence data, mainly in Brazil. An additional survey effort is necessary, especially in specific areas, as well as the conservation and data digitalization of biological collections. However, the techniques used here showed a great potential to be further explored in other analyses, involving different biological issues, other taxonomic groups and other data layers.

Der technologische Fortschritt ermöglicht es heute, die moleculare Konfiguration einzelner Zellen oder ganzer Gewebeproben zu untersuchen. Solche in großen Mengen produzierten hochdimensionalen Omics-Daten aus der Molekularbiologie lassen sich zu immer niedrigeren Kosten erzeugen und werden so immer häufiger auch in klinischen Fragestellungen eingesetzt. Personalisierte Diagnose oder auch die Vorhersage eines Behandlungserfolges auf der Basis solcher Hochdurchsatzdaten stellen eine moderne Anwendung von Techniken aus dem maschinellen Lernen dar. In der Praxis werden klinische Parameter, wie etwa der Gesundheitszustand oder die Nebenwirkungen einer Therapie, häufig auf einer ordinalen Skala erhoben (beispielsweise gut, normal, schlecht). Es ist verbreitet, Klassifikationsproblme mit ordinal skaliertem Endpunkt wie generelle Mehrklassenproblme zu behandeln und somit die Information, die in der Ordnung zwischen den Klassen enthalten ist, zu ignorieren. Allerdings kann das Vernachlässigen dieser Information zu einer verminderten Klassifikationsgüte führen oder sogar eine ungünstige ungeordnete Klassifikation erzeugen. Klassische Ansätze, einen ordinal skalierten Endpunkt direkt zu modellieren, wie beispielsweise mit einem kumulativen Linkmodell, lassen sich typischerweise nicht auf hochdimensionale Daten anwenden. Wir präsentieren in dieser Arbeit hierarchical twoing (hi2) als einen Algorithmus für die Klassifikation hochdimensionler Daten in ordinal Skalierte Kategorien. hi2 nutzt die Mächtigkeit der sehr gut verstandenen binären Klassifikation, um auch in ordinale Kategorien zu klassifizieren. Eine Opensource-Implementierung von hi2 ist online verfügbar. In einer Vergleichsstudie zur Klassifikation von echten wie von simulierten Daten mit ordinalem Endpunkt produzieren etablierte Methoden, die speziell für geordnete Kategorien entworfen wurden, nicht generell bessere Ergebnisse als state-of-the-art nicht-ordinale Klassifikatoren. Die Fähigkeit eines Algorithmus, mit hochdimensionalen Daten umzugehen, dominiert die Klassifikationsleisting. Wir zeigen, dass unser Algorithmus hi2 konsistent gute Ergebnisse erzielt und in vielen Fällen besser abschneidet als die anderen Methoden.

As abelhas e plantas apresentam diferentes graus de especialização em suas interações. Parceiros mais especialistas frequentemente apresentam uma história evolutiva mútua e sobreposição nas áreas de ocorrência. No entanto, a estrutura espacial dos ambientes nos quais esses grupos se distribuem é caracterizada por padrões complexos e dinâmicos. Para analisar a influência dos fatores que atuam na distribuição de espécies de abelhas e plantas associadas foram utilizadas a modelagem de distribuição de espécies, análise multivariada e ferramentas de sistemas de informações geográficas. Os resultados indicaram que a distribuição de gêneros estritamente associados, como é o caso de Peponapis e Cucúrbita, é influenciada pelo clima das áreas de ocupação, bem como provavelmente, por sua história evolutiva e pela domesticação das abóboras (Cucúrbita). Já os gêneros Krameria e Centris apresentam um padrão mais intrincado de distribuição, uma vez que a interação entre ambos é mais complexa. Centris é um grupo diverso que utiliza recursos florais de outras famílias botânicas além de Krameriaceae, o que provavelmente influencia seus padrões de distribuição. No entanto, os resultados obtidos para Krameria demonstraram de maneira geral, a influência de características climáticas na distribuição do grupo e uma provável dependência maior de Centris em alguns casos. Finalmente, foi também demonstrada a importância da inclusão de dados bióticos no processo da modelagem de distribuição, resultando no aumento da acurácia dos modelos e na alteração da projeção da distribuição para o futuro, considerando-se um cenário de mudança climática. Os resultados foram mais conspícuos quando foram consideradas interações mais estreitas entre espécies de abelhas parasitas e hospedeiras do gênero Bombus, do que entre Centris e Krameria. As técnicas utilizadas, em especial a modelagem de distribuição, representaram uma importante contribuição para a análise efetuada. No entanto, embora crescentemente utilizada, a modelagem de distribuição de espécies demanda técnicas e testes mais robustos para avaliar a acurácia dos modelos gerados. Além disso, um desafio adicional a ser vencido consiste no aumento e melhoria da qualidade dos pontos de ocorrência das espécies, principalmente no Brasil. Faz-se necessário um esforço adicional de coleta, especialmente em algumas áreas específicas, bem como, a conservação e digitalização dos dados das coleções biológicas. Porém, as técnicas utilizadas mostraram um grande potencial a ser explorado em outras análises, envolvendo questões biológicas diferentes, ou outros grupos taxonômicos e camadas de dados. / Bees and plants present different degrees of specialism in their interactions. More specialized partners generally present a mutual evolutionary history and overlap with their occurrence areas. Nevertheless, the spatial structure of environments occupied by them is characterized by complex and dynamic patterns. Species distribution modelling, multivariate analyses and geographical system information tools were used in order to analyze the influence of different factors that act in the geographical distribution of associated bees and plants. Results showed that the geographical distribution of close associated genera, such as Peponapis and Cucúrbita are influenced by the clime of occurrence areas, and also, by their evolutionary history and cucurbits domestication (squashes and pumpkins). On the other hand, Centris and Krameria genera presented a more intricate distribution pattern, since their interaction is more complex. Centris is a diverse group that uses other floral resources than those provided by the Krameriacea family, which has probably influenced its distribution, also. However, the results obtained for Krameria showed the influence of clime in its distribution and a stronger relationship with Centris in some cases. Finally, the importance of including biotic data in the species distribution modelling process was also demonstrated, resulting in a general increase in the models accuracy and also altering future scenarios projection, considering climate changes. Stronger interaction, such as the host-parasite bee species of Bombus showed more conspicuous results than those found for Krameria and Centris. The techniques, especially distribution modelling, made an important contribution to the analyses. However, in spite of being increasingly used, distribution modelling demands more robust tests and techniques to evaluate the accuracy of final models. Besides, an additional challenge to be achieved consists in the increase and improvement of species occurrence data, mainly in Brazil. An additional survey effort is necessary, especially in specific areas, as well as the conservation and data digitalization of biological collections. However, the techniques used here showed a great potential to be further explored in other analyses, involving different biological issues, other taxonomic groups and other data layers.

Approximately three rhinos are poached daily in South Africa. Rhino poaching is a serious problem that a ects not only the rhino population of South Africa, but also the rhino population of the world. South Africa has the largest rhino population and of those rhinos the largest number can be found in the Kruger National Park (KNP). The KNP has been hit the hardest by the poaching epidemic, losing 1,175 rhinos in 2015 alone. Two big challenges are the size of the park and the unknown locations of both the poachers and new poaching events. The KNP is the size of a small country and there are simply not enough rangers to patrol this area e ectively. A costly solution would be to employ more rangers, but a proposed alternative is to reduce the search space and thus ensure that the rangers are allocated to the high risk areas first. A mathematical model was developed in the form of a Bayesian network (BN) to infer the most important factors contributing to poaching events and to model the rhino poaching problem. This model can be used to predict the area in which a future poaching attack could take place and thereby reduce the search area of rangers. The model also serves as a vehicle to enhance the understanding of the problem and encourage reasoning and discussion amongst decision makers. The map of the KNP is divided into cells and each cell is given a poaching probability, based on the outcome of the BN. This probability map forms a heat map that can be shown to the control centre and rangers can then be sent to the respective hotspots on the map. This is a proactive approach, which is in stark contrast to the numerous reactive approaches attempted thus far. This is the first BN modelling approach to the rhino poaching problem, and it is also the first BN application to wildlife crime. Previous applications of BN have only gone so far as environmental modelling, but not wildlife crimes. In this study the rhino poaching problem was shifted from a complex, ill-structured space to a position where researchers can begin to address the underlying problems by using a causal model as the vehicle for understanding the complex interplay between the factors a ecting poaching events. / Ongeveer drie renosters word daagliks in Suid-Afrika gestroop. Renosterstroping is 'n ernstige probleem wat nie net die renosterbevolking van Suid-Afrika raak nie, maar ook die res van die wêreld. Suid-Afrika het die grootste renoster bevolking in die wêreld, en die grootste getal van dié renosters word in die Kruger Nasionale Park (KNP) aangetref. Die KNP word die ergste geraak deur die stropings epidemie en 1,175 renosters is in 2015 gestroop. Twee groot uitdagings is die grootte van die park, asook die onbekende posisies van beide die stropers en die nuwe stropingsaanvalle. Die KNP is die grootte van 'n klein land en daar is eenvoudig nie genoeg veldwagters om hierdie area e ektief te patrolleer nie. 'n Duur oplossing sou wees om meer veldwagters aan te stel, maar 'n alternatief is om die soekarea van die veldwagters te verklein en sodoende te verseker dat die veldwagters die hoë-risiko areas eerste, en meer gereeld, patrolleer. 'n Wiskundige model in die vorm van 'n Bayesiese netwerk (BN) is ontwikkel om die belangrikste faktore te bepaal wat bydra tot stropingsaanvalle en uiteindelik die probleem te modelleer. Hierdie model kan gebruik word om die area te voorspel waar 'n stropingsaanval moontlik kan plaasvind en die soekarea van die veldwagters te verminder. Dit dien ook as 'n kanaal om die begrip van die probleem te verbeter en redenasie en bespreking onder besluitnemers aan te moedig. Die kaart van die KNP word in selle verdeel en aan elke sel word 'n stropingswaarskynlikheid toegeken gebaseer op die uitkoms van die BN. Hierdie waarskynlikheidskaart vorm 'n "hittekaart" wat aan die kontrolesentrum gewys kan word, en veldwagters kan dan na die onderskeie responskolle op die kaart gestuur word. Hierdie pro-aktiewe benadering is in teenstelling met huidige reaktiewe benaderings. Hierdie is die eerste BN modellering benadering tot die renosterstropingsprobleem, en dit is ook die eerste BN toepassing tot natuurlewe-misdaad. Vorige toepassings van BNs het omgewingsmodellering aangespreek, maar nie natuurlewe-misdade nie. In hierdie studie word aangetoon hoe die renosterstropings probleem geskuif is vanaf 'n komplekse, swak gestruktureerde probleemruimte na 'n omgewing waar navorsers kan begin om die onderliggende probleme aan te spreek deur gebruik te maak van 'n kausale model as die voertuig van begrip om die komplekse wisselwerking tussen faktore wat 'n stropingsaanval beïnvloed, te verstaan. / Thesis (PhD)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

>Magister Scientiae - MSc / Wetlands have been recognised as one of the most intrinsically valuable and threatened ecosystems in the world. Global estimates indicate that wetlands are being lost or transformed at a rapid rate, exacerbated by projected climate change impacts. This has prompted the need to improve wetland mapping to address the conservation and management of these ecosystems effectively. However, this remains a challenge. Current mapping approaches estimates of wetland extent vastly underestimate the true extent. Ancillary data has been acknowledged to improve the accuracy of mapping the distribution of wetlands.

Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines.

In this research, we apply data mining techniques to a nationally-representative expenditure data from the US to predict very high-cost patients in the top 5 cost percentiles, among the general population. Samples are derived from the Medical Expenditure Panel Survey’s Household Component data for 2006-2008 including 98,175 records. After pre-processing, partitioning and balancing the data, the final MEPS dataset with 31,704 records is modeled by Decision Trees (including C5.0 and CHAID), Neural Networks. Multiple predictive models are built and their performances are analyzed using various measures including correctness accuracy, G-mean, and Area under ROC Curve. We conclude that the CHAID tree returns the best G-mean and AUC measures for top performing predictive models ranging from 76% to 85%, and 0.812 to 0.942 units, respectively. Among a primary set of 66 attributes, the best predictors to estimate the top 5% high-cost population include individual’s overall health perception, history of blood cholesterol check, history of physical/sensory/mental limitations, age, and history of colonic prevention measures. It is worthy to note that we do not consider number of visits to care providers as a predictor since it has a high correlation with the expenditure, and does not offer a new insight to the data (i.e. it is a trivial predictor). We predict high-cost patients without knowing how many times the patient was visited by doctors or hospitalized. Consequently, the results from this study can be used by policy makers, health planners, and insurers to plan and improve delivery of health services.

Modern motion capturing technologies are capable of collecting quantitative, biomechanical data on golf swings that can help to improve our understanding of golf theory and facilitate the establishing of new, optimized swing paradigms.This study explored the possibility of utilizing Microsoft’s Kinect sensor to analyse the biomechanics of golf swings. Following design-science research principles, it presents a software prototype capable of capturing, recording, analysing and comparing movement patterns using three-dimensional vector angles. The tracking accuracy and data validity of the software were then evaluated in a set of experiments in optimal and real-world conditions using actual golf swing recordings.The results indicate that the software is providing accurate data on joint vector angles with a clear profile view, while visually occluded and frontal angles are more difficult to determine precisely. The employed position detection algorithm demonstrated good results in both optimal and real-world environments. Overall, the presented software and its approach to position analysis and detection show great potential for use in further research efforts. / Program: Magisterutbildning i informatik