Made available in DSpace on 2015-03-05T14:01:49Z (GMT). No. of bitstreams: 0 Previous issue date: 29 / Nenhuma / Este trabalho tem por objetivo apresentar uma proposta para a previsão de vendas de produtos, com fundamentação teórica, por meio da utilização de Redes Neurais Artificiais, utilizando como estudo de caso uma indústria que desenvolve produtos para o ramo de telecomunicação. Atualmente, realizar previsões de venda nas empresas é fundamental para reduzir seus custos com gastos desnecessários em recursos humanos e materiais e aumentar sua liquidez, sem perder a qualidade que os clientes estão acostumados, no sentido de evitar atrasos nos prazos de entrega dos produtos. O problema abordado nesta dissertação é importante para quase todas as áreas das indústrias, uma vez que a correta previsão de vendas permite às indústrias uma melhor organização de seu setor produtivo, permitindo a antecipação da quantidade ideal de matéria-prima a ser adquirida, o alinhamento de sua linha de produção, de modo a não ocorrerem alterações bruscas em seus layouts de fábrica, e um maior controle de seus níveis de estoque, reduzindo e / This work has the objective to present a methodology to predict product sales using artificial neural networks, as a study case was treated the periodic sales volume of an industry which develops products to the telecommunication area. Nowadays, realize sales prediction in the companies is crucial to reduce the costs with human resources, materials and increase the liquidity, without loose the quality which the clients are familiarized, avoiding delays in the products deliveries. The problem which this dissertation tackles is important to several kinds of industries, once a precise sales prediction allow the industries to organize themselves in a better way. It allows the company to know anticipated the material to be acquired, align their productions lines, avoiding an abrupt change in the factory layouts and offers a better control of their stocks. The study realized and the solution proposed are based on Artificial Neural Networks, applied to predict product sales using the sales history and the insertion

Before a radiation treatment on a cancer patient can get accomplished the treatment planning system (TPS) needs to undergo a quality assurance (QA). The QA consists of a pre-treatment (PT-QA) on a synthetic phantom body. During the PT-QA, data is collected from the phantom detectors, a set of monitors (transmission detectors) and the angular state of the machine. The outcome of this thesis project is to investigate if it is possible to predict the radiation dose distribution on the phantom body based on the data from the transmission detectors and the angular state of the machine. The motive for this is that an accurate prediction model could remove the PT-QA from most of the patient treatments. Prediction difficulties lie in reducing the contaminated noise from the transmission detectors and correctly mapping the transmission data to the phantom. The task is solved by modeling an artificial neuron network (ANN), that uses a u-net architecture to reduce the noise and a novel model that maps the transmission values to the phantom based on the angular state. The results show a median relative dose deviation ~ 1%.

Úlceras cutâneas constituem um problema de saúde pública no mundo atual. A eficiência do seu tratamento é observada pela redução das áreas total, de fibrina (amarelo) e de granulação (vermelho) da úlcera, calculados manualmente e/ou por imagens, processos demorados e posteriores à consulta médica. O trabalho propõe uma nova técnica não-invasiva e automatizada de acompanhamento das úlceras por redes neurais artificiais (RNAs). Foram utilizadas imagens digitais do banco de imagens do ADUN (Ambulatório da Dermatologia de Úlceras Neurovasculares) do Hospital das Clínicas da FMRP-USP (Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo), escolhidas aleatoriamente, sendo 50 imagens para treinamento da RNA e 250 para o teste da RNA. Para validação da RNA foram criados os grupos: 1 (n=15 imagens poligonais com áreas e cores definidas previamente); 2 (n=15 imagens poligonais com áreas e cores definidas previamente, submetidas a variações de iluminação, brilho, contraste, saturação); 3 (n=15 imagens poligonais constituídas de texturas de fibrina e de granulação); 4 (n=15 imagens de úlceras cutâneas reais preenchidas totalmente em cor preta sua superfície). Para avaliar a sua aplicação clínica foram utilizadas 50 imagens padronizadas submetidas aos cálculos das áreas pela RNA. Os resultados da RNA foram comparados aos do programa Image J (segmentação manual) e/ou às medidas-padrão. Estatisticamente os programas foram considerados similares quando p > 0,05 pelo Teste t Student. Quando p < 0,05 e r positivo, considerou-se o coeficiente de correlação de Pearson. A base de imagens de úlceras cutâneas foi eficiente para a aquisição das imagens, para a criação e execução dos algoritmos de extração de cores, de treinamento e de teste da RNA. A rede neural artificial desenvolvida apresentou desempenho similar ao Image J e às medidas-padrão adotadas para a segmentação das figuras do grupo 1, sendo p > 0,05 para as áreas total, de fibrina e de granulação. Na avaliação de interferência de ruídos (grupo 2), foi verificado que tais fatores não interferiram na segmentação da área dos polígonos (p > 0,05), pela RNA e pelo Image J. Entretanto, apesar de interferirem na segmentação de cores de granulação, sendo p < 0,05, o coeficiente de correlação RNA/Image J foi de 0,90 com p < 0,0001. No grupo 3, os cálculos das áreas foram semelhantes pela RNA e pelo Image J (p > 0,05). Quando comparadas às áreas calculadas pelos programas às medidas-padrão, o coeficiente de correlação foi significante (p < 0,0001) para todas as áreas. A segmentação das áreas das úlceras do grupo 4 pela RNA foi validada quando comparada à segmentação manual pelo Image J (p> 0,05). A aplicação clínica da RNA sobre o banco de imagens foi semelhante ao Image J para a segmentação das áreas (p > 0,05). Enfim, a rede neural artificial desenvolvida no Matlab 7.0 mostrou desempenho eficaz e validado na segmentação das úlceras de perna quanto à automatização do cálculo das áreas total, de fibrina e de granulação, semelhante à oferecida manualmente pelo programa Image J. Além disso, mostrou-se de grande aplicação clínica devido a facilidade de sua utilização através da interface web criada, sua praticidade, não interferência do usuário (automatização), propriedades essas que a consolida como uma metodologia adequada para o acompanhamento dinâmico-terapêutico da evolução das úlceras cutâneas. / Cutaneous ulcers are a public health problem worldwide. The efficiency of their treatment is observed through the reduction on the total affected areas, slough (yellow) and granulation (red) of the ulcer, manually calculated and/or through images, which are delayed processes usually performed after medical consultation. This work proposes a new non-invasive and automated technique to follow-up ulcers through artificial neural networks (ANN). Digital images from the ADUN (Neurovascular Ulcers Dermatology Ambulatory) image bank - FMRP General Hospital (Ribeirão Preto Medical School - University of São Paulo) were used and randomly selected as follows: 50 images for ANN training and 250 for the ANN test. For the ANN validation, the following groups were created: 1 (n=15 polygonal images with areas and colors previously defined); 2 (n=15 polygonal images with areas and colors previously defined submitted to illumination, brightness, contrast and saturation variation); 3 (n=15 polygonal images composed of slough and granulation textures); 4 (n=15 images of actual cutaneous ulcers with their surface fully filled in black). To evaluate its clinical application, 50 standard images were used and submitted to calculation of areas using ANN. The ANN results were compared to those obtained with the Image J software (manual segmentation) and/or to standard measures. The programs were statistically considered similar when p > 0.05 through the t Student test. When p < 0.05 and r is positive, the Pearson correlation coefficient was considered. The cutaneous ulcer image bank was efficient for the acquisition of images, for the creation and execution of color extraction algorithms, ANN training and tests. The artificial neural network developed presented performance similar to that obtained with the Image J software and to standard measures adopted for the segmentation of figures from group 1, with p > 0.05 for total areas, slough and granulation. In the noise interference assessment (group 2), it was verified that such factors did not interfere in the polygons area segmentation (p > 0.05) through both ANN and Image J. However, although interfering in the color and granulation segmentation, with p < 0.05, the ANN/Image J correlation coefficient was of 0.90, with p < 0.0001. In group 3, the calculations of areas were similar through both ANN and Image J (p > 0.05). When compared to standard measures, the correlation coefficient was significant (p < 0.0001) for all areas. The segmentation of ulcer areas of group 4 through ANN was validated when compared to manual segmentation through Image J (p> 0.05). The clinical application of ANN on the image bank was similar to Image J for the segmentation of areas (p > 0.05). Finally, the Artificial Neural Network developed in Matlab 7.0 environment showed good performance and was validated in the segmentation of leg ulcers in relation to the automation of the calculation of total areas, slough and granulation, which was similar to that obtained with the Image J software. Moreover, it presented a large clinical application due to the easiness of its application through the web interface created and the non interference of the user (automation), properties that consolidate this technique as a suitable methodology for the dynamic-therapeutic follow-up of the evolution of cutaneous ulcers.

A rugosidade é um parâmetro de acabamento importante nos processos de fabricação por usinagem e é determinado de acordo com a aplicação técnica da superfície usinada. A rugosidade afeta atributos funcionais dos produtos como desgaste, atrito, reflexão da luz, capacidade de manter e espalhar um lubrificante, etc.. Como a inspeção da superfície é normalmente feita com rugosímetros após a operação de usinagem, essa tarefa consome tempo e demanda trabalho, gerando custo adicional ao produto. Assim, este trabalho tem como objetivo estimar os valores das rugosidades média (Ra) e total (Rt) geradas no processo de fresamento frontal a seco do aço SAE 1045 com fresa de topo reto via redes neurais artificiais (RNA). Dessa forma, os valores de rugosidade Ra e Rt podem ser obtidos somente informando os parâmetros do processo ao modelo. Foram considerados como variáveis de entrada do processo a velocidade de corte (vc), o avanço por dente (fz) e o raio de ponta da ferramenta (r). Após uma análise estatística, constatou-se que as variáveis de saída que melhor se correlacionavam com os valores de rugosidade foram a força média no eixo x (Fx) (direção de avanço) e a variação da força no eixo z (Fz) (direção axial). Os dados de força foram obtidos usando um sistema sensório constituído de plataforma piezelétrica, placa de aquisição de dados e computador com software apropriado. Portanto, os cinco parâmetros de entrada utilizados nos 16 modelos testados foram vc, fz, r, Fx e Fz. O algoritmo de treinamento usado foi o de Levemberg-Marquardt. Dentre os testados, os modelos com topologia 5-10-10-1 (cinco entradas e uma saída) apresentaram as melhores capacidade de estimação para os valores de Ra e Rt, mostrando a eficiência da técnica de modelagem da rugosidade por RNA. / The surface roughness is an important finishing parameter in the machining manufacturing processes and it is determined according with the technical application of the machined surface. The surface roughness affects functional attributes of parts such as wear, friction, light reflection, ability to spreading and retaining a lubricant etc. As the surface inspection is usually done with the rugosimeter after the machining operation, this task is time consuming and labor demand, generating additional cost to the product. Thus, this work aims to estimate the values of average roughness (Ra) and total roughness (Rt) generated in the dry end milling process of the SAE 1045 steel via artificial neural networks (ANN). Thus, the roughness values of Ra and Rt may be obtained only by informing the process parameters to the model. Cutting speed (vc), feed per tooth (fz) and tool nose radius (r) were considered as input variables. After statistical analysis, it was found that output variables that best correlate with roughness values were the average force on the x axis (Fx) (feed direction) and the force variation in the z-axis (Fz) (axial direction). The cutting force data signals were obtained using a sensory system composed by piezoelectric platform, data acquisition board and personal computer with appropriate software. Therefore, the five input parameters applied in the 16 models tested were vc, fz, r, Fx and Fz and the training algorithm used was the Levemberg-Marquardt. Among the models tested, those with 5-10-10-1 topology (five inputs and one output) showed the best capacity for estimation of the Ra and Rt values that can demonstrate the modeling technique effectiveness of the surface roughness using ANN.

No presente trabalho, é testada e avaliada a aplicação de Redes Neurais Artificiais no combate de trapaças (cheating, em inglês) do tipo speed cheating em jogos online massivos de múltiplos jogadores, também conhecidos como MMOG (Massively Multi- player Online Games). Os MMOG representam um modelo de negócio onde quantias significativas de recursos financeiros estão envolvidas, e crescem a cada dia. Os mode- los para o combate de trapaças, que possam afastar jogadores de jogos ou servidores, estão localizados na camada de rede, à nível de protocolo. Analisando o estado-da-arte, constatou-se que não existem trabalhos explorando a área de Inteligência Artificial para este fim, tornando-se assim relevante o estudo de sua aplicabilidade. As Redes Neurais Artificiais foram escolhidas por terem grande poder de abstração, generalização e plasti- cidade. Através dos resultados obtidos comparando-se duas abordagens de arquiteturas, as redes Perceptron de múltiplas camadas (MLP) e as redes com atraso no tempo focadas (FTLFN), é possível constatar que é viável a utilização das mesmas para este fim, tendo-se alcançado resultados positivos no combate de speed cheating em MMOGs. / In the present work, Artificial Neural Networks are tested and evaluated in order to avoid a specific type of cheating, called Speed Cheating, in massively multi-player online games (MMOG). The MMOG represent a business model where meaningful financial resources amounts are involved, and increase each day. The models to avoid cheating, that could keep off players from games and servers, are localized in the network layer, at the protocol level. Examining the state-of-art, it was observed that research explor- ing the Artificial Intelligence application to this goal becomes relevant. The Artificial Neural Networks were chosen by their significant abstraction, generalization and plas- ticity characteristics. Through the results’s comparison from two different architectures approaches, the multi layer Perceptron network (MLP) and the focused time lagged net- work (FTLFN), it was possible to conclude that their utilization avoiding speed cheating in MMOG is possible, once good results were found in this work.

Este trabalho apresenta o desenvolvimento de um fluxo de projeto de circuitos digitais integrados, visando a incluir células geradas automaticamente pela ferramenta ASTRAN. Como parte integrante deste novo fluxo, desenvolveu-se uma nova técnica de comparação entre células, utilizando Redes Neurais Artificiais, para a modelagem das células ASTRAN, esta técnica se mostrou flexível ao se adaptar a diversos tipos de células e com resultados robustos tendo 5% de desvio padrão e 4% para o erro relativo. Também, foi criada uma ferramenta capaz de substituir células comerciais por células ASTRAN, tendo como objetivo melhorar as características de potência consumida e área utilizada pelo circuito, e por fim gerando um circuito misto composto de células comerciais feitas à mão e células ASTRAN geradas automaticamente. O foco principal deste trabalho encontra-se na integração do fluxo de geração de células geradas automaticamente a um fluxo de síntese comercial de circuitos digitais. Os resultados obtidos mostraram-se promissores, obtendo-se ganhos em redução de área e potência dos circuitos analisados. Em média os circuitos tiveram uma redução de 3,77% na potência consumida e 1,25% menos área utilizada. Com um acréscimo de 0,64% por parte do atraso total do circuito. / This work presents the development of a design flow for digital integrated circuits, including cells generated automatically by the ASTRAN tool. Moreover, a new technique, using Artificial Neural Networks, was developed to perform a comparison between two different cells, i.e. commercial and ASTRAN’s cell. This technique proved to be flexible when adapting to several types of cells and with robust results having 5% of standard deviation and 4% for relative error. Also, a new tool was developed, capable of performing cell replacement between ASTRAN and commercial cells, to improve power consumption an used area. Finally a mixed circuit composed of handmade commercial cells and cells automatically generated by ASTRAN was generated. A target was to mix an automatic cell synthesis tool with commercial synthesis tools dedicated to standard cells. Comparisons have shown that our approach was able to produce satisfactory results related area and power consumption. In average the circuits had a reduction of 3.77% in the power consumed and 1.25% less used area. With an increase of 0.64% due to the total delay of the circuit.

Les images hyperspectrales (HSI) fournissent des informations spectrales détaillées sur les objets analysés. Étant donné que différents matériaux ont des signatures spectrales distinctes, les objets ayant des couleurs et des formes similaires peuvent être distingués dans le domaine spectral. Toutefois, l’énorme quantité de données peut poser des problèmes en termes de stockage et de transmission des données. De plus, la haute dimensionnalité des images hyperspectrales peut entraîner un surajustement du classificateur en cas de données d'apprentissage insuffisantes. Une façon de résoudre de tels problèmes consiste à effectuer une sélection de bande (BS), car elle réduit la taille du jeu de données tout en conservant des informations utiles et originales. Dans cette thèse, nous proposons trois méthodes de sélection de bande différentes. La première est supervisée, conçu pour utiliser seulement 20% des données disponibles. Pour chaque classe du jeu de données, une classification binaire un contre tous utilisant un réseau de neurones est effectuée et les bandes liées aux poids le plus grand et le plus petit sont sélectionnées. Au cours de ce processus, les bandes les plus corrélées avec les bandes déjà sélectionnées sont rejetées. Par conséquent, la méthode proposée peut être considérée comme une approche de sélection de bande orientée par des classes. La deuxième méthode que nous proposons est une version non supervisée du premier framework. Au lieu d'utiliser les informations de classe, l'algorithme K-Means est utilisé pour effectuer une classification binaire successive de l'ensemble de données. Pour chaque paire de grappes, un réseau de neurones à une seule couche est utilisé pour rechercher l'hyperplan de séparation, puis la sélection des bandes est effectuée comme décrit précédemment. Pour la troisième méthode de BS proposée, nous tirons parti de la nature non supervisée des auto-encodeurs. Pendant la phase d'apprentissage, le vecteur d'entrée est soumis au bruit de masquage. Certaines positions de ce vecteur sont basculées de manière aléatoire sur zéro et l'erreur de reconstruction est calculée sur la base du vecteur d'entrée non corrompu. Plus l'erreur est importante, plus les fonctionnalités masquées sont importantes. Ainsi, à la fin, il est possible d'avoir un classement des bandes spectrales de l'ensemble de données. / Hyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or objects under analysis. It is possible thanks to both numerous and contiguous bands contained in such images. Given that di_erent materials have distinct spectral signatures, objects that have similar colors and shape can be distinguished in the spectral domain that goes beyond the visual range. However, in a pattern recognition system, the huge amount of data contained in HSIs may pose problems in terms of data storage and transmission. Also, the high dimensionality of hyperspectral images can cause the overfitting of the classifer in case of insufficient training data. One way to solve such problems is to perform band selection(BS) in HSIs, because it decreases the size of the dataset while keeping both useful and original information. In this thesis, we propose three different band selection frameworks. The first one is a supervised one, and it is designed to use only 20% of the available training data. For each class in the dataset, a binary one-versus-all classification using a single-layer neural network is performed, and the bands linked to the largest and smallest coefficients of the resulting hyperplane are selected. During this process, the most correlated bands with the bands already selected are automatically discarded, following a procedure also proposed in this thesis. Consequently, the proposed method may be seen as a classoriented band selection approach, allowing a BS criterion that meets the needs of each class. The second method we propose is an unsupervised version of the first framework. Instead of using the class information, the K-Means algorithm is used to perform successive binary clustering of the dataset. For each pair of clusters, a single-layer neural network is used to find the separating hyperplane, then the selection of bands is done as previously described. For the third proposed BS framework, we take advantage of the unsupervised nature of autoencoders. During the training phase, the input vector is subjected to masking Noise - some positions of this vector are randomly flipped to zero and the reconstruction error is calculated based on the uncorrupted input vector. The bigger the error, the more important the masked features are. Thus, at the end, it is possible to have a ranking of the spectral bands of the dataset.

Por ser não-invasiva e de baixo custo, a Ecocardiografia tem se tornado uma técnica de diagnóstico muito utilizada para a determinação dos volumes sistólicos e diastólicos do ventrículo esquerdo a fim de se calcular, indiretamente, o volume de ejeção do ventrículo esquerdo, a razão de contração muscular das cavidades cardíacas, a fração de ejeção regional e global, a espessura do miocárdio e a massa ventricular. Para isso, torna-se necessária a detecção das bordas endocárdicas do ventrículo esquerdo, o que é dificultada pelo fato da imagem de Ecocardiografia possuir ruídos que prejudicam sua definição. Apesar de haver várias técnicas de segmentação de imagem, este trabalho propõe detectar as bordas do ventrículo esquerdo de imagens ecocardiográficas utilizando uma rede neural artificial para reconhecer padrões de bordas. A fim de acelerar o processo e facilitar o processamento, uma área retangular centrada dentro da janela acústica do paciente é determinada pelo operador com o uso do \'mouse\' na qual serão realizadas todas as análises e reconhecimentos de borda pela rede neural. Após a marcação dos pontos reconhecidos pela rede neural como bordas, utilizam-se técnicas de gradientes e contorno móvel para se conectar os pontos de maior probabilidade e traçar a borda do ventrículo esquerdo. Esta técnica mostrou-se eficaz quando comparados com as bordas traçadas pelo especialista, sendo um fator importante a prática do operador ao escolher adequadamente a área a ser analisada. Após treinamento com 50 amostras de padrões de \"borda\" e 10 amostras de padrões de \"não borda\", a técnica foi testada em 108 imagens, alcançando resultados com boa precisão e rapidez quando comparamos os resultados na determinação da área do ventrículo esquerdo com outras técnicas citadas na literatura nacional e internacional. / Being non-invasive and having low cost, the echocardiography has been largely applied as diagnostic technique for left ventricle systolic and diastolic volumes determination that indirectly are used to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, the regional and global ejection fraction, the myocardial thickness, the ventricular mass, etc. For this reason, the detection of the left ventricle endocardial borders become necessary, but hampered by the noise that impairs the echocardiography images definition. In spite of having many image segmentation techniques, this work intend to detect the borders of left ventricle on echocardiography images by using a artificial neural network to recognize border patterns. To accelerate the process and facilitate the procedure, the operator uses the mouse to define a rectangular region inside the acoustic window of the pacient where all analyses and border recognitions will be accomplished. After labeling the recognized points as \'border\', gradient techniques and mobile boundary are used to connect the points of greater probability and delineate the left ventricle border. This technique has proved to be efficient when compared to the borders traced by the specialist. The ability of the operator is important in choosing of the region to be analyzed. After training with 50 samples of \"border\" pattern and 10 samples of \"no-border\" pattern, this technique was tested on 108 images, achieving good results on precision and velocitiy when we compared the calculated left ventricle area with the results of other techniques published on national and international literature.

Predictable, robust genetic parts including constitutive promoters are one of the defining attributes of synthetic biology. Ideally, candidate promoters should cover a broad range of expression strengths and yield homogeneous output, whilst also being orthogonal to endogenous regulatory pathways. However, such libraries are not always readily available in non-model organisms, such as the industrially relevant genus Geobacillus. A multitude of different approaches are available for the identification and de novo design of prokaryotic promoters, although it may be unclear which methodology is most practical in an industrial context. Endogenous promoters may be individually isolated from upstream of well-understood genes, or bioinformatically identified en masse. Alternatively, pre-existing promoters may be mutagenised, or mathematical abstraction can be used to model promoter strength and design de novo synthetic regulatory sequences. In this investigation, bioinformatic, mathematic and mutagenic approaches to promoter discovery were directly compared. Hundreds of previously uncharacterised putative promoters were bioinformatically identified from the core genome of four Geobacillus species, and a rational sampling method was used to select sequences for in vivo characterisation. A library of 95 promoters covered a 2-log range of expression strengths when characterised in vivo using fluorescent reporter proteins. Data derived from this experimental characterisation were used to train Artificial Neural Network, Partial Least Squares and Random Forest statistical models, which quantifiably inferred the relationship between DNA sequence and function. The resulting models showed limited predictive- but good descriptive-power. In particular, the models highlighted the importance of sequences upstream of the canonical -35 and -10 motifs for determining promoter function in Geobacillus. Additionally, two commonly used mutagenic techniques for promoter production, Saturation Mutagenesis of Flanking Regions and error-prone PCR, were applied. The resulting sequence libraries showed limited promoter activity, underlining the difficulty of deriving synthetic promoters in species where understanding of transcription regulation is limited. As such, bioinformatic identification and deep-characterisation of endogenous promoter elements was posited as the most practical approach for the derivation of promoter libraries in non-model organisms of industrial interest.

This first part of this thesis concerns the discovery and parameter determination of three hot Jupiter planets, first detected with by the SuperWASP collaboration, and their planetary nature is confirmed with the modelling of radial velocity measurements and further ground-based transit lightcurves. WASP-92b, WASP-93b and WASP-118b are all hot Jupiters with short orbital periods – 2.17, 2.73 and 4.05 days respectively. The analysis in this thesis finds WASP-92b to have R[sub]p = 1.461 ± 0.077 R[sub]J and M[sub]p = 0.805 ± 0.068 M[sub]J; WASP-93b to have R[sub]p = 1.597 ± 0.077 R[sub]J and M[sub]p = 1.47 ± 0.029 M[sub]J, and WASP-118b to have R[sub]p = 1.440 ± 0.036 R[sub]J and M[sub]p = 0.514 ± 0.020 M[sub]J. The second part of this thesis presents three novel approaches to modelling the effect of stellar limb darkening when fitting exoplanet transit lightcurves. The first method trains a Gaussian Process to interpolate between pre-calculated limb darkening coefficients for the non-linear limb darkening law. The method uses existing knowledge of the stellar atmosphere parameters as the constraints of the determined limb darkening coefficients for the host star of the transiting exoplanet system. The second method deploys an artificial neural network to model limb darkening without the requirement of a parametric approximation of the form of the limb profile. The neural network is trained for a specific bandpass directly from the outputs of stellar atmosphere models, allowing predictions to be made for the stellar intensity at a given position on the stellar surface for values of the T[sub]eff , log g and [Fe/H]. The efficacy of the method is demonstrated by accurately fitting a transit lightcurve for the transit of Venus, and for a single transit lightcurve of TRES-2b. The final limb darkening modelling method proposes an adjustment to the neural network model to account for the fact that the stellar radius is not constant across wavelengths. The method also allows the full variation in light at the edge of the star to be modelled by not assuming a sharp boundary at the limb.