The increasing variety of data mining tools offers a large palette of types and representation formats for predictive models. Managing the models then becomes a big challenge, as well as reusing the models and keeping the consistency of model and data repositories. Sustainable access and quality assessment of these models become limited to researchers. The approach for the Data and Model Governance (DMG) makes easier to process and support complex solutions. In this thesis, contributions are proposed towards ensembles of models with a focus on model representation, comparison and usage. Predictive Toxicology was chosen as an application field to demonstrate the proposed approach to represent predictive models linked to data for DMG. Further analysing methods such as predictive models comparison and predictive models combination for reusing the models from a collection of models were studied. Thus in this thesis, an original structure of the pool of models was proposed to represent predictive toxicology models called Predictive Toxicology Markup Language (PTML). PTML offers a representation scheme for predictive toxicology data and models generated by data mining tools. In this research, the proposed representation offers possibilities to compare models and select the relevant models based on different performance measures using proposed similarity measuring techniques. The relevant models were selected using a proposed cost function which is a composite of performance measures such as Accuracy (Acc), False Negative Rate (FNR) and False Positive Rate (FPR). The cost function will ensure that only quality models be selected as the candidate models for an ensemble. The proposed algorithm for optimisation and combination of Acc, FNR and FPR of ensemble models using double fault measure as the diversity measure improves Acc between 0.01 to 0.30 for all toxicology data sets compared to other ensemble methods such as Bagging, Stacking, Bayes and Boosting. The highest improvements for Acc were for data sets Bee (0.30), Oral Quail (0.13) and Daphnia (0.10). A small improvement (of about 0.01) in Acc was achieved for Dietary Quail and Trout. Important results by combining all the three performance measures are also related to reducing the distance between FNR and FPR for Bee, Daphnia, Oral Quail and Trout data sets for about 0.17 to 0.28. For Dietary Quail data set the improvement was about 0.01 though, but this data set is well known as a difficult learning exercise. For five UCI data sets tested, similar results were achieved with Acc improvement between 0.10 to 0.11, closing more the gaps between FNR and FPR. As a conclusion, the results show that by combining performance measures (Acc, FNR and FPR), as proposed within this thesis, the Acc increased and the distance between FNR and FPR decreased.

This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First, a volumetric registration algorithm is developed which solves the registration as a state estimation problem. Using a static deformation model. To reduce computations, the similarity measure is calculated at some specific points called control points. These control points can be from a low resolution image grid or any irregular image grid. Our numerical analysis has shown that control points placed in the area without much information; i.e with small or no changes in image intensity, yield negligible deformation. Therefore, the selection of the control points can significantly impact the accuracy and computation complexity of the registration algorithms. An extension of the speeded up robust features (SURF) to 3D is proposed for enhanced selection of the control points in deformable image registration. The impact of this new control point selection method on the performance of the registration algorithm is analyzed by comparing it to the case where regular grid control points are used. The results show that the number of control points could be reduced by a factor of ten with new selection methodology without sacrificing performance. Second image registration method is proposed in which, based on a segmented pre-operative image, a deformation model of the breast tissue is developed and discretized in the spatial domain using the method of finite elements. The compression of the preoperative image is modeled by applying smooth forces on the surface of the breast where compression plates are placed. Image registration is accomplished by formulating and solving an optimization problem. The cost function is a similarity measure between the deformed preoperative image and intra-operative image computed at some control point and the decision variables are the tissue interaction forces. / Thesis / Master of Applied Science (MASc)

<p> The binary choice Fast Guess Model of Ollman and Yellott was generalized to a multiple choice model and six subjects were run in a choice reaction time task to test the model. Stimulus set sizes of two, four and six were used and response accuracy and speed motivation was manipulated through specific instructions which were changed from trial to trial. Three different motivational instructions were used. In all cases, subjects were to respond with maximum accuracy but were also told on each trial to either disregard the duration of their response, respond within 440 milliseconds or respond within 300 milliseconds.</p> <p> The generalized Fast Guess Model was rejected because response time parameters of the SCR state were found to change across response accuracy-speed motivation instructions and across stimulus set sizes. Implications of these results for other classes of models were also discussed.</p> / Thesis / Doctor of Philosophy (PhD)

Query optimization aims to select a query execution plan among all query paths for a given query. The query optimization of traditional relational database management systems (RDBMSs) relies on estimating the cost of the alternative query plans in the query plan search space provided by a cost model. The classic cost model (CCM) may lead the optimizer to choose query plans with poor execution time due to inaccurate cardinality estimations and simplifying assumptions. A learned cost model (LCM) based on machine learning does not rely on such estimations and learns the cost from runtime. While learned cost models are shown to improve the average performance, they may not guarantee that optimal performance will be consistently achieved. In addition, the query plans generated using the LCM may not necessarily outperform the query plans generated with the CCM. This thesis proposes a hybrid approach to solve this problem by striking a balance between the LCM and the CCM. The hybrid model uses the LCM when it is expected to be reliable in selecting a good plan and falls back to the CCM otherwise. The evaluation results of the hybrid model demonstrate promising performance, indicating potential for successful use in future applications.

The protection zone of the Franklin Rod is widely used in practice due to its effectiveness and the wide usage. The theories are based on the mathematical approaches, regardless of physical inherence of lightning strokes. The study of this thesis is based on computer analysis of the field distribution in pre-breakdown stage, laboratory experiments of various test configurations and mathematical modeling. The impact of the Franklin Rod¡¯s height and the polarity of impulse are considered. New equations are developed to calculate the striking distance to Franklin Rod both under negative and positive polarity lightning strokes. The lightning protection zone of the Franklin Rod can be predicted by a proposed elliptical model. The experimental data coincides to the calculated protection zone by proposed equations. Finally, the dual-rod lightning protection system is studied. The protection zone of the dual-rod protection system can be evaluated by a combination of Rolling Sphere Circular Model and elliptical model.

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