Large Scale Data Analytics with Language Integrated Query

Databases can easily reach petabytes (1,048,576 gigabytes) in scale. A system to enable users to efficiently retrieve or query data from multiple databases simultaneously is needed. This research introduces a new, cloud-based query framework, designed and built using Language Integrated Query, to query existing data sources without the need to integrate or restructure existing databases. Protein data obtained through the query framework proves its feasibility and cost effectiveness

Development of a data utility framework to support effective health data curation

Objectives: The value of healthcare data is being increasingly recognised, including the need to improve health dataset utility. There is no established mechanism for evaluating healthcare dataset utility making it difficult to evaluate the effectiveness of activities improving the data. To describe the method for generating and involving the user community in developing a proposed framework for evaluation and communication of healthcare dataset utility for given research areas. Methods: An initial version of a matrix to review datasets across a range of dimensions was developed based on previous published findings regarding healthcare data. This was used to initiate a design process through interviews and surveys with data users representing a broad range of user types and use cases, to help develop a focused framework for characterising datasets. Results: Following 21 interviews, 31 survey responses and testing on 43 datasets, five major categories and 13 subcategories were identified as useful for a dataset, including Data Model, Completeness and Linkage. Each sub-category was graded to facilitate rapid and reproducible evaluation of dataset utility for specific use-cases. Testing of applicability to >40 existing datasets demonstrated potential usefulness for subsequent evaluation in real-world practice. Discussion: The research has developed an evidenced-based initial approach for a framework to understand the utility of a healthcare dataset. It is likely to require further refinement following wider application and additional categories may be required. Conclusion: The process has resulted in a user-centred designed framework for objectively evaluating the likely utility of specific healthcare datasets, and therefore, should be of value both for potential users of health data, and for data custodians to identify the areas to provide the optimal value for data curation investment

Mathematical modelling of blood glucose regulation

Exercise is beneficial for all individuals; it lowers blood pressure, keeps the heart healthy and increases insulin sensitivity. Recent studies have shown the power that regular exercise has to improve metabolic health, which in turn works to prevent and to reverse the onset of the widespread epidemics of type 2 diabetes (T2DM). However, diabetics taking insulin are required to meticulously plan exercise around meals and intake of insulin as they face an increased risk of hypoglycaemia from physical activity, which can discourage them from taking part. This thesis describes the use of systems of ordinary differential equations to model the effects of exercise on the glucose regulatory system, for both healthy and diabetic individuals. A particular focus is given to the role of glucagon, whose role is often neglected in glucoregulatory models, and its ability to enhance hepatic glucose production and so to prevent hypoglycaemia. Models of glucose-insulin-glucagon dynamics are first developed to describe an Intravenous glucose tolerance test (IVGTT), as the processes involved are simpler than in exercise and already widely modelled for glucose and insulin, thus is a good basis for validating the incorporation of glucagon. Mathematical models are used as tools within biological applications as they allow for an investigation into the dynamics that are involved in complex regulatory processes. The mathematical models in this thesis serve as accurate tools to predict blood glucose levels during exercise for both a non-diabetic and type 1 diabetic individual (T1DM) and emphasise exercise as a key element in the prevention of T2DM. By mathematically modelling the system and the mechanisms that occur to maintain glucose homeostasis an insight is gained into what the principal factors are for the greatest increase in insulin sensitivity and for the reduction in the likelihood of either hypoglycaemic or hyperglycaemic episodes. This may lead to recommendations for exercise plans which not only provide the greatest benefits for everyday health ant to assist with preventing the onset of diabetes but also to offer safer regimes for individuals with T1DM

Learning predictive models from massive, semantically disparate data

Machine learning approaches offer some of the most successful techniques for constructing predictive models from data. However, applying such techniques in practice requires overcoming several challenges: infeasibility of centralized access to the data because of the massive size of some of the data sets that often exceeds the size of memory available to the learner, distributed nature of data, access restrictions, data fragmentation, semantic disparities between the data sources, and data sources that evolve spatially or temporally (e.g. data streams and genomic data sources in which new data is being submitted continuously). Learning using statistical queries and semantic correspondences that present a unified view of disparate data sources to the learner offer a powerful general framework for addressing some of these challenges. Against this background, this thesis describes (1) approaches to deal with missing values in the statistical query based algorithms for building predictors (Nayve Bayes and decision trees) and the techniques to minimize the number of required queries in such a setting. (2) Sufficient statistics based algorithms for constructing and updating sequence classifiers. (3) Reduction of several aspects of learning from semantically disparate data sources (such as (a) how errors in mappings affect the accuracy of the learned model and (b) how to choose an optimal mapping from among a set of alternative expert-supplied or automatically generated mappings) to the well-studied problems of domain adaptation and learning in presence of noise and (4) a software for learning predictive models from semantically disparate data

ROBOT MÓVIL MECX1 PARA LA DETECCIÓN DE PERSONAS EMPLEANDO MEMORIAS ASOCIATIVAS ALFA-BETA

ResumenEn presente trabajo de investigación propone emplear a las Memorias Asociativas Alpha-Beta (AMαβ) en la detección automática del cuerpo humano a partir de imágenes RGB-3D capturadas por el robot MECX1, las AMαβ son entrenadas con vectores característicos extraídos de dos tipos de imágenes, las imágenes positivas contienen personas bajo diferentes poses, distancias e iluminación, mientras que las imágenes negativas contienen objetos que el robot puede encontrar en su entorno de navegación. El rendimiento de las AMαβ es evaluado en dos pruebas, en la primera se determina la capacidad para recordar los vectores previamente aprendidos, los resultados muestran que la memoria fue capaz de recordar al 100% las formas de cuerpos humanos, así como de los  objetos con los que fue entrenada, en la segunda prueba se evalúa su capacidad para clasificar vectores que no aprendió anteriormente, obteniéndose una tasa de precisión promedio de 95.1%. Para la validación de los resultados y separación de los conjuntos de entrenamiento y prueba se empleó el método de K-fold-cross-validation.Palabra(s) Clave: cuerpo humano, detección, forma humana, memorias asociativas, reconocimiento. MECX1 MOBILE ROBOT FOR THE DETECTION OF PEOPLE USING ASSOCIATIVE MEMORIES ALPHA-BETA AbstractIn the present work, we propose to use Alpha-Beta Associative Memories (AMαβ) in the automatic detection of the human body from RGB-3D images captured by the robot MECX1, the AMαβ are trained with characteristic vectors extracted from two types of images, positive images contain people under different poses, distances and illumination, while negative images contain objects that the robot can find in its navigation environment. The performance of the AMαβ is evaluated in two tests, the first one determines the ability to remember previously learned vectors, the results show that memory was able to remember 100% human body forms as well as objects with the ones that were trained, in the second test we evaluated the memory capacity to classify vectors that were not previously learned, obtaining an average accuracy rate of 95.1%, K-fold-cross-validation method was used for the validation of the results and separation of the training and test sets.Keywords: associative memories, detection, human body, human shape

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Protein structure prediction and analysis are more significant for living organs to perfect asses the livingorgan functionalities. Several protein structure prediction methods use neural network (NN). However,the Hidden Markov model is more interpretable and effective for more biological data analysis comparedto the NN. It employs statistical data analysis to enhance the prediction accuracy. The current workproposed a protein prediction approach from protein images based on Hidden Markov Model andChapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein imagesbinarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently,two counting algorithms, namely the Flood fill and Warshall are employed to classify the proteinstructures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classifiedstructures for predicting the protein structure. The execution time and algorithmic performances aremeasured to evaluate the primary, secondary and tertiary protein structure prediction

Genetic algorithm in ab initio protein structure prediction using low resolution model : a review

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution