Computing and Information Systems - Theses
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ItemOntologies in neuroscience and their application in processing questions( 2019)Neuroscience is a vast, multi-dimensional and complex field of study based on both its medical importance and unresolved issues regarding how brain and the nervous system work. This is because of the huge amount of brain disorders and their burden on people and society. Furthermore, scientist have been excited about the function and structure of brain, ever since it was discovered to be responsible for all our emotions, thoughts and behaviour. Ontologies are concepts whose origins go back to philosophy and the concern with the nature and relation of being. They have emerged as promising tools for assistance with neuroscience research recently and provide additional data on a field of study. They connect each entity or element to other ones through descriptive relationships. Ontologies seem to suit the complex, multi-dimensional and still incomplete nature of neuroscience very well because of their characteristics. The first study shines light on applications of ontologies in neuroscience. It incorporated a systematic literature review and methodically reviewed over 1000 research papers from eight databases and three journals. After scanning all documents, 208 of them were selected. Then, a full text analysis was performed on the selected documents. This study found eight major applications for ontologies in neuroscience, most of them consisted of several subcategories. The analysis not only demonstrated the current applications of ontologies in neuroscience, but also their potential future in this field. The second study was set to represent neuroscience questions and then, classify them using ontologies. For this purpose, a questions set was gathered from two research teams and analysed. This, results in a set of dimensions which represents questions. Then, a question hierarchy was formed based on dimensions and questions were classified according to that hierarchy. Two different approaches were used for the classification including an ontology-based approach and a statistical approach. The ontology-based approach exceeded the statistical approach by 15.73% better classification results. The last study was designed to tackle and resolve questions with the assistance of ontologies. It first proposed a set of templates that acted as a translation mechanism for changing questions into machine readable code. Templates were based on the question hierarchy presented in the previous study. Second, this study created an integrated collection of resources including two domain ontologies (NIFSTD and NeuroFMA) and a neuroimaging annotation application (Freesurfer). Subsequently, the code created using templates was executed upon the integrated resource (knowledge base) to find the appropriate answer. While processing the questions, ontologies were used for disambiguation purposes too. At the end, all parts created in this study along with the question classification method created in the previous study were merged as different modules of a question processing model. In conclusion, this thesis reviewed all current ontology applications in neuroscience in detail and demonstrated the extent to which they can assist scientists in classifying and resolving questions. The results of this thesis show that applications of ontologies in neuroscience are diverse and cover a wide range; they are steadily becoming more used in this field; and they can be powerful semantic tools in performing different tasks in neuroscience.
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ItemDauphin A Programming Language for Statistical Signal Processing - from principles to practice( 2018)This dissertation describes the design and implementation of a new programming language called Dauphin for the signal processing domain. Dauphin's focus is on the primitive concepts and algorithmic structures of signal processing. In this language, random variables and probability distributions are as fundamental and easy to use as the numeric types of other languages. The basic algorithms of signal processing --- estimation, detection, classification and so on --- become the standard function calls. Too much time is expended by researchers in re-writing these basic algorithms for each application. Dauphin allows you to code these algorithms directly, so they can be coded once and put into libraries for future use. Ultimately, Dauphin aims to extend the power of the researcher by allowing them to focus on the real problems and simplify the process of implementing their ideas. The first half of this dissertation describes Dauphin and the design issues of existing languages used for signal processing that motivated its development. It includes a general investigation into programming language design and the identification of specific design criteria that impact signal processing programming. These criteria directed the features in Dauphin that support writing signal processing algorithms. Of equal importance, the criteria also provide a means to compare, with some objectivity, the suitability of different languages for signal processing. Following the discussion on language design, Dauphin's features are described in detail, then details related to Dauphin's implementation are presented, including a description of Dauphin's semantics and type system. The second half of the dissertation presents practical applications of the Dauphin language, focussing on three broad areas associated with signal processing: classification, estimation and Monte Carlo methods. These non-trivial applications, combined with examples throughout the dissertation, demonstrate that Dauphin is simple and natural to use, easy to learn and has sufficient expressiveness for general programming in the signal processing domain.
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ItemOn the analysis of interaction between decision variables( 2017)Many real-world design and decision-making problems are characterized by the interactions between decision variables. In engineering optimization problems, the effects of one input variable on the performance measure may be influenced by one or several other input variables. In classification problems, one feature by itself may be irrelevant to the class label, however when combined with one or many feature(s), it may become highly relevant to the class label. Identifying variable interactions is important yet non-trivial. In this thesis, the overarching goals are (1) to identify and quantify variable interactions in a given optimization or classification problem and (2) to use this information to effectively solve the problem. The methodologies used to meet these goals are a combination of theoretical inquiry, computational modelling and experimental validation of the proposed methods. To identify and quantify variable interactions in a `Black-box' Continuous Optimization Problem (BCOP), a novel Exploratory Landscape Analysis (ELA) measure is proposed based on the Maximal Information Coefficient (MIC). MIC can identify a wide range of functional relationships with high levels of accuracy. Then the proposed ELA measure is embedded into an algorithm design framework to effectively solve a BCOP. The experimental results confirm the effectiveness of the proposed ELA measure. The high computational cost is the main limitation of the proposed ELA measure, especially in large-scale BCOPs. Therefore I propose an eXtended Differential Grouping (XDG) method, which can be used to identify variable interactions based on non-linearity detection. The XDG method decomposes a large-scale BCOP into several sub-problems considering both direct and indirect variable interactions. When XDG is embedded into a Cooperative Co-evolution framework to solve large-scale BCOPs, it generates high quality solution. To further improve the efficiency of problem decomposition, a Recursive Differential Grouping (RDG) method is proposed, which avoids the need to check the pairwise interactions between decision variables. RDG recursively examines the interaction between a selected decision variable and the remaining variables, placing all interacting decision variables into the same group. The efficiency of RDG is shown both theoretically and empirically. In the final stage of this thesis, I shift the focus from optimization problems to the closely related classification (more specifically feature selection) problems, by investigating the interactions between features to improve classification accuracy. I relax the assumptions made on the distribution of features and class labels, and propose a novel feature selection method which considers the Mutual Information (MI) between three features. To reduce the computational cost, the MI between three features is estimated from the pairwise MI between features. The experimental results confirm the effectiveness of the proposed method. In summary, the interactions between decision variables have been investigated in the optimization and classification domains. Novel methods have been proposed to improve the accuracy and efficiency of identifying variable interactions in a BCOP, large-scale BCOP or feature selection problem. I then have shown that this information can be used to guide the design of search techniques to effectively solve the problem.