Insight Centre for Data Analytics - Masters by Research Theses

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    Implementation of an AI-assisted sonification algorithm on an edge device
    (University College Cork, 2023) O'Sullivan, Feargal; Popovici, Emanuel; Temko, Andriy; Qualcomm
    Oxygen deprivation at birth leads to brain injury, which can have serious consequences. It is the dominant cause of seizures. Quickly and accurately detecting seizures is a challenging problem for neonates. A severe shortage of medical professionals with the necessary expertise for Electroencephalogram (EEG) analysis leads to significant delays in decision-making and hence treatment. These problems are made worse in disadvantaged communities. Artificial intelligence (AI) techniques have been proposed to automate the process and compensate for the lack of available expertise. However, these models are ’black boxes', and their lack of explainability dampens the wide adoption by medical professionals. AI-assisted sonification adds explainability to any such automated methodology, empowering medical professionals to make accurate decisions regardless of their level of expertise in EEG analysis. The feasibility of an implementation of an AI-assisted sonification algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations. Furthermore, a neural network is analysed for the potential of low-precision implementation, enabling inference on specialised hardware.
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    A study of airline route data
    (University College Cork, 2020) Yu, Xiaochen; O'Sullivan, Janet; Wolsztynski, Eric
    For decades, with the advancement of airline information system construction, the aviation industry has successfully built a number of information systems. An enormous amount of data has been accumulated through the successful operation of these systems for the aviation sector. The effective use of these invaluable data assets has increasingly become a requirement for the relevant airline departments, and the focus of aviation industry. Revenue management is crucial for measuring the operational success of the airlines. However, the traditional forecasting methods cannot support processing of the underlying data that keeps changing over time, which have impaired the accuracy of the forecast, and thus, the credibility. The new airline passenger ticket revenue pricing methods proposed in this thesis have explored the possibility of solving the existing problems through advanced modeling techniques, and thus provide a view for better airline route planning and optimisation. First of all, the airline data is classified in a targeted manner. The factors of available seat kilometers, revenue passenger kilometers, load factors, total number of passengers, average fares, etc. collected from different time periods were used to establish a multiple linear regression model in the statistical software, R. Through empirical analysis it is found that the factors affecting the passenger ticket revenue, over different time periods of the same company, are different. Therefore, a multivariate linear regression model was established which was based on the data of different airlines in one specific time period. It was empirically found through this approach that different airlines had different factors affecting ticket revenue in the same time period. A multivariate linear regression model was established for analysing the data of the head office and the various branches at the same time period and through this it was found that the factors affecting the passenger ticket revenue of the head office and the branches at the same time period were different. The research results of this dissertation can provide scientific evidence that airlines should consider analysing real-time ticket data for ticket price and flight plan collected from the IT system to maximise the revenue.