Saudi Cultural Missions Theses & Dissertations
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Item Restricted Machine Learning for Peaks Detection in Nuclear Magnetic Resonance Spectra(University of Liverpool, 2024-07) Alghamdi, Hadeel; Lisitsa, Alexei; Barsukov, IgorThis research addresses the challenge of accurately detecting and automating the peak picking process in both pure and mixture Nuclear Magnetic Resonance (NMR) spectra. Peak picking is a crucial step in NMR analysis, but manual methods are often time-consuming and prone to errors, particularly in complex mixture spectra. Recent advancements in machine learning provide an opportunity to automate this process, improving both efficiency and accuracy; however, many of those methods focus more on one type of peak than the other and still require pre-processing steps. A machine learning system was developed to automate the detection and extraction of peaks in both pure and mixture NMR spectra, and it was systematically evaluated against several established techniques. The approach tackles key issues, including enhancing the detection of small peaks, and overlapping peaks, and managing the limited availability of labeled training data by generating synthetic datasets. Despite being trained on synthetic data, the model demonstrated strong performance on real NMR spectra, effectively automating peak detection. The model employs well-established machine learning techniques for object detection and segmentation, achieving 97% accuracy on synthetic data with no missed detections and few false positives, and 92% accuracy on real data. These results, compared to existing methods, suggest that the automated system can improve the accuracy and efficiency of peak picking in both pure and mixture NMR spectra, providing a valuable tool for researchers and practitioners in the field.11 0Item Restricted Nonlinear Models for Mixture Experiments Including Process Variables(University Of Southampton, 2024) Alzahrani, Shroug; Biedermann, StefanieThis present work is concerned with finding and assessing a class of models that flexibly fits data from mixture experiments and mixture-process variables experiments, and with providing guidelines for how to design mixture experiments and mixture-process variables experiments when these models are fitted. Most models in the literature are either based on polynomials and are therefore not very flexible, or have a large number of parameters that make the response surface interpretation difficult to understand. The modified fractional polynomial models are a recent class of models from the literature that are flexible and parsimonious but quite restrictive. We contribute to mixture experiments by proposing a new class of nonlinear models, the complement mixture fractional polynomial (CMFP) models, by making an additional transformation of the fractional polynomial, which results in less restrictive models while retaining (and indeed exceeding) the advantages of this class. Moreover, we suggest an extended form for the modified fractional polynomial models to fit data from mixture-process variables experiments.12 0