SACM - Canada
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9651
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Item Restricted Data-Efficient Deep Learning for Predictive Modelling of Conventional Single Slope Solar Stills: Leveraging Transfer Learning and Tailored Data Augmentation Strategies(Saudi Digital Library, 2025) Migaybil, Hashim; Gopaluni, BhushanConventional single-slope solar stills are essential for decentralized freshwater production, yet their performance optimization is limited by small datasets and the nonlinear dynamics of desalination. This doctoral thesis addresses these constraints by developing and evaluating data-efficient supervised machine learning frameworks to predict freshwater productivity (Pstd, L/m²·day). The study integrates a novel high-performance solar still design with two complementary learning paradigms: Transfer Learning (TL) and tailored Data Augmentation (DA). The research begins with the design and MATLAB/SIMULINK simulation of a photovoltaic-assisted single-slope solar still engineered for improved thermal performance. The hybrid system achieved a peak efficiency of 45%, and its 730-sample dataset served as the “source” domain for TL. The first paradigm introduces a cross-design TL framework. A source Artificial Neural Network (ANN) was pre-trained on the hybrid system simulation data, and its learned weights were transferred and fine-tuned to model a conventional solar still using only 365 experimental samples. The optimized TL-based ANN (5-64-64-1) outperformed both randomly initialized ANNs and Multiple Linear Regression (MLR), achieving an Overall Index of Model Performance (OIMP) of 0.872 and demonstrating superior predictive accuracy and generalization. The second paradigm develops a tailored DA strategy to directly expand the conventional still’s limited dataset. Gaussian noise–based jittering was applied to sequential inputs within a 7-day look-back window to generate synthetic training data for a one-dimensional Convolutional Neural Network (CNN-1D). The optimized CNN-1D model—comprising three 128-filter convolutional layers—substantially outperformed baseline CNN and Support Vector Regression (SVR) models, achieving an RMSE of 0.045 and an OIMP of approximately 0.97. A threshold-based classification method was also introduced to translate raw predictions into interpretable productivity categories. Overall, this thesis provides a comparative evaluation of TL and DA approaches, validating their effectiveness in addressing data scarcity in solar still modeling. Key contributions include a novel cross-design TL framework, a specialized DA technique for time-series solar still data, and highly accurate predictive models. The findings provide practical, cost-effective tools for optimizing conventional solar stills and underscore the broader potential of advanced machine learning in renewable energy–driven desalination.19 0Item Restricted Machine Learning Classififiers for Chronic Obstructive Pulmonary Disease Assessment Using Lung CT Data.(Western University, 2024-04-12) Alsurayhi, Halimah; Abbas, SamaniChronic Obstructive Pulmonary Disease (COPD) is a condition characterized by persistent inflammation and airflow blockages in the lungs, contributing to a significant number of deaths globally each year. To guide tailored treatment strategies and mitigate future risks, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) employs a multifaceted assessment system of COPD severity, considering patient's lung function, symptoms, and exacerbation history. COPD staging systems, such as the high-resolution eight-stage COPD system and the GOLD 2023 three staging systems, have been later developed based on these factors. Lung Computed Tomography (CT) is becoming increasingly crucial in investigating COPD as it can detect various COPD phenotypes, such as emphysema, bronchial wall thickening, and gas trapping. Deep learning techniques show promise in leveraging CT imaging to assess the severity of COPD. This thesis uses lung CT data in conjunction with machine learning techniques to classify COPD patients according to these staging systems. For the eight-stage system, both Neural Network and Convolutional Neural Network (CNN) approaches were employed for classification. To develop the Neural Network model, features were extracted from lung CT scans at inspiration and expiration breathing phases, including lung air features and COPD phenotypes features. The CNN model utilized a single lung CT scan at the expiration phase. The GOLD 2023 three staging system involves training separate CNN models using lung CT scans at expiration to predict symptom levels and COPD exacerbation risk. In this thesis, in addition to models trained from scratch, Transfer Learning was also employed to develop models for the eight-stage COPD classification, Symptom level prediction, and exacerbation risk prediction. The developed classifiers demonstrate reasonably high classification performance, indicating their potential for deployment in clinical settings to enhance COPD assessment using image data.16 0