Electronic Thesis or Dissertation

A common topological data analysis approach used in the experimental sciences involves creating machine learning pipelines that incorporate discriminating topological features derived from persistent homology (PH) of data samples, encoded in persistence diagrams (PDs) and associated topological feature vectors. Often the most computationally demanding step is computing PH through an algorithmic process known as boundary matrix reduction. In this work, we introduce several methods to generate topological feature vectors from unreduced boundary matrices. We compared the performance of classifiers trained on vectorizations of unreduced PDs to vectorizations of fully-reduced PDs across several benchmark ML datasets. We discovered that models trained on PDs built from unreduced diagrams can perform on par and even outperform those trained on full-reduced diagrams. This observation suggests that machine learning pipelines which incorporate topology-based features may benefit in terms of computational cost and performance by utilizing information contained in unreduced boundary matrices.

The Arabian Peninsula was under the influence of the Ottoman Empire from 1517 until its collapse in 1918. During this time, three attempts were made to establish a Saudi state, the last of which began in 1902 and ended with the unification of the third Saudi State in 1932. During this period, three Saudi States were formed. The first Saudi State was established in 1745 when the al-Diriyah Agreement was introduced. This landmark agreement was achieved when Imam Mohammed Ibn Saud formed an alliance with Sheikh Mohammed Ibn Abdul Wahhab, a religious and rebellious man who advocated for the pure interpretation of Islamic principles. This alliance enabled Ibn Saud to govern the state, but he left the religious and cultural aspects of the society under the authority of Ibn Abdul Wahhab. As a result, throughout centuries, the religious establishment has greatly influenced the affairs of all three Saudi States. However, following the seizure of the Grand Mosque by religious zealots in 1979, Saudi Arabia underwent changes that radicalized the religious establishment, causing myriad detrimental effects for Saudi women. Oppressed by the weight of unfair laws and obstacles, Saudi women challenged the status quo and fought for equal rights through various methods. While King Abdullah al-Saud introduced some reforms, more significant change was still to come. After King Abdullah’s death, King Salman and his son Mohammed Bin Salman—the Crown Prince and Prime Minster—initiated a series of sweeping reforms under the Vision 2030 initiative to empower women, diversify the economy, and modernize the Kingdom of Saudi. Some key aspects of these reforms were abolishing the Male Guardianship System and removing the ban on women’s driving.

Glioblastoma multiforme (GBM) is an aggressive and highly resistant brain tumour, necessitating advanced treatment approaches to improve patient outcomes. This thesis provides a comprehensive review of recent advancements in GBM treatment, including innovations in treatment planning, radiation therapy, and their impacts on patient survival. The study also involves a detailed analysis of five GBM patients, examining critical dosimetric and radiobiological parameters, including Dose Volume Histogram, CT and MRI Images, T1, T2, T3 and T4 images. These parameters are analyzed using key radiobiological models, such as the linear-quadratic model, and factors like α/β, dose per fraction, and survival fractions. Through this data analysis, the study aims to evaluate the effectiveness of the treatment protocols and their impact on tumour control probability (TCP) and normal tissue complication probability (NTCP). The results will contribute to the understanding of GBM radiotherapy outcomes and provide insights for optimizing future treatment strategies.

Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning (DL) along with the hybrid models for binary and multi-class intrusion detection. The standalone machine and deep learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were used. Furthermore, two hybrid models were created by combining machine learning techniques: RF, XGBoost, AdaBoost, KNN, and SVM and these hybrid models were voting based hybrid classifier. Where one is for binary, and the other one is for multi-class classification. These models were tested using precision, recall, accuracy, and F1-score criteria and compared the performance of each model. This work thoroughly explains how hybrid, standalone ML and DL techniques could improve IDS (Intrusion Detection System) in terms of accuracy and scalability in IoT (Internet of Things).

Human Activity Recognition (HAR) plays a crucial role in various applications, including healthcare, fitness tracking, security, and smart environments, by enabling the automatic classification of human actions based on sensor and visual data. This dissertation presents a comprehensive exploration of HAR utilizing machine learning, sensor-based data, and Fusion approaches. HAR involves classifying human activities over time by analyzing data from sensors such as accelerometers and gyroscopes. Recent advancements in computational technology and sensor availability have driven significant progress in this field, enabling the integration of these sensors into smartphones and other devices. The first study outlines the foundational aspects of HAR and reviews existing literature, highlighting the importance of machine learning applications in healthcare, athletics, and personal use. In the second study, the focus shifts to addressing challenges in handling large-scale, variable, and noisy sensor data for HAR systems. The research applies machine learning algorithms to the KU-HAR dataset, revealing that the LightGBM classifier outperforms others in key performance metrics such as accuracy, precision, recall, and F1 score. This study underscores the continued relevance of optimizing machine learning techniques for improved HAR systems. The study highlights the potential for future research to explore more advanced fusion techniques to fully leverage different data modalities for HAR. The third study focuses on overcoming common challenges in HAR research, such as varying smartphone models and sensor configurations, by employing data fusion techniques.

IoBT stands for the Internet of Battlefield Things. This concept extends the principles of the Internet of Things (IoT) for military and defense use. IoBT integrates smart devices, sensors, and technology on the battlefield to improve situational awareness, communication, and decision-making in military operations. Sensitive military data typically includes information crucial to national security, such as the location of soldiers and equipment. Unauthorized access to location data may compromise operational confidentiality and impede the element of surprise in military operations. Therefore, ensuring the security of location data is crucial for the success and efficiency of military operations. We propose two systems to address this issue.
First, we propose a novel deception-based scheme to enhance the location-information security of IoBT nodes. The proposed scheme uses a novel encryption method, dummy IDs, and dummy packets technology. We develop a mathematical model to evaluate our scheme in terms of safety time (ST), probability of failure (PF), and the probability of identifying the real packet in each location information update (PIRP). Then, we develop NetLogo simulations to validate the mathematical model. The proposed scheme increases ST, reduces PF and PIRP.

The Internet of Things (IoT) has undergone remarkable expansion in recent years, leading to a proliferation of devices capable of connecting to the internet, collecting data, and sharing information. However, this rapid growth has also introduced a myriad of security challenges, resulting in an uptick in cyber-attacks targeting IoT infrastructures. To mitigate these threats and ensure the integrity of data, researchers have been actively engaged in the development of robust Intrusion Detection Systems (IDS) utilizing various machine learning (ML) techniques. This dissertation presents a comprehensive overview of three distinct approaches toward IoT intrusion detection, each leveraging ML methodologies to enhance security measures. The first approach focuses on a multi-class classification algorithm, integrating models such as random forest, logistic regression (LR), decision tree (DT), and Xgboost. Through meticulous evaluation utilizing evaluation metrics including F1 score, recall, and precision under the Receiver Operating Characteristics (ROC) curve, this approach demonstrates a remarkable 99 % accuracy in detecting IoT attacks. In the second approach, a deep ensemble model comprising Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) architectures is proposed for intrusion detection in IoT environments. Evaluation on the UNSW 2018 IoT Botnet dataset showcases the proficiency of this approach, achieving an accuracy of 98.4 % in identifying malicious activities. Lastly, the dissertation explores a real-time Intrusion Detection System (IDS) framework deployed within the Pyspark architecture, aimed at efficiently detecting IoT attacks while minimizing detection time.

The Neonatal Intensive Care Unit (NICU) is an inherently stressful environment for parents, where their satisfaction is a critical indicator of the quality of care provided. However, limited research has explored the satisfaction levels of Saudi fathers and mothers in the NICU, which are influenced by Islamic cultural values, family dynamics, and societal norms. A convergent parallel mixed-methods design was employed to explore the differences between Saudi fathers and mothers by integrating quantitative data on satisfaction with qualitative insights from NICU parental experiences of satisfaction through the lens of Leininger’s Culture Care Theory (CCT). Quantitative data were collected through a cross-sectional descriptive correlation design using the Critical Care Family Satisfaction Survey (CCFSS), adapted for the Saudi context. Qualitative data were gathered through an ethnonursing design involving participant observations and semi-structured individual interviews. The study included 75 parents (34 fathers and 41 mothers) from King Fahad Medical City in Riyadh, Saudi Arabia. Quantitative data were analyzed using independent t-tests, while qualitative data (25 observations and 22 interviews) were analyzed according to Leininger’s four phases of the Data Analysis Enabler. The mixed-methods analysis employed a side-by-side comparison to present both findings in a parallel format.

Artificial intelligence is now a way of life meaning, it is hard to find any type of technology or technological advance that isn’t assisted by or powered by artificial intelligence and machine learning. From Siri on our iPhones to our computer tailored Netflix home screens to fast learning computerized and independent floor vacuums AI is everywhere you turn intruding on every aspect of daily functioning. As the pressure of said intrusion increases questions arise about whether all these advances can become crushing to humans. In some instances technology with AI components has been used to replace certain skill sets affecting the availability of employment surround jobs including, cashiers, hotel reception, customer service, taxi drivers, toll booths. And what about graphic design? Can a machine programmed with AI replace the creativity of a human spirit?
The research explores the tension between automated (artificial intelligence + machine learning) and manual, human initiated methods and practices in graphic design…
Can humans be removed from the process of graphic design? Expected outcome: No
How can the case study exploration coupled with the examination of certain considerations including ethical practices, human creativity, quality and originality demonstrate the necessity of human involvement.

Telemetry data has become a crucial resource for detecting abnormal driving behaviors, especially for elderly drivers with Mild Cognitive Impairment (MCI) or dementia. This thesis proposes a novel spatial deep learning method that combines traditional telematics features with Grid-Index Resolution (GIR) to enhance the detection of abnormal driving behavior. By utilizing grid-indexed spatial-temporal analysis, the approach aims to capture more intricate driving patterns, which are often missed by traditional methods that rely only on basic telematics data such as speed, direction, and distance.
The methodology integrates Simple Neural Networks (SNN) to process traditional telematics features and Convolutional Neural Networks (CNN) to handle spatial relationships through grid-based data. The fusion of these two feature sets into a combined model improves the model's ability to accurately classify normal and abnormal driving behaviors.
This thesis evaluates the proposed approach using a dataset collected over 3.5 years from elderly drivers, including those with MCI. Experimental results demonstrate that the combined model achieves a classification accuracy of 97%, outperforming existing methods. The findings suggest that integrating grid-based spatial-temporal analysis into deep learning models offers significant potential for improving road safety, insurance risk assessment, and targeted interventions for at-risk drivers.