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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.

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.

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.

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.

Short-circuit faults can cause significant damage to power grid infrastructure, resulting in costly maintenance for utility providers. Rapid identification of fault locations can help mitigate these damages and associated expenses. Recent studies have demonstrated that graph neural network (GNN) models, using phasor data from various points in a power grid, can accurately locate fault events by accounting for the grid’s topology—a feature not typically leveraged by other machine learning methods. However, despite their high performance, GNN models are often viewed as ”black-box” systems, making their decision logic difficult to interpret. This thesis demonstrates that explanation methods can be applied to GNN models to enhance their transparency by clarifying the reasoning behind fault location predictions. By systematically benchmarking several explanation techniques for a GNN model trained for fault location detection, we assess and recommend the most effective methods for elucidating fault detection predictions in power grid systems.

This study applies the Response Escalation Paradigm (REP) to examine jealousy scores across five stages of increasing relationship threat (Huelsnitz et al., 2018). Participants rated their jealousy in scenarios involving a romantic partner with either a human or AI rival in virtual reality (VR), among other conditions. Consistent with hypothesis 1, jealousy rates increased across the stages in each condition. Consistent with hypothesis 2, people reported higher jealousy for the human rival in VR compared to the AI rival in VR at Stage 1. Inconsistent with hypothesis 3, participants did not experience faster escalation (the rate of increase in jealousy as the level of threat increases across the scenarios) for the human rival in VR relative to the AI rival in VR. Exploratory analyses did not reveal significant gender differences in jealousy responses. Overall, results indicate that individuals feel jealous about AI rivals as they do with human rivals.

There is a growing interest and attention for organizations to move from a shareholder capitalist to stakeholder capitalist business model, in which delivering environmental, social, and governance (ESG) performance is becoming more pronounced. Because of this transition, there is growing pressure for managers at all levels to act in sustainable, socially responsible, and ethical (SRE) ways; yet the current management models are based on maximizing economic value and scientific management. Therefore, the attempts to try and manage 21st century organizations with 20th century management practices and the aspirations of responsible management education have not demonstrated the desired effect (Laasch & Gherardi, 2019), as seen by continued business failures. It is not yet understood whether it is possible for the everyday manager to effectively navigate the tensions and contradictions of trying to deliver economic value to shareholders, social and ecological value to other stakeholders, while simultaneously attending to regulatory policies and, in the process, avoiding burnout for overtasked managers. Conducted in a debt remediation call center in the Dominican Republic, this study used an eclectic design integrating abductive analysis with a cultural-historical activity theory methodology to explore responsible management practices at the lowest levels of management. The findings describe responsible managers as pressure moderators enacting four primary elements: 1) embracing a pluralistic logic of responsibility, 2) accepting tensions with resilience, 3) engaging practices dynamically to moderate pressures, and 4) learning primarily on the job amid real work. In this organization, moderating the pressures of emotional labor was the primary object of their attention.

Major academic institutions in the United States house unidentified human skeletal remains in their collections as a result of unethical obtainment, poor documentation, and lack of resources. This thesis explores the possible geographic provenances associated with two skulls, A11 and A12, kept in the Florida Atlantic University (FAU) Anthropology Department. This thesis utilizes strontium and oxygen isotope analysis collected from dental and enamel tissues to explore possible geographic provenance for A11 and A12.
Analyzing isotopic composition of human skeletal remains is an established method to reconstruct human processes, history, events, and lifeways. Strontium (expressed by the ratio: 87Sr/86Sr) and oxygen (expressed by the ratio: δ18O) stable isotope analysis is used to determine place of origin for human remains with unknown origin. Strontium and oxygen isotopes express geographic signatures, of an individual's food and drinking water ingested during childhood which can reflect the isotope signature of the environment (soil, water, geology) from where it originates.