SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted The impact of artificial intelligent (AI) on inventory management and cost efficiency in the supply chain.(Saudi Digital Library, 2025) almasaeed, murtadha; fang, liuThis dissertation discusses the impact of artificial intelligence on inventory management and cost efficiency. The research shows how AI improves in the supply chain such as forecasting, replenishment, and warehouse operations. At the same time, it shows the challenges facing the small and medium sized enterprises (SMEs). Furthermore, the research investigates the importance of trust and satisfaction that can affect the success of AI adoption. The study uses a quantitative survey to collect data from professionals in the supply chain from different organisations and industries. Various analyses were employed including descriptive statistics, reliability testing, t-tests, correlation, regression, and ANOVA tests. According to the findings, AI adoption has improved the inventory turnover, reduced delays, and lowered labour and logistics costs. However, there are some unexpected findings such as demand forecasting and automated replenishment, which did not show statistically significant evidence. This shows that system integration and the maturity of adoption are important to achieve all the benefits. The findings also show that trust and satisfaction have an important role. The trust showed to reduction in stockouts and the satisfaction improved as the company size and usage time increased. These findings match the Technology Acceptance Model (TAM) which shows user perception affects the adoption and the outcome of AI. In conclusion, the study shows improvement in the supply chain operations after AI adoption. However, the success of AI depends on some factors such as the company's resources, the level of adoption and how employees trust the technology. These results give the manager the full view of how to use AI effectively in the operations.9 0Item Restricted Metadata-Centric Cybersecurity Classification: A Fair Benchmark of LLMs and Classical Models(Saudi Digital Library, 2025) Binothman, Elyas; Chaudhry, Umair BilalCybersecurity breach classification supports triage and risk response but is hindered by heterogeneous reporting, class imbalance, and limited semantic coverage in traditional pipelines. Prior work has relied on rule-based heuristics and classical models (SVM, Random Forest) with heavy feature engineering, while recent LLM studies rarely evaluate breach metadata under identical, fair splits; severity labels are often absent or not reproducibly constructed. We present a metadata-centric benchmark on the Privacy Rights Clearinghouse chronology spanning two tasks: breach-type classification and severity tiering in three and five labels, with severity derived reproducibly from native fields using a Breach Level Index style mapping. All models share one preprocessing recipe and a single stratified 80/20 train–test split. We compare parameter-efficient transformers (DistilBERT and T5 with LoRA) against tuned tabular baselines (Linear SVM, Random Forest, compact ANN). On breach type, DistilBERT achieves the strongest results (Accuracy 0.943; Macro– F1 0.840), surpassing tabular baselines. For severity, a classweighted ANN on TF–IDF and categorical features attains the highest Macro–F1 at both granularities, while T5 shows high accuracy but low Macro–F1, indicating majority-class bias. The study contributes a unified PRC schema with transparent severity construction, a fair head-to-head comparison under identical conditions, and an efficiency-oriented training recipe suitable for modest hardware.11 0Item Restricted SMART TOURISM IN SAUDI ARABIA: EXPLORING THE INTEGRATION OF AI IN CULTURAL HERITAGE DESTINATIONS(Saudi Digital Library, 2025) Alotaibi, Hussain; Buhalis, DimitriosIn line with Saudi Arabia’s Vision 2030, the tourism sector is undergoing rapid transformation, with smart tourism emerging as a key pillar of innovation and development. This study investigates the integration of Artificial Intelligence (AI) technologies in cultural heritage tourism, with a focus on three significant heritage destinations: Al-Ula, Diriyah, and Historic Jeddah. While innovative tourism technologies such as AI-powered recommendation systems, augmented reality (AR), and sentiment analysis have the potential to enhance tourist experiences, increase visitor satisfaction, and support heritage preservation, their adoption within Saudi Arabia’s heritage sector remains underexplored. This research aims to assess international tourists’ perceptions of AI usefulness, satisfaction, and trust, and to examine their behavioural intentions and willingness to pay for AI-enhanced services. A quantitative survey method was employed, with a sample of 306 international tourists who interacted with AI services at the selected heritage sites. Data were analysed using frequency distribution, descriptive statistics, reliability analysis, ANOVA, and correlation tests. The findings are expected to provide empirical insights into the effectiveness of AI technologies in enhancing cultural tourism experiences while preserving authenticity. The study offers practical implications for tourism authorities, technology developers, and policymakers on how to strengthen innovative heritage tourism strategies in Saudi Arabia.25 0Item Restricted A CLOUD-BASED AI SYSTEM FOR SKILL GAP ANALYSIS AND TRAINING PATH RECOMMENDATION IN HR DEPARTMENTS(Saudi Digital Library, 2025) Alanazi, Abdullah Ramadan; AlYamani, AbdulghaniThis dissertation presents the development of a cloud-based artificial intelligence (AI) system designed to automate skill gap analysis and provide personalised training recommendations in Human Resource (HR) departments. The system integrates employee profiles, job role requirements, and training histories to identify competency gaps using a decision tree algorithm. The AI model achieved an accuracy of 0.86 and demonstrated strong interpretability and efficiency in recommending relevant training paths. Usability testing with HR professionals confirmed the system’s practicality and reliability in supporting workforce development and data-driven training strategies. The research contributes to the field of HR analytics by combining Human Capital Theory with Knowledge Discovery in Databases (KDD) to provide an explainable, scalable, and cloud-enabled HR decision-support framework.10 0Item Restricted Resilience of Saudi Financial Institutions Against AI-Driven Cyber Threats(Saudi Digital Library, 2025) ALshammar, Rushud; Adamos, VasileiosArtificial intelligence (AI) is increasingly exploited by cybercriminals, creating advanced threats that challenge the security of financial institutions. Saudi banks, central to Vision 2030’s digital transformation, face heightened risks from AI-driven attacks such as phishing, fraud detection evasion, and adversarial machine learning. The aim of this research was to evaluate the resilience of six major Saudi banks (NCB, Al Rajhi, SABB, Riyad Bank, BSF, and ANB)against AI-enabled cyber threats, with a focus on identifying gaps in current frameworks, assessing employee awareness, and recommending improvements. A quantitative, cross-sectional survey was employed, gathering data from banking professionals across cybersecurity, compliance, and risk management roles. The findings show that while AI-driven threats are widely recognised, frameworks are inconsistently applied, AI-powered defences are rare, and employee training lacks AI-specific content. These shortcomings reduce institutional agility and leave human awareness as the weakest layer of defence. The study is limited by its reliance on survey data, which restricts depth of institution-specific insights. It recommends mandatory AI-focused training, adoption of automated defence systems, and contextualised national frameworks. Future research should include longitudinal studies, case-specific analyses, and simulation-based testing to strengthen resilience in evolving threat environments.17 0Item Restricted Advancing narcolepsy diagnosis: Leveraging machine learning to identify novel neuro-biomarkers(Saudi Digital Library, 2024) Orkouby, Hadir; Bartsch, UllrichNarcolepsy is a rare neurological disorder with a well-identified pathophysiology that manifests as a sudden onset of sleep during wake behaviour. The current diagnostic pathways for narcolepsy involve complex assessments of sleep neurophysiology, including polysomnography and the multiple sleep latency (MSLT) test. These are cumbersome and work-intensive, and with limited resources within the NHS, this has led to increased waiting times for diagnosis and treatment of narcolepsy. This project harnessed the power of digital neuro-biomarkers and Artificial Intelligence (AI) to develop novel diagnostic markers for narcolepsy. Leveraging an open-source dataset of labelled archival polysomnography (PSG) recordings, including electroencephalography (EEG), I created a data analysis and classification pipeline to enhance diagnostic decision-making in clinical settings. This pipeline combines comprehensive data preprocessing and feature extraction with XGBoost and Random Forest (RF) classification models. The feature extraction process included selected time- series analysis features, spectral frequency ratios, cross-frequency coupling and moment-based statistical features of Intrinsic Mode Functions (IMFs) derived from empirical mode decomposition (EMD). The RF classifier emerged as the best model, achieving an accuracy of 82.5%, with a specificity of 82.5% and a sensitivity of 92.86%, by combining and averaging these feature sets and incorporating sleep stage labels during model training. These results underscore the potential of a novel approach using single-channel sleep EEG data from wearable devices. This innovative method simplifies the lengthy and costly pathway for narcolepsy diagnosis and also paves the way for developing new tools to diagnose sleep disorders automatically in non-clinical environments.14 0Item Restricted Predicting Delayed Flights for International Airports Using Artificial Intelligence Models & Techniques(Saudi Digital Library, 2025) Alsharif, Waleed; MHallah, RymDelayed flights are a pervasive challenge in the aviation industry, significantly impacting operational efficiency, passenger satisfaction, and economic costs. This thesis aims to develop predictive models that demonstrate strong performance and reliability, capable of maintaining high accuracy within the tested dataset and showcasing potential for application in various real-world aviation scenarios. These models leverage advanced artificial intelligence and deep learning techniques to address the complexity of predicting delayed flights. The study evaluates the performance of Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and their hybrid model (LSTM-CNN), which combine temporal and spatial pattern analysis, alongside Large Language Models (LLM, specifically OpenAI's Babbage model), which excel in processing structured and unstructured text data. Additionally, the research introduces a unified machine learning framework utilizing Gradient Boosting Machine (GBM) for regression and Light Gradient Boosting Machine (LGBM) for classification, aimed at estimating both flight delay durations and their underlying causes. The models were tested on high-dimensional datasets from John F. Kennedy International Airport (JFK), and a synthetic dataset from King Abdulaziz International Airport (KAIA). Among the evaluated models, the hybrid LSTM-CNN model demonstrated the best performance, achieving 99.91% prediction accuracy with a prediction time of 2.18 seconds, outperforming the GBM model (98.5% accuracy, 6.75 seconds) and LGBM (99.99% precision, 4.88 seconds). Additionally, GBM achieved a strong correlation score (R² = 0.9086) in predicting delay durations, while LGBM exhibited exceptionally high precision (99.99%) in identifying delay causes. Results indicated that National Aviation System delays (correlation: 0.600), carrier-related delays (0.561), and late aircraft arrivals (0.519) were the most significant contributors, while weather factors played a moderate role. These findings underscore the exceptional accuracy and efficiency of LSTM-CNN, establishing it as the optimal model for predicting delayed flights due to its superior performance and speed. The study highlights the potential for integrating LSTM-CNN into real-time airport management systems, enhancing operational efficiency and decision-making while paving the way for smarter, AI-driven air traffic systems.11 0Item Restricted Exploring the Impact of Artificial Intelligence on Risk Management Practices in Project Management within Small and Medium-Sized Enterprises (SMEs) in the IT Sector of the UK(Saudi Digital Library, 2025) Alburaq, Huda; Rutherford, CarrieThis dissertation investigates the impact of artificial intelligence (AI) on risk management practices in project management within small and medium-sized enterprises (SMEs) in the UK IT sector. The study addresses a gap in understanding how AI adoption influences risk identification, response time, and overall project performance. Using a positivist philosophy and quantitative design, data were collected through an online survey of 75 professionals from UK IT SMEs. Statistical analysis showed that AI implementation significantly improves risk identification effectiveness and reduces response times, confirming two research hypotheses. However, no direct link was found between AI adoption and overall project risk performance, suggesting that successful outcomes depend on additional factors such as organisational readiness, integration strategies, and complementary capabilities. The findings provide both theoretical and practical contributions, emphasising that SMEs should prioritise AI for risk identification and response while building comprehensive integration strategies. This research offers guidance for SMEs seeking to leverage AI in project management and highlights areas for future investigation.7 0Item Restricted AI Impersonation on social media Analysing Human Characteristics and Ethical Implications(Saudi Digital Library, 2025) Almuammar, Eyad; Fahad, AhmadThis study explores the behavioural, ethical, social, and regulatory implications of AI bots that impersonate humans on social media platforms. As artificial intelligence becomes increasingly integrated into online communication, AI-driven bots are being deployed to mimic human users, influence opinions, and automate engagement. While these technologies offer efficiency, they also raise serious concerns about misinformation, manipulation, transparency, and digital trust. Using a structured online questionnaire distributed via platforms such as Twitter (X), LinkedIn, and WhatsApp, this research gathered responses from 57 participants. The survey examined user perceptions across multiple dimensions, including their confidence in identifying bots, behavioural changes due to bot exposure, ethical concerns, perceived political influence, and expectations for regulation and education. Findings indicate that while many users feel moderately confident in recognizing bots, they also express reduced trust and engagement when bots are suspected. Ethical concerns particularly around privacy and undisclosed AI interaction were prominent, and users widely supported stronger regulation, transparency tools, and public education initiatives. The study concludes that AI bots pose a significant challenge to online authenticity and democratic discourse and highlights the need for multi-stakeholder governance to ensure safe and ethical deployment of such technologies.30 0Item Restricted Human Action Recognition Based on Convolutional Neural Networks and Vision Transformers(University of Southampton, 2025-05) Alomar, Khaled Abdulaziz; Xiaohao, CaiThis thesis explores the impact of deep learning on human action recognition (HAR), addressing challenges in feature extraction and model optimization through three interconnected studies. The second chapter surveys data augmentation techniques in classification and segmentation, emphasizing their role in improving HAR by mitigating dataset limitations and class imbalance. The third chapter introduces TransNet, a transfer learning-based model, and its enhanced version, TransNet+, which utilizes autoencoders for improved feature extraction, demonstrating superior performance over existing models. The fourth chapter reviews CNNs, RNNs, and Vision Transformers, proposing a novel CNN-ViT hybrid model and comparing its effectiveness against state-of-the-art HAR methods, while also discussing future research directions.33 0