SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
Browse
39 results
Search Results
Item Restricted Pixels to Pavements(University College London, 2024) Hababi, Abdullah; Selby, EllyThe convergence of machine learning (ML) and the built environment is redefining traditional design decision-making processes. This report explores the integration of ML within architecture, urban design, and urban planning, emphasizing its transformative potential as a design decision making tool. The report delves into the historical context of digital tools in architecture and examines how ML is currently utilized in the built environment. Through a detailed methodology, the report analyzes ML’s role as a computational design aid, as a design facilitator or augmenter, and as a co-designer. This report aims to connect the idea of machine learning’s use in design decision-making processes in the built Environment to my design project. The impact of a literature review and case studies has helped extract and implement different key methods of machine learning in various stages of my design project, such as the data manipulation stage, form finding stage, design intervention placement stage, and simulation analysis of and for design decisions stage. Critical analyses focus on the role of data quality, human agency, and the limitations of ML, such as algorithmic bias and the potential erosion of human creativity. This report contends that ML can profoundly influence and effectively dictate design decision making in both an architectural and urban design context, through its aid as a computational design tool, design facilitator, and co-designer. The discussion emphasizes the necessity of human expertise in interpreting ML outputs and proposes a collaborative approach between human intuition and ML capabilities. The report concludes by advocating for a continuous dialogue between technology and human creativity to ensure ML serves as a valuable tool in shaping the built environment rather than a replacement for human ingenuity.5 0Item Restricted Supervised Machine Learning. A Strategic Approach for Financial Fraud Detection(University of Nottingham, 2024-03) Bashehab, Omar Sami; Wang, HuamaoFinancial fraud is an increasingly concerning issue in the present day. The rapidly growing rate of fraudulent activities has led to significant financial losses for many stakeholders. Card-not-present (CNP) fraud has risen with the growth of digital sales. The same benefits attracting online banking and transactions have attracted fraudsters and cybercriminals. Consequently, the incentive for fraud detection for mitigating financial risk is evident. However, traditional detectors are outdated, and rule-based systems fail to keep up with the dynamic innovative methodologies of cybercriminals. Thus, the ML-based system is needed. However, various challenges exist within ML-based detectors. Firstly, datasets are typically highly imbalanced and secondly, a lack of real-world datasets makes research extremely difficult. To tackle these problems, different resampling methods such as RUS, ROS, SMOTE and a hybrid sampling approach (ROS + RUS) were used and evaluated. Furthermore, a novel dataset was used, augmented from an original PAYsim real-world synthetic data. Furthermore, predictive models such as Decision Tree, Logistic Regression, Random Forests, Support Vector Machine and (Gaussian) Naïve Bayes were used with the different resampling methods in a comparative approach. Finally, the importance of data preprocessing and feature engineering was explored and evaluated amongst the classifiers. The experimental results illustrate the Random Forest, with Grid Search CV optimisation and RUS as well as feature engineering performed the best. The methodological approach exhibited an increase in F1 score, True Positive Rate, Recall and Accuracy for the classifier. The final model outputted an F1 score of 69%, ROC-AUC of 88% and True Positive Rate (TPR) of 93%.57 0Item Restricted LIGHTREFINENET-SFMLEARNER: SEMI-SUPERVISED VISUAL DEPTH, EGO-MOTION AND SEMANTIC MAPPING(Newcastle University, 2024) Alshadadi, Abdullah Turki; Holder, ChrisThe advancement of autonomous vehicles has garnered significant attention, particularly in the development of complex software stacks that enable navigation, decision-making, and planning. Among these, the Perception [1] component is critical, allowing vehicles to understand their surroundings and maintain localisation. Simultaneous Localisation and Mapping (SLAM) plays a key role by enabling vehicles to map unknown environments while tracking their positions. Historically, SLAM has relied on heuristic techniques, but with the advent of the "Perception Age," [2] research has shifted towards more robust, high-level environmental awareness driven by advancements in computer vision and deep learning. In this context, MLRefineNet [3] has demonstrated superior robustness and faster convergence in supervised learning tasks. However, despite its improvements, MLRefineNet struggled to fully converge within 200 epochs when integrated into SfmLearner. Nevertheless, clear improvements were observed with each epoch, indicating its potential for enhancing performance. SfmLearner [4] is a state-of-the-art deep learning model for visual odometry, known for its competitive depth and pose estimation. However, it lacks high-level understanding of the environment, which is essential for comprehensive perception in autonomous systems. This paper addresses this limitation by introducing a multi-modal shared encoder-decoder architecture that integrates both semantic segmentation and depth estimation. The inclusion of high-level environmental understanding not only enhances scene interpretation—such as identifying roads, vehicles, and pedestrians—but also improves the depth estimation of SfmLearner. This multi-task learning approach strengthens the model’s overall robustness, marking a significant step forward in the development of autonomous vehicle perception systems.31 0Item Restricted Enhancing Network Security through Machine Learning and Threat Intelligence Integration in Next-Generation Firewall IDS/IPS Systems(Northumbria University, 2024-09-05) Sufi, Mohammed; Abosata, NassrThis dissertation explores how Machine Learning (ML) and real-time Threat Intelligence feeds can improve Next-Generation Firewall (NGFW) systems especially in increasing the accuracy and efficacy of Intrusion Detection and Prevention Systems which contribute in enhancing network security. Using threat intelligence feeds including IP addresses, domains, and URLs which come with related information’s such as the Indicators of Compromise (IoC) reputation scores, and threat categories like "malware" or "phishing,”. Thus, by using this information, applying supervised learning techniques enable to easily assess and classify threats into high-risk and low risk categories in order to reduce false positives, which result in enhancing threat detection and prevention accuracy. These classified threat feeds are dynamically updated, allowing the NGFW to protect against new threats by adjusting its security rules with appropriate countermeasures. The results show that combining ML with classified threat feeds improves the NGFW's capacity to detect and prevent threats, leading to more focused and responsive threat management.22 0Item Restricted Forecasting OPEC Basket Oil Price and Its Volatilities Using LSTM(University College London, 2024-09) Almazyad, Sulaiman; Hamadeh, LamaThe global economy is greatly affected by oil prices, which have an impact on everything from consumer goods prices to transportation expenses. Forecasting these prices accurately is crucial for energy security, company strategy, and economic planning. Traditional statistical models such as ARIMA and SARIMA have been used for such forecasts, but struggle with the non-linear patterns inherent in oil price movements. This research explores the use of Long Short-Term Memory (LSTM) networks, a specialized form of Recurrent Neural Network (RNN) built to manage longterm dependencies, in predicting the OPEC reference basket oil price and its associated volatility, ultimately improving the accuracy of these forecasts. The model is built upon historical datasets of the OPEC Reference Basket (ORB), and its efficacy is assessed using a variety of performance indicators, including RMSE, MAE, and MAPE. The outcomes reveal that the LSTM model is10 0Item Restricted Credit Card Fraud Prediction Using Machine Learning Model(University of Essex, 2024-08) Alanazi, Mohammed; Walton, MichaelThe widespread adoption of credit cards has significantly increased the frequency of fraudulent activities. This has resulted in considerable financial losses for both consumers and financial institutions. As the use of credit cards continues to grow, the challenge of protecting transactions against unauthorized access has become more serious than ever. This research focuses on creating a solution using machine learning to accurately and effectively identify fraudulent credit card transactions. It addresses the issue of uneven transaction data by employing advanced methods such as logistic regression, XGBoost, LightGBM, and a hybrid model. The research involves thorough data preparation, model development, and careful assessment using measures “such as accuracy, precision, recall, F1 score, and ROC AUC”. This research leverages sophisticated machine learning techniques and tackles the specific challenges associated with imbalanced data. The study aims to significantly enhance the detection of fraudulent transactions while reducing false positives. The ultimate goal is to boost the security of financial systems, thus providing better protection against fraud, and to improve trust and reliability in credit card transactions.46 0Item Restricted Leveraging Brain-Computer Interface Technology to Interpret Intentions and Enable Cognitive Human-Computer Interaction(Univeristy of Manchester, 2024) Alsaddique, Luay; Breitling, RainerIn this paper, I present the developed, integration, and evaluation of a Brain–Computer Interface (BCI) system which showcases the accessibility and usability of a BCI head- set to interact external devices and services. The paper initially provides a detailed survey of the history of BCI technology and gives a comprehensive overview of BCI paradigms and the underpinning biology of the brain, current BCI technologies, recent advances in the field, the BCI headset market, and prospective applications of the technology. The research focuses on leveraging BCI headsets within a BCI platform to interface with these external end-points through the Motor Imagery BCI paradigm. I present the design, implementation, and evaluation of a fully functioning, efficient, and versatile BCI system which can trigger real-world commands in devices and digital services. The BCI system demonstrates its versatility through use cases such as control- ling IoT devices, infrared (IR) based devices, and interacting with advanced language models. The system’s performance was quantified across various conditions, achiev- ing detection probabilities exceeding 95%, with latency as low as 1.4 seconds when hosted on a laptop and 2.1 seconds when hosted on a Raspberry Pi. The paper concludes with a detailed analysis of the limitations and potential im- provements of the newly developed system, and its implications for possible appli- cations. It also includes a comparative evaluation of latency, power efficiency, and usability, when hosting the BCI system on a laptop versus a Raspberry Pi.30 0Item Restricted A Peer-to-Peer Federated Learning Framework for Intrusion Detection in Autonomous Vehicles(Lancaster University, 2024-09) Alotaibi, Bassam; Bradbury, MatthewAs autonomous vehicles (AVs) increasingly rely on interconnected systems for enhanced functionality, they also face heightened cyberattack vulnerability. This study introduces a decentralized peer-to-peer federated learning framework to improve intrusion detection in AV environments while preserving data privacy. A novel soft-reordering one-dimensional Convolutional Neural Network (SR-1CNN) is proposed as the detection engine, capable of identifying known and unknown threats with high accuracy. The framework allows vehicles to communicate directly in a mesh topology, sharing model parameters asynchronously, thus eliminating dependency on centralized servers and mitigating single points of failure. The SR-1CNN model was tested on two datasets: NSL-KDD and Car Hacking, under both independent and non-independent data distribution scenarios. The results demonstrate the model’s robustness, achieving detection accuracies of 94.39% on the NSL-KDD dataset and 99.97% on the Car Hacking dataset in independent settings while maintaining strong performance in non-independent configurations. These findings underline the framework’s potential to enhance cybersecurity in AV networks by addressing data heterogeneity and preserving user privacy. This research contributes to the field of AV security by offering a scalable, privacy-conscious intrusion detection solution. Future work will focus on optimizing the SR-1CNN architecture, exploring vertical federated learning approaches, and validating the framework in real-world autonomous vehicle environments to ensure its practical applicability and scalability.28 0Item Restricted Negative Mixture Models via Squaring Representation and Learning(University of Nottingham, 2024) Almhmadi, Samaher; Raykov, YordanThe truths behind a real-world data can be faced by measuring the uncertainty around data. From probabilistic view, the uncertainty is used with respected to unsupervised learning as learning objectives under the probability distributions and inference. Mixture models enhanced the expressiveness of probability distributions. Mixture models have provided a general framework used for clustering data by building more complex probability distributions. We are begging with discussion of mixture distributions and introduced the latent variable concept. Mixture types with respect to the number of components and its formulation are discussed. Some example of Gaussian mixture models is exposed. Mixture types with respect to mixture coefficient are also discussed. We exposed the statistical inference problem of mixture models with different approaches such as, latent variable models, Markov chain Mote Carlo method and variational methods. Through our discussion, we exposed a several illustrative examples. Some concepts of probabilistic circuits: representation, formulation and the corresponding inference are also discussed. In thesis, we applied probabilistic circuits in probabilistic inference. Also, we discussed how the negative mixture is presented as probabilistic circuits. And its structure as tractable computational graphs. Also, we discussed the representation for the squared negative mixture models as efficiently tensorized computational graphs. As well as how can reduces the model size under including negative parameters in this class of functions. Mixture models and especially negative mixture model via squaring to learn the truths of real data was discussed. Due to Gaussian mixture models applied in several branches of science such as machine learning, data mining, pattern recognition and statistical analysis. And Gaussian mixture model and negative Gaussian mixture model are an important subclass for learning in data. In this thesis, we focused on discussion these models in two cases positive and negative case. For the representing the valid negative mixture models, we discuss a generic strategy to support negative parameters called squaring a base mixture. And then, this framework is extending to probabilistic circuits. Finally, we discuss the main idea of my thesis The main aim of this thesis is discussion the inference problem in the framework of mixture models. As well as the basic role which play each of positive mixture model and negative weight mixture model, especially standard Gaussian mixture model and negative weight Gaussian mixture model in inference problem. we expose this thesis in five subsequent chapters describe as follows. In Chapter 1: We discuss mixture motivation and mixture types. Also, we expose to some standard mixture models. In Chapter 2: We discuss mixture types with respected to its coefficients. When mixture coefficient is reduced to negative values for some not all coefficients then mixture model called negative weight mixture model. Also, in this chapter expos to the statistical inference problem of mixture models with different approaches such as latent variable models, Markov chain Mote Carlo (MCMC) method and variational methods. In Chapter 3: We discuss the important ideas around the problem of probabilistic inference. Information about the class of queries to computing interesting quantities of a probability distribution are discussed and makes a family of probabilistic model tractable. Different illustrative examples are exposed. The probabilistic circuits: representation and inference were discussed. At the end of this chapter discussed negative MMs via squaring and representing negative MMs as probabilistic circuits. In Chapter 4: We discuss Gaussian mixture models used to present subpopulations within an overall population. Also, we have known how Gaussian mixtures which is constituted a form of unsupervised learning. In the second part, we discussed the negative weight Gaussian mixture models under negative coefficients which make it more expressive than Gaussian mixture models by reducing the number of components and parameters. Also, the comparison between standard Gaussian mixture model and negative weight Gaussian mixture model are formulated under a real example. In Chapter 5: We discuss the important contributions of positive and negative weight mixture models especially positive and negative weight Gaussian mixture models. As well as the future works which can be developed in mixture framework.76 0Item Restricted Utilizing Data Analytics for Fraud Detection and Prevention in Online Banking Systems of Saudi Arabia(University of Portsmouth, 2024-09) Almotairy, Yazeed; Jiacheng, TanThis thesis addresses the critical issues of online banking and online banking fraud in Saudi Arabia. The thesis focusses on the older methodologies of the online banking systems in Saudi Arabia. The frauds are discussed in detail that are occurring in the online banking systems and are causing inconvenience to the users and account holders of the online banks and applications. In this thesis, online banking frauds are discussed thoroughly, and the traditional fraud detection methods are elaborated as well. The vulnerabilities in the current systems are explored. It discusses how the older systems are not performing well and why the new system encompasses the power of data analytics and machine learning. The methods proposed use a set of data analytics and machine learning algorithms and techniques to detect fraud or any fraudulent activity that a scammer or fraudster may perform. The results of this study explain how the proposed system can outperform the traditional methodologies being used in Saudi Arabian online banking systems. The proposed system can also enhance the user experience. The possible privacy and ethical concerns are also discussed. In the end, it is also discussed what the future prospects are for the researchers who are looking to enhance this research or want to work in the field of data analytics and machine learning to improve the security of the security of online banking applications. In conclusion, this thesis not only contributes to the body of knowledge on online banking frauds in Saudi Arabia and their detection but also features future research topics for new researchers.14 0