Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Advancing Action Recognition through Artificial Intelligence: A Comprehensive Approach for Home Safety Monitoring using Skeleton Data and Spatial Temporal Graph Convolutional Neural Networks(University College London, 2024-01-16) Alsawadi, Motasem S; Rio, MiguelAccidents resulting from falls are a pressing global concern, especially among the elderly, leading to fatalities, post-fall complications, and limitations in daily activities. Our work introduces an efficient action recognition system, with a primary focus on detecting falls in the fewest possible video frames. Instead of a relying in a single stage (e.g., the classification stage) to solve this issue, we break down the problem into smaller components to enhance the overall action recognition system's accuracy and efficiency. To improve the representation of actions, we utilize skeleton data extracted from RGB images, employing the Spatial Temporal-Graph Convolutional Network. We used the BlazePose topology for action recognition for the first time in the state-of-the art. Moreover, we introduce the Enhanced-BlazePose topology. This innovative approach can represent the actions more accurately. On the other hand, to improve the convolution operation effectiveness, we introduce three new skeleton partitioning strategies: the full-distance, the connection and the index splits. These contributions enhance our ability to recognize human body actions. Recognizing that an abundance of features can hinder machine learning algorithms' performance, we incorporate a feature selection layer, utilizing the Stochastic Fractal Search-Guided Whale Optimization Algorithm (SFS-GWOA) to identify critical joint movements during activities. This feature selection not only enhances performance but also reduces computational costs and processing time. Furthermore, our Multi-Stream Graph Recurrent Neural Network architecture, featuring LSTM units, models spatio-temporal features of skeleton data effectively. Our methodologies and approaches are rigorously evaluated using datasets from restricted and non-restricted environments, demonstrating promising results. Benchmark datasets include NTU-RGB+D, MultiCamera Fall, UR Fall, Kinetics, UCF-101, and HMDB-51. These findings contribute to advancing the field of fall detection and ADL recognition, with practical implications for enhancing the well-being of older individuals living alone.27 0Item Restricted The detection of cross site scripting through the application of machine learning techniques(Saudi Digital Library, 2023) Almutairi, Taher; Arabo, AbdullahiOne of the most significant vulnerabilities that web applications face is Cross- Site Scripting (XSS) attacks. Therefore, our study aimed to compare three distinct machine learning techniques that detect these attacks. These techniques included a hybrid model that combines feature selection information gain (IG), correlation coefficient (CC) and dimensionality reduction principal component analysis (PCA). The evaluation of these models utilized performance metrics such as accuracy, precision, recall, F1 score, area under the curve of the receiver operating characteristic curve (ROC AUC score), training time and prediction time. Ultimately, the hybrid SVM(IG+CC+PCA) model proved to be the most efficient and accurate. This information is important for anyone involved in web application development, as it highlights the effectiveness of machine learning in identifying and mitigating XSS attacks.30 0