SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Automatic Detection and Verification System for Arabic Rumor News on Twitter(University of Technology Sydney, 2026-04) Karali, Sami; Chin-Teng, LinLanguage models have been extensively studied and applied in various fields in recent years. However, the majority of the language use models are designed for and perform significantly better in English compared to other languages, such as Arabic. The differences between English and Arabic in terms of grammar, writing, and word-forming structures pose significant challenges in applying English-based language models to Arabic content. Therefore, there is a critical need to develop and refine models and methodologies that can effectively process Arabic content. This research aims to address the gaps in Arabic language models by developing innovative machine learning (ML) and natural language processing (NLP) methodologies. We apply the developed model to Arabic rumor detection on Twitter to test its effectiveness. To achieve this, the research is divided into three fundamental phases: 1) Efficiently collecting and pre-processing a comprehensive dataset of Arabic news tweets; 2) The refinement of ML models through an enhanced Convolutional Neural Network (ECNN) equipped with N-gram feature maps for accurate rumor identification; 3) The augmentation of decision-making precision in rumor verification via sophisticated ensemble learning techniques. In the first phase, the research meticulously develops a methodology for the collection and pre-processing of Arabic news tweets, aiming to establish a dataset optimized for rumor detection analysis. Leveraging a blend of automated and manual processes, the research navigates the intricacies of the Arabic language, enhancing the dataset’s quality for ML applications. This foundational phase ensures removing irrelevant data and normalizing text, setting a precedent for accuracy in subsequent detection tasks. The second phase is to develop an Enhanced Convolutional Neural Network (ECNN) model, which incorporates N-gram feature maps for a deeper linguistic analysis of tweets. This innovative ECNN model, designed specifically for the Arabic language, marks a significant departure from traditional rumor detection models by harnessing the power of spatial feature extraction alongside the contextual insights provided by N-gram analysis. Empirical results underscore the ECNN model’s superior performance, demonstrating a marked improvement in detecting and classifying rumors with heightened accuracy and efficiency. The culmination of the study explores the efficacy of ensemble learning methods in enhancing the robustness and accuracy of rumor detection systems. By synergizing the ECNN model with Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) networks within a stacked ensemble framework, the research pioneers a composite approach that significantly outstrips the capabilities of singular models. This innovation results in a state-of-the-art system for rumor verification that outperforms accuracy in identifying rumors, as demonstrated by empirical testing and analysis. This research contributes to bridging the gap between English-centric language models and Arabic language processing, demonstrating the importance of tailored approaches for different languages in the field of ML and NLP. These contributions signify a monumental step forward in the field of Arabic NLP and ML and offer practical solutions for the real-world challenge of rumor proliferation on social media platforms, ultimately fostering a more reliable digital environment for Arabic-speaking communities.17 0Item Restricted iBFog: Intelligent Blockchain-based Methodology for Verifiable Fog Selection and Participation(University of Technology Sydney, 2024-05-28) Alshuaibi, Enaam Abdulmonem O; Hussain, Farookh KhadeerFog computing has emerged as an important game-changing technology to address the resource challenges of the Internet of Things (IoT). However, the rapid increase in computational resource requirements at the edge of the network results in small-to-medium enterprises that provide fog services (FogSMEs) facing challenges in scalability, resource limitations, and network reliability. As a result of these challenges, FogSMEs are unable to meet modern data processing, security, and decision-making requirements. By exploring strategies that allow FogSMEs to maximize the benefits of the distributed nature of fog computing, in this thesis, we discuss volunteer computing as an innovative and cost-effective solution to improve their infrastructure. Leveraging idle computational resources from a global network of volunteer users, FogSMEs can achieve scalable, real-time services without significant investment in physical infrastructure. Research has identified significant gaps in the existing literature, including the absence of intelligent platforms to manage volunteer resources, dynamic selection mechanisms for volunteer nodes, and incentives to increase volunteer recruitment. To bridge the gaps in the recent literature, this thesis proposes an intelligent and reliable framework for selecting and verifying volunteer computing resources for fog scalability, named iBFog, by addressing three critical objectives: developing a trustworthy platform for managing volunteer nodes, designing an incentive mechanism to motivate participation, and implementing an intelligent selection mechanism for optimal node utilization. These objectives aim to overcome the challenges of fog scalability by ensuring efficient, secure, and reliable fog computing networks, especially for FogSMEs. This thesis contributes to the literature along three dimensions by including a systematic literature review to identify the need for an intelligent framework utilizing volunteer computing for fog scalability, the development of the iBFog framework that comprises a blockchain-based fog repository using Hyperledger Fabric, a game-based incentive module using Stackelberg game theory, and a ranking and selection module using three methods: a statistical method, a machine learning method, and a deep learning method. These components collectively address the identified research gaps, offering a comprehensive solution to the challenges of FogSME scalability. By intelligently managing, incentivizing and selecting volunteer computing resources, the iBFog framework advances the field of fog computing using a novel approach to enhancing its scalability. This framework not only addresses the immediate challenges of fog computing scalability but also sets the groundwork for future research and development in distributed computing environments.20 0Item Restricted Developing AI-Powered Support for Improving Software Quality(University of Wollongong, 2024-01-12) Alhefdhi, Abdulaziz Hasan M.; Dam, Hoa Khanh; Ghose, AdityaThe modern scene of software development experiences an exponential growth in the number of software projects, applications and code-bases. As software increases substantially in both size and complexity, software engineers face significant challenges in developing and maintaining high-quality software applications. Therefore, support in the form of automated techniques and tools is much needed to accelerate development productivity and improve software quality. The rise of Artificial Intelligence (AI) has the potential to bring such support and significantly transform the practices of software development. This thesis explores the use of AI in developing automated support for improving three aspects of software quality: software documentation, technical debt and software defects. We leverage a large amount of data from software projects and repositories to provide actionable insights and reliable support. Using cutting-edge machine/deep learning technologies, we develop a novel suite of automated techniques and models for pseudo-code documentation generation, technical debt identification, description and repayment, and patch generation for software defects. We conducted several intensive empirical evaluations which show the high effectiveness of our approach.30 0Item Restricted Audio-to-Video Synthesis using Generative Adversarial Network(University of New South Wales, 2024-01-23) Aldausari, Nuha; Mohammadi, Gelareh; Sowmya, Arcot; Marcus, NadineVideo generation is often perceived as stringing several image generators. However, in addition to visual quality, video generators must also consider motion smoothness and synchronicity with audio and text. Audio plays a crucial role in guiding visual content, as even slight discrepancies between audio and motion can be noticeable to human eyes. Thus, audio can be a self-supervised signal for learning the motion and building correlations between the audio and motion. While there are attempts to build promising audio-to-video generation models, these models typically rely on supervised signals such as keypoints. However, annotating keypoints as supervised signals takes time and effort. Thus, this thesis focuses on audio-based pixel-level video generation without keypoints. The primary goal of this thesis is to build models that generate a temporally and spatially coherent video from audio inputs. The thesis proposes multiple audio-to-video generator frameworks. The first proposed model, PhonicsGAN, uses GRU units for audio to generate pixel-based videos. The subsequent frameworks, each, address particular challenges while still pursuing the main objective. To improve the spatial quality of the generated videos, a model that adapts the image fusion concept to video generation is proposed. This model incorporates a multiscale fusion model that combines images with video frames. While the spatial quality of the video is important, the temporal aspect of the video frames should also be considered. To address this, a shuffling technique is proposed presenting each dataset sample with varied permutations to improve the video's temporal learning. We propose a new model that learns motion trajectory from sparse motion frames. AdaIN is utilised to adjust the motion in the content frame to the target frame to enhance the learning of video motion. All the proposed models are compared with state-of-the-art models to demonstrate their ability to generate high-quality videos from audio inputs. This thesis contributes to the field of video generation in several ways: Firstly, by providing an extensive survey on GAN-based video generation techniques. Secondly, by proposing and evaluating four pixel-based frameworks for enhanced audio-to-video generation output that each addresses important challenges in the field. Lastly, by collecting and publishing a new audio/visual dataset that can be used by the research community for further investigations in this area.24 0Item Restricted Automatic Generation of a Coherent Story from a Set of Images(Saudi Digital Library, 2023-12) Aljawy, Zainy; Mian, Ajaml; Hassan, Ghulam MubasharThis dissertation explores vision and language (V&L) algorithms. While (V&L) succeeds in image and video captioning tasks, the dynamic Visual Storytelling Task (VST) remains challenging. VST demands coherent stories from a set of images, requiring grammatical accuracy, flow, and style. The dissertation addresses these challenges. Chapter 2 presents a framework utilizing an advanced language model. Chapters 3 and 4 introduce novel techniques that integrate rich visual representation to enhance generated stories. Chapter 5 introduces a new storytelling dataset with a comprehensive analysis. Chapter 6 proposes a state-of-the-art Transformer-based model for generating coherent and informative story descriptions from image sets.52 0Item Restricted Leveraging Machine Learning for Enhanced Detection and Classification of Brain Pathologies Using EEG(Saudi Digital Library, 2023-11-09) Albaqami, Hezam; Hassan, Ghulam Mubashar; Datta, AmitavaMaintaining brain health is vital due to its role in controlling all body functions. This thesis introduces novel methods for the problem of automated brain diagnostic tasks using electroencephalogram (EEG). Several contributions have been made, including wavelet-based feature extraction methods and novel deep-learning architectures for detecting and classifying brain pathologies. Additionally, novel methods of feature dimensionality reduction, data fusion, and data augmentation are proposed. The proposed solutions are rigorously assessed using extensive EEG datasets consisting of patients from a wide demographic range to evaluate the generalization capabilities. This thesis offers significant contributions to biomedical signal processing for diagnostic tasks.46 0Item Restricted Deep Learning-Based Frameworks for Automated Identifying Depression Through Social Media(Saudi Digital Library, 2023-04-01) Zogan, Hamad; Xu, GuandongThe data generated by users on Twitter is precious for healthcare technology as it can reveal important patterns that can greatly benefit the field in multiple ways. Notably, most of the recent depression detection models are limited to detecting a large number of posts. The objective of this study is to integrate user posts and behavior to create a broad spectrum of behavioral, and semantic representations of users. This will enable the automatic selection of the most significant user-generated information and the development of an explainable deep-learning architecture to identify depression. Furthermore, this study aims to create new tasks that model user narratives in social media.61 0