SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted MISINFORMATION DETECTION IN THE SOCIAL MEDIA ERA(Howard University, 2024-04-22) Alzahrani, Amani; Rawat, Danda B.As social media becomes the main way of getting information, the spread of misinformation is a serious and widespread problem. Misinformation can take many forms, such as text, video, and audio, and it can travel quickly through different platforms, affecting the quality and trustworthiness of the information that users access around the world. Misinformation can have negative effects on how people think, act, and interact, and it can even endanger social peace. This study aims to tackle the complex problem of misinformation by presenting a comprehensive approach that addresses various forms of deceptive content on social media with a focus on Twitter ( currently X). Twitter stands out as a dynamic and influential microblogging service that enables users to share real-time updates, news, and opinions in concise 280-character messages known as tweets. We introduce a hybrid deep learning model that incorporates Feature-based models at both tweet and user levels, complemented by pre-trained text embedding models such as Global Vectors (GloVe) and Universal Sentence Encoders (USE). Through careful evaluation on a real-world dataset, our approach proves effective in detecting textual misinformation. Recognizing the vital need to verify the reliability of information on social media, we propose a method to assess user credibility. Our solution involves evaluating the credibility of users based on their profiles to enhance the rumors detection model. This study proposes a novel mechanism for assessing a user’s credibility. Additionally, we extended our study capabilities to address the challenges posed by deceptive video content spread on social media using DeepFake technology. As the rapid advancement of deepfake technology threatens the integrity of audio and video content, we present a novel approach combining Optical Flow (OF) algorithms with a Convolutional Neural Network (CNN) to enhance deepfake video detection. This comprehensive strategy addresses the diverse challenges posed by misinformation, credibility assessment, and deepfake detection in the dynamic landscape of social media.36 0Item Restricted Deep Learning-Based Digital Human Modeling And Applications(Saudi Digital Library, 2023-12-14) Ali, Ayman; Wang, PuRecent advancements in the domain of deep learning models have engendered remarkable progress across numerous computer vision tasks. Notably, there has been a burgeoning interest in the field of recovering three-dimensional (3D) human models from monocular images in recent years. This heightened interest can be attributed to the extensive practical applications that necessitate the utilization of 3D human models, including but not limited to gaming, human-computer interaction, virtual systems, and digital twin. The focus of this dissertation is to conceptualize and develop a suite of deep learning-based models with the primary objective of enabling the expeditious and high-fidelity digitalization of human subjects. This endeavor further aims to facilitate a multitude of downstream applications that leverage digital 3D human models. The endeavor to estimate a three-dimensional (3D) human mesh from a monocular image necessitates the application of intricate deep-learning models for enhanced feature extraction, albeit at the expense of heightened computational requirements. As an alternative approach, researchers have explored the utilization of a skeleton-based modality, which represents a lightweight abstraction of human pose, aimed at mitigating the computational intensity. However, this approach entails the omission of significant visual cues, particularly shape information, which cannot be entirely derived from the 3D skeletal representation alone. To harness the advantages of both paradigms, a hybrid methodology that integrates the benefits of 3D human mesh and skeletal information offers a promising avenue. Over the past decade, substantial strides have been made in the estimation of two-dimensional (2D) joint coordinates derived from monocular images. Simultaneously, the application of Convolutional Neural Networks (CNNs) for the extraction of intricate visual features from images has demonstrated its prowess in feature extraction. This progress serves as a compelling impetus for our investigation into a hybrid architectural framework that combines CNNs with a lightweight graph transformer-based approach. This innovative architecture is designed to elevate the 2D joint pose to a comprehensive 3D representation and recover essential visual cues essential for the precise estimation of pose and shape parameters. While SOTA results in 3D Human Pose Estimation (HPE) are important, they do not guarantee the accuracy and plausibility required for biomechanical analysis. Our innovative two-stage deep learning model is designed to efficiently estimate 3D human poses and associated kinematic attributes from monocular videos, with a primary focus on mobile device deployment. The paramount significance of this contribution lies in its ability to provide not only accurate 3D pose estimations but also biomechanically plausible results. This plausibility is essential for achieving accurate biomechanical analyses, thereby advancing various applications, including motion tracking, gesture recognition, and ergonomic assessments. Our work significantly contributes to enhancing our understanding of human movement and its interaction with the environment, ultimately impacting a wide range of biomechanics-related studies and applications. In the realm of human movement analysis, one prominent downstream task is the recognition of human actions based on skeletal data, known as Skeleton-based Human Action Recognition (HAR). This domain has garnered substantial attention within the computer vision community, primarily due to its distinctive attributes, such as computational efficiency, the innate representational power of features, and robustness to variations in illumination. In this context, our research demonstrates that, by representing 3D pose sequences as RGB images, conventional Convolutional Neural Network (CNN) architectures, exemplified by ResNet-50, when complemented by as tute training strategies and diverse augmentation techniques, can attain State-of-the-Art (SOTA) accuracy levels, surpassing the widely adopted Graph Neural Network models. The domain of radar-based sensing, rooted in the transmission and reception of radio waves, offers a non-intrusive and versatile means to monitor human movements, gestures, and vital signs. However, despite its vast potential, the lack of comprehensive radar datasets has hindered the broader implementation of deep learning in radar-based human sensing. In response, the application of synthetic data in deep learning training emerges as a crucial advantage. Synthetic datasets provide an expansive and practically limitless resource, enabling models to adapt and generalize proficiently by exposing them to diverse scenarios, transcending the limitations of real-world data. As part of this research’s trajectory, a novel computational framework known as "virtual radar" is introduced, leveraging 3D pose-driven physics-informed principles. This paradigm allows for the generation of high-fidelity synthetic radar data by merging 3D human models and the principles of Physical Optics (PO) approximation for radar cross-section modeling. The introduction of virtual radar marks a groundbreaking path towards establishing foundational models focused on the nuanced understanding of human behavior through privacy-preserving radar-based methodologies.16 0Item Restricted DEEP LEARNING APPROACHES FOR OBJECT TRACKING AND MOTION ESTIMATION OF ULTRASOUND IMAGING SEQUENCES(Saudi Digital Library, 2023) Alshahrani, Mohammed; Almekkawy, MohamedIn recent decades, object tracking and motion estimation in medical imaging have gained importance. It is a powerful tool that can be used to improve diagnostic accuracy and therapy efficiency. This importance has led researchers to search for faster and more accurate algorithms for object tracking. Different approaches have been used to perform object tracking, such as object detection, motion estimation, and similarity matching, which are the focus of this study. Different avenues can be used to address similarity matching. First, the classical method, which takes an object and searches for a similar object in the subsequent frame (because it is an object tracking in a video sequence) by examining all the sub-windows in the subsequent frame and measuring a cost function between the reference object and the sub-window. This approach is inefficient and cannot achieve real-time tracking. The deep learning method for similarity matching utilizes twin convolutional networks that produce a feature map that is later combined using a correlation layer. This layer provides a score map that points to a high-similarity area. This study examined and developed object tracking algorithms to track objects of interest in the human liver using a correlation filter-based neural network (CFNet). The dataset used in this study was CLUST-2D, which was provided by the Swiss Federal Institute of Technology in Zürich (ETH). It contains approximately 96 ultrasound sequences of the liver from different patients. Three versions of the CFNet network were tested in this study. First, baseline-CFNet was used for training. Baseline-CFNet struggled to track objects under significant displacements and deformations. To address this limitation of the baseline-CFNet, a second version was developed. Advanced-CFNet is the second version of CFNet implemented in this study. This is the main contribution of this study. This version incorporates a dynamic template update and motion prediction modules, which improve object tracking by preventing tracker drift and maintaining the template from being polluted with inappropriate appearances of the tracked object. The third version implemented in this study is Kalman-CFNet, which utilizes a linear Kalman filter to estimate an object's motion and enhance its robustness against unexpected motions. The comparative analysis demonstrated the superiority of Advanced-CFNet, as it achieved lower root mean square error (RMSE) values than the other methods, particularly in challenging scenarios. These findings highlight the effectiveness of the advanced CFNet for object tracking in liver ultrasound imaging.13 0