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 SEVERITY GRADING AND EARLY DETECTION OF ALZHEIMER’S DISEASE THROUGH TRANSFER LEARNING(Saudi Digital Library, 2025) Alqahtani, Saeed; Zohdy, MohamedAlzheimer’s disease (AD) is a neurological disorder that predominantly affects individuals aged 65 and older. It is one of the primary causes of dementia, and it contributes significantly and progressively to impairing and destroying brain cells. Recently, efforts to mitigate the impact of AD have focused with particular emphasis on early detection through computer aided diagnosis (CAD) tools. This study aims to develop deep learning models for the early detection and classification of AD cases into four categories: non-demented, moderate-demented, mild-demented, and very mild demented. Using Transfer Learning technique (TL), several models were implemented including AlexNet, ResNet-50, GoogleNet (InceptionV3), and SqueezeNet, by leveraging magnetic resonance images (MRI) and applying image augmentation techniques. A total of 12,800 images across the four classifications that were preprocessed to ensure balance and meet the specific requirements of each model. The dataset was split into 80% for training and 20% for testing. AlexNet achieved an average accuracy of 98.05%, GoogleNet (InceptionV3) reached 97.80%, ResNet-50 attained 91.11%, and SqueezeNet 86.37%. The use of transfer learning method addresses data limitations, allowing effective model training without the need for building from scratch, thereby enhancing the potential for early and accurate diagnosis of Alzheimer’s disease [1].14 0Item Restricted Industrial AI-based Workload Performance Analytics: Applications to Mixed Reality Multitasking(University of Illinois at Chicago, 2025) Abbas, Safanah; He, DavidImmersive technologies such as augmented and virtual reality are increasingly integrated into our daily lives. As this digital transformation progresses, understanding human reactions to these technologies becomes crucial, particularly in the context of human factors engineering, which prioritizes human safety and well-being. Mixed reality (MR), which blends the physical and virtual worlds, introduces new multitasking possibilities but also presents challenges. One critical aspect is the impact of MR multitasking on human workload, a key performance measure. This research employs an Industrial AI approach, combining traditional machine learning with advanced pre-trained models to develop predictive models for estimating human workload in MR environments. An experiment was conducted in which participants multitasked between a physical and a digital task within a defined timeframe. Workload data, collected via the NASA Task Load Index (NASA-TLX), was used alongside synthetic data generated by a Generative Adversarial Network (GAN) to create an enriched dataset. The combined real and synthetic data were then used to train predictive models, enhancing accuracy. To improve workload prediction, this study integrates pre-trained models such as BERT (Bidirectional Encoder Representations from Transformers) from large language models (LLMs) and CLIP (Contrastive Language-Image Pretraining) from computer vision applications, alongside traditional machine learning techniques like regression and neural networks. Evaluation using the Root Mean Square Error (RMSE) metric demonstrates that the proposed hybrid models incorporating transfer learning and pre-trained models significantly outperformed conventional methods. The deviations between actual and predicted values were minimal, indicating a more reliable workload estimation. This dissertation advances knowledge in human factors engineering by addressing a critical gap in workload prediction within MR multitasking environments. The findings provide insights into human-computer interaction in complex digital settings. Organizations implementing MR technologies can leverage these predictive models to better understand worker workload and optimize conditions for well-being and efficiency.8 0Item Restricted A Deep Learning Framework for Blockage Mitigation in mmWave Wireless(Portland State University, 2024-05-28) Almutairi, Ahmed; Aryafar, EhsanMillimeter-Wave (mmWave) communication is a key technology to enable next generation wireless systems. However, mmWave systems are highly susceptible to blockages, which can lead to a substantial decrease in signal strength at the receiver. Identifying blockages and mitigating them is thus a key challenge to achieve next generation wireless technology goals, such as enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC). This thesis proposes several deep learning (DL) frameworks for mmWave wireless blockage detection, mitigation, and duration prediction. First, we propose a DL framework to address the problem of identifying whether the mmWave wireless channel between two devices (e.g., a base station and a client device) is Lineof- Sight (LoS) or non-Line-of-Sight (nLoS). Specifically, we show that existing beamforming training messages that are exchanged periodically between mmWave wireless devices can also be used in a DL model to solve the channel classification problem with no additional overhead. We extend this DL framework by developing a transfer learning model (t-LNCC) that is trained on simulated data and can successfully solve the channel classification problem on any commercial-off-the-shelf (COTS) mmWave device with/without any real-world labeled data. The second part of the thesis leverages our channel classification mechanism from the first part and introduces new DL frameworks to mitigate the negative impacts of blockages. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. We go beyond those techniques by proposing DL frameworks that address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To do so, we developed two Gated Recurrent Unit (GRU) models that are trained using periodically exchanged messages in mmWave systems. Specifically, we first developed a GRU model that tackled the blockage mitigation problem in single-antenna clients wireless environment. Then, we proposed another GRU model to expand our investigation to cover more complex scenarios where both base stations and clients are equipped with multiple antennas and collaboratively mitigate blockages. Those two models are trained on datasets that are gathered using a commercially available mmWave simulator. Both models achieve outstanding results in selecting the optimal blockage mitigation method with an accuracy higher than 93% and 91% for single-antenna and multiple-antenna clients, respectively. We also show that the proposed methods significantly increases the amount of transferred data compared to several other blockage mitigation policies.17 0Item Restricted ENSEMBLE MACHINE LEARNING IN SPACE WEATHER ANALYTICS(New Jersey Institute of Technology, 2024) Alobaid, Khalid; Wang, JasonThis dissertation addresses several important space weather problems using ensemble learning techniques. An ensemble method combining multiple machine learning models is often more accurate than the individual machine learning models that form the ensemble method. There are several techniques for constructing an ensemble. With in-depth case studies, the dissertation demonstrates the usefulness and effectiveness of ensemble machine learning for space weather analytics, especially for predicting extreme space weather events such as coronal mass ejections (CMEs). The dissertation begins with an ensemble method for predicting the arrival time of CMEs from the Sun to Earth. The proposed method, named CMETNet, combines classical machine learning algorithms such as support vector regression, random forests, XGBoost and Gaussian process regression, along with a deep convolutional neural network (CNN), to perform multimodal learning. The classical machine learning algorithms are used to learn latent patterns from CME features and background solar wind parameters while the deep CNN is used to learn patterns hidden in CME images where the learned patterns are jointly used to make predictions. Experimental results show that CMETNet outperforms existing models, both machine learning based and physics based. Finally, the dissertation presents a fusion method, named DeepCME, to estimate two important properties of CMEs, namely, CME mass and kinetic energy. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. The fusion model extracts features from Large Angle and Spectrometric Coronagraph (LASCO) C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. To the best of current knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations. DeepCME can help scientists better understand CME dynamics. In conclusion, the dissertation showcases many applications of learning techniques including ensemble learning, deep learning, transfer learning and multimodal learning in space weather analytics. The tools and methods developed from the dissertation will make contributions to the understanding and forecasting of CME dynamics and CME geoeffectiveness.18 0Item Restricted Transforming Medical Image Segmentation with Enhanced U-Net Architectures and Adaptive Transfer Learning(Saudi Digital Library, 2023-05-25) Albishri, Ahmed; Yugyung, LeeMedical imaging has revolutionized healthcare by enabling accurate diagnosis, treatment planning, and monitoring of various diseases. Various modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound, visualize diverse anatomical structures and pathological conditions. However, challenges arise in medical image segmentation due to increasing complexity, variability, noise, artifacts, and scarcity of annotated data. The advent of AI, particularly deep learning with Convolutional Neural Networks (CNNs), has facilitated significant advancements in medical image segmentation. U-Net, a prominent CNN architecture, provides accurate segmentation results with relatively low training samples due to its encoder-decoder structure with skip connections. In addition, transfer learning further mitigates limitations imposed by scarce labeled data. In this thesis, we develop innovative custom U-Net models with advanced building blocks and transfer learning strategies, such as AM-UNet for human brain claustrum segmentation from MRI scans, TLU-Net for organ and tumor segmentation from CT scans, and OCU-Net for oral cancer tissue segmentation from whole slide images (WSI) stained with Hematoxylin and Eosin (H&E). Furthermore, we introduce the "U-Framework", a comprehensive guide in designing and optimizing U-Net models. This framework encompasses key decisions related to architecture, transfer learning, module selection and fine-tuning, and evaluation strategies. Finally, by comparing our models with state-of-the-art approaches on benchmark datasets, we demonstrate their significant potential to contribute to medical image segmentation.37 0