Saudi Cultural Missions Theses & Dissertations
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Item Restricted Open Innovation Ecosystems for Small Businesses in the UK(Bangor University, 2024) Almutairi, Ahmed; Mather, Carl; Jones, Stephen; Ghadiri, Amir; Bagheri, MashidThis study examines the role of open innovation in helping small and medium-sized enterprises (SMEs) in the United Kingdom (UK) address challenges and improve their growth and competitiveness. Open innovation, which integrates external knowledge and resources with internal capabilities, has become increasingly relevant due to its potential to foster creativity, reduce costs, and promote collaborative partnerships. SMEs in the UK, which represent 99.9% of all businesses and contribute over 52% of national turnover, are crucial to the economy but face obstacles such as limited financial resources, talent shortages, digital transformation pressures, and cybersecurity concerns. The research highlights how open innovation, through inbound, outbound, and coupled strategies, can offer solutions to these challenges. By leveraging external knowledge, SMEs can access new technologies, improve products, and expand into new markets. Additionally, the study explores the concept of an open innovation ecosystem, which encourages collaboration among businesses, research institutions, and stakeholders, creating a sustainable environment for innovation. The paper concludes by recommending strategies for SMEs to effectively adopt open innovation practices. These include leveraging government support, forming external partnerships, and developing a clear framework for adoption. Embracing open innovation can enhance SMEs' performance, foster market expansion, and contribute positively to the UK’s economic growth, enabling these businesses to navigate the rapidly changing business landscape and remain competitive.19 0Item Restricted Advanced Deep Learning Approach for Meter Classification in Arabic Poetry Using Bidirectional LSTM Networks(University of Leeds, 2024) Almutairi, Ahmed; Alsalka, AmmarThis dissertation develops and evaluates an advanced artificial intelligence system designed to classify Arabic poems based on their metrical patterns, using a Bidirectional Long Short- Term Memory (Bi-LSTM) network. Given the linguistic complexity of Arabic, which includes extensive morphological variations and rich phonetic patterns, this study addresses significant challenges in the automated classification of poetic meters. The research employs a robust methodology involving advanced text preprocessing techniques such as tokenization, sequence padding, and label encoding to prepare a comprehensive dataset for machine learning. The model is trained and optimized on Google Colab's TPU resources, which enhances computational efficiency and expedites the iterative refinement process. The effectiveness of the system is meticulously assessed through a variety of metrics, including accuracy, precision, recall, and F1-score, to ensure a thorough understanding of its performance across different poetic meters. Validation and testing phases are incorporated to evaluate the model's generalization abilities, utilizing confusion matrices and early stopping mechanisms to pinpoint areas for potential improvement. The findings demonstrate that Bi-LSTM networks are particularly effective in handling the complexities associated with Arabic poetic texts. This project advances the practical application of automatic poetic meter classification and encourage further scholarly exploration of deep learning techniques within Arabic literary studies. Ultimately, this dissertation highlights the potential of AI to enhance the accessibility and analytical depth of Arabic poetry, enriching the appreciation and understanding of this venerable literary tradition.10 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 0