Effat University Repository
Effat University Institutional Repository
Welcome to the Effat University Repository, a service of Effat Library and Cultural Museum. This repository provides global and open access to scholarship and research output created by Effat University faculty, graduate and undergraduate students.
While the University Repository is accessible to the public, submission is limited to Effat University community.
If you have any questions, please email us at repository@effatuniversity.edu.sa
Recent Submissions
Publication Future X Statdium(Effat University)Like people, cities have distinct personalities, rich histories, and desires that are just waiting to be heard. By creating a unified visual and experiential identity that appeals to both locals and tourists, city branding architecture serves as a link between these intangible attributes and the material world. It is a strategic framework that includes public spaces, cultural programs, urban design, and communication tactics to tell the city's story in an engaging and coherent way. It is not just a logo or tagline.This research paper proposes a Capstone project that follows the theme of City Branding and to design a project that contributes to the city branding and image in response to the directions of 2030 vision. Moreover, this paper will suggest a project in KSA, Riyadh city, study similar cases to the project type, propose a program, analyze a chosen site, and develop a design concept.Publication Unlocking the potential of Arabic NLP : High-quality dataset and preprocessing tool for Arabic large Language models(Effat University)Arabic remains one of the most widely spoken yet technologically underserved languages in the field of Natural Language Processing (NLP), especially within academic and formal domains. This project addresses two critical gaps in Arabic NLP: the scarcity of high-quality domain-specific Arabic datasets for low-resource LLMs and the lack of automated frameworks tailored to the complexities of the Arabic language. Arabic remains underrepresented in large-scale NLP research due to data sparsity, high morphological richness, and limited domain-specific corpora — particularly in academic and educational contexts. To bridge this gap, we developed a curated academic dataset that captures formal Arabic usage across disciplines, aimedat enhancing the training and evaluation of Arabic Large Language Models (LLMs). In parallel, we built a robust, modular framework for large-scale Arabic data preprocessing. This framework automates advanced linguistic refinement stages including deep normalization, morphological transformation, diacritization, and distributed deduplication across multiple GPUs, as well as semantic scoring using LLM-based annotators. By integrating data from Common Crawl and additional Arabic sources such as books and journals, and applying data-centric AI techniques and morphological analysis, the framework ensures high linguistic semantic coherence. Our output is a high-quality, academic-specific Arabic dataset. That was validated through intrinsic evaluations—grammar correctness, lexical diversity, readability, and topic coherence—and extrinsic evaluations on downstream tasks.These outcomes validate the framework’s effectiveness and its potential to accelerate the development of Arabic AI systems. This project supports Saudi Vision 2030 by advancing Arabic AI and aligning with SDG 4 through academic resource accessibility and SDG 9 through scalable NLP tools.Publication On the Aerodynamic Modeling of a VAWT(Sage, 1997)The J.-A. Bombardier Aeronautical Chair at École Polytechnique de Montréal is currently involved in a research program concerning Wind Turbines, Dynamic-Stall, Ice Accretion on Airfoils and Wings and Boundary-Layer Stability and Transition. The Chair Group, who has been engaged in the development of aerodynamic performance analysis of wind turbine machines since the 80's, has carried out a particular focus in the advanced aerodynamics applied to Vertical-Axis Wind Turbines (VAWT's). Computer codes have been developed to predict aerodynamic loads and performance of a Darrieus rotor including viscous, stochastic wind, and dynamic stall effects. Three dynamic stall models are included in our wind turbine load/performance computer codes: Gormont, MIT and Indicial models. A fundamental study of dynamic stall is presently undertaken by solving the unsteady Navier-Stokes equations. The different …Publication Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm(2025-01-16)The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future.Publication Cognitive Radio Receiver Design Employing Pipeline Successive Approximation ADC(2025-01-16)Smart cities require better cellular communication, incorporating a high data rate that satisfies the Internet, cloud computing, and the Internet of Things (IoT) requirements. A high data rate demands higher bandwidth with low latency. However, the already saturated or underutilized frequency spectrum hinders this realization. 5G and cognitive radio technology can enhance spectrum utilization to meet the high data demands of future cellular communication. Cognitive radio uses spectrum sensing to identify underutilized frequencies without interfering with licensed users, addressing the scarcity of the electromagnetic spectrum. In this paper, a cognitive radio receiver design with a SAR analog-to-digital converter, which is recognized for having a modest resolution for high bandwidth signals, is implemented. Moreover, the pipeline technique in the SAR design is proposed to increase the resolution to be suitable for 5G applications. The high-level model of the system is simulated using MATLAB Simulink software. The quantization error observed in the regular SAR ADC design is compared to that in the proposed pipeline SAR ADC, highlighting the enhanced accuracy and efficiency of the pipeline SAR ADC, which makes it suitable for spectrum sensing in next-generation cognitive radio systems.
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