Effat University Institutional Repository
Not a member yet
1865 research outputs found
Sort by
Drone-Based AI System for Real-Time Hazard Detection and Crowd Safety During Hajj and Umrah
No full textMass gatherings like Hajj and Umrah, which draw millions of pilgrims annually to Mecca, present complex
safety challenges due to extreme crowd density, high temperatures, and rapidly evolving risk environ ments. Traditional surveillance methods, like CCTV and manual monitoring, are generally inadequate
for real-time response. In this report, we present the development and evaluation of an AI-powered
drone system selected to detect threats and improve crowd safety during Hajj and Umrah. Integrat ing drone-based live monitoring and deep learning algorithms is proposed to identify critical threats
like overcrowding, associated heat stress, and medical emergencies. We developed a custom application
to perform real-time hazard classification, emergency alerting, and centralized data visualization. We
trained an augmented dataset of Hajj-related images with real and synthetic images using the deep
learning model that includes YOLOv5 for object detection, U-Net for segmentation, and ResNet50 for
severity analysis in the developed Android application. With edge deployment using Raspberry Pi and
NVIDIA Jetson Nano, the system operated for its accuracy promise in low-connectivity, high-density en vironments, with a latency of less than two seconds. Experimental results demonstrated high accuracy:
Fire hazards, crowd congestion, and medical emergencies were 92.3%, 88.7%, and 90.1%, respectively.
It consists of a scalable backend, a mobile dashboard, and an alert notification system for ease of use
and operational reliability for emergency responders and drone operators. The project fills significant
gaps in existing surveillance systems by adding real-time responsiveness, AI-based prediction, and inte gration with ground crews. This system aligns with Saudi Vision 2030’s aspiration to improve the safety
of pilgrimage and achieves a new benchmark in large-scale event safety management. Further research
includes extending hazard categories, including swarm drone intelligence, and increasing compatibility
between platforms for deployment on a larger scale internationally at mass gatherings
Cognitive Radio Receiver Design Employing Pipeline Successive Approximation ADC
No full textSmart 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
Poor governance and weak social cohesion in Somalia’s Climate-stressed settings: the mediating effects of economic inefficiencies and limited human development
No full textThis study provides critical insights into the intricate relationship between poor governance, economic inefficiencies, and weak social cohesion in Somalia’s climate-vulnerable and post-conflict setting. By employing Structural Equation Modeling (SEM), the research identifies how governance failures exacerbate economic stagnation and hinder human development, ultimately fragmenting social cohesion. The findings emphasize the urgent need for governance reforms, economic revitalization, and human development investments to break this cycle of instability. This study contributes to policy discourse by presenting a conceptual model that links governance, economic, and social dimensions, offering practical recommendations for fostering sustainable stability in fragile states.The purpose of this study is to investigate the complex relationship between poor governance and weak social cohesion in Somalia’s Climate-vulnerable setting, with a specific focus on how economic inefficiencies and limited human development mediate this dynamic. The study employs Structural Equation Modeling (SEM) to analyze survey data from Somalia, assessing the impact of poor governance, economic inefficiencies, and limited human development on social cohesion. The findings confirm that economic inefficiencies significantly contribute to weak social cohesion, while poor governance exacerbates both economic inefficiencies and limited human development. This cyclical relationship perpetuates social fragmentation, illustrating the critical need for governance reforms and targeted investments in human development to rebuild social cohesion. The study highlights the importance of integrated policy interventions that address governance reforms alongside economic revitalization and human development initiatives. Anti-corruption measures, equitable resource distribution, and community-based programs are essential to restoring social cohesion and fostering sustainable stability in post-conflict settings like Somalia. This research presents a conceptual model linking governance, economic, and social dimensions in a fragile post-conflict context, offering new insights into the cyclical effects of poor governance on recovery outcomes. This study reveals how governance failures and economic inefficiencies weaken social cohesion in fragile states
Explaining REIT returns in emerging economies: A Fama-French approach with foreign investment and political stability
No full textThis study examines the applicability of the Fama-French 3-factor model to Real Estate Investment Trusts (REITs) in emerging economies using monthly data from January 2016 to December 2023 for 23 REITs across five emerging markets. A Generalized Method of Moments (GMM) (system) approach assesses the impact of 12 explanatory variables, including traditional factors like market, value, size, and momentum premiums, as well as emerging market-specific factors such as the Morgan Stanley Capital International (MSCI) Emerging Markets Currency Index and Bloomberg Commodity Ex-Agriculture Index. Control variables like political stability, foreign direct investment, and portfolio investment are also included. The results show that value premium, foreign direct investment, portfolio investment, and commodity prices positively influence REIT excess returns, while momentum premium and political instability negatively affect them. These findings highlight the combined importance of traditional and emerging market-specific factors, emphasizing the critical role of stable political conditions for REIT performance. This research contributes valuable insights for investors and policymakers in understanding REIT dynamics in emerging markets
Labor market consequence of exports diversification and terms of trade shocks in EMDEs
No full textThis research examines the labor market consequences of international trade for emerging markets and developing economies (EMDEs) while using the Bayesian panel multivariate regression approach for the period 2011–2023 for a panel of 18 countries. It employs labor market variables such as average monthly earnings, labor force participation rate, unemployment rate, and labor productivity, along with trade-related variables such as terms of trade index, export diversification, and trade volume. While GDP and population growth rates are employed as control variables. The estimates reveal that trade-related variables have a positive and statistically significant impact on labor earnings, labor productivity, and unemployment. Furthermore, estimates reveal a positive and statistically significant impact of GDP growth on domestic labor earnings and productivity whereas population growth negatively impacts both labor market outcomes. This research provides insights for policymakers to follow export-oriented policies to enhance the earnings and productivity of the domestic labor force in EMDEs
Introduction to brain-computer interface: research trends and applications
No full textA broader review of the artificial intelligence (AI) and sensing methods used in brain–computer interfaces (BCIs) is given in this chapter. The brain and external equipment, such as computers and electrical gadgets, can communicate via the BCIs. Using a variety of noninvasive wearable sensors, it is now feasible to analyze neural signals from the brain to gain insights from brain patterns. The neural signals are preprocessed for noise removal and conditioning for the feature extraction and classification stages. The signal processing and analysis techniques are used for preconditioning and feature extraction. The classification is often carried out using the robust AI algorithms. The approaches covered have a variety of uses, from individual experiences to commercial applications. Particularly the applications of BCIs in assistive, mental state estimation, gaming, and entertainment are explored, showing how the signal processing and AI improve performance in various domains. The chapter also examines the latest AI-specific methods for BCIs, including deep learning and ensemble learning. This investigation lays the groundwork for future study and advancement in this exciting area by illuminating AI’s critical role in improving the BCIs
A Multi-Functional Empowerment and Entertainment Hub for young Patients
No full textThis research focuses on creating a bed unit for children with cancer that considers their comfort,
emotional well-being and practical needs. The goal is to improve the experience of cancer patients
and their families in the hospital by offering a comprehensive solution. The results of this study
can guide design efforts in oncology settings leading to better care and support for children
receiving cancer treatment.The primary goal of this research thesis is to cater to the requirements of children with illnesses such as cancer by creating a bed unit that will improve their comfort, mental health and overall care while staying in the hospital. Although the recovery rate for cancer in children is high; the psychosocial needs of cancer survivors and caregivers are often not understood by cancer care providers, they are not aware of or are not referred to appropriate resources, they are unaware of or do not consider psychosocial support an integral part of quality cancer care, and they are not adequately addressed by cancer care providers. To achieve this an in-depth study of existing pediatric bed units has been conducted to identify any challenges or shortcomings. Additionally qualitative research methods such as interviews and surveys with oncology patients, their families and healthcare providers have been employed to gain insights into the needs and preferences of this particular group. The gathered data has then been utilized to establish design specifications that will serve as a guide for developing the customized bed unit
Visualization in Big Data Analytics: Applying Hidden Markov Models to Big Data: An Appliance Modeling Case Study
No full textThis chapter presents a comprehensive comparative analysis of three widely used transformbased image compression techniques: Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), and Wavelet Transform. The compression transforms are tested under three threshold settings. Utilizing a dataset of 497 JPEG images from Kaggle, we systematically evaluated each method under three distinct threshold settings to examine their performance. The ROSE images dataset was chosen due to its abundant edges and intricate details, effectively distinguishing the differences between compressed images. Setting A employed the highest threshold value, resulting in minimal compression. Setting B had an intermediate threshold value, while Setting C utilized the lowest threshold value, leading to maximum compression. Lower threshold values discard more coefficients, thereby increasing the compression ratio, but potentially affecting image quality. The evaluation metrics used in this study include Mean Squared Error (MSE), Peak signal-to-noise ratio (PSNR), Signal-to-quantization noise ratio (SQNR), Structural Similarity Index (SSIM), Normalized Cross-Correlation (NCC), Compression Ratio (CR), Memory saving (MS), and contrast. The inherent properties of the Wavelet Transform, which efficiently captures both frequency and spatial information, suggest it has a greater potential to excel in balancing compression efficiency and image quality preservation. Both FFT and DCT transforms are anticipated to deliver moderate performance in terms of the performance metrics. They may exhibit more variability and less effectiveness in retaining high-frequency image details. These insights underscore the importance of selecting the appropriate compression technique based on the specific requirements of image quality and storage efficiency, particularly highlighting the advantages of the Wavelet Transform for high-fidelity applications such as medical imaging and archival storage
Electroencephalography-based emotion recognition with empirical mode decomposition and ensemble machine learning methods
No full textEmotion recognition stands as one of the most challenging tasks in pattern recognition, machine learning, and artificial intelligence. Incorporating emotion recognition in brain–computer interfaces (BCIs) is a recent trend. In fact, this phenomenon makes BCI systems more sensitive, flexible, and supportive of users’ emotional and cognitive demands. Emotion recognition leverages voices, images, and electroencephalography (EEG) signals for an automated identification of emotions, proving particularly valuable in diverse sectors. In today’s digital era, providing accurate insights into emotion recognition is crucial. Given the complexity of emotional activity, the application of advanced technologies and the utilization of signal processing and machine learning methodologies are essential for an effective analysis. Despite ongoing efforts to recognize emotional activities over the past decade, fundamental issues remain that need to be addressed to fully harness technology in understanding emotional states. This study explores recent advancements in signal processing and machine learning algorithms tailored for detecting emotional activity. It also discusses the challenges and critical considerations inherent in emotion recognition. Additionally, the chapter introduces several open concepts aimed at guiding future research efforts in addressing these challenges. Finally, specific examples of emotion recognition using EEG signals are presented, showcasing various AI and signal processing techniques employed in this domain