Effat University Institutional Repository
Not a member yet
1865 research outputs found
Sort by
EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers
No full textThis work is financially supported by Effat University.This chapter explores Wavelet Packet Decomposition (WPD) and Ensemble Classifiers to improve the accuracy and efficiency of P300 speller systems, which enable typing through EEG signals. This combination of Brain-Computer Interface (BCI) systems and P300 spellers represents a significant advancement in assistive technology, empowering individuals with severe motor limitations to communicate via brain signals. Traditional machine learning models, while effective, may suffer from overfitting and lower accuracy. To overcome these challenges, ensemble classifiers are utilized, leveraging diverse subsets of the dataset to enhance P300 recognition performance. The study employs multiscale principal component analysis for signal denoising, WPD for feature extraction, and ensemble models for BCI control systems. Through rigorous experimentation, the effectiveness of these strategies in improving spelling proficiency and reducing categorization errors is evaluated. The results demonstrate the potential of WPD and ensemble classifiers to enhance BCI-based communication systems, offering greater usability and effectiveness. The findings contribute valuable insights to the field of neurotechnology, promising advancements in improving the quality of life for individuals with movement disabilities. Overall, the use of ensemble learning models enhances the performance of the P300 speller, emphasizing the impact of WPD features combined with ensemble models on BCI recognition and paving the way for future assistive technology applications.Effat Universit
Word Recognition: A Comparative Study of Physical and Digital Media and Their Influence on improving QURAN Reading
No full textPreprocessing and feature extraction techniques for brain-computer interface
No full textIn order to improve communication and interaction between humans and computers, the signal processing and artificial intelligence (AI) are vital tools. For a seamless brain–computer interface (BCI), it integrates and analyzes the data from multiple sensors. The objective is to develop more efficient, natural, and intuitive interfaces that can comprehend and react to human input more effectively. Usually, these modalities consist of electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), and electroencephalography (EEG). In this context, the preprocessing and feature extraction methods play an important role. The aim of preprocessing is noise removal and focus on the most significant frequency content of the signal. The key preprocessing approaches are the digital filtering, wavelet transform, and multiscale principal component analysis (MSPCA). The feature extraction is vital in achieving accurate representation for modeling or identifying critical elements or intentions in the human body systems using machine or deep learning techniques. Feature extraction facilitates the identification and interpretation of relevant information from input data streams. This chapter explores various feature extraction techniques employed in BCI applications, ranging from parametric model-based methods to more complex approaches. Traditional techniques encompass the signal processing methods such as digital filtering and Fourier transform. The intended parametric model-based methods are the autoregressive, Yule-Walker, covariance, and modified covariance. Further considered approaches are the subspace-based methods, eigenvector, and time–frequency analysis, such as the short-time Fourier transform and different variants of wavelet transform. Additionally, the oscillatory mode decompositions and common spatial patterns are described. These methods are effective for extracting pertinent information from the input signals and, moreover, they enable the automated decision support through machine and deep learning methodologies for the contemporary BCIs
Power Extraction in Photovoltaic Systems using P&O-Based MPPT with DC-DC Buck Converter Integration
No full textSolar energy is an abundant, renewable resource that offers a sustainable alternative to fossil fuels. Photovoltaic (PV) systems, which convert sunlight into electricity, have gained global prominence due to technological advancements and decreasing costs. A typical PV system consists of solar panels, inverters, and energy storage. However, the efficiency of these systems is affected by variable sunlight and temperature conditions. Maximum Power Point Tracking technology works as an optimization tool for solar panel operations by finding their maximum operational efficiency. The research examines solar panel modeling together with MPPT algorithms and buck converter implementation for energy management between solar panels and storage systems. Simulations based on MATLAB/Simulink evaluated the performance output of a two-stage PV system which integrated an MPPT-controlled buck converter for performance assessment. The system evaluation results demonstrate its analysis of maximum power point while monitoring various conditions that optimize energy efficiency through irradiance and temperature variations. Off-grid and hybrid systems require energy storage according to research findings which demonstrates both lead-acid and lithiumion batteries as options. The paper explores solar energy integration challenges alongside methods to use advanced energy conversion systems and smart grid systems for enhancing future performance. The study highlights solar energy promising potential to be a key player in the global shift toward renewable energy. The simulation results further validate the system's strong performance, confirming its effectiveness in supporting this transition
Image Manipulation Detection System: Exposing AI-Generated Image Forgeries
No full textImage Manipulation Detection System
The rapid advancement of AI technologies has brought about a new era of dig-
ital creativity, enabling the creation and manipulation of images. However, this
progress also presents a double-edged sword as it opens avenues for misuse, particu-
larly in the fabrication of digital imagery for malicious purposes. The development
of effective image manipulation detection systems is crucial in safeguarding against
the growing problem of digital image fraud. This project aims to develop a sophis-
ticated image manipulation detection system, designed for integration into both
a web application and a Chrome extension. The tool will be an essential weapon
in the fight against the spread of misleading visual content, providing users with
a reliable way to verify the authenticity of images they encounter online. We will
employ an advanced algorithm and train a machine learning model to distinguish
between authentic and manipulated images. The application will include a feature
for users to upload images, after which the system will analyze and determine the
authenticity of the submitted image. The results, indicating whether the image is
genuine or altered, will then be displayed to the user
Blockchain Applications in Pharmaceutical Supply Chain: A Systematic Literature Review
No full textThe PSC (Pharmaceutical Supply Chain) is crucial to ensure that medicine is available worldwide. However, PSC still encounters challenges such as counterfeit medications, insufficient transparency, and poor track and trace systems. This dissertation explores blockchain technology in PSC by conducting a systematic literature review of articles published between 2004 and 2025. The goal of this research is to examine how blockchain can be applied in PSC, how combining IoT and blockchain can enhance PSC, what challenges and benefits can come from adopting blockchain technology in PSC, and how PSC can be utilized to prevent counterfeit medicines. A systematic literature review was conducted. 68 articles were identified using the PRISMA checklist, and 15 articles were reviewed. The findings reveal that adopting blockchain technology in the PSC enhances transparency, traceability, and security. It can also be used to prevent counterfeiting. But blockchain still has some barriers, such as scalability, the high cost of implementation, and cybersecurity threats. This dissertation identifies how integrating blockchain in PSC can improve its operations and prevent counterfeit drugs
EURASIP Journal on Wireless Communications and Networking
No full textCell-free massive MIMO networks offer significant advantages in spectral and energy
efficiency due to their macro-diversity and distributed architecture. However, the resil‑
ience of such systems is challenged by pilot contamination and multi-user interference,
particularly in dense deployments where pilot reuse is inevitable. This study proposes
a robust and scalable zero-forcing precoding technique based on two-dimensional
direction-of-arrival (2D-DOA) estimation to improve network reliability and interference
suppression without requiring channel state information (CSI) exchange among access
points (APs) or additional pilot overhead. The zero-forcing precoding is aided with DOA
information to separate between various users by exploiting the spatial diversity
of the correlated channels or allowing spatial multiplexing by antenna weights
adaptation based on the characteristics of the channels. By leveraging 2D-UESPRIT
and 2D-FDLSM algorithms, the proposed approach mitigates both intra- and intercell interference, enhancing system resilience against pilot contamination. A closedform expression for downlink spectral efficiency is derived, accounting for practical
limitations such as imperfect CSI. Simulation results show that the proposed method
achieves near-optimal performance—reaching up to 99.1% of the spectral efficiency
of a deterministic benchmark based on ideal, interference-free conditions—while sig‑
nificantly outperforming conventional systems under pilot contamination. These find‑
ings demonstrate that integrating 2D-DOA-based precoding enhances the robustness
and adaptability of cell-free massive MIMO systems, contributing to the development
of resilient wireless networks capable of sustaining high performance under real-world
constraints.
Keywords: Cell-free Massive-MIMO, Zero forcing precoding, 2D-direction of arrivalUniveristy of jedda
Insights Into Digital Business, Human Resource Management, and Competitiveness.
No full textThe digital and green transition is reshaping industries and economies, driving innovation and sustainable growth across the globe. By leveraging emerging technologies and intellectual capital, governments and companies can foster long-term competitiveness and resilience in a rapidly evolving landscape. Understanding how different regions navigate this shift provides valuable insights into best practices and potential challenges. The alignment of human resource and knowledge management strategies with digital transformation plays a crucial role in ensuring inclusive, adaptive, and future-ready economies. This transition not only boosts economic progress but also addresses global environmental and social goals, paving the way for more sustainable development.
Insights Into Digital Business, Human Resource Management, and Competitiveness analyzes business, human resource management, and information technologies in different regions and discusses implications for companies and governments. It is crucial to understand the key role of new and emerging technologies for the digital transformation of economies and societies and build more resilient and fair societies. Covering topics such as social capital, corporate social responsibility, and circular economy, this book is an excellent resource for industry leaders, policymakers, business owners, human resource managers, professionals, researchers, scholars, academicians, and more.The digital and green transition is reshaping industries and economies, driving innovation and sustainable growth across the globe. By leveraging emerging technologies and intellectual capital, governments and companies can foster long-term competitiveness and resilience in a rapidly evolving landscape. Understanding how different regions navigate this shift provides valuable insights into best practices and potential challenges. The alignment of human resource and knowledge management strategies with digital transformation plays a crucial role in ensuring inclusive, adaptive, and future-ready economies. This transition not only boosts economic progress but also addresses global environmental and social goals, paving the way for more sustainable development.
Insights Into Digital Business, Human Resource Management, and Competitiveness analyzes business, human resource management, and information technologies in different regions and discusses implications for companies and governments. It is crucial to understand the key role of new and emerging technologies for the digital transformation of economies and societies and build more resilient and fair societies. Covering topics such as social capital, corporate social responsibility, and circular economy, this book is an excellent resource for industry leaders, policymakers, business owners, human resource managers, professionals, researchers, scholars, academicians, and more
The relationship between Mental Health and the experiences of expatriates and foreign nationals residing in specific neighbourhoods throughout Saudi Arabia.
No full textThis study explores the impact of neighborhood-level factors on the mental health of expatriates and non-Saudi residents in Saudi Arabia. With a rapidly growing and diverse population, Saudi Arabia presents a unique setting for investigating how social determinants such as neighborhood deprivation, social networks, and gender affect mental well-being among expatriates. A mixed-methods approach was adopted, combining quantitative survey data with qualitative insights to assess mental health outcomes in different residential areas across Saudi cities. The findings reveal that perceived social exclusion, limited community belonging, and gendered experiences significantly influence reported levels of stress, anxiety, and depression. Furthermore, neighborhood characteristics such as safety, cleanliness, and access to social services were strongly correlated with mental wellbeing. The study highlights the importance of inclusive urban planning and targeted mental health policies to address the challenges faced by diverse populations in urban environments. These findings contribute to the broader discourse on migrant mental health and emphasize the need for culturally sensitive mental health interventions in host countries