Effat University Institutional Repository
Not a member yet
1865 research outputs found
Sort by
Artificially intelligent detection of retinal pigment sign using P3S-Net for retinitis pigmentosa analysis
No full textImage segmentation is a major and crucial problem in medical imaging. It is the foundation for clinical diagnostic methods, treatments, and computer-assisted disease diagnosis (CAD). An ocular condition known as retinal pigmentosa (RP) first results in night blindness and persistent degradation of the retinal pigment. An advanced deep learning method for computer-aided disease diagnosis that detects pigment signs (PS) in RP patients, enabling ophthalmologists to identify and treat the condition on or before schedule. Many researchers now use costly CAD approaches; however, the suggested solution, which uses a fundus image dataset, is quick, easy, and affordable. This paper presents a large kernel pigment sign semantic segmentation network (P3S-Net) to enhance segmentation performance and provide a useful receptive field. P3S-Net is a residual network with less trainable parameters due to its large kernel architecture. Moreover, these large-kernel receptive fields yield improved segmentation results with early, intermediate, and late feature information fusion. P3S-Net has an order of magnitude less trainable parameters of 155k in certain scenarios and is smaller than current medical image segmentation techniques. Utilizing the publicly available Retinal Images for Pigment Signs (RIPS) dataset for retinal pigment detection and segmentation, 4-fold cross-validation tests were conducted to assess the suggested P3S-Net. The performance of the suggested network is frequently better and more competitive when compared to cutting-edge techniques
The Accreditation as an Evolutionary and Developmental Process in Higher Education: Transforming Academic Strategic Visions to Social Impact
No full textThis chapter examines the evolution of accreditation as a dynamic and developmental process in higher education, emphasizing its transformative role in aligning academic goals with societal needs. It refers to the importance of education for individual and societal development, explaining the increasing complexity of higher education in a rapidly changing global context. The chapter also highlights the historical evolution from implicit quality assurance practices to formalized accreditation standards that enable innovation, inclusivity, and responsiveness. It makes a deep analysis of accreditation standards and examines how accreditation integrates in the current period social responsibility, technological adaptation, and interdisciplinary approaches. The benefits of accreditation are highlighted for various stakeholders, including students, institutions, and society, while addressing key challenges like resource constraints, the integration of AI, and the need for international cooperation.This chapter examines the evolution of accreditation as a dynamic and developmental process in higher education, emphasizing its transformative role in aligning academic goals with societal needs. It refers to the importance of education for individual and societal development, explaining the increasing complexity of higher education in a rapidly changing global context. The chapter also highlights the historical evolution from implicit quality assurance practices to formalized accreditation standards that enable innovation, inclusivity, and responsiveness. It makes a deep analysis of accreditation standards and examines how accreditation integrates in the current period social responsibility, technological adaptation, and interdisciplinary approaches. The benefits of accreditation are highlighted for various stakeholders, including students, institutions, and society, while addressing key challenges like resource constraints, the integration of AI, and the need for international cooperation
DIGITAL RISK AND FINANCIAL INCLUSION: BALANCE BETWEEN INNOVATION AND PROTECTING DIGITAL BANKING SAUDI CUSTOMERS
No full textImpact of Input Sequence Types on Healthcare Intrusion Prediction Models
No full textPrediction models are vital for sensing zero-day and even n-day cyberattacks, particularly in healthcare infrastructure. Most existing research focuses on developing classifiers also known as IDS to enhance detection and accuracy. However, predictive intrusion models for healthcare remain underexplored, with limited studies investigating the comparative performance of univariate and multivariate inputs against single-step and multi-step outputs in time series models. This study aims to address these gaps by evaluating the accuracy and error performance of selected predictive models across various input and output configurations. The methodology involves transforming input data sequences into univariate 1∗ n and multivariate m ∗ n formats, establishing single-step and multi-step splitting functions, and evaluating these configurations using the benchmark CIRA-CIC-DoHBrw-2020 dataset. Algorithms including Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Transformer Encoder-Decoder, Vector Output LSTM (GRU core), and CNN were applied, with results visualized to assess performance. The findings reveal that the Multivariate LSTM model, when trained on a sequence of multivariate inputs, demonstrates superior predictive performance, achieving low MAE error rates of 0.4% for single-step predictions and 0.1% for multi-step predictions. Additionally, GRU and Transformer models exhibit heightened sensitivity to specific input sequence configurations. In conclusion, our study demonstrates that Transformer Encoder-Decoder based prediction models exhibit exceptional prediction performance. This effectiveness is attributed to their ability to capture contextual and critical information from input sequences. These findings provide valuable insights for designing advanced intrusion prediction models, paving the way for improved prediction capabilities in future systems.
Author Keywords
Intrusion prediction model
intrusion detection system (IDS)
multivariate
univariate
data visualization
machine learning in cybersecurity
intrusion prediction in healthcareEffat Universit
Leveraging Distributed Deep Learning Techniques for Fine-grained Aspect Sentiment Analysis in MOOC Learners' Reviews
No full textDeveloping comprehensive analytics for Massive Open Online Courses (MOOCs) is essential for improving course design and enhancing learner engagement. In this work, we introduce MOOCSense, a multi-stage sentiment analysis module designed to analyze MOOC learner reviews and contribute to generating detailed MOOC analytics. In the first stage, we employ a mapping algorithm that extracts key MOOC-specific terms and central semantic phrases from the reviews. In the second stage, we propose a novel Centroid-Based Learning approach combined with the BERT (CLB) model to capture both implicit and explicit sentiment polarity in learner reviews, leveraging BERT’s deep contextual understanding of natural language. By focusing on the central semantics of each review, our approach uncovers the emotional drivers behind learner engagement or dissatisfaction. This dual-stage module enables more accurate sentiment association with specific course aspects, enriching MOOC analytics with valuable insights. Experimental results demonstrate the effectiveness of our approach across various MOOC datasets, achieving an accuracy of 92%, making it a promising solution for generating in-depth learning analytics and supporting course improvement strategies
Automatic Portable Food Heater: A New Way to Heat Meals, Everywhere You Go
No full textConcerned with providing instant solutions for meals and how they can be reheated, the
Automatic Portable Food Heater has been developed. This device is built to be user-friendly and
portable, particularly for those in transit, and endeavours to ensure that meals are heated up to the
ideal temperature they are to be eaten in whether one is in the office or at home. There is an
initiative to make use of technology that will help in the prevention of wastage and unnecessary
use of resources through techniques such as temperature control and cost-effective heaters. Most
of the current heating solutions have shortcomings, such as their inability to be moved and are
difficult to use, e.g., microwave ovens or heaters.
The research delves into the depth of customer requirements and enables the features that the
segment requires to enhance efficiency and performance, such as changing temperature and low
weight. These insights are believed to result in a product that is a blend of what customers want
and how it is beneficial to them in terms of improving one’s healthy eating habits by providing
an opportunity to have hot food more simply.
The discourse from this specific inquiry extends the areas of culinary and consumer technologies
allowing one to visualise indistinct possibilities in the realm of portable food technology. This
research is instrumental in actively enhancing the existing measures concerning eating habits,
hence particularly useful to people grappling with nutrition and the demands of their work
Psychometric Properties of the Arabic DissociativeSymptoms Scale—Brief Across Five Arab Countries
No full textBackground
Dissociation, involving disruptions in cognition, perception, and identity, is closely linked to trauma and various psychiatric disorders but remains underrecognized, especially in non-Western contexts. Although tools like the Dissociative Symptoms Scale—Brief (DSS—B) have improved assessment, validated Arabic-language versions are lacking. Given rising mental health concerns and limited resources in the Arab world, this study aims to evaluate the psychometric properties of the Arabic-translated DSS—B to support culturally appropriate diagnosis and research on dissociation.
Methods
In this cross-sectional study, participants from KSA, Egypt, Lebanon, Palestine, and Jordan were recruited via snowball sampling and completed an online survey. The DSS—B was translated into Arabic using a forward-backward method and reviewed by experts for cultural and semantic accuracy. Participants also completed validated Arabic versions of the Jong-Gierveld Loneliness Scale, Patient Health Questionnaire-4, and the Brief Irritability Test.
Results
Among 1494 participants (mean age = 24.97; 74.5% female), Palestinians showed the highest dissociative symptoms. Confirmatory factor analysis confirmed good model fit, excellent reliability (ω = 0.93; α = 0.92), and strong convergent validity average variance extracted (AVE = 0.70). Measurement invariance across genders and countries was supported, with no significant gender differences in scores. Dissociation was positively correlated with depression-anxiety (r = 0.57), irritability (r = 0.51), and loneliness (r = 0.45), confirming concurrent validity, while discriminant validity was also established.
Conclusion
This study validates the Arabic DSS—B as a reliable, valid, and culturally adaptable tool for assessing dissociation in Arab populations, reinforcing its clinical and research utility. Future research should explore its generalizability in underrepresented groups, use longitudinal and clinician-based assessments, and investigate neurobiological underpinnings to deepen understanding and application of dissociation measurement globally
Meowseum
No full textArtistic drive and obsession are profound and often underexplored aspects of the creative process, deeply intertwined with the cognitive and emotional experiences of artists. This research aims to investigate how inspiration and motivation influence the cognitive processes of creative individuals, leading to a relentless pursuit of perfection that can border on obsession. Understanding these dynamics is crucial, not only for the academic field of psychology and creativity studies but also for practical applications in supporting artists' mental health. This study will identify management strategies to enhance the well-being and productivity of artists and it is particularly significant in informing the thematic core of an animated capstone film that explores this artistic drive and obsession. A comprehensive understanding of this nuanced issue is essential for the accurate portrayal of the mental health struggles faced by both acquired and innate artists. Moreover, this project aims to contribute to the broader discourse on mental health in cinema by shedding light on an often-overlooked side. The film seeks to offer a unique perspective on the emotional and psychological sacrifices associated with artistic endeavors in order to deliver a compelling message about the importance of self-care, halting self-comparison as well as mental health awareness