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Loneliness and susceptibility to social pain mediate the association between autistic traits and psychotic experiences in young non-clinical adults
No full textUnderstanding of the mechanisms involved in the occurrence of psychotic experiences (PEs) in highly autistic individuals is crucial for identifying appropriate prevention and intervention strategies. This study aimed to investigate the mediating role of susceptibility to social pain and loneliness in the relationship between autistic traits (ATs) and PEs in adults from the general population of 12 Arab countries. This cross-sectional study is part of a large-scale multi-country research project. A total of 7646 young adults (age range 18–35 years, mean age of 22.55 ± 4.00 years and 75.5% females) from twelve Arab countries (i.e., Algeria, Bahrain, Egypt, Iraq, Jordan, Kingdom of Saudi Arabia, Kuwait, Lebanon, Morocco, Oman, Palestine, and Tunisia) were included. Mediation analyses showed that, after adjusting over confounding variables, both loneliness (indirect effect: Beta = 0.18; Boot SE = 0.02; Boot CI 0.14; 0.21) and social pain (indirect effect: Beta = 0.03; Boot SE = 0.01; Boot CI 0.001; 0.05) partially mediated the association between ATs and PEs. Higher ATs were significantly associated with more loneliness and susceptibility to social pain, and directly associated with more severe PEs. Finally, higher loneliness and susceptibility to social pain were significantly associated with greater PEs scores. Findings indicated that individuals with higher ATs tend to experience greater loneliness and feel more pain from rejection, which can in turn be associated with higher levels of PEs. Interventions targeting susceptibility to social pain and loneliness as a means of mitigating PEs among highly autistic adults should be considered
In Artificial Intelligence, Sustainable Technologies, and Business Innovation: Opportunities and Challenges of Digital Transformation
No full text00Cryptocurrency adoption in Jordan poses unique challenges and opportunities in a market where awareness of cryptocurrencies may be limited. This paper examines the impact of the User Experience Questionnaire (UEQ) dimensions—perspicuity, efficiency, dependability, stimulation, and novelty—on promoting cryptocurrency adoption in the Jordanian context. The study uses structural equation modeling (SEM) to analyze the relationships between these dimensions and cryptocurrency adoption intentions. The results indicate positive relationships between user experience dimensions and adoption intentions, highlighting the importance of ease of use, efficiency, trust, engagement, and innovation in shaping users’ attitudes toward cryptocurrency adoption. The findings underscore the need for cryptocurrency platforms to provide superior user-centric user experiences and address concerns around usability, security, and innovation to boost their adoption in Jordan and similar markets. This study provides a new direction in the literature by analyzing the factors influencing cryptocurrency adoption in emerging economies and providing practical recommendations for those concerned with promoting cryptocurrency adoption at the global level
EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods
No full textIn recent years, there has been growing interest in using electroencephalography (EEG) signals for secure authentication due to their unique characteristics and potential applications in security systems. While brain signals have long been studied in clinical contexts, their utilization as biometric identifiers in automated recognition systems has only recently garnered attention within the scientific community. Brain signals offer distinct advantages that are not present in conventional biometrics such as face, iris, and fingerprints, including compliance with privacy regulations, resilience to spoofing attempts, continuous identification capabilities, inherent liveness detection, and universality, making them an appealing option. Nonetheless, several challenges must be addressed, including understanding the uniqueness and stability of brain responses, developing stimulation protocols, and managing the noninvasiveness of the acquisition process. This chapter proposes an EEG-based secure authentication mechanism employing Discrete Wavelet Transform (DWT) in conjunction with ensemble machine learning methods. EEG signals, known for their unique characteristics and resistance to spoofing attacks, offer promising potential for secure authentication systems. The discrete wavelet transform is utilized for feature extraction from EEG signals, capturing their intricate temporal and spectral patterns. Ensemble machine learning methods are then employed to effectively classify EEG signals and authenticate users. The proposed approach aims to enhance the accuracy and robustness of EEG-based biometric identification systems by integrating multiple classifiers and leveraging the complementary strengths of each method. Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying individuals based on their EEG signals, highlighting its potential for applications in security, access control, and authentication systems. This research contributes to the advancement of biometric identification technology and underscores the promise of EEG signals as a reliable secure authentication.Effat Universit
Stages prediction of Alzheimer’s disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data
No full textDetection of Alzheimer’s Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitive Impairment (MCI). However, the progressive nature of AD makes it challenging to identify its causal factors. Modern diagnostic workflows for AD use cognitive tests, neurological examinations, and biomarker-based methods, e.g., cerebrospinal fluid (CSF) analysis and positron emission tomography (PET) imaging. While these methods are effective, non-invasive imaging techniques like Magnetic Resonance Imaging (MRI) are gaining importance. Deep Learning (DL) approaches for evaluating alterations in brain structure have focused on combining MRI and Convolutional Neural Networks (CNNs) within the spatial architecture of DL. This combination has garnered significant research interest due to its remarkable effectiveness in automating feature extraction across various multilayer perceptron models. Despite this, MRI’s noisy and multidimensional nature requires an intelligent preprocessing pipeline for effective disease prediction. Our study aims to detect different stages of AD from the multidimensional neuroimaging data obtained through MRI scans using 2D and 3D CNN architectures. The proposed preprocessing pipeline comprises skull stripping, spatial normalization, and smoothing. It is followed by a novel and efficient pixel count-based frame selection and cropping approach, which renders a notable dimension reduction. Furthermore, the learnable resizer method is applied to enhance the image quality while resizing the data. Finally, the proposed shallow 2D and 3D CNN architectures extract spatio-temporal attributes from the segmented MRI data. Furthermore, we merged both the CNNs for further comparative analysis. Notably, 2D CNN achieved a maximum accuracy of 93%, while 3D CNN reported the highest accuracy of 96.5%
Classification of motor imagery tasks in brain-computer interface using ensemble learning
No full textBrain–computer interfaces (BCIs) utilize brain activity instead of regular neuromuscular channels to facilitate environmental interaction. Because of this, BCIs offer a promising communication manner that enables users with disabilities to operate smart home systems and gadgets. It has been demonstrated that motor abilities can be improved, and rehabilitation from movement disorders can benefit from motor imagery (MI), which is the mental practice of movements. MI training can be more effective with BCIs providing real-time feedback. Those with physical disabilities can live much better thanks to Human Machine Interactions (HMIs) enabled by BCIs, which will allow them to do things like grab objects, turn on lights, and change fan speed using just their brain impulses. Machine learning is essential to recognize and convert intentions in brain signals into control commands for assistive devices. To help bring BCI-based assistive technology to reality, this chapter focuses primarily on brain signals collected from the scalp using electroencephalogram (EEG). For noise reduction, it employs multiscale principal component analysis (MSPCA). Feature extraction is done using wavelet packet decomposition (WPD). Subsequently, subband statistical analysis is carried out to reduce dimensions. The ensemble learning-based classifiers then process the prepared feature set to identify the MI tasks
EOG compression in polysomnographic recordings based on the Lempel-Ziv-Welch algorithm
No full textNocturnal polysomnography (PSG) is a neurophysiological technique that studies sleep by recording multiple physiological parameters. One is the electrical signal, called the electrooculogram (EOG), generated from eye movement. An extensive PSG signal recording, typically around 8 h, requires a massive volume of data to be transmitted and stored; compression is therefore required. This study aims to compress EOG signals effectively, providing high-quality reconstruction with low bit rates and acceptable distortions. The Sleep Disorders Research Center dataset is employed to verify the applicability of the devised method. The solution is founded on the Lempel-Ziv-Welch (LZW) algorithm, developed with MATLAB software. The signal is compressed using this algorithm and subsequently reconstructed. The algorithm’s performance is evaluated using five parameters: compression performance (CP), L2 energy retained in the compressed signal, percent root-mean-square difference (PRD) in the reconstruction, compressed signal size, and runtime. The findings of the experiment, which used 22 EOGs of different subjects, comprising 11 individuals with psychophysiological insomnia and 11 individuals without this condition, demonstrated that the LZW algorithm produced an average CP of 84.65% while retaining almost 100.44% of the signal energy and a PRD of 6.20% in the reconstructed signal
The Impact of Marketing Mix (7Ps) on Customer Satisfaction in the Healthcare Sector: A Study of Demographic and Professional Correlations
No full textThe service sector plays a crucial role in contributing to a nation’s GDP, with the healthcare sector experiencing rapid economic growth. Customer satisfaction in healthcare centers is essential and expected to remain a priority. To enhance satisfaction, implementing marketing mix strategies across sectors is vital. This study explores the 7Ps’ impact on customer satisfaction, considering gender and age factors. Using quantitative techniques and a questionnaire, correlations between satisfaction and variables like the 7Ps, gender, and age were examined. Significant factors identified include process, promotion, place, product, and people. Logistic regression revealed that people and process significantly influence satisfaction. The study underscores process as the primary factor in marketing strategies, offering a key element to improve service and satisfaction. Future research should expand by segmenting the 7Ps based on gender and age groups. These findings provide valuable insights for tailoring healthcare services to better meet diverse patient needs
Noor Al-Ayoon Centre
No full textBy attending to the specific demands of the world's visually impaired population, this
extensive initiative aims to transform the interior design of Blind Centers. It is impossible to
exaggerate the importance of providing accessible settings, given that there are 295 million
visually impaired people and 43 million blind people globally. Our rebuilt Blind Center aims to
close the gap between physical environments and the different requirements of the visually
impaired by using universal design and accessibility principles with great care. This will
promote independence, growth, and community among the visually impaired
ECG-based emotion recognition using CWT and deep learning
No full textThis work is financially supported by the Effat University.Emotion recognition can enhance the Human-Machine Interaction (HMI) in several aspects such as an enriched personalization with intention-based adaptation and responsiveness. In this context, several valuable studies have been conducted by exploring the physiological signals. This chapter discusses the significance of assessing the Autonomic Nervous System (ANS) through physiological indicators like electrocardiogram (ECG), Galvanic Skin Response (GSR), Blood Pressure (BP), and respiration rates, with particular emphasis on ECG and GSR due to their insights into various pathological and psychophysiological conditions. While ECG provides detailed heart electrical activity information, GSR reflects ANS activity through sweat gland function. The simplicity, effectiveness, affordability, and noninvasiveness of these measures make them preferable, although automatic interpretation is crucial for accurately identifying patterns associated with specific mental and physiological states. This chapter aims to improve healthcare applications and human-computer interaction by investigating the possibilities of emotion recognition using AI algorithms applied to ECG and GSR signals. Machine learning and deep learning algorithms evaluate ECG and GSR data to categorize emotions. These techniques have shown promise in various fields, including affective computing, mental health assessment, and human-computer interaction. To validate their effectiveness in differentiating emotions for multiple applications, the chapter shows how to create an effective ecosystem for real-time emotion recognition from ECG and GSR signals using a blend of wavelet transform, convolutional neural networks, and transfer learning.Effat Universit