THE ROLE OF VOLUNTARY WORK IN THE IMPROVEMENT OF THE SOCIAL CAPITAL AND SUSTAINABLE DEVELOPMENT IN SAUDI SOCIETY

Purpose: The present study aimed to identify the role of voluntary work in improving the social capital and enhancing sustainable development in the Saudi community. Methodology: Participants were (245) volunteers who were members of the Saudi Volunteer Organization (Takatuf). Their ages were between (20-26) years and were all dwelling in Riyadh. The descriptive survey method was used and the main study instrument was a questionnaire developed by the researchers. Main Findings: Analysis of the gathered data to answer the main two questions in the study revealed that voluntary work has a significant role in improving the social capital and achieving sustainable development in the Saudi community. Novelty/Originality of this study: In light of these findings, this study showed the importance of urging people in charge to seriously take into consideration this vital issue related to voluntary work and provide all possible support to institutions of voluntary work in Saudi Arabia in order to help to spread the culture of voluntary work. Moreover, people in charge were recommended to view voluntary work from the perspective of its effective contribution to the development of the social capital and achievement of sustainable development within the community, rather than the perspective of voluntary work as a charitable action

Autism Spectrum Disorder Detection Based on Wavelet Transform of BOLD fMRI Signals using Pre-trained Convolution Neural Network

Autism spectrum disorder (ASD) is a mental disorder and the main problem in ASD treatment has no definite cure, and one possible option is to control its symptoms. Conventional ASD assessment using questionnaires may not be accurate and required evaluation of trained experts. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported for ASD detection. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. This study aims to present several convolutional neural networks as tools for ASD detection based on temporal dynamic features classification and improve the ASD prediction results. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as GoogLenet, DenseNet201, ResNet18, and ResNet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbours (KNN). The best prediction results are 85.9% accuracy achieved by extracted the features from DenseNet201 network and classified these features by KNN classifier. Comparison with previous studies, has indicated the good  potential of the proposed model for diagnosis of  ASD cases. From another perspective, the presented method can be applied for analysis of rs-fMRI data on other type of brain disorders