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International Journal of Advanced Computer Science and Applications(IJACSA), 3(2), 2012 ; Often referred as Government to Citizen (G2C) e-government services, many governments around the world are developing and utilizing ICT technologies to provide information and services to their citizens. In Saudi Arabia (KSA) e-government projects have been identified as one of the top government priority areas. However, the adoption of e-government is facing many challenges and barriers including technological, cultural, organizational which must be considered and treated carefully. This paper explores the key factors of user adoption of e-government services through empirical evidence gathered by survey of 460 Saudi citizens including IT department employees from different public sectors. Based on the analysis of data collected the researchers were able to identify some of the important barriers and challenges from these different perspectives. As a result, this study has generated a list of possible recommendations for the public sector and policy-makers to move towards successful adoption of e-government services in Saudi Arabia. ; http://thesai.org/Downloads/Volume3No2/Paper%201%20-%20A%20Comprehensive%20Analysis%20of%20E-government%20services%20adoption%20in%20Saudi%20Arabia%20Obstacle%20and%20Challenges.pdf

More and more governments around the world are introducing e-government as a means of reducing costs, improving services, saving time and increasing effectiveness and efficiency in the public sector Therefore e-government has been identified as one of the top priorities for Saudi government and all its agencies. However, the adoption of e-government is facing many challenges and barriers such as technological, cultural, organizational, and social issues which must be considered and treated carefully by any government contemplating its adoption. This paper reports on a pilot study amongst online (e-ready) citizens to identify the challenges and barriers that affect the adoption of e-government services especially from their perspective in Saudi society. Based on the analysis of data collected from an online survey the researcher was able to identify some of the important barriers and challenges from the e-ready citizen perspective. As a result, this study has generated a list of possible strategies to move towards successful adoption of egovernment services in Saudi Arabia.

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual's location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual's PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.

The elderly population is increasing throughout the globe, resulting in higher healthcare costs. Potential inappropriate medication (PIM) prescriptions are a major health problem affecting the elderly persons. Due to limited studies in PIM use in primary care and home healthcare in Saudi Arabia, we aim to examine the extent of PIM prescription for and use by elderly patients. This study was carried out with 798 elderly patients, arbitrarily selected from Prince Sultan Medical Military City through the patient register. The mean age of the patients were in the range of 75.2 ± 5.5; 37.8% were males and 62.2% were females. The elderly patients are affected majorly with diabetes (73.9%), hypertension (83.2%) and lipid abnormalities (73.8%). The maximum patients involved in this study were affected with lower hemoglobin levels i.e. 99.2%. Renal impairment was found in 64% and iron supplements were the most commonly used in 23.1%, followed by analgesics and opioids (17%). The 52.5% of participants were using one or more PIMs. Kidney was the only functions and had influence on prescribed decisions. This study indicates PIM is a concern in elderly patients attending clinics and home residents and commonly prescribed ones are atypical antipsychotics, iron overdose, benzodiazepines and opioids. Prescription of drug–drug interactions, cascades and inappropriate drug doses results in preventable adverse effects.

BACKGROUND: Numerous studies worldwide have reported COVID-19 in children; however, the clinical symptoms and consequences of COVID-19 in children have only been reported in a few studies in Saudi and gulf region. Therefore, we aimed to investigate the clinical features and outcomes of COVID-19 infection in children and the therapeutic interventions used. METHODS: This retrospective cohort study included 96 patients with confirmed severe acute respiratory syndrome coronavirus 2 infection aged ≤14 years who were admitted to a tertiary governmental care hospital in Riyadh, Saudi Arabia between March 2020 and November 2020. Data on children with COVID-19, including demographics, comorbidities, symptoms, imaging and laboratory results, therapies, and clinical outcomes, were analyzed. RESULTS: Of 96 children admitted with a confirmed diagnosis of COVID-19, 63.8% were aged ≤ 3 years, 52.1% were male, 56.2% had an unknown source of infection, and 51% had no comorbidities. Most cases had severe infection (71.88%) as they required oxygen, 10.42% of whom were critical. The most common symptoms were respiratory-related (98%), and the common physical sign was fever (49%). High D-dimer (90.7%) and C-reactive protein (72.09%) levels were found in most cases. Oxygen (71.88%) was the most commonly used treatment. Most patients were discharged home and fully recovered (97.92%). We reported two deaths (2.08%). CONCLUSIONS: Our findings showed that the majority of the admitted children with COVID-19 were ≤3 years of age (52.1%) and infected with an unknown source (56.2%). Moreover, the majority of the cases had severe COVID-19 infection as they required oxygen (71.88%), although they had favorable outcomes. However, some cases were critical and resulted in death. Future studies will be crucial to better understand the disease spectrum and potential therapeutic options for COVID-19 in children.

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model ...