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Register a new userSuccess Factors Contributing to eGovernment Adoption in Saudi Arabia: G2C approach
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Added by
Ibrahim AbuNadi
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Saudi Arabia is predetermined to implement eGovernment and provide world-class government services to citizens by 2010. However, this initiative will be meaningless if the people did not adopt these electronic services. Therefore, the purpose of this study is to determine success factors that will facilitate the adoption of eGovernment in Saudi Arabia. The results of the literature review have been deployed into surveys with Saudi eGovernment users. The discussion of the analysis from results obtained from the practical study has provided a framework that encompasses the eGovernment adoption success factors for Saudi Arabia.
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In this paper, we propose Ensemble Learning models to identify factors contributing to preterm birth. Our work leverages a rich dataset collected by a NIEHS P42 Center that is trying to identify the dominant factors responsible for the high rate of premature births in northern Puerto Rico. We investigate analytical models addressing two major challenges present in the dataset: 1) the significant amount of incomplete data in the dataset, and 2) class imbalance in the dataset. First, we leverage and compare two types of missing data imputation methods: 1) mean-based and 2) similarity-based, increasing the completeness of this dataset. Second, we propose a feature selection and evaluation model based on using undersampling with Ensemble Learning to address class imbalance present in the dataset. We leverage and compare multiple Ensemble Feature selection methods, including Complete Linear Aggregation (CLA), Weighted Mean Aggregation (WMA), Feature Occurrence Frequency (OFA), and Classification Accuracy Based Aggregation (CAA). To further address missing data present in each feature, we propose two novel methods: 1) Missing Data Rate and Accuracy Based Aggregation (MAA), and 2) Entropy and Accuracy Based Aggregation (EAA). Both proposed models balance the degree of data variance introduced by the missing data handling during the feature selection process while maintaining model performance. Our results show a 42% improvement in sensitivity versus fallout over previous state-of-the-art methods.
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Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the `digital divide`. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.
Pandemic is an outbreak that happens over a large geographic area affecting a greater portion of the population as new pathogens appear for which people have less immune and no vaccines are available. It can spread from person to person in a very short time, and in fact, the health workers are at greater risk of infection because of the patients who carry the disease. In the 21st century, where everyone is connected through digital technologies, Information and Communication Technology (ICT) plays a critical role in improving health care for individuals and larger communities. ICT has currently been severed in a variety of application domains which signifies its importance as a major technological paradigm, and it has drawn higher attention for its potential to alleviate the burden on healthcare systems caused by a rise in chronic diseases, aging and increased population and pandemic situations. This paper surveys and offers substantial knowledge about how effective ICT Healthcare strategy can be used to manage global pandemics by presenting a four-phased framework, which can be deployed to alleviate the strain on healthcare during a pandemic. In addition, we discuss how ICT powered technologies can be used towards managing a pandemic during the transformation of simple disease outbreak into a global pandemic.
We report air temperature and humidity changes during the two solar eclipses of 26 December 2019, and of 21 June 2020, respectively, in the cities of Al-Hofuf and Riyadh in Saudi Arabia. During the December eclipse the Sun rose already eclipsed (91.53% of the area covered) while the June eclipse, although also annular in other places of the Arabian Peninsula, was just partial at Riyadh (area covered 72.80%). This difference apparently affected the observed response on the recorded variables of temperature, relative humidity (RH) and vapor pressure (VP) in the two events. Change in these variables went unnoticed for the first eclipse since it was within the natural variability of the day; yet for the other, they showed clearly some trend alterations, which we analyze and discuss. A decrease in temperature of 3.2 {deg}C was detected in Riyadh; however, RH and VP showed an oscillation that we explain in the light of a similar effect reported in other eclipses. We found a time lag of about 15 min measured from the eclipse central phase in this city. We made an inspection of related fluctuations and dynamics from the computed rates of the temporal variation of temperature and RH. Trying to identify the influence of solar eclipses in similar environments we have made a broad inter-comparison with other observations of these variables in the Near East, northern Africa and in the United States. We compare our results with results obtained by other authors working with the December eclipse but in the United Arab Emirates and Oman, which showed dissimilar results. These inter-comparisons show how effectively the lower atmosphere can respond to a solar eclipse within a desert environment and others similar. As a preamble, a historical revision of temperature and humidity in the context of eclipse meteorology is also included.
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