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AbstractPurpose -The purpose of this paper is to examine the challenges encountered in e-government implementation, as well as the potential opportunities available in the context of Jordanian society. Design/methodology/approach -A detailed examination and analysis of Jordan's published e-government vision and strategy is presented, together with a review of other relevant literature. Findings -The findings and implications of this study reveal Jordan is still lagging behind in utilising information and communication technologies for delivering government services online. Practical implications -An understanding of the current status of e-government in Jordan can help policy makers in the country pursue development of the public sector organisations on the one hand, and would be of importance for Jordan's economic future success on the other. Originality/value -This is believed to be the most up-to-date and comprehensive analysis of Jordan's plans and assessment of its level of readiness for delivery of e-government services.
Abstract-Despite the predictive performance of AnalogyBased Estimation (ABE) in generating better effort estimates, there is no consensus on: (1) how to predetermine the appropriate number of analogies, (2) which adjustment technique produces better estimates. Yet, there is no prior works attempted to optimize both number of analogies and feature distance weights for each test project. Perhaps rather than using fixed number, it is better to optimize this value for each project individually and then adjust the retrieved analogies by optimizing and approximating complex relationships between features and reflects that approximation on the final estimate. The Artificial Bees Algorithm is utilized to find, for each test project, the appropriate number of closest projects and features distance weights that are used to adjust those analogies' efforts. The proposed technique has been applied and validated to 8 publically datasets from PROMISE repository. Results obtained show that: (1) the predictive performance of ABE has noticeably been improved; (2) the number of analogies was remarkably variable for each test project. While there are many techniques to adjust ABE, Using optimization algorithm provides two solutions in one technique and appeared useful for datasets with complex structure.
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