电子政务中的循环知识管理系统:以人工神经网络评价魁北克独立公投民意调查为例

Manzur Ashraf, H. Ali, Mahfuz Ashraf
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引用次数: 7

摘要

电子政务将城市和农村联系在一起,打破了距离的障碍,实现了高效的行政管理。令人惊讶的是,电子政务并没有像电子商务和电子学习那样对人们产生足够的影响。从软件工程的角度来看,电子政务具有许多不同于电子商务和电子学习的特点。电子商务和电子学习被动就足够了,而电子政务需要积极主动。电子商务和电子学习涉及信息管理,而电子政务涉及知识管理。知识管理是对信息、技能、经验、创新和智慧的管理。基于知识管理系统(KMS)的电子政务模型建立在知识获取、知识共享、知识增强和知识保存的知识管理周期之上。一些流行的电子政务模式使用“知识中心”的概念来共享知识。我们的模型共享支持知识动力学定律的知识。基于信息管理系统(IMS)的电子政务发展模式采用螺旋或迭代过程等循环过程。本文研究了基于知识管理系统的电子政务发展模式,即基于路径的电子政务发展模式。这个模型提供了一个环境,让系统与人一起成长。为了模拟所提出的KMS模型,我们使用人工神经网络分析了基于加拿大魁北克省关于独立(单方面行动或谈判)的公投问题的民意调查的场景。
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Cyclic knowledge management system in e-governance: a case study of evaluating polls on referendum-independence of quebec using ANN
E-Governance brings urban and rural together and breaks the barrier of distance to lead an efficient administration. Surprisingly, E-Governance have not made enough impact on the people as e-Commerce and e-Learning have done. From the software engineering point of view, E-Governance has a lot of characteristics, which are different from e-Commerce and e-Learning. It is enough for e-Commerce and e-Learning to be reactive whereas E-Governance needs to be proactive. While e-Commerce and e-Learning involve Information Management, E-Governance involves Knowledge Management (KM). Knowledge management is the management of information, skill, experience, innovation, and intelligence. Our model of E-Governance based on Knowledge Management System (KMS) is built on KM cycle of Knowledge capturing, Knowledge sharing, Knowledge enhancing, and Knowledge preserving. A few popular models of E-Governance use a concept of "knowledge centers" to share knowledge. Our model shares knowledge that supports the law of knowledge dynamics. The development model of E-Governance based on Information Management System (IMS) applies a cycle-based process such as spiral or iterative process. This paper studies a development model of KMS-based E-Governance, which is path-based. This model provides an environment where the system grows with the people. To simulate the proposed KMS model, we analyzed a scenario based on the poll on Referendum question of Quebec, CA regarding independence (unilateral action or negotiation) using Artificial Neural Network.
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