Effective Implementation of Knowledge Management Systems (KMS) in Government Schemes/Programs of Selected Sectors Using Soft Computing

M. Chanda, Neelotpaul Banerjee, Gautam Bandyopadhyay
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引用次数: 1

Abstract

Soft Computing (SC) technique consisting of several fields of Artificial Intelligence namely, Artificial Neural Networks (ANNs), Fuzzy Logic (FL), Genetic Algorithms (GA), Machine Learning (ML) and Probabilistic Reasoning (PR) is a new paradigm in computing based on a creative mix of comparatively newer computing techniques. Here, we have taken into consideration several important schemes/ programs of selected sectors pertaining to different Central Ministries/ Departments of Government of India and observed that proper Knowledge Management System (KMS) can be developed and implemented in such cases using ANN based soft computing input output model as an effective tool for the same. Further, we have analyzed how various input and output parameters (both physical and financial) associated with each such selected scheme/ program are related to each other in the light of their basic objectives and the way these can be appropriately represented by an ANN based soft computing input output model in which the expected outputs corresponding to certain inputs can be calculated/ estimated.
在选定行业的政府计划/计划中,运用软计算有效推行知识管理系统
软计算(SC)技术由人工智能的几个领域组成,即人工神经网络(ann),模糊逻辑(FL),遗传算法(GA),机器学习(ML)和概率推理(PR)是基于相对较新的计算技术的创造性组合的计算新范式。在这里,我们考虑了与印度政府不同中央部委/部门有关的选定部门的几个重要方案/计划,并观察到在这种情况下,使用基于人工神经网络的软计算输入输出模型作为有效工具,可以开发和实施适当的知识管理系统(KMS)。此外,我们分析了与每个这样的选择方案/程序相关的各种输入和输出参数(物理和财务)如何根据其基本目标相互关联,以及如何通过基于人工神经网络的软计算输入输出模型适当地表示这些参数,其中可以计算/估计与某些输入对应的预期输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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