具有改进空间的课程成果评估与实现:工程学院纳入成果导向型教育的实用案例研究样本

Krishnamoorthy Somasundaram, Vinoth Kumar Balasubramanian, Paulraj Sivakumar
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引用次数: 0

摘要

机器学习是帮助智慧城市在各个阶段部署的技术之一。诊断是一个关键的阶段,来确保一个项目的实施适应城市的现实诊断;这一步骤需要作出重大的财政承诺。本文在使用自学习技术的同时,部署了一种智能环境组件的节俭诊断方法。此外,通过机器学习,报告了对这一新概念的评估和监管成熟度。在不久的将来,机器学习将在实现这种概念方面发挥至关重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment and Attainment of Course Outcomes With Scope For Improvement: A Sample Pragmatic Case Study for Incorporating Outcome Based Education in Engineering Colleges
Machine learning is one of the technologies coming to help the deployment of smart cities in all phases. The diagnosis is a crucial phase that comes to ensure the implementation of a project adapted to the reality of the city diagnosed; this step requires a significant financial commitment. This paper comes to deploy a frugal diagnostic approach of the smart environment component while using self-learning techniques. In addition, assessments are reported and regulatory maturity with respect to this new concept is explored through machine learning. In the near future machine, learning will play a crucial role in the implementation of this kind of concept.
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