高等教育机构功能可持续智能信息系统的机器学习技术

I. V. Zamriy
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引用次数: 0

摘要

市场的发展产生了对综合数据存储数据库、分析工具和文档管理系统的功能广泛的综合企业信息系统的需求。在当今条件下,选择企业信息系统的问题往往是一个关键的战略决策,在很大程度上决定了企业的效率。因此,智能信息系统的开发、支持和优化是一个迫切需要解决的问题,确保在分析现状的基础上做出最优决策,以实现一定的目标。为此,考虑基于云技术和机器学习的智能数据分析聚类算法的优化,以提高高校智能信息系统的效率。在特定任务的条件下,最佳算法的选择应该由决策者合理地进行,因为分析后对获得的结果进行解释和评价的过程是极其重要的。在这一阶段,主要作用是由调查主题领域的专家发挥,他除了使用标准外,还可以根据对关键指标的先验想法和知识,对结果进行进一步的核查,以便作出进一步的决策。为了获得最大的结果,需要一种复杂的数据分析方法,其中包括使用专家的先验知识进行数据预处理和结果解释,以及使用专门的算法。本文分析了存在的不足,对优化算法进行了优化和并行化,以期利用智能信息系统的云计算和机器学习,提高处理大数据阵列的能力,提高高校智能信息系统的活动效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MACHINE LEARNING TECHNOLOGIES OF THE FUNCTIONALLY SUSTAINABLE INTELLECTUAL INFORMATION SYSTEM OF THE INSTITUTION OF HIGHER EDUCATION
Market development has created a need for widely functional integrated corporate information systems that combine data storage databases, analytical tools, and document management systems. In today's conditions, the question of choosing a corporate information system is often a key strategic decision that largely determines the efficiency of the enterprise. Therefore, the development, support and optimization of intelligent information systems is an urgent issue of ensuring optimal decision-making based on the analysis of current situations to achieve a certain goal. For this purpose, the optimization of clustering algorithms of intelligent data analysis based on cloud technologies and machine learning to increase the efficiency of the intelligent information system of a higher education institution is considered. The choice of the best algorithm under the conditions of a specific task should be reasonably carried out by the person who makes the decision, since the processes of interpretation and evaluation of the obtained results after their analysis are extremely important. At this stage, the main role is played by an expert in the subject field under investigation, who, in addition to using the criteria, can, based on a priori ideas and knowledge of key target indicators, perform additional verification of the results for further decision-making. To achieve the maximum result, a complex approach to data analysis is required, which includes both the use of a priori knowledge of specialists for pre-processing of data and interpretation of results, and the use of specialized algorithms. The paper analyzed the shortcomings, optimized and parallelized the optimized algorithms in order to improve the ability to process a large array of data and increase the effect of the activity of the intelligent information system of the institution of higher education using cloud computing and machine learning of the intelligent information system.
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