Polymorphism prediction method based on big data artificial intelligence algorithm

Peng Wang, Yang Wu, Wen Wang, Yao Yang, Guanglu Feng
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Abstract

In the innovation and development of modern information technology, big data analysis method with artificial intelligence technology as the core has been widely used in all fields. At present, big data analysis has achieved excellent results in practical exploration. It not only completes cluster analysis, association analysis, classification prediction of big data efficiently, but also realizes distributed deep learning in Map Reduce, Spark and other platforms, and uses Map Reduce programming framework to study the application advantages of deep learning models. As an important resource for modern social and economic development, big data information contains not only rich experience and knowledge, but also speeds up social and economic development in a certain sense. Therefore, it is necessary to strengthen the research and innovation of big data analysis methods. On the basis of understanding big data artificial intelligence algorithms, this paper mainly studies polymorphism prediction methods with big data artificial intelligence algorithms as the core, so as to understand the correlation between information in a limited time, mine the hidden content of a large amount of information, and make effective decisions according to actual characteristics.
基于大数据人工智能算法的多态性预测方法
在现代信息技术的创新发展中,以人工智能技术为核心的大数据分析方法已广泛应用于各个领域。目前,大数据分析在实践探索中取得了优异的成绩。它不仅高效地完成了大数据的聚类分析、关联分析、分类预测,而且在Map Reduce、Spark等平台上实现了分布式深度学习,并利用Map Reduce编程框架研究了深度学习模型的应用优势。大数据信息作为现代社会经济发展的重要资源,不仅蕴含着丰富的经验和知识,而且在一定意义上加快了社会经济的发展。因此,有必要加强大数据分析方法的研究和创新。在了解大数据人工智能算法的基础上,本文主要研究以大数据人工智能算法为核心的多态性预测方法,从而在有限的时间内了解信息之间的相关性,挖掘大量信息的隐藏内容,并根据实际特点做出有效的决策。
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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