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Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms

机译:深度学习算法应用多铁路决策融合方法的大数据分析

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Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system's potentiality and efficacy. In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteriabased decisionmaking problems. This paper aims to provide various applications that involve the concepts of deep learning techniques and exploiting the multicriteria approaches for issues that are facing in big data analytics by proposing new studies with the fusion approaches of datadriven techniques.
机译:数据正在发展,随着各种类型的设备,例如网络,云计算,物联网(物联网),执行器和传感器等各种类型的设备的速度和通信的快速进展。数据和通信内容的增量与速度,速度,大小和价值的等同性,以提供有用和有意义的知识,有助于解决未来的具有挑战性的任务和最新问题。此外,基于多标准的决策是解决与大数据分析中的替代效果有关的各种问题的关键问题之一。它倾向于找到基于最新机器学习技术的解决方案,该技术包括基于多标准的决策和深度学习机制等算法,在提供对大数据的洞察中的洞察中。另一方面,衍生是为了使其与近似来增加运行时的二元性,并提高整个系统的潜力和功效。实质上,几个领域,包括商业,农业,信息技术和计算机科学,利用深度学习和多铁的决策问题。本文旨在提供各种应用,涉及深度学习技术的概念,利用了通过提出数据分析的新研究,利用多种机构方法,提出与DataDRIVEN技术的融合方法进行新的研究。

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