Adaptive internet of things and machine learning techniques for managing the complexity of intelligent systems big data

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Fuzzy Logic and Intelligent Systems Pub Date : 2021-03-06 DOI:10.3233/JIFS-189844

Ahmed A. Elngar

{"title":"Adaptive internet of things and machine learning techniques for managing the complexity of intelligent systems big data","authors":"Ahmed A. Elngar","doi":"10.3233/JIFS-189844","DOIUrl":null,"url":null,"abstract":"The underlying concept of the Internet of Things 7 (IoT), several studies IoT will dramatically change 8 our daily life. It can be imagined that the era of the 9 Internet of Intelligent Systems will be coming to us 10 soon. The development of IoT, however, has reached 11 a crossroads. Without intelligence, IoT systems will 12 act as an ordinary information system the reactions 13 of which are based on a set of predefined rules. They 14 may not be the services we are looking for. Besides, 15 there is a growing awareness that the complexity of 16 managing Intelligent Systems Big Data is one of the 17 main challenges in the developing field of the Inter18 net of Things (IoT). Complexity arises from several 19 aspects of the Big Data life cycle, such as gather20 ing data, storing them onto cloud servers. Among 21 the intelligent technologies, how to handle the mas22 sive amount of data generated by the systems and 23 devices of the IoT has been widely considered. Many 24 technologies, such as data mining, big data analytics, 25 statistical and other analysis technologies, have also","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"35 1","pages":"1-1"},"PeriodicalIF":1.5000,"publicationDate":"2021-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fuzzy Logic and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JIFS-189844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

The underlying concept of the Internet of Things 7 (IoT), several studies IoT will dramatically change 8 our daily life. It can be imagined that the era of the 9 Internet of Intelligent Systems will be coming to us 10 soon. The development of IoT, however, has reached 11 a crossroads. Without intelligence, IoT systems will 12 act as an ordinary information system the reactions 13 of which are based on a set of predefined rules. They 14 may not be the services we are looking for. Besides, 15 there is a growing awareness that the complexity of 16 managing Intelligent Systems Big Data is one of the 17 main challenges in the developing field of the Inter18 net of Things (IoT). Complexity arises from several 19 aspects of the Big Data life cycle, such as gather20 ing data, storing them onto cloud servers. Among 21 the intelligent technologies, how to handle the mas22 sive amount of data generated by the systems and 23 devices of the IoT has been widely considered. Many 24 technologies, such as data mining, big data analytics, 25 statistical and other analysis technologies, have also

查看原文本刊更多论文

自适应物联网和机器学习技术，用于管理智能系统大数据的复杂性

物联网(IoT)的基本概念，一些研究表明物联网将极大地改变我们的日常生活。可以想象，智能系统互联网的时代将很快向我们走来。然而，物联网的发展已经走到了十字路口。如果没有智能，物联网系统将作为一个普通的信息系统，其中的反应是基于一组预定义的规则。它们可能不是我们想要的服务。此外，人们越来越意识到，管理智能系统大数据的复杂性是物联网(IoT)发展领域的17个主要挑战之一。复杂性来自大数据生命周期的几个方面，比如收集数据、将数据存储到云服务器上。在21项智能技术中，如何处理物联网系统和设备产生的大量数据已被广泛考虑。许多24项技术，如数据挖掘、大数据分析、25项统计和其他分析技术，也得到了发展

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Fuzzy Logic and Intelligent Systems COMPUTER SCIENCE, THEORY & METHODS-

CiteScore

2.80

自引率

23.10%

发文量

期刊介绍： The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.