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Machine Learning And Knowledge Extraction

Machine Learning And Knowledge ExtractionSCIE

国际简称:MACH LEARN KNOW EXTR  参考译名:机器学习与知识提取

  • CiteScore分区

    Q1

  • JCR分区

    Q2

基本信息:
ISSN:2504-4990
是否OA:开放
是否预警:否
出版信息:
出版地区:Switzerland
出版商:MDPI AG
出版语言:English
研究方向:Multiple
评价信息:
影响因子:4
CiteScore指数:8.5
SNIP指数:1.82
发文数据:
Gold OA文章占比:100.00%
研究类文章占比:88.04%
年发文量:92
英文简介 期刊介绍 CiteScore数据 JCR分区 常见问题

英文简介Machine Learning And Knowledge Extraction期刊介绍

Machine Learning and Knowledge Extraction is an international academic journal dedicated to the fields of machine learning, data mining, knowledge discovery, and artificial intelligence. This journal is committed to publishing original research papers, review articles, and case studies, with the aim of promoting theoretical development and practical applications in these fields.

This journal covers multiple aspects of machine learning, including but not limited to deep learning, clustering analysis, classification algorithms, regression analysis, reinforcement learning, and neural networks. At the same time, the journal also focuses on the application of knowledge extraction and data mining techniques in fields such as bioinformatics, medicine, social sciences, and business intelligence, emphasizing the extraction of useful information and patterns from big data to support decision-making and innovative research. The editorial team and review experts of the magazine come from around the world, and they are all leading figures in their respective fields, ensuring the high quality and innovation of the journal content. Through rigorous peer review, the journal ensures the academic value and scientific accuracy of published articles, providing a platform for researchers, engineers, scholars, and industry experts worldwide to share the latest research findings and cutting-edge technologies.

期刊简介Machine Learning And Knowledge Extraction期刊介绍

《Machine Learning And Knowledge Extraction》该刊近一年未被列入预警期刊名单,目前已被权威数据库SCIE收录,得到了广泛的认可。

该期刊投稿重要关注点:

  • 预计审稿时间: 7 Weeks
  • SCIE
  • 非预警

Cite Score数据(2024年最新版)Machine Learning And Knowledge Extraction Cite Score数据

  • CiteScore:8.5
  • SJR:0
  • SNIP:1.822
学科类别 分区 排名 百分位
大类:Engineering 小类:Engineering (miscellaneous) Q1 14 / 151

91%

大类:Engineering 小类:Artificial Intelligence Q1 63 / 301

79%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础,针对的是前四年发表的论文的引文。CiteScore的意义在于,它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法,而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

JCR分区Machine Learning And Knowledge Extraction JCR分区

2023-2024 年最新版
按JIF指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ESCI Q2 64 / 197

67.8%

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ESCI Q2 45 / 169

73.7%

学科:ENGINEERING, ELECTRICAL & ELECTRONIC ESCI Q2 100 / 352

71.7%

按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ESCI Q2 81 / 198

59.34%

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ESCI Q2 72 / 169

57.69%

学科:ENGINEERING, ELECTRICAL & ELECTRONIC ESCI Q2 140 / 354

60.59%

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异,此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此,JCR将期刊按照学科门类和影响因子分为不同的分区,这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

投稿常见问题