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Knowledge-based Systems

Knowledge-based SystemsSCIE

国际简称:KNOWL-BASED SYST  参考译名:基于知识的系统

  • 中科院分区

    1区

  • CiteScore分区

    Q1

  • JCR分区

    Q1

基本信息:
ISSN:0950-7051
E-ISSN:1872-7409
是否OA:未开放
是否预警:否
TOP期刊:是
出版信息:
出版地区:NETHERLANDS
出版商:Elsevier
出版语言:English
出版周期:Bimonthly
出版年份:1987
研究方向:工程技术-计算机:人工智能
评价信息:
影响因子:7.2
H-index:94
CiteScore指数:14.8
SJR指数:2.219
SNIP指数:2.226
发文数据:
Gold OA文章占比:8.59%
研究类文章占比:99.54%
年发文量:874
自引率:0.125
开源占比:0.0405
出版撤稿占比:0
出版国人文章占比:0.46
OA被引用占比:0.0266...
英文简介 期刊介绍 CiteScore数据 中科院SCI分区 JCR分区 发文数据 常见问题

英文简介Knowledge-based Systems期刊介绍

Knowledge-Based Systems is an international, interdisciplinary and applications-oriented journal. This journal focuses on systems that use knowledge-based (KB) techniques to support human decision-making, learning and action; emphases the practical significance of such KB-systems; its computer development and usage; covers the implementation of such KB-systems: design process, models and methods, software tools, decision-support mechanisms, user interactions, organizational issues, knowledge acquisition and representation, and system architectures.

This journal's current leading topics are but not limited to:

• Big data techniques and methodologies, data-driven information systems, and knowledge acquisition

• Cognitive interaction and intelligent human interfaces

• Recommender systems and E-service personalization

• Intelligent decision support systems, prediction systems and warning systems

• Computational and artificial intelligence based systems and uncertain information processes

• Swarm intelligence and evolutionary computing

• Knowledge engineering, machine learning-based systems and web semantics

The journal also welcomes papers describing novel applications of knowledge based systems in any human endeavor: ranging from financial technology to engineering to health science or any other domain impacted by Artificial Intelligence technologies and its associated techniques and systems.

期刊简介Knowledge-based Systems期刊介绍

《Knowledge-based Systems》自1987出版以来,是一本计算机科学优秀杂志。致力于发表原创科学研究结果,并为计算机科学各个领域的原创研究提供一个展示平台,以促进计算机科学领域的的进步。该刊鼓励先进的、清晰的阐述,从广泛的视角提供当前感兴趣的研究主题的新见解,或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道计算机科学领域的最新进展和新发现新突破等。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:

Cite Score数据(2024年最新版)Knowledge-based Systems Cite Score数据

  • CiteScore:14.8
  • SJR:2.219
  • SNIP:2.226
学科类别 分区 排名 百分位
大类:Decision Sciences 小类:Information Systems and Management Q1 8 / 148

94%

大类:Decision Sciences 小类:Management Information Systems Q1 8 / 131

94%

大类:Decision Sciences 小类:Software Q1 28 / 407

93%

大类:Decision Sciences 小类:Artificial Intelligence Q1 31 / 350

91%

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

中科院SCI分区Knowledge-based Systems 中科院分区

中科院 2023年12月升级版 综述期刊:否 Top期刊:是
大类学科 分区 小类学科 分区
计算机科学 1区 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能 2区

中科院分区表 是以客观数据为基础,运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据,得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次,类似于“优、良、及格”等。最开始,这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Knowledge-based Systems 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 SCIE Q1 27 / 197

86.5%

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 SCIE Q1 34 / 198

83.08%

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

发文数据

2023-2024 年国家/地区发文量统计
  • 国家/地区数量
  • CHINA MAINLAND1020
  • Spain129
  • USA116
  • Australia100
  • England77
  • India61
  • Canada58
  • Japan56
  • Iran44
  • Singapore39

本刊中国学者近年发表论文

  • 1、Multi-relation graph convolutional network for Alzheimer's disease diagnosis using structural MRI

    Author: Zhang, Jin; He, Xiaohai; Qing, Linbo; Chen, Xiang; Liu, Yan; Chen, Honggang

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 270, Issue , pp. -. DOI: 10.1016/j.knosys.2023.110546

  • 2、Light field angular super-resolution based on intrinsic and geometric information

    Author: Wang, Lingyu; Ren, Lifei; Wei, Xiaoyao; Yang, Jiangxin; Cao, Yanlong; Cao, Yanpeng

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 270, Issue , pp. -. DOI: 10.1016/j.knosys.2023.110553

  • 3、MSBPR: A multi-pairwise preference and similarity based Bayesian personalized ranking method for recommendation

    Author: Zeng, Liang; Guan, Jiewen; Chen, Bilian

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 260, Issue , pp. -. DOI: 10.1016/j.knosys.2022.110165

  • 4、Multiplex network community detection algorithm based on motif awareness

    Author: Li, Chunying; Guo, Xiaojiao; Lin, Weijie; Tang, Zhikang; Cao, Jinli; Zhang, Yanchun

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 260, Issue , pp. -. DOI: 10.1016/j.knosys.2022.110136

  • 5、Ranking influential spreaders based on both node k-shell and structural hole

    Author: Zhao, Zhili; Li, Ding; Sun, Yue; Zhang, Ruisheng; Liu, Jun

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 260, Issue , pp. -. DOI: 10.1016/j.knosys.2022.110163

  • 6、Data and knowledge co-driving for cancer subtype classification on multi-scale histopathological slides

    Author: Yu, Bo; Chen, Hechang; Zhang, Yunke; Cong, Lele; Pang, Shuchao; Zhou, Hongren; Wang, Ziye; Cong, Xianling

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 260, Issue , pp. -. DOI: 10.1016/j.knosys.2022.110168

  • 7、Hyperparameter optimization through context-based meta-reinforcement learning with task-aware representation

    Author: Wu, Jia; Liu, Xiyuan; Chen, Senpeng

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 260, Issue , pp. -. DOI: 10.1016/j.knosys.2022.110160

  • 8、Semi-supervised learning with pseudo-negative labels for image classification

    Author: Xu, Hao; Xiao, Hui; Hao, Huazheng; Dong, Li; Qiu, Xiaojie; Peng, Chengbin

    Journal: KNOWLEDGE-BASED SYSTEMS. 2023; Vol. 260, Issue , pp. -. DOI: 10.1016/j.knosys.2022.110166

投稿常见问题

通讯方式:ELSEVIER SCIENCE BV, PO BOX 211, AMSTERDAM, NETHERLANDS, 1000 AE。