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Neural Networks

Neural NetworksSCIE

国际简称:NEURAL NETWORKS  参考译名:神经网络

  • 中科院分区

    1区

  • CiteScore分区

    Q1

  • JCR分区

    Q1

基本信息:
ISSN:0893-6080
E-ISSN:1879-2782
是否OA:未开放
是否预警:否
TOP期刊:是
出版信息:
出版地区:ENGLAND
出版商:Elsevier Ltd
出版语言:English
出版周期:Monthly
出版年份:1988
研究方向:工程技术-计算机:人工智能
评价信息:
影响因子:6
H-index:128
CiteScore指数:13.9
SJR指数:2.605
SNIP指数:2.442
发文数据:
Gold OA文章占比:19.75%
研究类文章占比:98.15%
年发文量:595
自引率:0.0769...
开源占比:0.1207
出版撤稿占比:0
出版国人文章占比:0.35
OA被引用占比:0.0623...
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Neural Networks期刊介绍

Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters and reviews, as well as letters to the editor, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.

期刊简介Neural Networks期刊介绍

《Neural Networks》自1988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)Neural Networks Cite Score数据

  • CiteScore:13.9
  • SJR:2.605
  • SNIP:2.442
学科类别 分区 排名 百分位
大类:Neuroscience 小类:Cognitive Neuroscience Q1 4 / 115

96%

大类:Neuroscience 小类:Artificial Intelligence Q1 35 / 350

90%

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Neural Networks 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 NEUROSCIENCES 神经科学 2区 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Neural Networks 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 38 / 197

81%

学科:NEUROSCIENCES SCIE Q1 34 / 310

89.2%

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 28 / 198

86.11%

学科:NEUROSCIENCES SCIE Q1 32 / 310

89.84%

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 MAINLAND408
  • USA133
  • England58
  • Japan55
  • Australia39
  • Spain33
  • South Korea32
  • Italy30
  • GERMANY (FED REP GER)28
  • France27

本刊中国学者近年发表论文

  • 1、Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligence

    Author: Yang, Yang; Huang, Jie; Hu, Dexiu

    Journal: NEURAL NETWORKS. 2023; Vol. 163, Issue , pp. 165-177. DOI: 10.1016/j.neunet.2023.04.005

  • 2、Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection

    Author: Zhu, Dehui; Du, Bo; Hu, Meiqi; Dong, Yanni; Zhang, Liangpei

    Journal: NEURAL NETWORKS. 2023; Vol. 163, Issue , pp. 205-218. DOI: 10.1016/j.neunet.2023.02.002

  • 3、Unsupervised graph-level representation learning with hierarchical contrasts

    Author: Ju, Wei; Gu, Yiyang; Luo, Xiao; Wang, Yifan; Yuan, Haochen; Zhong, Huasong; Zhang, Ming

    Journal: NEURAL NETWORKS. 2023; Vol. 158, Issue , pp. 359-368. DOI: 10.1016/j.neunet.2022.11.019

  • 4、Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer's disease

    Author: Liu, Chaoqiang; Huang, Fei; Qiu, Anqi

    Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 14-24. DOI: 10.1016/j.neunet.2022.10.032

  • 5、Factorizing time-heterogeneous Markov transition for temporal recommendation?

    Author: Wen, Wen; Wang, Wencui; Hao, Zhifeng; Cai, Ruichu

    Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 84-96. DOI: 10.1016/j.neunet.2022.11.032

  • 6、Efficient Perturbation Inference and Expandable Network for continual learning

    Author: Du, Fei; Yang, Yun; Zhao, Ziyuan; Zeng, Zeng

    Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 97-106. DOI: 10.1016/j.neunet.2022.10.030

  • 7、Variable three-term conjugate gradient method for training artificial neural networks

    Author: Kim, Hansu; Wang, Chuxuan; Byun, Hyoseok; Hu, Weifei; Kim, Sanghyuk; Jiao, Qing; Lee, Tah Hee

    Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 125-136. DOI: 10.1016/j.neunet.2022.12.001

  • 8、Representation learning for continuous action spaces is beneficial for efficient policy learning

    Author: Zhao, Tingting; Wang, Ying; Sun, Wei; Chen, Yarui; Niu, Gang; Sugiyama, Masashi

    Journal: NEURAL NETWORKS. 2023; Vol. 159, Issue , pp. 137-152. DOI: 10.1016/j.neunet.2022.12.009

投稿常见问题

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