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Neural Networks

SCIE

國(guó)際簡(jiǎn)稱(chēng):NEURAL NETWORKS  參考譯名:神經(jīng)網(wǎng)絡(luò)

主要研究方向:工程技術(shù)-計(jì)算機(jī):人工智能  非預(yù)警期刊  審稿周期: 約12.0個(gè)月 約10.5周

《神經(jīng)網(wǎng)絡(luò)》(Neural Networks)是一本由Elsevier Ltd出版的以工程技術(shù)-計(jì)算機(jī):人工智能為研究特色的國(guó)際期刊,發(fā)表該領(lǐng)域相關(guān)的原創(chuàng)研究文章、評(píng)論文章和綜述文章,及時(shí)報(bào)道該領(lǐng)域相關(guān)理論、實(shí)踐和應(yīng)用學(xué)科的最新發(fā)現(xiàn),旨在促進(jìn)該學(xué)科領(lǐng)域科學(xué)信息的快速交流。該期刊是一本未開(kāi)放期刊,近三年沒(méi)有被列入預(yù)警名單。該期刊享有很高的科學(xué)聲譽(yù),影響因子不斷增加,發(fā)行量也同樣高。

  • 1區(qū) 中科院分區(qū)
  • Q1 JCR分區(qū)
  • 595 年發(fā)文量
  • 6 IF影響因子
  • 未開(kāi)放 是否OA
  • 128 H-index
  • 1988 創(chuàng)刊年份
  • Monthly 出版周期
  • English 出版語(yǔ)言

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.

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Neural Networks期刊信息

  • ISSN:0893-6080
  • 出版語(yǔ)言:English
  • 是否OA:未開(kāi)放
  • E-ISSN:1879-2782
  • 出版地區(qū):ENGLAND
  • 是否預(yù)警:
  • 出版商:Elsevier Ltd
  • 出版周期:Monthly
  • 創(chuàng)刊時(shí)間:1988
  • 開(kāi)源占比:0.1207
  • Gold OA文章占比:19.75%
  • OA被引用占比:0.0623...
  • 出版國(guó)人文章占比:0.35
  • 出版撤稿占比:0
  • 研究類(lèi)文章占比:98.15%

Neural Networks CiteScore評(píng)價(jià)數(shù)據(jù)(2024年最新版)

CiteScore SJR SNIP CiteScore 指數(shù)
13.9 2.605 2.442
學(xué)科類(lèi)別 分區(qū) 排名 百分位
大類(lèi):Neuroscience 小類(lèi):Cognitive Neuroscience Q1 4 / 115

96%

大類(lèi):Neuroscience 小類(lèi):Artificial Intelligence Q1 35 / 350

90%

名詞解釋?zhuān)?/b>CiteScore 是衡量期刊所發(fā)表文獻(xiàn)的平均受引用次數(shù),是在 Scopus 中衡量期刊影響力的另一個(gè)指標(biāo)。當(dāng)年CiteScore 的計(jì)算依據(jù)是期刊最近4年(含計(jì)算年度)的被引次數(shù)除以該期刊近四年發(fā)表的文獻(xiàn)數(shù)。例如,2022年的 CiteScore 計(jì)算方法為:2022年的 CiteScore =2019-2022年收到的對(duì)2019-2022年發(fā)表的文件的引用數(shù)量÷2019-2022年發(fā)布的文獻(xiàn)數(shù)量 注:文獻(xiàn)類(lèi)型包括:文章、評(píng)論、會(huì)議論文、書(shū)籍章節(jié)和數(shù)據(jù)論文。

Neural Networks中科院評(píng)價(jià)數(shù)據(jù)

中科院 2023年12月升級(jí)版

Top期刊 綜述期刊 大類(lèi)學(xué)科 小類(lèi)學(xué)科
計(jì)算機(jī)科學(xué) 1區(qū) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 NEUROSCIENCES 神經(jīng)科學(xué) 2區(qū) 2區(qū)

中科院 2022年12月升級(jí)版

Top期刊 綜述期刊 大類(lèi)學(xué)科 小類(lèi)學(xué)科
計(jì)算機(jī)科學(xué) 1區(qū) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 NEUROSCIENCES 神經(jīng)科學(xué) 2區(qū) 2區(qū)

中科院 2021年12月舊的升級(jí)版

Top期刊 綜述期刊 大類(lèi)學(xué)科 小類(lèi)學(xué)科
計(jì)算機(jī)科學(xué) 2區(qū) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 NEUROSCIENCES 神經(jīng)科學(xué) 2區(qū) 2區(qū)

中科院 2021年12月基礎(chǔ)版

Top期刊 綜述期刊 大類(lèi)學(xué)科 小類(lèi)學(xué)科
工程技術(shù) 2區(qū) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 NEUROSCIENCES 神經(jīng)科學(xué) 2區(qū) 2區(qū)

中科院 2021年12月升級(jí)版

Top期刊 綜述期刊 大類(lèi)學(xué)科 小類(lèi)學(xué)科
計(jì)算機(jī)科學(xué) 2區(qū) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 NEUROSCIENCES 神經(jīng)科學(xué) 2區(qū) 2區(qū)

中科院 2020年12月舊的升級(jí)版

Top期刊 綜述期刊 大類(lèi)學(xué)科 小類(lèi)學(xué)科
計(jì)算機(jī)科學(xué) 2區(qū) COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 NEUROSCIENCES 神經(jīng)科學(xué) 2區(qū) 2區(qū)

Neural Networks JCR評(píng)價(jià)數(shù)據(jù)(2023-2024年最新版)

按JIF指標(biāo)學(xué)科分區(qū) 收錄子集 分區(qū) 排名 百分位
學(xué)科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 38 / 197

81%

學(xué)科:NEUROSCIENCES SCIE Q1 34 / 310

89.2%

按JCI指標(biāo)學(xué)科分區(qū) 收錄子集 分區(qū) 排名 百分位
學(xué)科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SCIE Q1 28 / 198

86.11%

學(xué)科:NEUROSCIENCES SCIE Q1 32 / 310

89.84%

Neural Networks歷年數(shù)據(jù)統(tǒng)計(jì)

影響因子
中科院分區(qū)

Neural Networks中國(guó)學(xué)者發(fā)文選摘

  • 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

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