影响因子:
0.2
ISSN:
print: 2405-6456
on-line: 2405-6464
研究领域:
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
自引率:
0.00%
Gold OA文章占比:
1.28%
原创研究文献占比:
100.00%
SCI收录类型:
Emerging Sources Citation Index (ESCI) || Scopus (CiteScore)
期刊介绍英文:
Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
CiteScore:
CiteScoreSJRSNIPCiteScore排名
0.90.1510.142
学科
排名
百分位
大类:Computer Science
小类:Artificial Intelligence
309 / 350
11%
大类:Computer Science
小类:Computer Networks and Communications
355 / 395
10%
大类:Computer Science
小类:Software
377 / 407
7%
发文信息
WOS期刊分区
学科分类
Q4COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
历年影响因子
2022年0.3000
2023年0.2000
历年发表
2012年86
2013年66
2014年0
2015年28
2016年23
2017年20
2018年25
2019年31
2020年21
2021年24
2022年35
投稿信息
出版国家(地区):
Netherlands
出版商:
IOS Press

Web Intelligence - 最新文献

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Efficient IoT-based heart disease prediction framework with Weight Updated Trans-Bidirectional Long Short Term Memory-Gated Recurrent Unit

Pub Date : 2024-05-16 DOI: 10.3233/web-230063 K. Sasirekha, D. Asha, P. Sivaganga, R. Harini
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