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Machine Learning-Science and Technology

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期刊簡介
期刊名稱Machine Learning-Science and Technology Machine Learning-Science and Technology

According to the latest JCR data, this journal is indexed in the JCR. A previous title of this journal is )
LetPub Score
7.1
50 ratings
Rate

Reputation
8.3

Influence
6.2

Speed
7.0

期刊簡稱MACH LEARN-SCI TECHN
ISSN2632-2153
h-indexN.A.
CiteScore
CiteScoreSJRSNIPCiteScore Rank
9.101.5061.403
Subject fieldQuartilesRankPercentile
Category: Computer Science
Subcategory: Software
Q170 / 407
Category: Computer Science
Subcategory: Human-Computer Interaction
Q126 / 145
Category: Computer Science
Subcategory: Artificial Intelligence
Q173 / 350

自引率 (2023-2024)6.30%自引率趨勢
掲載範囲
Machine Learning: Science and Technology™ is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories:


i) advance the state of machine learning-driven applications in the sciences,

or

ii) make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.

Particular areas of scientific application include (but are not limited to):
• Physics and space science

• Design and discovery of novel materials and molecules

• Materials characterisation techniques

• Simulation of materials, chemical processes and biological systems

• Atomistic and coarse-grained simulation

• Quantum computing

• Biology, medicine and biomedical imaging

• Geoscience (including natural disaster prediction) and climatology

• Particle Physics

• Simulation methods and high-performance computing


Conceptual or methodological advances in machine learning methods include those in (but are not limited to):
• Explainability, causality and robustness

• New (physics inspired) learning algorithms

• Neural network architectures

• Kernel methods

• Bayesian and other probabilistic methods

• Supervised, unsupervised and generative methods

• Novel computing architectures

• Codes and datasets

• Benchmark studies
官方網站https://iopscience.iop.org/journal/2632-2153
在線稿件提交https://mc04.manuscriptcentral.com/mlst-iop
開放訪問Yes
出版商IOP PUBLISHING LTD
主題領域Multiple
出版國/地區ENGLAND
發行頻率四半期刊行
創刊年2020
每年文章數194每年文章數趨勢
黃金OA百分比99.53%
OA Related Info
APC: Yes( USD2610; EUR2255; GBP1900; )
APC waiver:Check Notes
Other charges: No
Keywords: machine learning、artificial intelligence、computational physics、reinforcement learning、neural networks、applied algorithms
Useful LinksAims & ScopeAuthor InstructionsEditorial BoardAnonymous peer review
Web of Science 四分位
2023-2024
WOS Quartile: Q1

CategoryEditionJIF QuartileJIF RankingJIF Percentage
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCESCIEQ136/197
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSSCIEQ123/169
MULTIDISCIPLINARY SCIENCESSCIEQ115/134
索引 (SCI or SCIE)Science Citation Index Expanded
鏈接到PubMed Central (PMC)https://www.ncbi.nlm.nih.gov/nlmcatalog?term=2632-2153%5BISSN%5D
平均審稿時間 *來自出版商的數據: Submission to first decision before peer review: 3 days; Submission to first decision after peer review: 49 days;
來自作者的數據: 13 Weeks
競爭力 *來自作者的數據:
參考鏈接
相關期刊 【Machine Learning-Science and Technology】CiteScore趨勢
自引率趨勢 每年文章數趨勢
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*所有的審稿過程指標,如接受率和審稿速度,僅限於用戶提交的稿件。因此,這些指標可能無法準確反映期刊的競爭力或速度。
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  •  

    Machine Learning-Science and Technology Machine Learning-Science and Technology
    明年預測:
    穩步上升 無變化 逐步下降  刷新
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