Machine Learning And Knowledge Extraction

SCIE

国际简称：MACH LEARN KNOW EXTR 参考译名：机器学习与知识提取

主要研究方向：Multiple 非预警期刊审稿周期：7 Weeks

《机器学习与知识提取》(Machine Learning And Knowledge Extraction)是一本由MDPI AG出版的以Multiple为研究特色的国际期刊，发表该领域相关的原创研究文章、评论文章和综述文章，及时报道该领域相关理论、实践和应用学科的最新发现，旨在促进该学科领域科学信息的快速交流。该期刊是一本未开放期刊，近三年没有被列入预警名单。

3区中科院分区
Q1 JCR分区
175 年发文量
8.4 IF影响因子
未开放 是否OA
English 出版语言

Machine Learning and Knowledge Extraction is an international academic journal dedicated to the fields of machine learning, data mining, knowledge discovery, and artificial intelligence. This journal is committed to publishing original research papers, review articles, and case studies, with the aim of promoting theoretical development and practical applications in these fields.

This journal covers multiple aspects of machine learning, including but not limited to deep learning, clustering analysis, classification algorithms, regression analysis, reinforcement learning, and neural networks. At the same time, the journal also focuses on the application of knowledge extraction and data mining techniques in fields such as bioinformatics, medicine, social sciences, and business intelligence, emphasizing the extraction of useful information and patterns from big data to support decision-making and innovative research. The editorial team and review experts of the magazine come from around the world, and they are all leading figures in their respective fields, ensuring the high quality and innovation of the journal content. Through rigorous peer review, the journal ensures the academic value and scientific accuracy of published articles, providing a platform for researchers, engineers, scholars, and industry experts worldwide to share the latest research findings and cutting-edge technologies.

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Machine Learning And Knowledge Extraction期刊信息

ISSN：2504-4990
出版语言：English
是否OA：未开放
出版地区：Switzerland
是否预警：否
出版商：MDPI AG
开源占比：0.9929
Gold OA文章占比：100.00%
OA被引用占比：
出版国人文章占比：0
出版撤稿占比：
研究类文章占比：92.57%

Machine Learning And Knowledge Extraction CiteScore评价数据（2026年6月最新版）

CiteScore

SJR

SNIP

CiteScore 指数

12.7

1.548

3.033

学科类别	分区	排名	百分位
大类：Engineering 小类：Engineering (miscellaneous)	Q1	22 / 300	92%
大类：Engineering 小类：Artificial Intelligence	Q1	59 / 570	89%

名词解释：CiteScore 是衡量期刊所发表文献的平均受引用次数，是在 Scopus 中衡量期刊影响力的另一个指标。当年CiteScore 的计算依据是期刊最近4年（含计算年度）的被引次数除以该期刊近四年发表的文献数。例如，2022年的 CiteScore 计算方法为：2022年的 CiteScore =2019-2022年收到的对2019-2022年发表的文件的引用数量÷2019-2022年发布的文献数量注：文献类型包括：文章、评论、会议论文、书籍章节和数据论文。

Machine Learning And Knowledge Extraction中科院评价数据

《新锐期刊分区表》（2026年3月发布）

Top期刊	综述期刊	大类学科		小类学科
否	否	计算机科学	3区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 ENGINEERING, ELECTRICAL & ELECTRONIC 工程：电子与电气	3区 3区 3区

Machine Learning And Knowledge Extraction JCR评价数据

2025-2026年最新版

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	ESCI	Q1	32 / 210	85
学科：COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	ESCI	Q1	23 / 185	87.8
学科：ENGINEERING, ELECTRICAL & ELECTRONIC	ESCI	Q1	36 / 369	90.4
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	ESCI	Q2	62 / 210	70.71
学科：COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	ESCI	Q2	65 / 185	65.14
学科：ENGINEERING, ELECTRICAL & ELECTRONIC	ESCI	Q2	116 / 371	68.87

Machine Learning And Knowledge Extraction历年数据统计

影响因子

中科院分区

Machine Learning And Knowledge Extraction中国学者发文选摘

1、Debiased Multiplex Tokenization Using Mamba-Based Pointers for Efficient and Versatile Map-Free Visual Relocalizatio
Author: Wang, Wenshuai; Liu, Hong; Li, Shengquan; Jiang, Peifeng; Che, Dandan; Ding, Runwei

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2026; Vol. 8, Issue 3, pp. -. DOI: 10.3390/make8030083
2、Differential Equation Ensemble Discovery for Modeling Active Matter Based on Robotic Swarm Dat
Author: Bashkova, Xeniya; Molodtsova, Anastasia; Olekhno, Nikita; Hvatov, Alexander

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2026; Vol. 8, Issue 3, pp. -. DOI: 10.3390/make8030072
3、SkySeg-Net: Sky Segmentation-Based Row-Terminal Recognition in Trellised Orchard
Author: Gu, Haiyang; Wang, Yong; Liu, Huaiyang; Tian, Tong; Geng, Changxing; Shi, Yun

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2026; Vol. 8, Issue 2, pp. -. DOI: 10.3390/make8020046
4、Visual Perception and Robust Autonomous Following for Orchard Transportation Robots Based on DeepDIMP-ReI
Author: Shen, Renyuan; Wang, Yong; Liu, Huaiyang; Gu, Haiyang; Geng, Changxing; Shi, Yun

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2026; Vol. 8, Issue 2, pp. -. DOI: 10.3390/make8020039
5、MBS: A Modality-Balanced Strategy for Multimodal Sample Selectio
Author: Xu, Yuntao; Chen, Bing; Hu, Feng; Liu, Jiawei; Zhao, Changjie; Wu, Hongtao

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2026; Vol. 8, Issue 1, pp. -. DOI: 10.3390/make8010017
6、Knowledge Bases and Representation Learning Towards Bug Triagin
Author: Wang, Qi; Yan, Weihao; Li, Yanlong; Ge, Yizheng; Liu, Yiwei; Yin, Peng; Tan, Shuai

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2025; Vol. 7, Issue 2, pp. -. DOI: 10.3390/make7020057
7、Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Dat
Author: Chen, Wenli; Lin, Zirou; Zheng, Lishan; Ho, Mei-Yee Mavis; Ali, Farhan; Teo, Wei Peng

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2025; Vol. 7, Issue 2, pp. -. DOI: 10.3390/make7020051
8、ADTime: Adaptive Multivariate Time Series Forecasting Using LLM
Author: Pei, Jinglei; Zhang, Yang; Liu, Ting; Yang, Jingbin; Wu, Qinghua; Qin, Kang

Journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION. 2025; Vol. 7, Issue 2, pp. -. DOI: 10.3390/make7020035