期刊名：Machine Learning and Knowledge Extraction

期刊主页：https://www.mdpi.com/journal/make

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1.

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

用于人类活动识别的深度学习方法综述

https://www.mdpi.com/2504-4990/6/2/40

Kaseris, M.; Kostavelis, I.; Malassiotis, S. A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition. Mach. Learn. Knowl. Extr. 2024, 6, 842-876. https://doi.org/10.3390/make6020040

2.

Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models

推进医学影像中的人工智能可解释性：像素级可解释性与Grad-CAM模型的比较分析

https://www.mdpi.com/2504-4990/7/1/12

Ennab, M.; Mcheick, H. Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models. Mach. Learn. Knowl. Extr. 2025, 7, 12. https://doi.org/10.3390/make7010012

3.

SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis

SHAP（沙普利加性解释）在滚动轴承故障诊断中的高效特征选择

https://www.mdpi.com/2504-4990/6/1/16

Santos, M.R.; Guedes, A.; Sanchez-Gendriz, I. SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis. Mach. Learn. Knowl. Extr. 2024, 6, 316-341. https://doi.org/10.3390/make6010016

4.

More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

能力越强，善意越少？不同社会背景下对AI系统的信任感知

https://www.mdpi.com/2504-4990/6/1/17

Novozhilova, E.; Mays, K.; Paik, S.; Katz, J.E. More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts. Mach. Learn. Knowl. Extr. 2024, 6, 342-366. https://doi.org/10.3390/make6010017

5.

Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction

基于算术优化算法特征选择的多层感知器神经网络用于心血管疾病预测

https://www.mdpi.com/2504-4990/6/2/46

Alghamdi, F.A.; Almanaseer, H.; Jaradat, G.; Jaradat, A.; Alsmadi, M.K.; Jawarneh, S.; Almurayh, A.S.; Alqurni, J.; Alfagham, H. Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction. Mach. Learn. Knowl. Extr. 2024, 6, 987-1008. https://doi.org/10.3390/make6020046

6.

Exploring the Intersection of Machine Learning and Big Data: A Survey

探索机器学习与大数据的交叉综述

https://www.mdpi.com/2504-4990/7/1/13

Dritsas, E.; Trigka, M. Exploring the Intersection of Machine Learning and Big Data: A Survey. Mach. Learn. Knowl. Extr. 2025, 7, 13. https://doi.org/10.3390/make7010013

7.

Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education

用符号化教育知识增强深度神经网络：迈向可信且可解释的教育人工智能

https://www.mdpi.com/2504-4990/6/1/28

Hooshyar, D.; Azevedo, R.; Yang, Y. Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education. Mach. Learn. Knowl. Extr. 2024, 6, 593-618. https://doi.org/10.3390/make6010028

8.

Empowering Brain Tumor Diagnosis through Explainable Deep Learning

通过可解释深度学习赋能脑肿瘤诊断

https://www.mdpi.com/2504-4990/6/4/111

Li, Z.; Dib, O. Empowering Brain Tumor Diagnosis through Explainable Deep Learning. Mach. Learn. Knowl. Extr. 2024, 6, 2248-2281. https://doi.org/10.3390/make6040111

9.

Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning

客户流失预测：机器学习和深度学习领域最新进展、趋势与挑战的系统综述

https://www.mdpi.com/2504-4990/7/3/105

Imani, M.; Joudaki, M.; Beikmohammadi, A.; Arabnia, H.R. Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning. Mach. Learn. Knowl. Extr. 2025, 7, 105. https://doi.org/10.3390/make7030105

10.

Uncertainty in XAI: Human Perception and Modeling Approaches

可解释人工智能中的不确定性：人类感知与建模方法

https://www.mdpi.com/2504-4990/6/2/55

Chiaburu, T.; Haußer, F.; Bießmann, F. Uncertainty in XAI: Human Perception and Modeling Approaches. Mach. Learn. Knowl. Extr. 2024, 6, 1170-1192. https://doi.org/10.3390/make6020055

11.

A Cognitive Load Theory (CLT) Analysis of Machine Learning Explainability, Transparency, Interpretability, and Shared Interpretability

基于认知负荷理论的机器学习可解释性、透明度与共享可解释性分析

https://www.mdpi.com/2504-4990/6/3/71

Fox, S.; Rey, V.F. A Cognitive Load Theory (CLT) Analysis of Machine Learning Explainability, Transparency, Interpretability, and Shared Interpretability. Mach. Learn. Knowl. Extr. 2024, 6, 1494-1509. https://doi.org/10.3390/make6030071

12.

AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview

用于重症监护室的人工智能进展——聚焦脓毒症预测的概述

https://www.mdpi.com/2504-4990/7/1/6

Stylianides, C.; Nicolaou, A.; Sulaiman, W.A.; Alexandropoulou, C.-A.; Panagiotopoulos, I.; Karathanasopoulou, K.; Dimitrakopoulos, G.; Kleanthous, S.; Politi, E.; Ntalaperas, D.; et al. AI Advances in ICU with an Emphasis on Sepsis Prediction: An Overview. Mach. Learn. Knowl. Extr. 2025, 7, 6. https://doi.org/10.3390/make7010006

13.

Knowledge Graphs and Their Reciprocal Relationship with Large Language Models

知识图谱及其与大语言模型的互惠关系

https://www.mdpi.com/2504-4990/7/2/38

Dehal, R.S.; Sharma, M.; Rajabi, E. Knowledge Graphs and Their Reciprocal Relationship with Large Language Models. Mach. Learn. Knowl. Extr. 2025, 7, 38. https://doi.org/10.3390/make7020038

14.

Dataset Dependency in CNN-Based Copy-Move Forgery Detection: A Multi-Dataset Comparative Analysis

基于卷积神经网络的复制-移动伪造检测中的数据集依赖性：多数据集比较分析

https://www.mdpi.com/2504-4990/7/2/54

Dell’Olmo, P.V.; Kuznetsov, O.; Frontoni, E.; Arnesano, M.; Napoli, C.; Randieri, C. Dataset Dependency in CNN-Based Copy-Move Forgery Detection: A Multi-Dataset Comparative Analysis. Mach. Learn. Knowl. Extr. 2025, 7, 54. https://doi.org/10.3390/make7020054

15.

Comparative Analysis of Perturbation Techniques in LIME for Intrusion Detection Enhancement

局部可解释模型（LIME）中扰动技术的比较分析用于增强入侵检测

https://www.mdpi.com/2504-4990/7/1/21

Bacevicius, M.; Paulauskaite-Taraseviciene, A.; Zokaityte, G.; Kersys, L.; Moleikaityte, A. Comparative Analysis of Perturbation Techniques in LIME for Intrusion Detection Enhancement. Mach. Learn. Knowl. Extr. 2025, 7, 21. https://doi.org/10.3390/make7010021

16.

Behind the Algorithm: International Insights into Data-Driven AI Model Development

算法背后：数据驱动AI模型开发的国际视角

https://www.mdpi.com/2504-4990/7/4/122

Ziv, L.; Nakash, M. Behind the Algorithm: International Insights into Data-Driven AI Model Development. Mach. Learn. Knowl. Extr. 2025, 7, 122. https://doi.org/10.3390/make7040122

期刊简介：

主编：Prof. Dr. Andreas Holzinger, BOKU University, Austria; University of Technology, Austria; University of Alberta, Canada

期刊创刊于2019年，涵盖机器学习方法及其应用，从数据预处理到结果可视化的整个机器学习和知识获取与发现的流程，重点关注隐私、数据保护、安全性、数据挖掘、自然语言、神经网络和熵等。