期刊名：Algorithms

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

期刊名：algorithms

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

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英文标题：Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review

中文标题：量子计算与机器学习在医学决策中的应用：综合综述

文章链接：https://www.mdpi.com/1999-4893/18/3/156

MDPI引用格式：Chow, J.C.L. Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review. Algorithms 2025, 18, 156. https://doi.org/10.3390/a18030156

英文标题：Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model

中文标题：基于四分之一汽车模型的主动、半主动与被动悬架性能研究

文章链接：https://www.mdpi.com/1999-4893/18/2/100

MDPI引用格式：Samaroo, K.; Awan, A.W.; Marimuthu, S.; Iqbal, M.N.; Daniel, K.; Shabbir, N. Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model. Algorithms 2025, 18, 100. https://doi.org/10.3390/a18020100

英文标题：A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing

中文标题：面向云制造的可扩展传感器数据采集与实时处理框架

文章链接：https://www.mdpi.com/1999-4893/18/1/22

MDPI引用格式：Pacella, M.; Papa, A.; Papadia, G.; Fedeli, E. A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing. Algorithms 2025, 18, 22. https://doi.org/10.3390/a18010022

英文标题：High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting

中文标题：城市用水需求预测的高性能计算与并行算法

文章链接：https://www.mdpi.com/1999-4893/18/4/182

MDPI引用格式：Myllis, G.; Tsimpiris, A.; Aggelopoulos, S.; Vrana, V.G. High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting. Algorithms 2025, 18, 182. https://doi.org/10.3390/a18040182

英文标题：GATransformer: A Graph Attention Network-Based Transformer Model to Generate Explainable Attentions for Brain Tumor Detection

中文标题：GATransformer：一种基于图注意力网络的Transformer模型，用于生成可解释注意力的脑肿瘤检测

文章链接：https://www.mdpi.com/1999-4893/18/2/89

MDPI引用格式：Tehsin, S.; Nasir, I.M.; Damaševi?ius, R. GATransformer: A Graph Attention Network-Based Transformer Model to Generate Explainable Attentions for Brain Tumor Detection. Algorithms 2025, 18, 89. https://doi.org/10.3390/a18020089

英文标题：Optimization of PFMEA Team Composition in the Automotive Industry Using the IPF-RADAR Approach

中文标题：基于IPF-RADAR方法的汽车行业PFMEA团队构成优化

文章链接：https://www.mdpi.com/1999-4893/18/6/342

MDPI引用格式：Komatina, N.; Marinkovic, D. Optimization of PFMEA Team Composition in the Automotive Industry Using the IPF-RADAR Approach. Algorithms 2025, 18, 342. https://doi.org/10.3390/a18060342

英文标题：DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection

中文标题：DDL R-CNN：用于旋转目标检测的动态方向学习R-CNN

文章链接：https://www.mdpi.com/1999-4893/18/1/21

MDPI引用格式：Su, W.; Jing, D. DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection. Algorithms 2025, 18, 21. https://doi.org/10.3390/a18010021

英文标题：Impossibility Results for Byzantine-Tolerant State Observation, Synchronization, and Graph Computation Problems

中文标题：拜占庭容错状态观测、同步与图计算问题的不可能性结果

文章链接：https://www.mdpi.com/1999-4893/18/1/26

MDPI引用格式：Kshemkalyani, A.D.; Misra, A. Impossibility Results for Byzantine-Tolerant State Observation, Synchronization, and Graph Computation Problems. Algorithms 2025, 18, 26. https://doi.org/10.3390/a18010026

英文标题：A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks

中文标题：基于深度神经网络的机器人机械臂逆运动学、控制与路径规划综述（含障碍与无障碍情况）

文章链接：https://www.mdpi.com/1999-4893/18/1/23

MDPI引用格式：Calzada-Garcia, A.; Victores, J.G.; Naranjo-Campos, F.J.; Balaguer, C. A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks. Algorithms 2025, 18, 23. https://doi.org/10.3390/a18010023

英文标题：SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models

中文标题：SMOTE 与 SMOTEENN：处理回归模型类别不平衡的重采样算法性能研究

文章链接：https://www.mdpi.com/1999-4893/18/1/37

MDPI引用格式：Husain, G.; Nasef, D.; Jose, R.; Mayer, J.; Bekbolatova, M.; Devine, T.; Toma, M. SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models. Algorithms 2025, 18, 37. https://doi.org/10.3390/a18010037

英文标题：Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics

中文标题：Apache Spark MLlib优化：大规模模型在大数据分析中的预测性能

文章链接：https://www.mdpi.com/1999-4893/18/2/74

MDPI引用格式：Theodorakopoulos, L.; Karras, A.; Krimpas, G.A. Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics. Algorithms 2025, 18, 74. https://doi.org/10.3390/a18020074

英文标题：Integrating Artificial Intelligence, Internet of Things, and Sensor-Based Technologies: A Systematic Review of Methodologies in Autism Spectrum Disorder Detection

中文标题：人工智能、物联网与传感器技术的融合：自闭症谱系障碍检测方法的系统综述

文章链接：https://www.mdpi.com/1999-4893/18/1/34

MDPI引用格式：Bouchouras, G.; Kotis, K. Integrating Artificial Intelligence, Internet of Things, and Sensor-Based Technologies: A Systematic Review of Methodologies in Autism Spectrum Disorder Detection. Algorithms 2025, 18, 34. https://doi.org/10.3390/a18010034

英文标题：Application Framework and Optimal Features for UAV-Based Earthquake-Induced Structural Displacement Monitoring

中文标题：基于无人机的地震诱发结构位移监测的应用框架与最佳特征

文章链接：https://www.mdpi.com/1999-4893/18/2/66

MDPI引用格式：Ji, R.; Sorosh, S.; Lo, E.; Norton, T.J.; Driscoll, J.W.; Kuester, F.; Barbosa, A.R.; Simpson, B.G.; Hutchinson, T.C. Application Framework and Optimal Features for UAV-Based Earthquake-Induced Structural Displacement Monitoring. Algorithms 2025, 18, 66. https://doi.org/10.3390/a18020066

英文标题：Pneumonia Disease Detection Using Chest X-Rays and Machine Learning

中文标题：基于胸部X光片与机器学习的肺炎检测

文章链接：https://www.mdpi.com/1999-4893/18/2/82

MDPI引用格式：Usman, C.; Rehman, S.U.; Ali, A.; Khan, A.M.; Ahmad, B. Pneumonia Disease Detection Using Chest X-Rays and Machine Learning. Algorithms 2025, 18, 82. https://doi.org/10.3390/a18020082

英文标题：Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System

中文标题：面向简化飞机燃油系统的智能多故障诊断

文章链接：https://www.mdpi.com/1999-4893/18/2/73

MDPI引用格式：Li, J.; King, S.; Jennions, I. Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System. Algorithms 2025, 18, 73. https://doi.org/10.3390/a18020073

英文标题：Knowledge Discovery in Predicting Martensite Start Temperature of Medium-Carbon Steels by Artificial Neural Networks

中文标题：基于人工神经网络的中碳钢马氏体起始温度预测中的知识发现

文章链接：https://www.mdpi.com/1999-4893/18/2/116

MDPI引用格式：Wang, X.-S.; Maurya, A.K.; Ishtiaq, M.; Kang, S.-G.; Reddy, N.G.S. Knowledge Discovery in Predicting Martensite Start Temperature of Medium-Carbon Steels by Artificial Neural Networks. Algorithms 2025, 18, 116. https://doi.org/10.3390/a18020116

期刊简介：Algorithms (ISSN 1999-4893; CODEN: ALGOCH) 是一本开放获取期刊，涵盖计算机科学、计算数学、人工智能、自动化与控制系统、理论、方法及跨学科应用、数据与信息系统以及软件工程等领域。Algorithms为算法及其应用相关研究提供了一个先进的交流平台。期刊发表综述论文、常规研究论文、简短通讯，以及特定主题的专刊论文。Algorithms的宗旨是鼓励科学家尽可能详细地发表其实验和理论研究成果。因此，期刊对论文的篇幅没有限制。作者应提供完整的实验细节，以便结果能够被重复验证。

期刊主编：Frank Werner, Otto-von-Guericke-University, Germany

Frank Werner 教授自20世纪80年代起，长期从事调度问题的精确与近似求解研究。其早期工作包括针对流水车间调度问题开发遗传算法。此后，他的研究方向涵盖了复杂性问题、不确定性条件下的调度问题、列车调度问题以及多种图论问题。

他曾主持并参与多个重要科研项目，资助机构包括德国研究基金会（DFG）、欧盟（INTAS）以及白俄罗斯共和国基础研究基金会等。自2019年以来，Werner 教授担任Algorithms （MDPI） 主编。同时，他也是 International Journal of Production Research、Journal of Scheduling 以及 Operations Research and Decisions 的副主编，并担任另外18种国际期刊的编委或顾问编委。

此外，他曾担任14本国际期刊特刊的客座编辑，并受邀参加超过180场国际学术会议的程序委员会工作。学术成果方面，Werner 教授出版了2本数学教材，撰写或主编了17部学术著作，并在国际期刊上发表了300余篇学术论文。

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