Journal of Computational Science最新文献_第3页

BAHA: Binary artificial hummingbird algorithm for feature selection 用于特征选择的二元人工蜂鸟算法

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-31 DOI: 10.1016/j.jocs.2025.102686

Ali Hamdipour , Abdolali Basiri , Mostafa Zaare , Seyedali Mirjalili

{"title":"BAHA: Binary artificial hummingbird algorithm for feature selection","authors":"Ali Hamdipour , Abdolali Basiri , Mostafa Zaare , Seyedali Mirjalili","doi":"10.1016/j.jocs.2025.102686","DOIUrl":"10.1016/j.jocs.2025.102686","url":null,"abstract":"<div><div>Datasets classification accuracy depends on their features. The presence of irrelevant and redundant features in the dataset leads to the reduction of classification accuracy. Identifying and removing such features is the main purpose in feature selection, which is an important step in the data science lifecycle. The objective of the Wrapper feature selection method is to reduce the number of selected feature (NSF) while improving the classification accuracy by working on a set of features. The feature selection is a challenging and computationally expensive problem that falls under the NP-complete category, so it requires computationally cheap and efficient algorithm to solve it. The artificial hummingbird algorithm (AHA) is a biological inspired optimization technique that mimics the unique flight capabilities and intelligent foraging tactics of hummingbirds in nature. Since feature selection is inherently a binary problem. In this paper, the binary form of the AHA meta-heuristic algorithm is proposed to show that binarizing the AHA meta-heuristic algorithm improves its performance for solving feature selection problems. The proposed method is tested on a standard benchmark dataset and compared with four state-of-the-art feature selection algorithms: Automata-based improved equilibrium optimizer with U-shaped transfer function (AIEOU), Whale optimization approaches for wrapper feature selection (WOA-CM), Ring theory-based harmony search (RTHS), and Adaptive switching gray-whale optimizer (ASGW). The results show the effectiveness of the proposed algorithm in searching for optimal features subset. The source code for the algorithm being proposed is accessible to the public on <span><span>https://github.com/alihamdipour/baha</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102686"},"PeriodicalIF":3.7,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection and root cause analysis using convolutional autoencoders: A real case study 使用卷积自编码器的异常检测和根本原因分析：一个真实案例研究

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-30 DOI: 10.1016/j.jocs.2025.102685

Piero Danti , Alessandro Innocenti , Sascha Sandomier

{"title":"Anomaly detection and root cause analysis using convolutional autoencoders: A real case study","authors":"Piero Danti , Alessandro Innocenti , Sascha Sandomier","doi":"10.1016/j.jocs.2025.102685","DOIUrl":"10.1016/j.jocs.2025.102685","url":null,"abstract":"<div><div>Anomaly detection is the process of identifying unusual patterns in data that may indicate a deviation from the expected norm. This paper proposes a semi-supervised deep learning solution to detect anomalies of a YANMAR energy device that produces heat and power utilizing an internal combustion engine supplied with natural gas. The main equipment of the analysis is a 20 <span><math><mrow><mi>k</mi><msub><mrow><mi>W</mi></mrow><mrow><mi>e</mi></mrow></msub></mrow></math></span> micro-cogeneration unit installed in the energy plant of a facility school. More in detail, the dataset considered in this work consists of 12 features temporally acquired every 15 min. The authors exploit a deep learning architecture, an autoencoder with 1-D convolutional layers to retain temporal correlations, trained to learn the normal behavior of the cogenerator and report unseen operations. In consideration of the fact that autoencoders tend to yield false positives, a Fast-Fourier-Transform-based technique has been applied to filter spurious detections and improve the algorithm’s robustness. As the last contribution, a naive methodology to address the root cause of the anomalies has been explained and its effectiveness has been proved in a real malfunctioning of the CHP.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102685"},"PeriodicalIF":3.7,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved K-means algorithm based on persistent homology 基于持久同源性的改进K-means算法

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-29 DOI: 10.1016/j.jocs.2025.102680

NingNing Peng, Shanjunshu Gao, Xingzi Yin, Xueyan Zhan

引用次数: 0

The variational multiscale element free Galerkin method for three-dimensional steady magnetohydrodynamics duct flows 三维稳定磁流体管道流动的变分多尺度无单元伽辽金方法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-28 DOI: 10.1016/j.jocs.2025.102683

Xiaohua Zhang , Yujie Fan

{"title":"The variational multiscale element free Galerkin method for three-dimensional steady magnetohydrodynamics duct flows","authors":"Xiaohua Zhang , Yujie Fan","doi":"10.1016/j.jocs.2025.102683","DOIUrl":"10.1016/j.jocs.2025.102683","url":null,"abstract":"<div><div>Magnetohydrodynamics (MHD) has extensive applications in diverse fields, making the study of three-dimensional (3D) MHD problems crucial. For MHD flows, when the Hartmann (<span><math><mrow><mi>H</mi><mi>a</mi></mrow></math></span>) number is large, leading to a convection-dominated regime where convection terms overcome diffusion. In such scenarios, standard Galerkin methods fail to suppress non-physical oscillations in solutions, as they lack inherent stabilization mechanisms for strong convection. This paper introduces the variational multiscale element free Galerkin (VMEFG) method to solve 3D steady MHD equations. The VMEFG method inherits the advantage of the element free Galerkin (EFG) method in avoiding the complex meshing process, which is particularly challenging for complex 3D problems. Moreover, compared with the EFG method, it shows enhanced stability in dealing with convection-dominant problems and can automatically generate stabilized parameters, outperforming other stabilization techniques. To verify the numerical stability and accuracy of the VMEFG method, several numerical experiments on various domains including cubic, annular cubic, spherical, and annular spherical domains were conducted and compared with EFG solutions and existing literature results. The results indicate that the VMEFG method can effectively avoid numerical oscillations and maintain stability for 3D MHD problems at high <span><math><mrow><mi>H</mi><mi>a</mi></mrow></math></span> number, providing a reliable and efficient solution for such problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102683"},"PeriodicalIF":3.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A demonstration on the construction of modular neural network using elevator system that operates based on reinforcement learning 基于强化学习的电梯系统模块化神经网络的构建演示

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-26 DOI: 10.1016/j.jocs.2025.102678

Ning Ning Chung , Hamed Taghavian , Mikael Johansson , Lock Yue Chew

引用次数: 0

Data-driven enhancement of the Hastings–Powell model using sparse identification algorithm 使用稀疏识别算法的Hastings-Powell模型的数据驱动增强

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-26 DOI: 10.1016/j.jocs.2025.102682

Nitu Kumari, Anurag Singh

{"title":"Data-driven enhancement of the Hastings–Powell model using sparse identification algorithm","authors":"Nitu Kumari, Anurag Singh","doi":"10.1016/j.jocs.2025.102682","DOIUrl":"10.1016/j.jocs.2025.102682","url":null,"abstract":"<div><div>A significant challenge in various fields of science and engineering is extracting governing equations from data. Prey-predator models are particularly complex due to their nonlinear behavior, making traditional analytical methods insufficient for accurately capturing their dynamics. In this study, we introduce a data-driven approach to model the intricate dynamics of Hastings–Powell model solely from time series data. This article explores the application of the sparse identification of nonlinear dynamics (SINDy) and its extension, the SINDy-PI (parallel, implicit) method, in a model representing a chaotic food chain. The main goal is to determine the governing equations that describe the chaotic dynamics of the prey-predator populations. Hence, this study uses the parameters wherein the dynamics exhibit chaotic behavior. The method of SINDy was developed with the aim of identifying governing equations of nonlinear dynamical systems. In both methods, a library of potential terms are created and then a regression problem is solved. We have employed both methods as our model incorporates not only nonlinear terms but also rational terms. Our results shows that SINDy method is unable to find the exact form of governing equations but SINDy-PI method has the capability to accurately capture the authentic structure of the governing equations. In addition, we applied model selection techniques to identify the most parsimonious model possible. Through the application of SINDy and SINDy-PI, this research contributes to the advancement of data-centric approaches in ecological modeling, offering insights into the intricate dynamics of multi-species interactions within ecosystems. Further, for this study to be more realistic, utilizing real-world data from three-species would have been ideal. However, due to non-availability of three species real data, simulated data set has been used for validation purpose.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102682"},"PeriodicalIF":3.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An h-adaptive collocation method for Physics-Informed Neural Networks 物理信息神经网络的h-自适应配置方法

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-25 DOI: 10.1016/j.jocs.2025.102684

Jan Trynda , Paweł Maczuga , Albert Oliver-Serra , Luis Emilio García-Castillo , Robert Schaefer , Maciej Woźniak

引用次数: 0

Sensitivity analysis of high-dimensional models with correlated inputs 具有相关输入的高维模型敏感性分析

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-25 DOI: 10.1016/j.jocs.2025.102681

Juraj Kardoš , Wouter Edeling , Diana Suleimenova , Derek Groen , Olaf Schenk

{"title":"Sensitivity analysis of high-dimensional models with correlated inputs","authors":"Juraj Kardoš , Wouter Edeling , Diana Suleimenova , Derek Groen , Olaf Schenk","doi":"10.1016/j.jocs.2025.102681","DOIUrl":"10.1016/j.jocs.2025.102681","url":null,"abstract":"<div><div>Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input–output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions in the state-of-the-art sensitivity analysis methods. Consequently, the results obtained ignoring the correlations provide values which do not reflect the true contributions of the input parameters. This study proposes an approach to address the parameter correlations using a polynomial chaos expansion method and Rosenblatt and Cholesky transformations to reflect the parameter dependencies. Treatment of the correlated variables is discussed in context of variance and derivative-based sensitivity analysis. We demonstrate that the sensitivity of the correlated parameters can not only differ in magnitude, but even the sign of the derivative-based index can be inverted, thus significantly altering the model behavior compared to the prediction of the analysis disregarding the correlations. Numerous experiments are conducted using workflow automation tools within the VECMA toolkit.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102681"},"PeriodicalIF":3.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A computational analysis of traffic cluster dynamics using a percolation-based approach in urban road networks 城市道路网络中基于渗流的交通集群动态计算分析

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-24 DOI: 10.1016/j.jocs.2025.102675

Yongsung Kwon , Minjin Lee , Mi Jin Lee , Seung-Woo Son

{"title":"A computational analysis of traffic cluster dynamics using a percolation-based approach in urban road networks","authors":"Yongsung Kwon , Minjin Lee , Mi Jin Lee , Seung-Woo Son","doi":"10.1016/j.jocs.2025.102675","DOIUrl":"10.1016/j.jocs.2025.102675","url":null,"abstract":"<div><div>Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102675"},"PeriodicalIF":3.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A structural feature-based approach for comprehensive graph classification 基于结构特征的综合图分类方法

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-24 DOI: 10.1016/j.jocs.2025.102679

Saiful Islam , Md. Nahid Hasan , Pitambar Khanra

{"title":"A structural feature-based approach for comprehensive graph classification","authors":"Saiful Islam , Md. Nahid Hasan , Pitambar Khanra","doi":"10.1016/j.jocs.2025.102679","DOIUrl":"10.1016/j.jocs.2025.102679","url":null,"abstract":"<div><div>The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102679"},"PeriodicalIF":3.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0