Journal of Computational Science最新文献_第10页

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 : 2025-10-01 Epub 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":"2025-10-01","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

Integrating fuzzy rough set-based entropies for identifying drug-resistant miRNAs in cancer 基于模糊粗糙集熵的肿瘤耐药mirna识别集成

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-07-21 DOI: 10.1016/j.jocs.2025.102673

Joginder Singh , Shubhra Sankar Ray

{"title":"Integrating fuzzy rough set-based entropies for identifying drug-resistant miRNAs in cancer","authors":"Joginder Singh , Shubhra Sankar Ray","doi":"10.1016/j.jocs.2025.102673","DOIUrl":"10.1016/j.jocs.2025.102673","url":null,"abstract":"<div><div>MicroRNAs (miRNAs) are key biomarkers in cancer diagnosis and treatment. Identification of drug-resistant miRNAs may help in effective treatment of cancer. Two new z score based fuzzy rough relevance and redundancy entropies are developed and then a weighted framework is introduced to integrate the entropies for ranking and selecting miRNAs in classifying control and drug resistant patients. Here, two key components of soft computing, fuzzy set and rough set are utilized. The methodology is called a weighted framework for integrating fuzzy rough set-based relevance and redundancy entropies (WFIFRRRE). The z score is used to compute the fuzzy membership of expression values required for both entropies. Fuzziness deals with the overlapping nature of miRNA expression profiles and rough set helps in determining the exact class size. The weights in WFIFRRRE, assigned to relevance and redundancy entropies, are determined in a supervised manner to maximize the <span><math><mi>F</mi></math></span> score used for validating the classification performance in discriminating the control and drug-resistant patients. The weights are varied from 0 to 1 in steps of 0.01 which enables an integration between relevance and redundancy entropies. A subset of miRNAs is selected from the ranked list and the performance is evaluated using three benchmark classifiers on eight drug-resistant cancer datasets. Experimental results show that WFIFRRRE provides better prediction accuracy than the popular methods used for comparison. The classification accuracy in terms of <span><math><mi>F</mi></math></span> score, achieved by WFIFRRRE, ranges from 0.74 to 1.0, 0.75 to 1.0, and 0.73 to 1.0 using random forest, Naive Bayes, and linear SVM classifiers, respectively. The resultant set of miRNAs obtained using WFIFRRRE is also verified with the help of existing biological studies. The source code of WFIFRRRE is available at <span><span>https://www.isical.ac.in/ shubhra/WFIFRRRE.html</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102673"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724960","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

Data classification with dynamically growing and shrinking neural networks 动态增长和收缩神经网络的数据分类

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-07-01 DOI: 10.1016/j.jocs.2025.102660

Szymon Świderski , Agnieszka Jastrzębska

{"title":"Data classification with dynamically growing and shrinking neural networks","authors":"Szymon Świderski , Agnieszka Jastrzębska","doi":"10.1016/j.jocs.2025.102660","DOIUrl":"10.1016/j.jocs.2025.102660","url":null,"abstract":"<div><div>The issue of data-driven neural network model construction is one of the core problems in the domain of Artificial Intelligence. A standard approach assumes a fixed architecture with trainable weights. A conceptually more advanced assumption is that we not only train the weights but also find out the optimal model architecture. We present a new method that realizes just that. This article is an extended version of our conference paper titled “Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search (Świderski and Jastrzebska, 2024). In the paper, we show in detail how to create a neural network with a procedure that allows dynamic shrinking and growing of the model while it is being trained. The decision-making mechanism for the architectural design is governed by the Monte Carlo tree search procedure, which simulates network behavior and allows comparing several candidate architecture changes to choose the best one. The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. This is attributed to the architecture’s ability to adapt dynamically, allowing independent modifications for each time series. To enhance the reproducibility of our method, we publish open-source code of the proposed method. It was prepared in Python. Experimental evaluations in visual pattern and multivariate time series classification tasks revealed highly promising performance, underscoring the method’s robustness and adaptability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102660"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564047","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

Deep Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure 具有傅里叶特征映射的深度里兹方法：一种解决微观结构变分模型的深度学习方法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-06-02 DOI: 10.1016/j.jocs.2025.102631

Ensela Mema , Ting Wang , Jaroslaw Knap

引用次数: 0

Deep Link Strength Prediction: Leveraging line graph transformations and neural networks 深度链接强度预测：利用线图转换和神经网络

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-07-12 DOI: 10.1016/j.jocs.2025.102661

Zhixin Ming , Jie Li , Jing Wang

{"title":"Deep Link Strength Prediction: Leveraging line graph transformations and neural networks","authors":"Zhixin Ming , Jie Li , Jing Wang","doi":"10.1016/j.jocs.2025.102661","DOIUrl":"10.1016/j.jocs.2025.102661","url":null,"abstract":"<div><div>Predicting link strengths in complex networks is a fundamental challenge, crucial for understanding network dynamics and optimizing real-world applications. Traditional approaches often rely on shallow structural features, limiting their ability to model intricate dependencies. To address these limitations, we propose Deep Link Strength Prediction (DLSP), a novel framework that integrates line graph transformations with graph convolutional networks (GCNs) to enhance the predictive capability of link weight estimation. DLSP redefines the task by transforming edge-centric information into node-level representations, facilitating effective learning of complex structural patterns. DLSP follows a multi-phase approach: first, a localized subgraph around the target link is extracted and encoded using a weighted node labeling scheme, preserving local structural and attribute-driven properties. Next, the labeled subgraph undergoes a line graph transformation, mapping link dependencies into node representations, thereby enabling a structured embedding space. A GCN is then employed to extract rich hierarchical representations, capturing both micro and macro-level graph structures. Finally, these learned embeddings are passed through a dense neural network to estimate the target link strength, framing the problem as a continuous-valued regression task. Unlike existing methods that rely on handcrafted features or isolated node embeddings, DLSP explicitly models link dependencies through graph-aware transformations, leading to superior predictive performance. Extensive experiments conducted on six diverse network datasets demonstrate that DLSP consistently outperforms state-of-the-art methods, showcasing its robustness, scalability, and potential for real-world applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102661"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632597","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

Fast and power efficient GPU-based explicit elastic wave propagation analysis by low-ordered orthogonal voxel finite element with INT8 Tensor Cores 基于INT8张量核低阶正交体素有限元的快速高效gpu显式弹性波传播分析

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-07-01 DOI: 10.1016/j.jocs.2025.102659

Tsuyoshi Ichimura , Kohei Fujita , Muneo Hori , Maddegedara Lalith

{"title":"Fast and power efficient GPU-based explicit elastic wave propagation analysis by low-ordered orthogonal voxel finite element with INT8 Tensor Cores","authors":"Tsuyoshi Ichimura , Kohei Fujita , Muneo Hori , Maddegedara Lalith","doi":"10.1016/j.jocs.2025.102659","DOIUrl":"10.1016/j.jocs.2025.102659","url":null,"abstract":"<div><div>There is a strong need for faster and more power-efficient explicit elastic wavefield simulations for large and complex three-dimensional media using a structured finite element method. Such wavefield simulations are suitable for GPUs, which have been improving their computational performance in recent years, and the use of GPUs is expected to speed up such simulations. However, there is still room for speedup and improving energy efficiency of such simulations using GPUs, since the performance of GPUs is not fully exploited just by its simple use, and the conventional method involves some numerical dispersion. In this paper, we propose a method for fast and efficient explicit structured-mesh wavefield simulation on GPUs by utilizing INT8 Tensor Cores and reducing numerical dispersion. We implemented the proposed method on GPUs and evaluated its performance in detail using an application example that simulates a real problem, and showed that it is faster and more efficient than conventional methods on many-node CPU-based systems and multiple GPU-based systems. This paper is the extended version of Ichimura et al. (2024).<span><span>[1]</span></span></div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102659"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534397","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

Artificial neural network discrete-time biomass controller for a continuous stirred tank reactor 连续搅拌槽式反应器的人工神经网络离散时间生物质控制器

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-07-23 DOI: 10.1016/j.jocs.2025.102688

Hale Hapoglu , Egemen Ander Balas , Semin Altuntaş

{"title":"Artificial neural network discrete-time biomass controller for a continuous stirred tank reactor","authors":"Hale Hapoglu , Egemen Ander Balas , Semin Altuntaş","doi":"10.1016/j.jocs.2025.102688","DOIUrl":"10.1016/j.jocs.2025.102688","url":null,"abstract":"<div><div>The employment of stirred tank reactors in the field of treatment technology is well-established. In this regard, a bioreactor model is commonly utilized for conducting simulations, identifying parameters, and developing control applications. Control of biomass concentration is independent of scale through manipulation of the dilution rate. To enable discrete-time control, an equivalent model incorporating a zero-order hold element and a 0.1-h sampling time has been formulated for controlling biomass concentration. In this study, the various well-known controllers performed effectively to track set points. Further, to mitigate the effects of load disturbances, the generalized predictive controller, the proportional integral derivative controller, and the controllers designed based on pole placement have been employed to obtain process control responses. The performance of these controllers has been evaluated through a weighted aggregate sum product assessment technique that employs an analytical hierarchy process. Due to the significant nonlinearity present in the closed loop bioprocess with substrate inhibition, the feedforward artificial neural network controller is trained using a closed-loop dataset, and its performances are compared with the conventional controllers. The controller has demonstrated its ability to manage realistic feed fluctuations without risking upset to the culture. The biomass concentration showed only minor deviations, settling swiftly back to the desired value by smoothly adjusting the dilution rate. This controller with tansig and purelin functions overcomes nonlinearities and time delays better than conventional controllers. The results suggest that the artificial neural network controller offers the desired simplicity and effectiveness for industrial applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102688"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711668","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 : 2025-10-01 Epub 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":"2025-10-01","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

Making hierarchically aware decisions on short findings for automatic summarisation 为自动总结的简短发现做出层次分明的决策

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-08-16 DOI: 10.1016/j.jocs.2025.102692

Emrah Inan

{"title":"Making hierarchically aware decisions on short findings for automatic summarisation","authors":"Emrah Inan","doi":"10.1016/j.jocs.2025.102692","DOIUrl":"10.1016/j.jocs.2025.102692","url":null,"abstract":"<div><div>An impression in a typical radiology report emphasises critical information by providing a conclusion and reasoning based on the findings. However, the findings and impression sections of these reports generally contain brief texts, as they highlight crucial observations derived from the clinical radiograph. In this scenario, abstractive summarisation models often experience a degradation in performance when generating short impressions. To address this challenge in the summarisation task, our work proposes a method that combines well-known fine-tuned text classification and abstractive summarisation language models. Since fine-tuning a language model requires an extensive, well-defined training dataset and is a time-consuming task dependent on high GPU resources, we employ prompt engineering, which uses prompt templates to programme language models and improve their performance. Our method first predicts whether the given findings text is normal or abnormal by leveraging a fine-tuned language model. Then, we apply a radiology-specific BART model to generate the summary for abnormal findings. In the zero-shot setting, our method achieves remarkable results compared to existing approaches on a real-world dataset. In particular, our method achieves scores of 37.43 for ROUGE-1, 21.72 for ROUGE-2, and 35.52 for ROUGE-L.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102692"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852163","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

Fractional order malaria epidemic model: Qualitative and computational study to determine the dynamics for sensitivity prevalence 分数阶疟疾流行模型：确定敏感性流行动态的定性和计算研究

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-10-01 Epub Date: 2025-07-04 DOI: 10.1016/j.jocs.2025.102656

Muhammad Farman , Nezihal Gokbulut , Aamir Shehzad , Kottakkaran Sooppy Nisar , Evren Hincal , Aceng Sambas

{"title":"Fractional order malaria epidemic model: Qualitative and computational study to determine the dynamics for sensitivity prevalence","authors":"Muhammad Farman , Nezihal Gokbulut , Aamir Shehzad , Kottakkaran Sooppy Nisar , Evren Hincal , Aceng Sambas","doi":"10.1016/j.jocs.2025.102656","DOIUrl":"10.1016/j.jocs.2025.102656","url":null,"abstract":"<div><div>In this study, we created a nonlinear mathematical model with eight compartments to understand the dynamics of malaria transmission in North Cyprus region using the Caputo fractional operator. Because of their memory and genetic features, fractional-order models are regarded to be more adaptable than integer-order models. To explore the malaria compartmental model, we use the stability theory of fractional-order differential equations with the Caputo operator. A full explanation of the proposed model’s qualitative and quantitative analysis is offered, as well as a brief overview of its essential aspects and a theoretical evaluation. The Lipschitz criterion and well-known fixed point theorems are used to prove the existence and uniqueness of solutions. In addition to establishing equilibrium points, sensitivity analysis of reproductive number parameters is carried out. The proposed system has been validated in terms of Ulam–Hyers–Rassias. To deal with chaotic circumstances a linear feedback control strategy directs system dynamics near equilibrium points. To verify the existence of bifurcation, we apply bifurcation principles. The study uses numerical methodology based on Newton polynomial interpolation method to graphically model the solutions. The study analyzes system behavior by investigating parameter alterations at various fractional orders while retaining model stability. The long-term memory effect, represented by the Caputo fractional order derivative, has no influence on steady point stability, but solutions get closer to equilibrium faster at higher fractional-orders.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102656"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579929","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