Journal of Computational Science最新文献_第10页

Sharkovskii’s theorem and the limits of digital computers for the simulation of chaotic dynamical systems 沙尔科夫斯基定理和数字计算机模拟混沌动力学系统的极限

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-10-03 DOI: 10.1016/j.jocs.2024.102449

Peter V. Coveney

引用次数: 0

Bivariate Jacobi polynomials depending on four parameters and their effect on solutions of time-fractional Burgers’ equations 取决于四个参数的二元雅可比多项式及其对时间分数布尔格斯方程解的影响

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-30 DOI: 10.1016/j.jocs.2024.102450

Khadijeh Sadri , David Amilo , Muhammad Farman , Evren Hinçal

{"title":"Bivariate Jacobi polynomials depending on four parameters and their effect on solutions of time-fractional Burgers’ equations","authors":"Khadijeh Sadri , David Amilo , Muhammad Farman , Evren Hinçal","doi":"10.1016/j.jocs.2024.102450","DOIUrl":"10.1016/j.jocs.2024.102450","url":null,"abstract":"<div><div>The utilization of time-fractional Burgers’ equations is widespread, employed in modeling various phenomena such as heat conduction, acoustic wave propagation, gas turbulence, and the propagation of chaos in non-linear Markov processes. This study introduces a novel pseudo-operational collocation method, leveraging two-variable Jacobi polynomials. These polynomials are obtained through the Kronecker product of their one-variable counterparts, concerning both spatial (<span><math><mi>x</mi></math></span>) and temporal (<span><math><mi>t</mi></math></span>) domains. The study explores the impact of four parameters (<span><math><mrow><mi>θ</mi><mo>,</mo><mi>ϑ</mi><mo>,</mo><mi>σ</mi><mo>,</mo><mi>ς</mi><mo>></mo><mo>−</mo><mn>1</mn></mrow></math></span>) on the accuracy of resulting approximate solutions, marking the first examination of such influence. Collocation nodes in a tensor approach are constructed employing the roots of one-variable Jacobi polynomials of varying degrees in <span><math><mi>x</mi></math></span> and <span><math><mi>t</mi></math></span>. The study delves into analyzing how the distribution of these roots affects the outcomes. Consequently, pseudo-operational matrices are devised to integrate both integer and fractional orders, presenting a novel methodological advancement. By employing these matrices and appropriate approximations, the governing equations transform into an algebraic system, facilitating computational analysis. Furthermore, the existence and uniqueness of the equations under study are investigated and the study estimates error bounds within a Jacobi-weighted space for the obtained approximate solutions. Numerical simulations underscore the simplicity, applicability, and efficiency of the proposed matrix spectral scheme.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102450"},"PeriodicalIF":3.1,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416160","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

Learnability of state spaces of physical systems is undecidable 物理系统状态空间的可学性是不可判定的

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-26 DOI: 10.1016/j.jocs.2024.102452

Petr Spelda, Vit Stritecky

引用次数: 0

AFF-BPL: An adaptive feature fusion technique for the diagnosis of autism spectrum disorder using Bat-PSO-LSTM based framework AFF-BPL：使用基于 Bat-PSO-LSTM 框架的自适应特征融合技术诊断自闭症谱系障碍

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-24 DOI: 10.1016/j.jocs.2024.102447

Kainat Khan, Rahul Katarya

{"title":"AFF-BPL: An adaptive feature fusion technique for the diagnosis of autism spectrum disorder using Bat-PSO-LSTM based framework","authors":"Kainat Khan, Rahul Katarya","doi":"10.1016/j.jocs.2024.102447","DOIUrl":"10.1016/j.jocs.2024.102447","url":null,"abstract":"<div><div>Autism spectrum disorder (ASD) is a neurological condition revealed by deficiencies in physical well-being, social communication, hyperactive behavior, and increased sensitivity. The delayed diagnosis of ASD showcases a significant obstacle in mitigating the severity of its impact. Individuals with ASD often exhibit restricted and repetitive behavioral patterns. In this context, we proposed a novel adaptive feature fusion technique with a BAT-PSO-LSTM-based network for the diagnosis of autism spectrum disorder. Our focus is on three distinct autism screening datasets namely, Toddlers, Children, and Adults for comprehensive analysis of techniques. Bat and PSO concurrently select features and the selected features will go through an adaptive feature fusion algorithm and an LSTM-based classifier. This research addresses various challenges encountered in the existing techniques including concerns related to overfitting, faster training, interpretability, generalization capability, and reduced computation time. The work incorporates baseline techniques like a neural network, CNN, and LSTM with evaluations based on key parameters like precision, specificity, accuracy, sensitivity, and f1-score. The experimental simulations reveal that AFF-BPL outperforms considered baseline techniques achieving remarkable accuracy on all three datasets. Specifically, the model attains the accuracy of 0.992, 0.989, and 0.986 on toddler, children, and adult datasets respectively. Additionally, the exploration of functional and structural images will provide deeper insights into the underlying mechanism of ASD.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102447"},"PeriodicalIF":3.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328168","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-driven robust optimization in the face of large-scale datasets: An incremental learning approach 面对大规模数据集的数据驱动稳健优化：增量学习法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-21 DOI: 10.1016/j.jocs.2024.102432

Somayeh Danesh Asgari, Emran Mohammadi, Ahmad Makui, Mostafa Jafari

{"title":"Data-driven robust optimization in the face of large-scale datasets: An incremental learning approach","authors":"Somayeh Danesh Asgari, Emran Mohammadi, Ahmad Makui, Mostafa Jafari","doi":"10.1016/j.jocs.2024.102432","DOIUrl":"10.1016/j.jocs.2024.102432","url":null,"abstract":"<div><div>One of the most significant current discussions in optimization under deep uncertainty is integrating machine learning and data science into robust optimization, which has led to the emergence of a new field called Data-Driven Robust Optimization (DDRO). When creating data-driven uncertainty sets, it considers a dataset’s complexity, hidden information, and inherent form. One of the more practical machine learning algorithms for creating data-driven uncertainty sets is support vector clustering (SVC). This algorithm has no prerequisites for preliminary information to generate uncertainty sets with arbitrary geometry. More scenarios can reduce risk when developing SVC-based uncertainty sets. However, the lack of a systematic way to manage the large number of these scenarios hinders the employment of SVC. This paper puts forward an incremental learning algorithm based on support vector clustering, called Incremental Support Vector Clustering (ISVC), to construct an uncertainty set incrementally and efficiently using large datasets. This approach’s novelty and main contributions include incrementally constructing uncertainty sets and dynamic management of outliers. In order to update the temporarily stored Bounded Support Vectors (BSV) and identify outliers, the idea of BSV-archive is offered, where the revision-and-recycle operation is tailored to do just that. As a result, some of the newly acquired information is preserved. Experiments on large-scale datasets demonstrate that the proposed ISVC approach can create an uncertainty set comparable to that of an SVC-based method while using significantly less time.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102432"},"PeriodicalIF":3.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319878","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

VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN VEGF-ERCNN：利用集合残差 CNN 预测血管内皮生长因子的深度学习模型

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-20 DOI: 10.1016/j.jocs.2024.102448

Farman Ali , Majdi Khalid , Atef Masmoudi , Wajdi Alghamdi , Ayman Yafoz , Raed Alsini

{"title":"VEGF-ERCNN: A deep learning-based model for prediction of vascular endothelial growth factor using ensemble residual CNN","authors":"Farman Ali , Majdi Khalid , Atef Masmoudi , Wajdi Alghamdi , Ayman Yafoz , Raed Alsini","doi":"10.1016/j.jocs.2024.102448","DOIUrl":"10.1016/j.jocs.2024.102448","url":null,"abstract":"<div><p>Vascular Endothelial Growth Factor (VEGF), a signaling protein family, is essential in angiogenesis, regulating the growth and survival of endothelial cells that create blood vessels. VEGF is critical in osteogenesis for coordinating blood vessel growth with bone formation, resulting in a well-vascularized environment that promotes nutrition and oxygen delivery to bone-forming cells. Predicting VEGF is crucial, yet experimental methods for identification are both costly and time-consuming. This paper introduces VEGF-ERCNN, an innovative computational model for VEGF prediction using deep learning. Two datasets were generated using primary sequences, and a novel feature descriptor called multi fragmented-position specific scoring matrix-discrete wavelet transformation (MF-PSSM-DWT) was developed to extract numerical characteristics from these sequences. Model training is performed via deep learning techniques such as generative adversarial network (GAN), gated recurrent unit (GRU), ensemble residual convolutional neural network (ERCNN), and convolutional neural network (CNN). The VEGF-ERCNN outperformed other competitive predictors on both training and testing datasets by securing the highest 92.12 % and 83.45 % accuracies, respectively. Accurate prediction of VEGF therapeutic targeting has transformed treatment techniques, establishing it as a crucial participant in both health and disease.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102448"},"PeriodicalIF":3.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274659","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 new space–time localized meshless method based on coupling radial and polynomial basis functions for solving singularly perturbed nonlinear Burgers’ equation 基于径向基函数和多项式基函数耦合的新型时空局部无网格方法，用于求解奇异扰动非线性布尔格斯方程

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-19 DOI: 10.1016/j.jocs.2024.102446

Hani Hafidi , Ahmed Naji , Abdelkrim Aharmouch , Fatima Ghafrani

{"title":"A new space–time localized meshless method based on coupling radial and polynomial basis functions for solving singularly perturbed nonlinear Burgers’ equation","authors":"Hani Hafidi , Ahmed Naji , Abdelkrim Aharmouch , Fatima Ghafrani","doi":"10.1016/j.jocs.2024.102446","DOIUrl":"10.1016/j.jocs.2024.102446","url":null,"abstract":"<div><div>In this paper, the singularly perturbed nonlinear Burgers’ problem (SPBP) with small kinematic viscosity <span><math><mrow><mn>0</mn><mo><</mo><mi>ϵ</mi><mo>≪</mo><mn>1</mn></mrow></math></span> is solved using a new Space–Time Localized collocation method based on coupling Polynomial and Radial Basis Functions (STLPRBF). To our best knowledge, it is the first time that the solution of SPBP is accurately approximated using the space–time meshless method without applying any adaptive refinement technique. The method is based on solving the problem without distinguishing between space and time variables, which eliminates the need for time discretization schemes. To address the inherent non-linearity of the problem, the method employs an iterative algorithm based on quasilinearization technique. The efficiency and accuracy of the proposed method are demonstrated by solving different examples of one- and two-dimensional SPBP with very small <span><math><mi>ϵ</mi></math></span> up to <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>10</mn></mrow></msup></mrow></math></span>. Additionally, the numerical convergence of the method with respect to <span><math><mi>ϵ</mi></math></span> and also to the number of collocation points has been investigated. The comparison of the STLPRBF results with other published ones is presented.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102446"},"PeriodicalIF":3.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315014","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

Implementation of the emulator-based component analysis 实施基于仿真器的组件分析

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-18 DOI: 10.1016/j.jocs.2024.102437

Anton Vladyka, Eemeli A. Eronen, Johannes Niskanen

引用次数: 0

A feasible numerical computation based on matrix operations and collocation points to solve linear system of partial differential equations 基于矩阵运算和配位点的可行数值计算，用于求解线性偏微分方程系

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-17 DOI: 10.1016/j.jocs.2024.102445

Seda Çayan , Mehmet Sezer

引用次数: 0

A Markov random field model for change points detection 用于变化点检测的马尔可夫随机场模型

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2024-09-07 DOI: 10.1016/j.jocs.2024.102429

Zakariae Drabech, Mohammed Douimi, Elmoukhtar Zemmouri

{"title":"A Markov random field model for change points detection","authors":"Zakariae Drabech, Mohammed Douimi, Elmoukhtar Zemmouri","doi":"10.1016/j.jocs.2024.102429","DOIUrl":"10.1016/j.jocs.2024.102429","url":null,"abstract":"<div><p>Detecting Change Points (CPs) in data sequences is a challenging problem that arises in a variety of disciplines, including signal processing and time series analysis. While many methods exist for PieceWise Constant (PWC) signals, relatively fewer address PieceWise Linear (PWL) signals due to the challenge of preserving sharp transitions. This paper introduces a Markov Random Field (MRF) model for detecting changes in slope. The number of CPs and their locations are unknown. The proposed method incorporates PWL prior information using MRF framework with an additional boolean variable called Line Process (LP), describing the presence or absence of CPs. The solution is then estimated in the sense of maximum a posteriori. The LP allows us to define a non-convex non-smooth energy function that is algorithmically hard to minimize. To tackle the optimization challenge, we propose an extension of the combinatorial algorithm DPS, initially designed for CP detection in PWC signals. Also, we present a shared memory implementation to enhance computational efficiency. Numerical studies show that the proposed model produces competitive results compared to the state-of-the-art methods. We further evaluate the performance of our method on three real datasets, demonstrating superior and accurate estimates of the underlying trend compared to competing methods.</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"83 ","pages":"Article 102429"},"PeriodicalIF":3.1,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167258","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