Journal of Computational Mathematics and Data Science最新文献_第3页

Usage of the Kullback–Leibler divergence on posterior Dirichlet distributions to create a training dataset for a learning algorithm to classify driving behaviour events 使用后验Dirichlet分布上的Kullback–Leibler散度为学习算法创建训练数据集，以对驾驶行为事件进行分类

Journal of Computational Mathematics and Data Science Pub Date : 2023-08-01 DOI: 10.1016/j.jcmds.2023.100081

M. Cesarini , E. Brentegani , G. Ceci , F. Cerreta , D. Messina , F. Petrarca , M. Robutti

引用次数: 0

Solving the Dirichlet problem for the Monge–Ampère equation using neural networks 用神经网络求解Monge–Ampère方程的Dirichlet问题

Journal of Computational Mathematics and Data Science Pub Date : 2023-08-01 DOI: 10.1016/j.jcmds.2023.100080

Kaj Nyström, Matias Vestberg

引用次数: 0

A Steffensen type optimal eighth order multiple root finding scheme for nonlinear equations 非线性方程的Steffensen型最优八阶多重寻根格式

Journal of Computational Mathematics and Data Science Pub Date : 2023-06-01 DOI: 10.1016/j.jcmds.2023.100079

Fiza Zafar , Sofia Iqbal , Tahira Nawaz

引用次数: 1

Towards Deep Interpretable Features 走向深层可解释特征

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2022.100067

Robert Hu, Dino Sejdinovic

引用次数: 1

Distance geometry for word representations and applications 单词表示和应用的距离几何

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2022.100073

Sammy Khalife , Douglas S. Gonçalves , Leo Liberti

{"title":"Distance geometry for word representations and applications","authors":"Sammy Khalife , Douglas S. Gonçalves , Leo Liberti","doi":"10.1016/j.jcmds.2022.100073","DOIUrl":"https://doi.org/10.1016/j.jcmds.2022.100073","url":null,"abstract":"<div><p>Many machine learning methods used for the treatment of sequential data often rely on the construction of vector representations of unitary entities (e.g. words in natural language processing, or <span><math><mi>k</mi></math></span>-mers in bioinformatics). Traditionally, these representations are constructed with optimization formulations arising from co-occurrence based models. In this work, we propose a new method to embed these entities based on the Distance Geometry Problem: find object positions based on a subset of their pairwise distances or inner products. Considering the empirical Pointwise Mutual Information as a surrogate for the inner product, we discuss two Distance Geometry based algorithms to obtain word vector representations. The main advantage of such algorithms is their significantly lower computational complexity in comparison with state-of-the-art word embedding methods, which allows us to obtain word vector representations much faster. Furthermore, numerical experiments indicate that our word vectors behave quite well on text classification tasks in natural language processing as well as regression tasks in bioinformatics.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"6 ","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50188316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Complex dynamics of two prey–one predator model together with fear effect and harvesting efforts in preys 两个猎物的复杂动力学——一个捕食者模型以及猎物的恐惧效应和收获努力

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2022.100071

Ashok Mondal , A.K. Pal , G.P. Samanta

引用次数: 0

On Newton’s Rule of signs 关于牛顿符号规则

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2023.100076

Emil M. Prodanov

引用次数: 0

JoMIC: A joint MI-based filter feature selection method JoMIC：一种基于MI的联合滤波器特征选择方法

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2023.100075

Khumukcham Robindro , Urikhimbam Boby Clinton , Nazrul Hoque , Dhruba K. Bhattacharyya

{"title":"JoMIC: A joint MI-based filter feature selection method","authors":"Khumukcham Robindro , Urikhimbam Boby Clinton , Nazrul Hoque , Dhruba K. Bhattacharyya","doi":"10.1016/j.jcmds.2023.100075","DOIUrl":"https://doi.org/10.1016/j.jcmds.2023.100075","url":null,"abstract":"<div><p>Feature selection (FS) is a common preprocessing step of machine learning that selects informative subset of features which fuels a model to perform better during prediction or classification. It helps in the design of an intelligent and expert system used in computer vision, image processing, gene expression data analysis, intrusion detection and natural language processing. In this paper, we introduce an effective filter method called Joint Mutual Information with Class relevance (JoMIC) using multivariate Joint Mutual Information (JMI) and Mutual Information (MI). Our method considers both JMI and MI of a non selected feature with selected ones w.r.t a given class to select a feature that is highly relevant to the class but non redundant to other selected features. We compare our method with seven other filter-based methods using the machine learning classifiers viz., Logistic Regression, Support Vector Machine, K-nearest Neighbor (KNN), Decision Tree, Random Forest, Naïve Bayes, and Stochastic Gradient Descent on various datasets. Experimental results reveal that our method yields better performance in terms of accuracy, Matthew’s Correlation Coefficient (MCC) and F1-score over 16 benchmark datasets, as compared to other competent methods. The superiority of our proposed method is that it uses an effective objective function that combines both JMI and MI to choose the relevant and non redundant features.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"6 ","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50188317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Scrutinization of unsteady MHD fluid flow and entropy generation: Hybrid nanofluid model 非定常MHD流体流动和熵产生的精细化：混合纳米流体模型

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2023.100074

Hiranmoy Maiti , Amir Yaseen Khan , Sabyasachi Mondal , Samir Kumar Nandy

{"title":"Scrutinization of unsteady MHD fluid flow and entropy generation: Hybrid nanofluid model","authors":"Hiranmoy Maiti , Amir Yaseen Khan , Sabyasachi Mondal , Samir Kumar Nandy","doi":"10.1016/j.jcmds.2023.100074","DOIUrl":"https://doi.org/10.1016/j.jcmds.2023.100074","url":null,"abstract":"<div><p>The goal of the present paper is to scrutinize the heat transport of an unsteady stagnation-point flow of a hybrid nanofluid over a stretching/shrinking disk in the presence of a variable magnetic field and thermal radiation. The entropy generation analysis for the system is also made. The suitable similarity transformation is utilized for the reduction of a set of governing equations which are solved numerically by using fourth order Runge–Kutta procedure with shooting technique. The impact of different physical parameters under the realistic passive control of nanoparticles on the flow, heat transfer and concentration profiles are analyzed and demonstrated graphically. The analysis of the results obtained shows that multiple solutions exist for a certain parametric domain. With the increase in magnetic parameter (<span><math><mi>M</mi></math></span>), fluid velocity is found to increase for the first solution whereas opposite behavior is observed for the second solution. Also as <span><math><mi>M</mi></math></span> increases, the temperature at a point decreases for the first solution branch. Further, the influences of Reynolds number, Brinkmann number and volumetric concentrations on entropy generation are sketched and discussed. The novel result that emerges from the analysis is that among two solution branches, the first solution branch is linearly stable and physically meaningful whereas the second solution branch is linearly unstable and cannot be realized physically.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"6 ","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50188318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Classification of hyperspectral images with copulas 利用copula对高光谱图像进行分类

Journal of Computational Mathematics and Data Science Pub Date : 2023-01-01 DOI: 10.1016/j.jcmds.2022.100070

C. Tamborrino , F. Mazzia

{"title":"Classification of hyperspectral images with copulas","authors":"C. Tamborrino , F. Mazzia","doi":"10.1016/j.jcmds.2022.100070","DOIUrl":"https://doi.org/10.1016/j.jcmds.2022.100070","url":null,"abstract":"<div><p>In the last decade, supervised learning methods for the classification of remotely sensed images (RSI) have grown significantly, especially for hyper-spectral (HS) images. Recently, deep learning-based approaches have produced encouraging results for the land cover classification of HS images. In particular, the Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have shown good performance. However, these methods suffer for the problem of the hyperparameter optimization or tuning that requires a high computational cost; moreover, they are sensitive to the number of observations in the learning phase. In this work we propose a novel supervised learning algorithm based on the use of copula functions for the classification of hyperspectral images called CopSCHI (Copula Supervised Classification of Hyperspectral Images). In particular, we start with a dimensionality reduction technique based on Singular Value Decomposition (SVD) in order to extract a small number of relevant features that best preserve the characteristics of the original image. Afterward, we learn the classifier through a dynamic choice of copulas that allows us to identify the distribution of the different classes within the dataset. The use of copulas proves to be a good choice due to their ability to recognize the probability distribution of classes and hence an accurate final classification with low computational cost can be conducted. The proposed approach was tested on two benchmark datasets widely used in literature. The experimental results confirm that CopSCHI outperforms the state-of-the-art methods considered in this paper as competitors.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"6 ","pages":"Article 100070"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50188312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0