Journal of Computational Mathematics and Data Science最新文献

{"title":"Broken adaptive ridge method for variable selection in generalized partly linear models with application to the coronary artery disease data","authors":"Christian Chan ,&nbsp;Xiaotian Dai ,&nbsp;Thierry Chekouo ,&nbsp;Quan Long ,&nbsp;Xuewen Lu","doi":"10.1016/j.jcmds.2025.100127","DOIUrl":"10.1016/j.jcmds.2025.100127","url":null,"abstract":"<div><div>Motivated by the CATHGEN data, we develop a new statistical method for simultaneous variable selection and parameter estimation in the context of generalized partly linear models for data with high-dimensional covariates. The method is referred to as the broken adaptive ridge (BAR) estimator, which is an approximation of the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>-penalized regression by iteratively performing reweighted squared <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>-penalized regression. The generalized partly linear model extends the generalized linear model by incorporating a non-parametric component, allowing for the construction of a flexible model to capture various types of covariate effects. We employ the Bernstein polynomials as the sieve space to approximate the non-parametric functions so that our method can be implemented easily using the existing R packages. Extensive simulation studies suggest that the proposed method performs better than other commonly used penalty-based variable selection methods. We apply the method to the CATHGEN data with a binary response from a coronary artery disease study, which motivated our research, and obtained new findings in both high-dimensional genetic and low-dimensional non-genetic covariates.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"17 ","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145242528","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}

{"title":"Enhanced access level-based Circular Replication for CDN performance using CRANNS","authors":"Meenakshi Gupta ,&nbsp;Atul Garg","doi":"10.1016/j.jcmds.2025.100126","DOIUrl":"10.1016/j.jcmds.2025.100126","url":null,"abstract":"<div><div>The reliance of users on the Internet or web and need of fast access for official or personal use is increasing load on servers which also increases the challenges for developers to provide fast access. Content Delivery Networks (CDNs) playing a crucial role to overcome these challenges by helping content providers deliver web content efficiently to end-users through geographically distributed surrogate servers (SS). This requires selection of effective web contents from the origin server (OS) for replication on surrogate servers. In this work, optimizing content replication techniques named Circular Replication among Neighbor Surrogate Servers (CRANSS) is proposed. This technique considers access level of surrogate servers (SS) based on their association with neighbor SS for contents replication decision also CRANSS evaluates the access levels of surrogate servers based on their association with neighboring SS. It also allows for strategic content replication decisions and considers storage capacity of SS and requests pattern of end-users. For evaluation the proposed technique, Network simulator — ns-2 used, and 10 surrogate servers (SS) with one origin​ server (OS) were set. The results of CRANSS are compared with random, round-robin and popularity-based methods. The simulation results show that average response time (1.65 to 1.99), completed response requests (95.06 to 95.43) and load imbalance index (14.27 to 17.25) is better in proposed system. This proposed technique ensures enhancing the overall web experience by providing faster, optimal use of resources and more reliable access to web content. The aim is to ensure efficient utilization of resources keeping in view end-users perceived Quality of Service (QoS) of accessing web content as per the needs of today’s digital landscape.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026827","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}

{"title":"Integrating interpolation techniques with deep learning for accurate brain tumor classification","authors":"Soumyarashmi Panigrahi,&nbsp;Dibya Ranjan Das Adhikary,&nbsp;Binod Kumar Pattanayak","doi":"10.1016/j.jcmds.2025.100124","DOIUrl":"10.1016/j.jcmds.2025.100124","url":null,"abstract":"<div><div>Artificial Intelligence (AI)-powered Computer vision techniques have revolutionized Medical Image Analysis (MIA), enabling accurate detection, diagnosis, and treatment of various disorders such as brain tumors. Brain tumors are a worldwide primary health concern that affects thousands of people. Precisely identifying and diagnosing brain tumors is vital for effective management and life expectancy. Current advances in AI, particularly in Deep Learning (DL) methods have shown immense possibilities to analyze medical images, including MRI. However, the quality of the MRI images significantly impact the overall accuracy of the classification framework. To tackle this issue, we investigated the effect of various Interpolation Techniques (IT) on enhancing Magnetic Resonance Imaging (MRI) image quality, including Nearest Neighbour IT, Bilinear IT, Bicubic IT, and Lanczos IT. Furthermore, we employed Transfer Learning to leverage pre-trained Convolutional Neural Networks (CNNs) architectures, specifically DenseNet201. We proposed a modified DenseNet201 model by adding additional layers and extracting features from the interpolated brain MRI images. We used two publicly available brain tumor datasets. Our experimental results illustrated that the combination of Lanczos IT and fine-tuned DenseNet201 attained the highest accuracy of 99.21% and 99.60% in Dataset-1 and Dataset-2, respectively, for brain tumor classification. Our analysis highlights the importance of image interpolation techniques in improving medical image quality and ultimately improving diagnostic accuracy. Our findings have significant implications for the development of AI-powered decision support systems in medical imaging, enabling healthcare professionals to make more accurate diagnoses and informed treatment decisions.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724957","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}

{"title":"Advanced rainfall nowcasting using 3D convolutional LSTM networks on satellite data","authors":"Abhay B. Upadhyay,&nbsp;Saurin R. Shah,&nbsp;Rajesh A. Thakker","doi":"10.1016/j.jcmds.2025.100125","DOIUrl":"10.1016/j.jcmds.2025.100125","url":null,"abstract":"<div><div>This paper introduces an innovative method for rainfall nowcasting using a deep learning model that combines 3D Convolutional Neural Networks (3D-CNN) with Long Short-Term Memory (LSTM) model. The primary objective is to improve the accuracy and timeliness of short-term rainfall predictions. The 3D-CNN component is responsible for extracting spatial features from complex weather data, while the LSTM component captures temporal dependencies across time steps. This hybrid architecture, referred to as the 3D-Conv-LSTM model, has demonstrated high effectiveness for nowcasting applications. The model processes weather data stored in Network Common Data Form (NetCDF) files and integrates satellite imagery to enhance forecast precision. This dual-data approach enables the model to learn intricate spatiotemporal patterns and relationships often missed by traditional techniques. Through extensive experimentation and validation, the proposed model exhibits superior performance in predicting precipitation events compared to conventional methods. The model achieved a Mean Squared Error (MSE) of 0.0003, Peak Signal-to-Noise Ratio (PSNR) of 42.11, Root Mean Square Error (RMSE) of 0.019, and a Structural Similarity Index Measure (SSIM) of 0.99, indicating excellent prediction quality. Furthermore, the computation time for training and inference was recorded 18 min, demonstrating the model’s efficiency. These results confirm a significant improvement in forecast accuracy, which is critical for disaster preparedness and resource management in weather-sensitive regions.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703263","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}

{"title":"Capturing patterns and radical changes in long-distance mobility by Flickr data","authors":"Anton Galich","doi":"10.1016/j.jcmds.2025.100122","DOIUrl":"10.1016/j.jcmds.2025.100122","url":null,"abstract":"<div><div>In contrast to daily travel behaviour, long-distance mobility constitutes a poorly understood area in transport research. Only few national household travel surveys include sections on long-distance travel and these usually focus on the trip to the destination without gathering information about mobility behaviour at the destination. Other sources of data on mobility are either restricted to the national level such as cell phone data or to specific modes of transport such as international flight statistics or floating car data. In addition, the outbreak of the COVID-19 pandemic in 2020 has illustrated how difficult it is to grasp abrupt changes in mobility behaviour.</div><div>Against this background this paper investigates the potential of Flickr data for capturing patterns and radical changes in long-distance mobility. Flickr is a social media online platform allowing its users to upload photos and to comment on their own and other users’ photos. It is mainly used for sharing holiday and travel experiences. The results show that Flickr constitutes a viable source of data for capturing patterns and radical changes in long-distance mobility. The distribution of the travel distances, the travel destinations as well as reduction of the mileage of all holiday trips in 2020 in comparison to 2019 due to the pandemic calculated on the basis of the Flickr data is very similar to the same indicators determined on the basis of a national household travel survey, official passenger flight statistics, and other official transportation statistics.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513831","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}

{"title":"An in-depth analysis of the IRPSM-Padé algorithm for solving three-dimensional fluid flow problems","authors":"Abdullah Dawar ,&nbsp;Hamid Khan ,&nbsp;Muhammad Ullah","doi":"10.1016/j.jcmds.2025.100123","DOIUrl":"10.1016/j.jcmds.2025.100123","url":null,"abstract":"<div><div>In this article, a comparative analysis of the IRPSM-Padé and DTM-Padé methods has been conducted by solving the fluid flow problem over a bi-directional extending sheet. The fluid flow is expressed by the partial differential equations (PDEs) which are then converted to ordinary differential equations (ODEs) by mean of similarity variables. Both the IRPSM-Padé and DTM-Padé methods are tested at [3,3] and [6,6] Padé approximants. Tables and Figures are used to examine the outcomes and show the consistency and accuracy of both approaches. The outcomes of IRPSM-Padé [3,3] and [6,6] with the same order of approximations closely match the outcomes of DTM-Padé [3,3] and [6,6] using Padé approximants. The significant degree of agreement between the two methods indicates that IRPSM-Padé and DTM-Padé handle the fluid flow problem in a comparable manner. The findings of the IRPSM-Padé and DTM-Padé methods show a strong degree of agreement, indicating the accuracy and dependability of the more recent technique (IRPSM-Padé). The obtained CPU time shows that the DTM consistently perform better that IRPSM in terms of computational efficiency. The total CPU time for IRPSM is nearly three-times greater than that of DTM, indicating that IRPSM demands more computational effort. The recorded times accurately reflect the computational efficiency of IRPSM and DTM because the Padé approximation simply improves the results rationalization and has no influence on CPU time. The residual errors analysis demonstrates that the IRPSM-Padé technique produces exceptionally precise approximations, with errors decreasing as the Padé order increases. Furthermore, the numerical assessment demonstrates that higher Padé orders improve the accuracy and stability of the IRPSM-Padé.</div></div><div><h3>Computational Implementation:</h3><div>Mathematica 14.1 was used to carry out numerical simulations, the DTM-Padé method, and the IRPSM-Padé method. Mathematica’s integrated symbolic and numerical solvers, including the ND Solve function for numerical validation, were used to solve the governing equations. Additionally, plots, such as mesh visualizations and absolute error graphs, were created using Mathematica’s built-in plotting capabilities without the usage of third-party programs.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491666","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}

{"title":"On exact line search method for a polynomial matrix equation","authors":"Chacha Stephen Chacha","doi":"10.1016/j.jcmds.2025.100120","DOIUrl":"10.1016/j.jcmds.2025.100120","url":null,"abstract":"<div><div>In this work, we investigate the elementwise minimal non-negative (EMN) solution of the matrix polynomial equation using an exact line search (ELS) technique to enhance the convergence of the Newton method. Nonnegative solutions to matrix equations are essential in engineering, optimization, signal processing, and data mining, driving advancements and improving efficiency in these fields. While recent advancements in solving matrix equations with nonnegative constraints have emphasized iterative methods, optimization strategies, and theoretical developments, efficiently finding the EMN solution remains a significant challenge. The proposed method integrates the Newton method with an exact line search (ELS) strategy to accelerate convergence and improve solution accuracy. Numerical experiments demonstrate that this approach requires fewer iterations to reach the EMN solution compared to the standard Newton method. Moreover, the method shows improved stability, particularly when dealing with ill-conditioned input matrices and very small tolerance errors.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189659","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}

{"title":"A neural network approach for solving the Monge–Ampère equation with transport boundary condition","authors":"Roel Hacking ,&nbsp;Lisa Kusch ,&nbsp;Koondanibha Mitra ,&nbsp;Martijn Anthonissen ,&nbsp;Wilbert IJzerman","doi":"10.1016/j.jcmds.2025.100119","DOIUrl":"10.1016/j.jcmds.2025.100119","url":null,"abstract":"<div><div>This paper introduces a novel neural network-based approach to solving the Monge–Ampère equation with the transport boundary condition, specifically targeted towards optical design applications. We leverage multilayer perceptron networks to learn approximate solutions by minimizing a loss function that encompasses the equation’s residual, boundary conditions, and convexity constraints. Our main results demonstrate the efficacy of this method, optimized using L-BFGS, through a series of test cases encompassing symmetric and asymmetric circle-to-circle, square-to-circle, and circle-to-flower reflector mapping problems. Comparative analysis with a conventional least-squares finite-difference solver reveals the competitive, and often superior, performance of our neural network approach on the test cases examined here. A comprehensive hyperparameter study further illuminates the impact of factors such as sampling density, network architecture, and optimization algorithm. While promising, further investigation is needed to verify the method’s robustness for more complicated problems and to ensure consistent convergence. Nonetheless, the simplicity and adaptability of this neural network-based approach position it as a compelling alternative to specialized partial differential equation solvers.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204762","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}

{"title":"Machine learning-driven market value prediction for European football players","authors":"Abdullah Tamim ,&nbsp;Md. Wadud Jahan ,&nbsp;Md. Rashid Shahriar Chowdhury ,&nbsp;Ahammad Hossain ,&nbsp;Md. Mizanur Rahman ,&nbsp;A.H.M. Rahmatullah Imon","doi":"10.1016/j.jcmds.2025.100118","DOIUrl":"10.1016/j.jcmds.2025.100118","url":null,"abstract":"<div><div>Football is globally recognized as the most widely practiced and watched sport. Precise player value is crucial for clubs seeking to maximize their player acquisition strategy and overall success in football. Conventional player valuation methodologies are mainly dependent on expert judgments and subjective assessments, missing the objectivity and precision provided by data-driven approaches. This study seeks to close this disparity by utilizing machine learning techniques to predict the market valuations of football players. The analysis is conducted using an extensive dataset sourced from the FIFA 22 video game, which was obtained via sofifa.com. The collection includes more than 16,000 players. The Machine Learning (ML) techniques used in this study are Multiple Linear Regression (MLR), Ridge Regression (RR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The machine learning algorithms undergo training using 80% of the samples and are subsequently tested using the remaining 20% of the samples. We evaluate each algorithm’s performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) value. Numerical results show that the RFR model demonstrates superior performance by achieving the lowest MAE, MSE, RMSE, and the highest R² value across all samples. The RFR effectively captures non-linear interactions and reliably prevents overfitting. This research finding will enhance the existing knowledge in sports economics by demonstrating how ML can be used to anticipate the market prices of football players with better accuracy. This will provide football teams with valuable insights to make more strategic decisions.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194593","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}

{"title":"An improved descent hybrid gradient-based projection algorithm for nonlinear equations and signal recovery problems","authors":"M. Koorapetse,&nbsp;P. Kaelo,&nbsp;T. Diphofu,&nbsp;S. Lekoko,&nbsp;T. Yane,&nbsp;B. Modise,&nbsp;C.R. Sam","doi":"10.1016/j.jcmds.2025.100117","DOIUrl":"10.1016/j.jcmds.2025.100117","url":null,"abstract":"<div><div>Derivative-free projection methods for solving nonlinear monotone equations have recently gained favor with researchers. Based on a hybrid conjugate gradient algorithm and the projection techniques, in this work, we present a descent derivative-free projection method for finding solutions to large-scale nonlinear monotone equations. The proposed method satisfies the descent condition and, under some suitable assumptions, its global convergence is established. The presented method’s efficacy is demonstrated through numerical experiments. Results show that, compared to other methods with similar structure, the method performs better. The method is further applied to an application in signal recovery, and it is proving to be efficient.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134386","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}