Journal of Computational Mathematics and Data Science最新文献

Efficiency of the multisection method 多分段法的效率

Journal of Computational Mathematics and Data Science Pub Date : 2024-11-02 DOI: 10.1016/j.jcmds.2024.100106

J.S.C. Prentice

引用次数: 0

Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of Autistic Spectrum Disorder 贝叶斯优化一维卷积神经网络 (1D CNN)，用于自闭症谱系障碍的早期诊断

Journal of Computational Mathematics and Data Science Pub Date : 2024-10-19 DOI: 10.1016/j.jcmds.2024.100105

Temidayo Oluwatosin Omotehinwa , Morolake Oladayo Lawrence , David Opeoluwa Oyewola , Emmanuel Gbenga Dada

{"title":"Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of Autistic Spectrum Disorder","authors":"Temidayo Oluwatosin Omotehinwa , Morolake Oladayo Lawrence , David Opeoluwa Oyewola , Emmanuel Gbenga Dada","doi":"10.1016/j.jcmds.2024.100105","DOIUrl":"10.1016/j.jcmds.2024.100105","url":null,"abstract":"<div><div>Autistic Spectrum Disorder (ASD) is a challenging neurological development disorder, which involves poor social interaction, communication, and repetitive behaviours. If autism is identified early enough it can be treated with better outcomes but present diagnostic tests are dependent on subjective opinion, consume a lot of time, and are vague. This study is aimed at optimizing one-dimensional convolutional neural networks (1D CNN) to improve the precision and speed of early ASD diagnosis. Four ASD datasets representing different age groups — toddlers, children, adolescents, and adults were modelled using one-dimensional convolutional neural networks (1D CNN). These datasets are accessible to the public on the UCI Machine Learning Repository and Kaggle, they consist of behavioural features relevant to ASD diagnosis. Each dataset underwent feature selection, categorical encoding, and missing value handling. Then, baseline 1D CNN with predefined hyperparameters was modelled on each of the datasets. Subsequently, the baseline models were optimized using the Tree-structured Parzen Estimator (TPE). An interactive web-based ASD diagnostic tool was developed, where user inputs are processed through age-specific pre-trained optimized models to determine ASD probability. The optimized 1D CNN models significantly outperformed the baseline models across all age groups and achieved scores of 100% in accuracy, precision, recall, F1-score, MCC, and AUC ROC. This implies that the optimized models can reliably identify people in various age groups who have and do not have ASD. The development of an interactive web-based diagnostic tool extends the practical utility of the models, making them accessible for clinical and at-home use.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"13 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel color space representation extracted by NMF to segment a color image 用 NMF 提取的新颖色彩空间表示法分割彩色图像

Journal of Computational Mathematics and Data Science Pub Date : 2024-10-18 DOI: 10.1016/j.jcmds.2024.100104

Ciro Castiello , Nicoletta Del Buono , Flavia Esposito

引用次数: 0

Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition 通过拓扑数据分析和低阶张量分解增强磁共振成像脑肿瘤检测和分类能力

Journal of Computational Mathematics and Data Science Pub Date : 2024-10-03 DOI: 10.1016/j.jcmds.2024.100103

Serena Grazia De Benedictis , Grazia Gargano , Gaetano Settembre

{"title":"Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition","authors":"Serena Grazia De Benedictis , Grazia Gargano , Gaetano Settembre","doi":"10.1016/j.jcmds.2024.100103","DOIUrl":"10.1016/j.jcmds.2024.100103","url":null,"abstract":"<div><div>The advent of artificial intelligence in medical imaging has paved the way for significant advancements in the diagnosis of brain tumors. This study presents a novel ensemble approach that uses magnetic resonance imaging (MRI) to identify and categorize common brain cancers, such as pituitary, meningioma, and glioma. The proposed workflow is composed of a two-fold approach: firstly, it employs non-trivial image enhancement techniques in data preprocessing, low-rank Tucker decomposition for dimensionality reduction, and machine learning (ML) classifiers to detect and predict the type of brain tumor. Secondly, persistent homology (PH), a topological data analysis (TDA) technique, is exploited to extract potential critical areas in MRI scans. When paired with the ML classifier output, this additional information can help domain experts to identify areas of interest that might contain tumor signatures, improving the interpretability of ML predictions. When compared to automated diagnoses, this transparency adds another level of confidence and is essential for clinical acceptance. The performance of the system was quantitatively evaluated on a well-known MRI dataset, with an overall classification accuracy of 97.28% using an extremely randomized trees model. The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"13 ","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artifact removal from ECG signals using online recursive independent component analysis 利用在线递归独立成分分析去除心电信号中的伪影

Journal of Computational Mathematics and Data Science Pub Date : 2024-09-27 DOI: 10.1016/j.jcmds.2024.100102

K. Gunasekaran , V.D. Ambeth Kumar , Mary Judith A.

{"title":"Artifact removal from ECG signals using online recursive independent component analysis","authors":"K. Gunasekaran , V.D. Ambeth Kumar , Mary Judith A.","doi":"10.1016/j.jcmds.2024.100102","DOIUrl":"10.1016/j.jcmds.2024.100102","url":null,"abstract":"<div><div>The diagnosis of cardiac abnormalities and monitoring of heart health heavily rely on Electrocardiogram (ECG) signals. Unfortunately, these signals frequently encounter interference from diverse artifacts, impeding precise interpretation and analysis. To overcome this challenge, we suggest a novel method for real-time artifact removal from ECG signals through the utilization of Online Recursive Independent Component Analysis (ORICA). Our study outlines a systematic preprocessing pipeline, adaptively estimating the mixing matrix and demixing matrix of the ICA model while streaming data is processed. Additionally, we explore the selection of appropriate ICA components and the use of relevant feature extraction techniques to enhance the quality of extracted cardiac signals. This research presents a promising solution for removing artifacts from ECG signals in real-time, paving the way for improved cardiac diagnostics and monitoring systems. Comparative analyses demonstrate significant improvements in the accuracy of subsequent ECG analysis and interpretation following the application of our ORICA-based preprocessing.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"13 ","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging feed-forward neural networks to enhance the hybrid block derivative methods for system of second-order ordinary differential equations 利用前馈神经网络增强二阶常微分方程系统的混合分块导数法

Journal of Computational Mathematics and Data Science Pub Date : 2024-09-20 DOI: 10.1016/j.jcmds.2024.100101

Sabastine Emmanuel , Saratha Sathasivam , Muideen O. Ogunniran

引用次数: 0

On resolution coresets for constrained clustering 关于约束聚类的分辨率核心集

Journal of Computational Mathematics and Data Science Pub Date : 2024-09-01 DOI: 10.1016/j.jcmds.2024.100100

Maximilian Fiedler, Peter Gritzmann, Fabian Klemm

引用次数: 0

Fast empirical scenarios 快速经验方案

Journal of Computational Mathematics and Data Science Pub Date : 2024-09-01 DOI: 10.1016/j.jcmds.2024.100099

Michael Multerer , Paul Schneider , Rohan Sen

引用次数: 0

Estimating data complexity and drift through a multiscale generalized impurity approach 通过多尺度广义杂质法估算数据复杂性和漂移

Journal of Computational Mathematics and Data Science Pub Date : 2024-08-26 DOI: 10.1016/j.jcmds.2024.100098

Diogo Costa , Eugénio M. Rocha , Nelson Ferreira

{"title":"Estimating data complexity and drift through a multiscale generalized impurity approach","authors":"Diogo Costa , Eugénio M. Rocha , Nelson Ferreira","doi":"10.1016/j.jcmds.2024.100098","DOIUrl":"10.1016/j.jcmds.2024.100098","url":null,"abstract":"<div><p>The quality of machine learning solutions, and of classifier models in general, depend largely on the performance of the chosen algorithm, and on the intrinsic characteristics of the input data. Although work has been extensive on the former of these aspects, the latter has received comparably less attention. In this paper, we introduce the Multiscale Impurity Complexity Analysis (MICA) algorithm for the quantification of class separability and decision-boundary complexity of datasets. MICA is both model and dimensionality-independent and can provide a measure of separability based on regional impurity values. This makes it so that MICA is sensible to both global and local data conditions. We show MICA to be capable of properly describing class separability in a comprehensive set of both synthetic and real datasets and comparing it against other state-of-the-art methods. After establishing the robustness of the proposed method, alternative applications are discussed, including a streaming-data variant of MICA (MICA-S), that can be repurposed into a model-independent method for concept drift detection.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"12 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415824000099/pdfft?md5=54b719dae828872e98af24740cf27e23&pid=1-s2.0-S2772415824000099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structured stochastic curve fitting without gradient calculation 无需梯度计算的结构化随机曲线拟合

Journal of Computational Mathematics and Data Science Pub Date : 2024-07-26 DOI: 10.1016/j.jcmds.2024.100097

Jixin Chen

引用次数: 0