Mathematical Biosciences and Engineering最新文献_第3页

Robust control and data reconstruction for nonlinear epidemiological models using feedback linearization and state estimation.

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-02 DOI: 10.3934/mbe.2025006

Balázs Csutak, Gábor Szederkényi

{"title":"Robust control and data reconstruction for nonlinear epidemiological models using feedback linearization and state estimation.","authors":"Balázs Csutak, Gábor Szederkényi","doi":"10.3934/mbe.2025006","DOIUrl":"https://doi.org/10.3934/mbe.2025006","url":null,"abstract":"It has been clearly demonstrated over the past years that control theory can provide an efficient framework for the solution of several complex tasks in epidemiology. In this paper, we present a computational approach for the state estimation based reference tracking control and historical data reconstruction using nonlinear compartmental epidemic models. The control model is given in nonlinear input-affine form, where the manipulable input is the disease transmission rate influenced by possible measures and restrictions, while the observed or computed output is the number of infected people. The control design is built around a simple SEIR model and relies on a feedback linearization technique. We examine and compare different control setups distinguished by the availability of state information, complementing the directly measurable data with an extended Kalman filter used for state estimation. To illustrate the capabilities and robustness of the proposed method, we carry out multiple case studies for output tracking and data reconstruction on Swedish and Hungarian data, all in the presence of serious model and parameter mismatch. Computation results show that a well-designed feedback, even in the presence of significant observation uncertainties, can sufficiently reduce the effect of modeling errors.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 1","pages":"109-137"},"PeriodicalIF":2.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational physics and imaging in medicine.

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-02 DOI: 10.3934/mbe.2025005

James C L Chow

引用次数: 0

Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models.

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-01 Epub Date: 2024-12-17 DOI: 10.3934/mbe.2025002

Rami Al-Hajj, Gholamreza Oskrochi, Mohamad M Fouad, Ali Assi

{"title":"Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models.","authors":"Rami Al-Hajj, Gholamreza Oskrochi, Mohamad M Fouad, Ali Assi","doi":"10.3934/mbe.2025002","DOIUrl":"https://doi.org/10.3934/mbe.2025002","url":null,"abstract":"Forecasting wind speed plays an increasingly essential role in the wind energy industry. However, wind speed is uncertain with high changeability and dependency on weather conditions. Variability of wind energy is directly influenced by the fluctuation and unpredictability of wind speed. Traditional wind speed prediction methods provide deterministic forecasting that fails to estimate the uncertainties associated with wind speed predictions. Modeling those uncertainties is important to provide reliable information when the uncertainty level increases. Models for estimating prediction intervals of wind speed do not differentiate between daytime and nighttime shifts, which can affect the performance of probabilistic wind speed forecasting. In this paper, we introduce a prediction framework for deterministic and probabilistic short-term wind speed forecasting. The designed framework incorporates independent machine learning (ML) models to estimate point and interval prediction of wind speed during the daytime and nighttime shifts, respectively. First, feature selection techniques were applied to maintain the most relevant parameters in the datasets of daytime and nighttime shifts, respectively. Second, support vector regressors (SVRs) were used to predict the wind speed 10 minutes ahead. After that, we incorporated the non-parametric kernel density estimation (KDE) method to statistically synthesize the wind speed prediction errors and estimate the prediction intervals (PI) with several confidence levels. The simulation results validated the effectiveness of our framework and demonstrated that it can generate prediction intervals that are satisfactory in all evaluation criteria. This verifies the validity and feasibility of the hypothesis of separating the daytime and nighttime data sets for these types of predictions.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 1","pages":"23-51"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stochastic models of population growth.

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-01 Epub Date: 2024-12-16 DOI: 10.3934/mbe.2025001

Katarzyna Pichór, Pejman Sanaei

引用次数: 0

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-01 Epub Date: 2024-12-25 DOI: 10.3934/mbe.2025004

Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

{"title":"Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.","authors":"Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul","doi":"10.3934/mbe.2025004","DOIUrl":"https://doi.org/10.3934/mbe.2025004","url":null,"abstract":"Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00-99.00%) and specificity (89.63-99.00%) across all tasks highlighted the model's robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 1","pages":"73-105"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical modeling of microtube-driven regrowth of gliomas after local resection.

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2025-01-01 Epub Date: 2024-12-24 DOI: 10.3934/mbe.2025003

Alexandra Shyntar, Thomas Hillen

引用次数: 0

Correction to "Data augmentation based semi-supervised method to improve COVID-19 CT classification" [Mathematical Biosciences and Engineering 20(4) (2023) 6838-6852]. 修正“基于数据增强的改进COVID-19 CT分类的半监督方法”[数学生物科学与工程20(4)(2023)6838-6852]。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-12-13 DOI: 10.3934/mbe.2024345

Xiangtao Chen, Yuting Bai, Peng Wang, Jiawei Luo

引用次数: 0

Correction to "IMC-MDA: Prediction of miRNA-disease association based on induction matrix completion" [Mathematical Biosciences and Engineering 20(6) (2023) 10659-10674]. 对“IMC-MDA：基于诱导矩阵补全的mirna -疾病关联预测”的更正[数学生物科学与工程20(6)(2023)10659-10674]。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-12-13 DOI: 10.3934/mbe.2024346

Zejun Li, Yuxiang Zhang, Yuting Bai, Xiaohui Xie, Lijun Zeng

引用次数: 0

A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI. 海马脑MRI自动分割的半监督深度神经模糊迭代学习系统。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-12-11 DOI: 10.3934/mbe.2024344

M Nisha, T Kannan, K Sivasankari

{"title":"A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.","authors":"M Nisha, T Kannan, K Sivasankari","doi":"10.3934/mbe.2024344","DOIUrl":"https://doi.org/10.3934/mbe.2024344","url":null,"abstract":"The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"21 12","pages":"7830-7853"},"PeriodicalIF":2.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting the classical target cell limited dynamical within-host HIV model - Basic mathematical properties and stability analysis. 重新审视宿主内经典靶细胞有限动力学HIV模型-基本数学性质和稳定性分析。

IF 2.6 4区工程技术

Mathematical Biosciences and Engineering Pub Date : 2024-12-11 DOI: 10.3934/mbe.2024343

Benjamin Wacker

{"title":"Revisiting the classical target cell limited dynamical within-host HIV model - Basic mathematical properties and stability analysis.","authors":"Benjamin Wacker","doi":"10.3934/mbe.2024343","DOIUrl":"https://doi.org/10.3934/mbe.2024343","url":null,"abstract":"In this article, we reconsider the classical target cell limited dynamical within-host HIV model, solely taking into account the interaction between $ {rm{CD}}4^{+} $ T cells and virus particles. First, we summarize some analytical results regarding the corresponding dynamical system. For that purpose, we proved some analytical results regarding the system of differential equations as our first main contribution. Specifically, we showed non-negativity and boundedness of solutions, global existence in time and global uniqueness in time and examined stability properties of two possible equilibria. In particular, we demonstrated that the virus-free equilibrium and the plateau-phase equilibrium are locally asymptotically stable using the Routh-Hurwitz criterion under appropriate conditions. As our second main contribution, we underline our theoretical findings through some numerical experiments with standard Runge-Kutta time stepping schemes. We conclude this work with a summary of our main results and a suggestion of an extension for more complex dynamical systems with regard to HIV-infection.","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"21 12","pages":"7805-7829"},"PeriodicalIF":2.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0