Search

Your search keyword '"Weber, Gerhard-Wilhelm"' showing total 1,591 results
Start Over Author "Weber, Gerhard-Wilhelm"

Refine your results

more »

more »

more »

more »

more »
1,591 results on '"Weber, Gerhard-Wilhelm"'

225. Modeling of Tumor-Immune Nonlinear Stochastic Dynamics with Hybrid Systems with Memory Approach

Author

GÖKGÖZ, Nurgül, ÖKTEM, Hakan, and WEBER, Gerhard-wilhelm

Subjects
Matematik, Hybrid systems,Regime switching,Pattern memorization,Multistationarity,Regulatory dynamical systems,Medicine, Mathematics
Abstract

In this paper, we address the well-known Tumor-Immune Model of Kuznetsovet al., converting it into a stochastic form, and for simulation purposes we employEuler-Maruyama discretization process. Such a modeling, for being realistic inbiology and medicine, requires the implication of memory components. We also explainhow to calculate the state transition time and we elaborate on how to reducethe system dynamics after the state transition. In fact, we establish and evaluateStochastic Kuznetsov et al. model, and we describe how to demonstrate the stabilityof the numerical method, addressing tumor growth in spleen of mice. This workends with a conclusion and a prospective view at future research and application,with special focus on medicine and neuroscience of tumor analysis and treatment.

Published
2020

241. Editorial

Author

Vasant, Pandian, primary, Weber, Gerhard‐Wilhelm, additional, and Litvinchev, Igor S., additional

Published
2020
Full Text
View/download PDF

247. Editorial

Author

Stein, Oliver, Still, Georg, Terlaky, Tamás, and Weber, Gerhard-Wilhelm

Published
2008
Full Text
View/download PDF

248. Recovering source term of the time-fractional diffusion equation.

Author

Partohaghighi, Mohammad, Karatas Akgül, Esra, Weber, Gerhard-Wilhelm, Yao, Guangming, and Akgül, Ali

Subjects
FUNCTIONAL equations, VECTOR spaces, INVERSE problems, HEAT equation, ENERGY function
Abstract

In this paper, a computational approach is suggested to obtain unknown space–time-dependent source term of the fractional diffusion equations. We assign a time-dependent source term and a linear space with the zero components which represent a series of boundary functions. In linear space, an energy border functional equation is obtained. After that, some numerical examples are provided to verify the accuracy of the method. Also, two tables are presented to display the values of solutions. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

249. A new outlier detection method based on convex optimization: application to diagnosis of Parkinson's disease.

Author

Taylan, Pakize, Yerlikaya-Özkurt, Fatma, Bilgiç Uçak, Burcu, and Weber, Gerhard-Wilhelm

Subjects
PARKINSON'S disease, OUTLIER detection, DIAGNOSIS, TIKHONOV regularization, REGRESSION analysis, STATISTICAL models, CONIC sections
Abstract

Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results