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arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. We assume that, for numerical reasons, one has to time-discretize the diffusion process which typically leads to filtering that is subject to discretization bias. The approach in [16] establishes that when only having access to the time discretized diffusion it is possible to remove the discretization bias with an estimator of finite variance. We improve on the method in [16] by introducing a modified estimator based on the recent work of [17]. We show that this new estimator is unbiased and has finite variance. Moreover, we conjecture and verify in numerical simulations that substantial gains are obtained. That is, for a given mean square error (MSE) and a particular class of multi-dimensional diffusion, the cost to achieve the said MSE falls. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Parameter Estimation for Partially Observed McKean-Vlasov Diffusions

arXiv

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arXiv 2024年

作者： Jasra, Ajay Maama, Mohamed Tempone, Raul School of Data Science The Chinese University of Hong Kong CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Chair of Mathematics for Uncertainty Quantification RWTH Aachen University Aachen52062 Germany

In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5] we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovian stochastic approximation methodology. New Markov chain Monte Carlo algorithms for the POMV model are introduced facilitating the application of [5]. We prove, under assumptions, that the expectation of our estimator is biased, but with expected small and controllable bias. Our approach is implemented on several examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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Multilevel Monte Carlo Methods for the Grad-Shafranov Free Boundary Problem

SSRN

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SSRN 2023年

作者： Elman, Howard C. Liang, Jiaxing Sánchez-Vizuet, Tonatiuh Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Applied Mathematics & Statistics and Scientific Computing Program University of Maryland College Park United States Department of Mathematics The University of Arizona United States

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costsby performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductionstypically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the model. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Multilevel Monte Carlo methods for the Grad-Shafranov free boundary problem

arXiv

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arXiv 2023年

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costs by performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductions typically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the *** Codes 65Z05, 65C05, 62P35, 35R35, 35R60 © 2023, CC BY.

关键词： Monte Carlo methods

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification

Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

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IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Shu Liu Qiang Wu Xin Yang Computational and Data Science PhD Program Middle Tennessee State University Murfreesboro TN USA Department of Mathematics University of Tennessee Knoxville TN USA Department of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method.

关键词： Training Support vector machines Neuroimaging Autism Machine learning algorithms Functional magnetic resonance imaging Vectors Robustness Classification algorithms Sustainable development

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

引用

2023 IEEE International Conference on Data and Software Engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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Expert System Research for Prostate Cancer : A Literature Review

Expert System Research for Prostate Cancer : A Literature Re...

引用

Networking, Electrical Engineering, computer science, and Technology (IConNECT), International Conference on

作者： Rusliyawati Rusliyawati Sutyarso Sutyarso Agus Wantoro Doctoral Program of Mathematics and Natural Science Faculty University of Lampung Bandar Lampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandar Lampung Indonesia Faculty of Mathematics and Natural Sciences University of Lampung Bandar Lampung Indonesia

In the field of computer science, expert systems have attracted many computer scientists. In recent years, expert systems have been widely used in the medical field, particularly for diagnosing and treating various diseases, especially prostate cancer. Reality has shown that expert systems hold great promise for accurate diagnosis and treatment of prostate cancer. This research summarizes several studies related to the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores potential future research. Articles and reviews on the application of expert systems in prostate cancer from 2015 to 2022 were selected from the PubMed, Embase, and Web of science databases. Microsoft Excel 2019 and GraphPad Prism 8 were used for target variable analysis. A total of 37 articles were selected for this research. Research on artificial intelligence in prostate cancer has grown exponentially in recent years, particularly in Indonesia. Image data usage is more predominant (62%) than numerical data types in prostate cancer research. Artificial intelligence research in prostate cancer, especially in the field of treatment cases, and the use of Deep Neural Network (DNN), Human Neural Network (HNN), Fully Convolutional Network (FCN), QT Environment for C++, Artificial Neural Network (ANN) algorithms, are highly feasible for further research.

关键词：

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Optimization and Computing Model of Fish Resource Supply Chain Distribution Network 5

Optimization and Computing Model of Fish Resource Supply Cha...

引用

5th International Conference on Computing and Applied Informatics, ICCAI 2020

作者： Nurdin Zarlis, M. Tulus Efendi, S. Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sumatera Utara Medan Indonesia

Aceh is a province that is rich in fishery resource potential. Fish resources become one of the leading commodities, therefore we need a supply chain optimization model. Optimization is one of the technologies widely used in supply chain network management. Fish resource supply chain becomes very important because it is caused by several factors, including the diversity of types of products from fish resources and their short expiry. The steps used in this research is literature review, determine parameters and decision variables, formulate the objective function and constraint function, modeling and model implementation. The purpose of this research is to get an optimization model in planning and managing the fish resource supply chain by using mixed integer linear programming, so that the resulting model can solve problems in the limitations of fish resources in certain areas, can distribute all consumer demand quickly. This new model can minimize transportation costs to suppliers, minimize transportation costs from suppliers to distribution centers and minimize transportation costs from distribution centers to consumers, and minimize product inventory costs to suppliers and to distribution centers. © Published under licence by IOP Publishing Ltd.

关键词： Supply chains

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A new two-parameter family of discrete distributions

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Heliyon 2025年第3期11卷 e41459页

作者： Haddari, Allaeddine Zeghdoudi, Halim Pakyari, Reza Mathematics Department Faculty of Mathematics and Computer Science Batna 2 University Batna Algeria LaPS Laboratory Badji Mokhtar–Annaba University Annaba 23000 Algeria Statistics Program Department of Mathematics and Statistics College of Arts and Sciences Qatar University Doha 2713 Qatar

This paper introduces a new family of discrete distributions with two shape parameters, termed the Generalized Two-Parameter Discrete Distribution (GTPDD). The raw and central moments are derived from a general expression for its rth factorial moments. Additionally, the paper provides expressions for the moment generating function, coefficient of variation, skewness, kurtosis, index of dispersion and the hazard function. Parameter estimation is addressed using the maximum likelihood estimation method, and the uniqueness of the maximum likelihood estimators is also verified. The applicability of the proposed distribution is illustrated through the modeling of two real data sets, one from engineering and the other from survival analysis. © 2024

关键词： Maximum likelihood estimation Moments Monte Carlo simulation Poisson distribution Poisson-Lindley distribution

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