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Lassa hemorrhagic fever model using new generalized Caputo-type fractional derivative operator

International Journal of Modeling, Simulation, and Scientific Computing 2021年第6期12卷 129-149页

作者： Pushpendra Kumar Vedat Suat Erturk Abdullahi Yusuf Tukur Abdulkadir Sulaiman Department of Mathematics and Statistics School of Basic and Applied Sciences Central University of Punjab BathindaPunjab 151001India Department of Mathematics Ondokuz Mayis University Atakum 55200SamsunTurkey Department of Computer Engineering Biruni University Istanbul Turkey Department of Mathematics Science Faculty Federal University DutseJigawa 7156Nigeria

In some of the previous decades, we have observed that mathematical modeling hasbecome one of the most interesting research fields and has attracted many *** this regard, thousands of researchers have proposed different varieties of mathematicalmodels to study the dynamics of a number of real-world problems. This research workis framed to analyzing the structure of the well-known Lassa hemorrhagic epidemic;adangerous epidemic for pregnant women, via new generalized Caputo type nonintegerorder derivative with the help of a modified Predictor–Corrector scheme. Lassa hemorrhagic disease is an epidemical and biocidal fever, whose negative impacts were initiallyrecognized in the countries of Africa. This virus has killed many pregnant women ascompared to the Ebola epidemic. It was noticed that Lassa virus was isolated in Verocell cultures from a blood pattern, and after 12 days it was ejective, after the climb ofthe sickness. In this research study, necessary theorems and lemmas are reminded toprove the existence of a unique solution and stability of given fractional approximationscheme. All necessary results are reminded to confirm the effectiveness of the proposedapproximation algorithm by graphical observations for various fractional-order *** our practical calculations, we plotted the graphs for two different values of naturaldeath rate along with various values of given fractional-order operator. Our major target is to show the importance of the proposed modified version of the Predictor–Correctoralgorithm in epidemic studies by exploring the given Lassa hemorrhagic fever dynamics.

关键词： Lassa hemorrhagic fever epidemic mathematical model new generalized Caputo fractional derivatives predictor–corrector algorithm

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Some Notes on the Similarity of Priority Vectors Derived by the Eigenvalue Method and the Geometric Mean Method 26

Some Notes on the Similarity of Priority Vectors Derived by ...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Mazurek, Jirí Kulakowski, Konrad Ernst, Sebastian Strada, Michal Silesian University in Opava School of Business Administration in Karvina Department of Informatics and Mathematics Czech Republic AGH University of Science and Technology Department of Applied Computer Science Kraków Poland Aptiv Services Poland S.A. Kraków Poland

This paper examines the differences in ordinal rankings obtained from a pairwise comparison matrix using the eigenvalue method and the geometric mean method. First, we introduce several propositions on the (dis)similarity of both rankings concerning the matrix size and its inconsistency expressed by the Koczkodaj's inconsistency index. Further on, we examine the relationship between differences in both rankings and Kendall's rank correlation coefficient t and Spearman's rank coefficient ?. Apart from theoretical results, intuitive numerical examples and Monte Carlo simulations are also provided. © 2022 The Authors. Published by Elsevier B.V.

关键词： Monte Carlo methods

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A unified weighting framework for evaluating nearest neighbour classification

arXiv

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arXiv 2023年

作者： Lenz, Oliver Urs Bollaert, Henri Cornelis, Chris Leiden Institute of Advanced Computer Science Leiden University Leiden2333 CA Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

We present the first comprehensive and large-scale evaluation of classical (NN), fuzzy (FNN) and fuzzy rough (FRNN) nearest neighbour classification. We standardise existing proposals for nearest neighbour weighting with kernel functions, applied to the distance values and/or ranks of the nearest neighbours of a test instance. In particular, we show that the theoretically optimal Samworth weights converge to a kernel. Kernel functions are closely related to fuzzy negation operators, and we propose a new kernel based on Yager negation. We also consider various distance and scaling measures, which we show can be related to each other. Through a systematic series of experiments on 85 real-life classification datasets, we find that NN, FNN and FRNN all perform best with Boscovich distance, and that NN and FRNN perform best with a combination of Samworth rank- and distance-weights and scaling by the mean absolute deviation around the median (r1), the standard deviation (r2) or the semi-interquartile range (r∞∗ ), while FNN performs best with only Samworth distance-weights and r1- or r2-scaling. However, NN achieves comparable performance with Yager-21 distance-weights, which are simpler to implement than a combination of Samworth distance- and rank-weights. Finally, FRNN generally outperforms NN, which in turn performs systematically better than FNN. © 2023, CC BY-NC-ND.

关键词： Nearest neighbor search

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对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学对比分析

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Journal of Central South University 2023年第8期30卷 2616-2626页

作者： KHALID Abdulkhaliq M-alharbi HINA Gul MUHAMMAD Ramzan SEIFEDINE Kadry ABDULKAFI Mohammed-saeed Mechanical Engineering Department College of EngineeringUmm Al-Qura UniversityMakkah 24382Sandi Arabia Department of Computer Science Bahria UniversityIslamabad 44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题... 详细信息

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题由一组方程组成,借助MATLAB bvp4c包进行求解。为了使参数与相关剖面的影响可视化,给出了图形说明,并利用图表来评估关键参数变化是如何影响相关场及其相应的物理量。通过对盐1和盐2的Dufour参数的多次估计,可以推断出流体流速的波动。验证了流体动力流相对于流体磁流的优势。此外,还将特定情景下已发表的结果与当前研究结果进行了比较。

关键词：三重扩散纳米流体二次混合对流可变导热系数非线性热辐射

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Doubly-robust inference and optimality in structure-agnostic models with smoothness

arXiv

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arXiv 2024年

作者： Bonvini, Matteo Kennedy, Edward H. Dukes, Oliver Balakrishnan, Sivaraman Department of Statistics Rutgers State University of New Jersey United States Department of Statistics & Data Science Carnegie Mellon University United States Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

We study the problem of constructing an estimator of the average treatment effect (ATE) with observational data. The celebrated doubly-robust, augmented-IPW (AIPW) estimator generally requires consistent estimation of both nuisance functions for standard root-n inference, and moreover that the product of the errors of the nuisances should shrink at a rate faster than n−1/2. A recent strand of research has aimed to understand the extent to which the AIPW estimator can be improved upon (in a minimax sense). Under structural assumptions on the nuisance functions, the AIPW estimator is typically not minimax-optimal, and improvements can be made using higher-order influence functions (Robins et al, 2017). Conversely, without any assumptions on the nuisances beyond the mean-square-error rates at which they can be estimated, the rate achieved by the AIPW estimator is already optimal (Balakrishnan et al, 2023;Jin and Syrgkanis, 2024). We make three main contributions. First, we propose a new hybrid class of distributions that combine structural agnosticism regarding the nuisance function space with additional smoothness constraints. Second, we calculate minimax lower bounds for estimating the ATE in the new class, as well as in the pure structure-agnostic one. Third, we propose a new estimator of the ATE that enjoys doubly-robust asymptotic linearity;it can yield asymptotically valid Wald-type confidence intervals even when the propensity score or the outcome model is inconsistently estimated, or estimated at a slow rate. Under certain conditions, we show that its rate of convergence in the new class can be much faster than that achieved by the AIPW estimator and, in particular, matches the minimax lower bound rate, thereby establishing its optimality. Finally, we complement our theoretical findings with simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Autonomous Navigation of Mobile Robot with Obstacle Avoidance: A Review 1

引用

3rd International Conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2020

作者： Bijli, Mahvish Kumar, Neerendra Department of Computer Science & IT Central University of Jammu Jammu India Doctoral School of Applied Informatics & Applied Mathematics Obuda University Budapest Hungary

ISBN: (数字)9789811614835

ISBN: (纸本)9789811614828

Navigation of mobile robot with obstacle avoidance is a successful research area owing to its comprehensive applications. Secure and smooth mobile robot navigation through different (static and dynamic) environments for single and multiple robot system to attain its goal with following secure path and producing a most fulfilling end result is the principal purpose of navigation. Many techniques are developed for mobile robot navigation. This paper proposes the soft computing techniques used in mobile robot navigation namely fuzzy logic, neural network and neuro-fuzzy. This paper concludes with strength, limitations, efficiency and tabular data of each methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Towards Efficient Training of Graph Neural Networks: A Multiscale Approach

arXiv

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arXiv 2025年

作者： Gal, Eshed Eliasof, Moshe Schönlieb, Carola-Bibiane Haber, Eldad Treister, Eran Department of Computer Science Ben-Gurion University of the Negev Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Earth Ocean and Atmospheric Sciences University of British Columbia Canada

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning and inferring from graph-structured data, and are widely used in a variety of applications, often considering large amounts of data and large graphs. However, training on such data requires large memory and extensive computations. In this paper, we introduce a novel framework for efficient multiscale training of GNNs, designed to integrate information across multiscale representations of a graph. Our approach leverages a hierarchical graph representation, taking advantage of coarse graph scales in the training process, where each coarse scale graph has fewer nodes and edges. Based on this approach, we propose a suite of GNN training methods: such as coarse-to-fine, sub-to-full, and multiscale gradient computation. We demonstrate the effectiveness of our methods on various datasets and learning tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Multilevel Particle Filters for Partially Observed McKean-Vlasov Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Jasra, Ajay 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 School of Data Science The Chinese University of Hong Kong Shenzhen China

In this paper we consider the filtering problem associated to partially observed McKean-Vlasov stochastic differential equations (SDEs). The model consists of data that are observed at regular and discrete times and the objective is to compute the conditional expectation of (functionals) of the solutions of the SDE at the current time. This problem, even the ordinary SDE case is challenging and requires numerical approximations. Based upon the ideas in [3, 12] we develop a new particle filter (PF) and multilevel particle filter (MLPF) to approximate the afore-mentioned expectations. We prove under assumptions that, for ϵ > 0, to obtain a mean square error of O(ϵ2) the PF has a cost per-observation time of O(ϵ−5) and the MLPF costs O(ϵ−4) (best case) or O(ϵ−4 log(ϵ)2) (worst case). Our theoretical results are supported by numerical experiments. © 2024, CC BY.

关键词： Mean square error

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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

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Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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Towards Efficient and Practical Multi-party Computation under Inconsistent Trust in TEEs

Towards Efficient and Practical Multi-party Computation unde...

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IEEE Symposium on Security and Privacy

作者： Xuanwei Hu Rujia Li Yi Liu Qi Wang Department of Computer Science and Engineering Southern University of Science and Technology Institute for Advanced Study Tsinghua Univesity College of Cyber Security Jinan University National Center for Applied Mathematics Shenzhen Southern University of Science and Technology

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Secure multi-party computation (MPC) allows joint computations on sensitive data while guaranteeing privacy and correctness. In recent years, a series of MPC protocols assisted by trusted execution environments (TEEs) have been proposed to reduce overhead brought by costly cryptographic techniques. However, existing protocols either generally assume consistent trust in TEEs among all participating parties, or require dedicated designs for different applications. This prevents the protocols from being deployed in practice. To address these challenges, in this work, we propose a generic MPC protocol without assuming consistent trust in TEEs while fully utilizing heterogeneous TEEs to improve efficiency. To this end, we propose a security model to capture parties' inconsistent trust in TEEs and prove the security of our protocol under a simpler variant of the UC framework (SUC framework). In addition, we instantiate our protocol for secure aggregation based on a state-of-the-art information-theoretically secure protocol SwiftAgg+. Evaluation results among 64 parties deployed on Azure virtual machines show that our protocol reduces the running time of SwiftAgg+ by 66%. The running time of parties in our protocol is reduced by at most 91% compared to that required in SwiftAgg+.

关键词： Analytical models Privacy Data privacy Protocols Computational modeling Multi-party computation Virtual machines Security

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