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arXiv 2024年

作者： Shylaja, G. Naidu, Kesavulu V. Venkatesh, B. Mallikarjunaiah, S.M. Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru560035 India Department of Mathematics & Statistics Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States

Accurate triangulation of the domain plays a pivotal role in computing the numerical approximation of the differential operators. A good triangulation is the one which aids in reducing discretization errors. In a standard collocation technique, the smooth curved domain is typically triangulated with a mesh by taking points on the boundary to approximate them by polygons. However, such an approach often leads to geometrical errors which directly affect the accuracy of the numerical approximation. To restrict such geometrical errors, isoparametric, subparametric, and iso-geometric methods were introduced which allow the approximation of the curved surfaces (or curved line segments). In this paper, we present an efficient finite element method to approximate the solution to the elliptic boundary value problem (BVP), which governs the response of an elastic solid containing a v-notch and inclusions. The algebraically nonlinear constitutive equation along with the balance of linear momentum reduces to second-order quasi-linear elliptic partial differential equation. Our approach allows us to represent the complex curved boundaries by smooth one-of-its-kind point transformation. The main idea is to obtain higher-order shape functions which enable us to accurately compute the entries in the finite element matrices and vectors. A Picard-type linearization is utilized to handle the nonlinearities in the governing differential equation. The numerical results for the test cases show considerable improvement in the accuracy. © 2024, CC BY.

关键词： Finite element method

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Multimodal Deep Learning for Online Meme Classification

Multimodal Deep Learning for Online Meme Classification

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Han, Stephanie Leal-Arenas, Sebastian Zdravevski, Eftim Cavalcante, Charles C. Boukouvalas, Zois Corizzo, Roberto American University Department of Computer Science WashingtonDC United States University of Pittsburgh Department of Linguistics PittsburghPA United States University Ss. Cyril and Methodius Faculty of Computer Science and Engineering Skopje Macedonia Federal University of Ceara Department of Teleinformatics Engineering Ceara Fortaleza Brazil American University Department of Mathematics and Statistics WashingtonDC United States

ISBN: (纸本)9798350362480

Memes possess a humorous intent, yet they can also be used for malicious purposes. Analysing meme data has the potential to enhance content monitoring, identify emerging topics, and support content moderation in online platforms. Memes also represent an interesting use case for multimodal machine learning, as they combine text and image data. In this study, we explored the linguistic characteristics and analysed the convergent themes of five meme classes through common word extraction. Moreover, we compared the effectiveness of various machine learning models, i.e., unimodal (text or image) and multimodal (early fusion, late fusion) in binary and multiclass meme classification tasks. Our results on a large meme dataset showed that memes heavily adhered to current affairs, demonstrated by the high frequency of topical words across meme classes. Regarding model accuracy, early fusion achieved superior accuracy over late fusion in meme classification. Binary models outperformed multi-class classification methods. However, fusion models did not consistently surpass the accuracy of independent text or image-based models. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated Deep Learning Empowered Breast Cancer Diagnosis UsingBiomedical Mammogram Images

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Computers, Materials & Continua 2022年第6期71卷 4221-4235页

作者： JoséEscorcia-Gutierrez Romany F.Mansour Kelvin Belen Javier Jiménez-Cabas Meglys Pérez Natasha Madera Kevin Velasquez Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Mechatronics Engineering Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic *** the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging *** mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death *** the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false *** resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital *** goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital *** proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image *** addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction ***,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 *** wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast *** ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance *** simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

关键词： Breast cancer digital mammograms deep learning wavelet neural network Resnet 34 disease diagnosis

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Mean Field Games on Dense and Sparse Networks: The Graphexon MFG Equations

Mean Field Games on Dense and Sparse Networks: The Graphexon...

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American Control Conference (ACC)

作者： Peter E. Caines Minyi Huang Department of Electrical and Computer Engineering McGill University Montreal QC Canada School of Mathematics and Statistics Carleton University Ottawa ON Canada

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

For sequences of networks embedded in the unit cube $[0,1]^{m}$ in $\mathbb{R}^{m}$ (more generally compact sets in Riemannian manifolds), a notion related to that of graphons was introduced in [Caines, CDC 2022] in terms of (weak) measure limits of (sub-) sequences of empirical measures of vertex densities (vertexons) on $[0,1]^{m}$ and the associated (weak) measure limits of (sub-) sequences of empirical measures of edge densities (graphexons) on $\lceil 0,1\rceil^{2n}$ , both of which exist regardless of sparsity or density of the limit graphs. This paper presents an extension of the Graphon Mean Field Game (GMFG) theory of [Caines-Huang, SICON, 2021] to the vertexon-graphexon MFG set-up (here denoted GXMFG). In particular, for sparse limit graphexons, an LQG GXMFG example is presented where the influence between agent populations on neighbouring nodes is modeled via a first order PDE.

关键词： Manifolds Density measurement Atmospheric measurements Games Particle measurements Control systems Mathematical models

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Clustering-based Low Rank Approximation Method

arXiv

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arXiv 2025年

作者： Zhu, Yujun Zhu, Jie Arshad, Hizba Wang, Zhongming Ming, Ju School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan China Department of Mathematics and Statistics Florida International University MiamiFL United States

We propose a clustering-based generalized low rank approximation method, which takes advantage of appealing features from both the generalized low rank approximation of matrices (GLRAM) and cluster analysis. It exploits a more general form of clustering generators and similarity metrics so that it is more suitable for matrix-structured data relative to conventional partitioning methods. In our approach, we first pre-classify the initial matrix collection into several small subset clusters and then sequentially compress the matrices within the clusters. This strategy enhances the numerical precision of the low-rank approximation. In essence, we combine the ideas of GLRAM and clustering into a hybrid algorithm for dimensionality reduction. The proposed algorithm can be viewed as the generalization of both techniques. Theoretical analysis and numerical experiments are established to validate the feasibility and effectiveness of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Matrix algebra

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Rician Channel Modelling for Super Wideband MIMO Communications

Rician Channel Modelling for Super Wideband MIMO Communicati...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Smith, Peter J. Bandara, Sachitha C. Khordad, Erfan Evans, Robin Senanayake, Rajitha University of Melbourne Department of Electrical and Electronic Engineering Melbourne Australia School of Mathematics and Statistics Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798350368369

Recent developments in Multiple-Input-Multiple-Output (MIMO) technology include packing a large number of antenna elements in a compact array to access the bandwidth benefits provided by higher mutual coupling (MC). The resulting super-wideband (SW) systems require a circuit-theoretic framework to handle the MC and channel models which span extremely large bands. Hence, in this paper, we make two key contributions. First, we develop a physically-consistent Rician channel model for use with SW systems. Secondly, we express the circuit-theoretic models in terms of a standard MIMO model, so that insights into the effects of antenna layouts, MC, and bandwidth can be made using standard communication theory. For example, we show the bandwidth widening resulting from the new channel model. In addition, we show that MC distorts line-of-sight paths which has beamforming implications. We also highlight the interaction between spatial correlation and MC and show that tight coupling reduces spatial correlations at low frequencies. © 2025 IEEE.

关键词： Electric network parameters

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Review of UAVs for efficient agrochemical spray application

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International Journal of Agricultural and Biological engineering 2025年第1期18卷 1-9页

作者： Narayan Raosaheb Gatkal Sachin Madhukar Nalawade Girishkumar Balasaheb Bhanage Ramesh Kumar Sahni Avdhoot Ashok Walunj Pravin Bhaskar Kadam Musrrat Ali Department of Farm Machinery and Power Engineering Dr.Annasaheb Shinde College of Agricultural Engineering and TechnologyMahatma Phule Krishi VidyapeethRahuri 413722MaharashtraIndia Centre for Advanced Agricultural Science and Technology for Climate Smart Agriculture and Water Management(CAAST-CSAWM) Dr.Annasaheb Shinde College of Agricultural Engineering and TechnologyMahatma Phule Krishi VidyapeethRahuri 413722AhmednagarMaharashtraIndia Central Institute of Agricultural Engineering Bhopal 462038Madya PradeshIndia Department of Mathematics and Statistics College of ScienceKing Faisal UniversityAl-Ahsa 31982Saudi Arabia

Insect infestation attacks in agricultural ecosystems are becoming more common because of global warming as well as farmland environmental circumstances,necessitating the development of new crop production *** application is one of the most common strategies for protecting the entire growing period of plants or shrubs against pests and pathogens in *** rapid,effective,and profitable application of plant control substances via unmanned aerial vehicle(UAV)crop spraying is anticipated to be a key new *** compared to ground spraying,UAV spraying saves chemicals,water,time,does not damage crop plants or balls of crop,and does not create soil *** using UAV,pesticide drift and deposition must be managed in order to use pesticides safely,effectively,and *** paper focuses on agrochemical spraying by unmanned aerial vehicles and the key parameters that influence spray effectiveness,such as the operating parameters of nozzle type,flying speed,flight height,type of nozzle,and type of UAV model,for reducing drift and increasing application *** multirotor UAV is most suitable for spraying due to its fast operation,safety,not requiring a runway for takeoff and landing,and lower cost as compared to fixed-wing and *** can also be used for crop disease identification,soil health monitoring,livestock monitoring,field mapping,*** paper aims to review the development of various UAV models,optimization of operating parameters,effect of nozzle on UAV spraying,characterization of droplet deposition,drift reduction technology,UAV-based remote sensing for plant protection,and cost comparison of UAV to conventional ground sprayer.

关键词： deposition drift flight height flying speed nozzle unmanned aerial vehicle(UAV)

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Generalized Linear Mixed Models by penalized Lasso in modelling the scores of Indonesian students 2

Generalized Linear Mixed Models by penalized Lasso in modell...

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2nd Annual Conference of Science and Technology, ANCOSET 2020

作者： Santi, V.M. Notodiputro, K.A. Sartono, B. Rahyu, W. Statistics Study Program Faculty of Mathematics and Natural Sciences Universitas Negeri Jakarta Jakarta13220 Indonesia Department of Statistics Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia

The Generalized linear mixed model (GLMM) is an extension of the generalized linear model by adding random effects to linear predictors to accommodate clustered or over dispersion. Severe computational problems in the GLMM modelling cause its use restricted for only a few predictors. When many predictors are available, the estimators become very unstable. Therefore, the procedure for selecting relevant variables is essential in modelling. The use of penalty techniques for selecting variables in mixed models is still rarely applied. In this article, the penalized Lasso approach proposed to handle these kinds of problems. The proposed methods select variables and estimate coefficients simultaneously in GLMM. Based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and standard error criteria, it was found that glmmLasso has a better performance than GLMM. For the factors affecting Indonesian's student scores, where glmmLasso produces three significant covariates for the GLMM model while GLMM without penalized Lasso has five covariates, which means that the GLMM model is more complicated than glmmLasso. Gender, school quality based on National Examination (UN) scores and the opportunity for students to investigate to test their ideas are essential covariates as factors that influence the rating of Indonesian students. © Published under licence by IOP Publishing Ltd.

关键词： Students

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LEVERAGING BLACK-BOX MODELS TO ASSESS FEATURE IMPORTANCE IN UNCONDITIONAL DISTRIBUTION

arXiv

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arXiv 2024年

作者： Zhou, Jing Li, Chunlin School of Engineering Mathematics and Physics University of East Anglia United Kingdom Department of Statistics Iowa State University United States

Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to compute the feature importance curves relevant to the unconditional distribution of outcomes, while leveraging the power of pre-trained black-box predictive models. The feature importance curves measure the changes across quantiles of outcome distribution given an external impact of change in the explanatory features. Through extensive numerical experiments and real data examples, we demonstrate that our approximation method produces sparse and faithful results, and is computationally efficient. Copyright © 2024, The Authors. All rights reserved.

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An Additive Shared Frailty Model for Recurrent Gap Time Data in the Presence of Zero-Recurrence Subjects 26th

An Additive Shared Frailty Model for Recurrent Gap Time Data...

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26th Congress of the Portuguese Statistical Society, SPE 2023

作者： Sousa-Ferreira, Ivo Abreu, Ana Maria Rocha, Cristina Department of Mathematics Faculty of Exact Sciences and Engineering University of Madeira Funchal Portugal CEAUL – Faculty of Sciences University of Lisbon Lisbon Portugal CIMA University of Madeira Funchal Portugal Department of Statistics and Operational Research Faculty of Sciences University of Lisbon Lisbon Portugal CEAUL – Faculty of Sciences University of Lisbon Lisbon Portugal

ISBN: (纸本)9783031689482

A new shared frailty model for recurrent gap time data is introduced, assuming that frailty acts additively on a Weibull rate function derived from a non-homogeneous Poisson process. The frailty is included with two purposes: to handle within-subject correlation and to accommodate zero-recurrence subjects. With this intention, we assume that the frailty has a non-central chi-squared distribution with zero degrees of freedom. The proposed model has two special cases without frailty, namely the Weibull rate model and the classical homogeneous Poisson process. Furthermore, since the model is fully specified, the maximum likelihood method is applied for parameters estimation based on a marginal likelihood function. Particular attention is devoted to the likelihood construction for bivariate recurrent event data. An application to a well-known data set is provided to elucidate the practical contribution of the new survival model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Degrees of freedom (mechanics)

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