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A Diffuse-Interface Lattice Boltzmann Method for the Dendritic Growth with Thermosolutal Convection

Communications in computational Physics 2023年第4期33卷 1164-1188页

作者： Chengjie Zhan Zhenhua Chai Baochang Shi Ping Jiang Shaoning Geng Dongke Sun School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074P.R.China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and TechnologyWuhan 430074P.R.China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074P.R.China The State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074P.R.China School of Mechanical Engineering Southeast UniversityNanjing 211189P.R.China

In this work,we proposed a diffuse-interface model for the dendritic growth with thermosolutal *** this model,the sharp boundary between the fluid and solid dendrite is firstly replaced by a thin but nonzero thickness diffuse interface,which is described by the order parameter,and the diffuse-interface based governing equations for the dendritic growth are *** solve the model for the dendritic growth with thermosolutal convection,we also developed a diffuse-interface multirelaxation-time lattice Boltzmann(LB)*** this method,the order parameter in the phase-field equation is combined into the force caused by the fluid-solid interaction,and the treatment on the complex fluid-solid interface can be *** addition,four LB models are designed for the phase-field equation,concentration equation,temperature equation and the Navier-Stokes equations in a unified ***,we performed some simulations of the dendritic growth to test the present diffuse-interface LB method,and found that the numerical results are in good agreements with some previous works.

关键词： Dendritic growth diffuse interface lattice Boltzmann method phase-field method.

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Neural Cryptography with Fog computing Network for Health Monitoring Using IoMT

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Computer Systems science & engineering 2023年第1期44卷 945-959页

作者： G.Ravikumar K.Venkatachalam Mohammed A.AlZain Mehedi Masud Mohamed Abouhawwash Department of Computer Science and Engineering Coimbatore Institute of Engineering and TechnologyCoimbatore641109India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Sleep apnea syndrome(SAS)is a breathing disorder while a person is *** traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a *** typically provides accurate results,but it is expensive and time ***,for people with Sleep apnea(SA),available beds and laboratories are ***,it may produce inaccurate ***,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)*** paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen *** diagnosis information is securely stored in the IoMT fog computing *** can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing *** proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

关键词： Sleep apnea polysomnography IOMT fog node security neural network KNN signature encryption sensor

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FINITE ELEMENT ERROR ESTIMATES FOR THE NONLINEAR SCHRÖDINGER-POISSON MODEL

arXiv

引用

arXiv 2023年

作者： Cui, Tao Lu, Wenhao Pan, Naiyan Zheng, Weiying LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Science University of Chinese Academy of Sciences Beijing100190 China

In this paper, we study a priori error estimates for the finite element approximation of the nonlinear Schrödinger-Poisson model. The electron density is defined by an infinite series over all eigenvalues of the Hamiltonian operator. To establish the error estimate, we present a unified theory of error estimates for a class of nonlinear problems. The theory is based on three conditions: 1) the original problem has a solution u which is the fixed point of a compact operator A, 2) A is Fréchet-differentiable at u and I − A′[u] has a bounded inverse in a neighborhood of u, and 3) there exists an operator Ah which converges to A in the neighborhood of u. The theory states that Ah has a fixed point uh which solves the approximate problem. It also gives the error estimate between u and uh, without assumptions on the well-posedness of the approximate problem. We apply the unified theory to the finite element approximation of the Schrödinger-Poisson model and obtain optimal error estimate between the numerical solution and the exact solution. Numerical experiments are presented to verify the convergence rates of numerical *** Codes 65M60, 65L15, 37L65 Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation theory

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A Survey Analysis on Dental Caries Detection from RVG Images Using Deep Learning 1

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8th International Conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Nageswari, P. Pareek, Piyush Kumar Suresh Kumar, A. Aditya, Pai H. Guru Prasad, M.S. Kandasamy, Manivel Department of Mathematics Mahendra Institute of Technology Tamil Nadu Namakkal637503 India Computer Science and Engineering NITTE Meenakshi Institute of Technology Bangalore560064 India Karnataka Bengaluru562112 India Unitedworld School of Computational Intelligence Karnavati University Gujarat Gandhinagar382422 India

ISBN: (数字)9789819976331

ISBN: (纸本)9789819976324

A rapid developing area of study in the healthcare industry is medical imaging. Medical imaging is crucial for the early identification, diagnosis, and treatment of disorders. Ultrasound, magnetic resonance, computed tomography, X-rays, and other imaging techniques fall under this category. Dental caries is one of the most common conditions affecting people of all ages globally. Using radiovisiography (RVG) or dental X-ray images, it could be challenging to spot dental caries in its early stages. Practically, all medical fields use deep learning to predict or identify certain diseases. This research work has proposed and investigated a customized convolution neural network (CCNN) for caries detection from dental X-ray images. Few datasets are available for dental image processing. A total of 1300 X-ray images, 500 intra-oral photographic images, and 150 OPG images were collected from two medical practitioners with ground truth and used as a dataset for dental disease detection and image segmentation. This research work has demonstrated that image enhancement techniques play an important role in improving the quality of dental radiograph images. This research work explores different image augmentation techniques, which contribute to increasing the size of the dataset and improving accuracy. Image segmentation plays an important role in further classification and dental disease detection. Various deep learning models, including AlexNet, EfficientNetB0 and B7, Inception3, VGG-16, and the proposed CCNN model, are used to train the dataset. With more accuracy, a comparison between the deep learning model and the suggested CCNN model has been conducted. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Applying Graph Neural Networks to ERP Systems with a Focus on Business Centric Development 1

Applying Graph Neural Networks to ERP Systems with a Focus o...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Umanesan, R. Harshavardhana, C.N. Somashekar, P. Siva Kota Reddy, P. Dhiliphan Kumar, M. Singh, Ramanjeet Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Government First Grade College for Women Department of Mathematics Holenarasipura India Department of Mathematics Mysuru India Jss Science and Technology University Department of Mathematics Mysuru India Department of Management Studies Madurai India Chandigarh University Gharuan Mohali India

ISBN: (纸本)9798350352931

Software for enterprise resource planning (ERP) has grown in popularity as a means to streamline corporate processes that are critical for survival in the modern, globalized marketplace. In order to cater to the specific needs of their global customer base in terms of both price and availability, suppliers in the textile industry are under increasing pressure to enhance their manufacturing management practices. This task is becoming more dependent on the careful selection and usage of information technology. Preprocessing, feature extraction, and training are the three phases that make up this method. Time difference relevance and K-L distance are components of preprocessing. The feature selection approach employs Kernel Principal Component Analysis for the factor extraction phase. An GNN was used for the model's training. The proposed technique outperforms CNN and SVM with an average accuracy of 9 2. 1 6percent. © 2024 IEEE.

关键词： Textile industry

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MONOLITHIC MULTIGRID FOR REDUCED MAGNETOHYDRODYNAMIC EQUATIONS

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Journal of computational mathematics 2021年第3期39卷 453-470页

作者： Xiaodi Zhang Weiying Zheng LSEC NCMISInstitute of Computational Mathermatics and Scientific ComputingAcademy of Mathematics and Systerns ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Science University of Chinese Academy of SciencesBeijing 100049China

In this paper,the monolithic multigrid method is investigated for reduced magnetohydrodynamic *** propose a diagonal Braess-Sarazin smoother for the finite element discrete system and prove the uniform convergence of the MMG method with respect to mesh sizes.A multigrid-preconditioned FGMRES method is proposed to solve the magnetohydrodynamic *** turns out to be robust for relatively large physical *** extensive numerical experiments,we demonstrate the optimality of the monolithic multigrid method with respect to the number of degrees of freedom.

关键词： Monolithic multigrid Magnetohydrodynamic equations Diagonal Braess-Sarazin smoother Finite element method

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Enhanced Fruit Fly Optimization Algorithm for Wrapper Based Feature Selection in Cancer Prediction

Enhanced Fruit Fly Optimization Algorithm for Wrapper Based ...

引用

Cognitive, Green and Ubiquitous computing (IC-CGU), International Conference on

作者： P. Saranya D. Viji Aditya Jangiti Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil nadu India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil nadu India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

Nowadays, smart healthcare appliances generate vast amount of medical data. Such huge amount of data requires classification methodologies, through which disease diagnostics can be carried out. Chronic diseases like cancer can be predicted in advance to help the patients to take treatment in initial stages of the disease. This article proposes an improvised feature selection and classification -method for cancer prediction. Here we propose an enhanced fruit fly optimization algorithm in KNN classifier for searching strategy with wrapper methods for feature selection. Lung cancer data set, breast cancer data set and leukemia data set has been taken from the UCI repository for testing the performance of the proposed model. For Lung cancer data set, accuracy, specificity, sensitivity, and fscore value observed are 96.97 %, 96.28 %, 96.35 %, and 96.34% respectively. For breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 99.97 %, 98.2 %, 96.85 %, and 90.35% respectively. For Breast cancer dataset, accuracy, specificity, sensitivity, and fscore value observed are 96.67%, 98.28 %, 95.85 %, and 93.85% respectively. The proposed method works well with various dataset and shows prominent accuracy improvement over the existing systems.

关键词： Sensitivity Machine learning algorithms Lung cancer Medical services Feature extraction Prediction algorithms Breast cancer

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Adaptive neural network basis methods for partial differential equations with low-regular solutions

arXiv

引用

arXiv 2024年

作者： Huang, Jianguo Wu, Haohao Zhou, Tao School of Mathematical Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

This paper aims to devise an adaptive neural network basis method for numerically solving a second-order semilinear partial differential equation (PDE) with low-regular solutions in two/three dimensions. The method is obtained by combining basis functions from a class of shallow neural networks and the resulting multi-scale analogues, a residual strategy in adaptive methods and the non-overlapping domain decomposition method. At the beginning, in view of the solution residual, we partition the total domain Ω into K + 1 non-overlapping subdomains, denoted respectively as {Ωk}Kk=0, where the exact solution is smooth on subdomain Ω0 and low-regular on subdomain Ωk (1 ≤ k ≤ K). Secondly, the low-regular solutions on different subdomains Ωk (1 ≤ k ≤ K) are approximated by neural networks with different scales, while the smooth solution on subdomain Ω0 is approximated by the initialized neural network. Thirdly, we determine the undetermined coefficients by solving the linear least squares problems directly or the nonlinear least squares problem via the Gauss-Newton method. The proposed method can be extended to multi-level case naturally. Finally, we use this adaptive method for several peak problems in two/three dimensions to show its high-efficient computational performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Partial differential equations

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InFlamPred: a machine learning framework for anti-inflammatory small molecule prediction

引用

Chemical Papers 2025年 1-13页

作者： Selvam, Subathra Balaji, Priya Dharshini Uthra, R. Annie Anupama, C. G. Sohn, Honglae Madhavan, Thirumurthy Computational Biology Laboratory Department of Genetic Engineering School of Bioengineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Chemistry and Department of Carbon Materials Chosun University Gwangju South Korea

Several anti-inflammatory small molecules have been discovered and utilized in the treatment of various inflammatory and autoimmune diseases. Despite experimental identification of numerous anti-inflammatory peptides, the development of peptide-based drugs remains expensive, time-consuming, and labor-intensive. Small molecules offer higher stability compared to peptides, owing to their chemical synthesis or natural sources. Consequently, there is an urgent need to develop advanced machine learning (ML) methods utilizing a large-scale dataset consisting of experimentally acquired small molecule to enhance precision and efficiency. This study introduces a predictive ML-method, named InFlamPred (Anti-inflammatory Small Molecule Predictor), tailored for anti-inflammatory small molecules. The proposed ML classifier facilitates compound screening in inflammatory diseases. We trained five different ML classifiers—RF, KNN, LGBM, DT, and XGB achieving an overall accuracy ranging from 61 to 75%. Notably, the LGBM, RF, and XGB models demonstrated strong performance on the training dataset, with XGB maintaining high accuracy and robustness on the test dataset, achieving an accuracy of 75% and an AUC of 81%. When evaluated on an external validation dataset consisting of 80 compounds (equally distributed between active and inactive classes), the XGB model effectively identified all active and inactive molecules. These results highlight the reliability and generalizability of the proposed ML approach in accurately predicting small molecules with anti-inflammatory potential.

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Can Efficient Fourier-Transform Techniques Favorably Impact on Broadband computational Electromagnetism?

arXiv

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arXiv 2024年

作者： Anderson, Thomas G. Lyon, Mark Yin, Tao Bruno, Oscar P. Department of Computational Applied Mathematics and Operations Research Rice University Houston77098 United States Department of Mathematics & Statistics University of New Hampshire Durham03824 United States LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Computing and Mathematical Sciences California Institute of Technology Pasadena91125 United States

In view of recently demonstrated joint use of novel Fourier-transform techniques and effective high-accuracy frequency domain solvers related to the Method of Moments, it is argued that a set of trans-formative innovations could be developed for the effective, accurate and efficient simulation of problems of wave propagation and scattering of broadband, time-dependent wavefields. This contribution aims to convey the character of these methods and to highlight their applicability in computational modeling of electromagnetic configurations across various fields of science and *** Codes 65R10, 65R20 Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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