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L1-predictor–corrector method for ψ-Caputo type fractional differential equations

computational and Applied mathematics 2025年第5期44卷 1-26页

作者： Sivalingam, S.M. Govindaraj, V. Sousa, J. Vanterler da C. Hendy, A.S. Department of Mathematics National Institute of Technology Puducherry Karaikal609609 India Department of Mathematics Aerospace Engineering PPGEA-UEMA DEMATI-UEMA MA São Luís65054 Brazil Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University 19 Mira St. Yekaterinburg620002 Russia Department of Mechanics and Mathematics Western Caspian University Baku1001 Azerbaijan Department of Mathematics and Computer Science Faculty of Science Benha University Benha13511 Egypt

This paper proposes a three-point L1-based predictor–corrector scheme for the ψ-Caputo derivative. The L1-based rule and linear spline-based interpolation are utilized to perform the discretization of the derivative. The Daftardar-Gejji Jafari scheme (DGJ) is used to frame the iterative scheme. The error in the discretization procedure and the proposed approach is theoretically verified. The stability of the proposed approach is given in detail. Several numerical examples are solved to prove the numerical efficacy of the approach. An example is given by using the properties of the ψ-Caputo derivative to compare the proposed approach. Further, the computer virus model and the Chen system are simulated to demonstrate the method’s accuracy for a system of equations. © The Author(s) under exclusive licence to Sociedade Brasileira de Matemática Aplicada e Computacional 2025.

关键词： Iterative methods

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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AG Codes Have No List-Decoding Friends: Approaching the Generalized Singleton Bound Requires Exponential Alphabets

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IEEE Transactions on Information Theory 2025年第4期71卷 2443-2451页

作者： Alrabiah, Omar Guruswami, Venkatesan Li, Ray University of California at Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94709 United States University of California at Berkeley Department of Electrical Engineering and Computer Science Department of Mathematics Simons Institute for the Theory of Computing BerkeleyCA94709 United States Santa Clara University Department of Mathematics and Computer Science Santa ClaraCA95053 United States

A simple, recently observed generalization of the classical Singleton bound to list-decoding asserts that rate R codes are not list-decodable using list-size L beyond an error fraction L/L+1 (1-R) (the Singleton bound being the case of L=1 , i.e., unique decoding). We prove that in order to approach this bound for any fixed L > 1 , one needs exponential alphabets. Specifically, for every L > 1 and R ∈ (0,1) , if a rate R code can be list-of-L decoded up to error fraction L/L+1 (1-R - ∈ ) , then its alphabet must have size at least exp (ΩL,R(1\ ∈)) . This is in sharp contrast to the situation for unique decoding where certain families of rate R algebraic-geometry (AG) codes over an alphabet of size O(1\ ∈2) are unique-decodable up to error fraction (1-R-\ ∈ )2 . Our bounds hold even for subconstant ∈ ≥ 1/n , implying that any code exactly achieving the L-th generalized Singleton bound requires alphabet size 2ΩL,R(n) . Previously this was only known only for L=2 under the additional assumptions that the code is both linear and MDS. Our lower bound is tight up to constant factors in the exponent—with high probability random codes (or, as shown recently, even random linear codes) over exp(OL(1\ ∈)) -sized alphabets, can be list-of-L decoded up to error fraction L/L+1 (1-R -∈ ) . © 1963-2012 IEEE.

关键词： Errors

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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1st International Conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Optimizing solar radiation prediction: a novel SVM approach for renewable energy systems

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Basha, S. Sharief Rao, A. Nagaraja Department of Mathematics School of Advanced Science Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The global transition toward renewable energy sources, driven by fossil fuel depletion and environmental imperatives, has positioned solar power as a crucial sustainable alternative. This study presents an advanced methodological framework for solar radiation (SR) prediction utilizing Support Vector Machine (SVM) techniques. The research analyzes comprehensive solar radiation data collected from India’s Vellore district throughout 2023, implementing a novel Support Vector Regression (SVR) approach with both polynomial and radial basis function (RBF) kernels. While conventional methodologies predominantly focus on minimizing training errors, our approach emphasizes reducing the generalization error bound to enhance predictive performance. The proposed model incorporates diverse meteorological parameters for SR prediction, demonstrating superior generalization capabilities compared to traditional approaches. Empirical results indicate that the SVR_rbf model significantly outperformed both SVR_poly and artificial neural network (ANN) models in SR prediction accuracy. The model's efficacy is validated through rigorous performance metrics: RMSE (10.3032), MAE (4.2728), MAPE (0.0152), and R² (0.9795). This research advances the understanding of machine learning applications in renewable energy forecasting, particularly for regions with limited meteorological data availability. Research findings enhance solar energy utilization and contribute to the transition towards sustainable energy. Future research directions may explore the integration of additional environmental parameters and the model’s adaptability across diverse geographical contexts. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Artificial neural network Predictive models Solar radiation Support vector regression

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Gaussian weighting—based random walk segmentation and DCNN method for brain tumor detection and classification

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Multimedia Tools and Applications 2025年第8期84卷 4675-4702页

作者： Rani, K. Vijila Sumathy, G. Shoba, L.K. Sivalakshmi, P. Department of ECE Udaya School of Engineering Tamil Nadu Nagercoil India Department of Computational Intelligence SRM Institute Of Science and Technology Kattankulathur CampusChengalpattu Dt Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of ECE R.M.K College of Engineering and Technology Tamil Nadu Chennai India

Cancer is a disease that develops when cells divide abnormally. Cancerous cells can spread to surrounding organ tissues, the bloodstream, and the lymphatic system. This process is known as metastasis, and it shortens the patients' lives. Brain cancer is a type of cancer that develops in the brain and nervous system. Typically, MR (Magnetic Resonance) scan images are used by doctors to assess the condition of tissues that have grown abnormally within the human body. A novel segmentation method with classification is specifically suggested here for assessing the tumor site. To improve the quality of an image, it first uses top hat filtering to perform the morphological process. The brain tumor region is subsequently extracted from the morphologically filtered output using the Gaussian Weighted-Based Random Walk (GWBRW) segmentation technique. Lastly, a classification operation is carried out by the Deep Convolutional Neural Network (DCNN) classifier. Five performance measures are then used to compare the segmented output with the original images. The segmentation-based assessment estimations show that the segmentation achieves higher results, with average values of 22.8160, 0.2133, and 0.9704 for PSNR, Dice, and SSIM, respectively, and lower results, with average values of 19.3997 and 422.0844 for RMSE and MSE. The segmentation results show that the suggested computation enhances the tumor region's characteristics. Eventually, the Deep Convolutional Neural Network classifier successfully classifies 96.1% of the predictions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical engineering of Southern Medical University(SMU-dataset),and The National institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Effect of leading-edge and trailing-edge camber morphing on gust load for an elastic wing

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Chinese Journal of Aeronautics 2025年第04期 208-224页

作者： Yuting DAI Yating HU You WU Chen SONG Chao YANG School of Aeronautic Science and Engineering Beihang University Tianmushan Laboratory CAEP Software Center for High-Performance Numerical Simulation Institute of Applied Physics and Computational Mathematics

This paper investigates the influence of the spanwise-distributed trailing-edge camber morphing on the dynamic stall characteristics of a finite-span wing at Re=2×10～*** mathematical model of the spanwise-distributed trailing-edge camber morphing is established based on Chebyshev polynomials,and the deformed wing surface is modeled by a spline surface according to the rib's morphing in the chordwise *** computational Fluid Dynamics(CFD)method is adopted to obtain flow-field results and aerodynamic *** SST-γ model is introduced and the overset mesh technique is *** numerical results show that the spanwisedistributed trailing-edge morphing obviously changes the aerodynamic and energy transfer characteristics of the dynamic *** when the phase difference between the trailing-edge motion and the wing pitch is-π/2,the interaction between the three-dimensional(3-D) Leading-Edge Vortex(LEV) and Trailing-Edge Vortex(TEV) is strengthened,and the work done by the aerodynamic force turns *** indicates that the trailing-edge deformation has the potential to suppress the oscillation amplitude of stall *** also found that as the trailing-edge camber morphing varies more complexly along the spanwise direction,the suppression effect decreases accordingly.

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Domination Number in Sierpiński Networks

Domination Number in Sierpiński Networks

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International Conference on computational engineering, ICCE 2023

作者： Anitha, J. Rajasingh, Indra Sharon, Jane Olive Department of Mathematics Rajalakshmi Institute of Technology Chennai India Department of Mathematics for Excellence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031777639

If there is at least one neighbour in K for each vertex in V that is not in K, then a set K⊆V(G) is a dominating set (DS) in G(V,E). The minimal cardinality of a DS of G is the domination number(DN) of G, represented by γ(G). In this paper, we determine the γ(G) for Sierpiński networks, Str and the divide-and-swap cubic networks, DSCr. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 68R10 81Q30 82B20 82C20 Divide-and-swap cubic networks Dominating set Sierpiński Networks

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