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Electronics and Renewable Systems (ICEARS), International Conference on

作者： G. Bhupal Raj Muzeeb Khan Patan Ashwani Kumar Gupta C.Lakshmi Gopinath D Ajay Singh Yadav School of Agriculture SR University Warangal Department of EEE College of Engineering and Technology University of Technology and Applied Sciences Muscat Oman Faculty of Business and Commerce Chandigarh University Unnao Uttar Pradesh Saveetha Engineering College Chennai Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad U.P

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques.

关键词： Deep learning Accuracy Biological system modeling Prevention and mitigation Disasters Predictive models Market research Terrain factors Floods Forecasting

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Inverse Design with Dynamic Mode Decomposition

arXiv

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arXiv 2025年

作者： Zhu, Yunpeng Cheng, Liangliang Jing, Anping Huo, Hanyu Lang, Ziqiang Zhang, Bo Kutz, J. Nathan School of Engineering and Material Science Queen Mary University of London LondonE1 4NS United Kingdom ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands School of Mechanical Engineering Ningxia University Yinchuan750021 China School of Chemistry and Material Science University of Science and Technology of China Hefei230026 China School of Electrical and Electronic Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Department of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA United States

We introduce a computationally efficient method for the automation of inverse design in science and engineering. Based on simple least-square regression, the underlying dynamic mode decomposition algorithm can be used to construct a low-rank subspace spanning multiple experiments in parameter space. The proposed inverse design dynamic mode composition (ID-DMD) algorithm leverages the computed low-dimensional subspace to enable fast digital design and optimization on laptop-level computing, including the potential to prescribe the dynamics themselves. Moreover, the method is robust to noise, physically interpretable, and can provide uncertainty quantification metrics. The architecture can also efficiently scale to large-scale design problems using randomized algorithms in the ID-DMD. The simplicity of the method and its implementation are highly attractive in practice, and the ID-DMD has been demonstrated to be an order of magnitude more accurate than competing methods while simultaneously being 3-5 orders faster on challenging engineering design problems ranging from structural vibrations to fluid dynamics. Due to its speed, robustness, interpretability, and ease-of-use, ID-DMD in comparison with other leading machine learning methods represents a significant advancement in data-driven methods for inverse design and optimization, promising a paradigm shift in how to approach inverse design in *** Codes 37M05, 37M10, 37M21 Copyright © 2025, The Authors. All rights reserved.

关键词： Dynamic mode decomposition

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Some hybrid Riemannian conjugate gradient methods with restart strategy

Franklin Open

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Franklin Open 2025年 10卷

作者： Salihu, Nasiru Kumam, Poom Salisu, Sani Khammahawong, Konrawut Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand Department of Mathematics Faculty of Sciences Modibbo Adama University Yola 652105 Nigeria KMUTT-Fixed Point Research Laboratory Room SCL 802 Science Laboratory Building Department of Mathematics Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand Department of Mathematics Faculty of Natural and Applied Sciences Sule Lamido University Kafin Hausa Jigawa Nigeria Applied Mathematics for Science and Engineering Research Unit (AMSERU) Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand

Hybrid nonlinear conjugate gradient (CG) methods are known to be efficient and have less memory requirement for solving optimization problems in Euclidean spaces. The formulations strategically switch between two or more CG parameters to enhance their general structures. In this work, some hybrid Riemannian CG methods are presented, which are sufficiently descent irrespective of the line search technique employed and converges globally using scaled version of the nonexpensive condition. Finally, the numerical experiments are performed to confirm the strength of the schemes by considering some Riemannian optimization problems. The numerical results obtained when compared to some classical Riemannian CG methods indicated the efficiency of the proposed hybrid Riemannian CG methods. © 2025 The Authors

关键词： Global convergence Retraction Riemannian conjugate gradient method Riemannian optimization Riemannian wolfe conditions Vector transport

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A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia

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computers in Biology and Medicine 2025年 189卷 109924-109924页

作者： Anitha, T. Aanjankumar, S. Dhanaraj, Rajesh Kumar Pamucar, Dragan Simic, Vladimir Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India VIT Bhopal University Bhopal- Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Applied Mathematical Science College of Science and Technology Korea University Sejong30019 Korea Republic of Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City320315 Taiwan

— In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in the automated prediction of heart disease. A deep bi-directional capsule network (Bi-CapsNet) uses a new method based on an intelligent deep learning (DL) classifier model to make the classification process very accurate. Initially, the input ECG signal data are acquired and the preprocessing steps such as DC drift, normalization, LPF filtering, spectrogram analysis, and artifact removal are applied. After preprocessing the data, the Deep Ensemble CNN-RNN approach is employed for feature extraction. Finally, the Deep Bi-CapsNet model is used to predict and classify the cardiac arrhythmia. For performance validation, the dataset is referred to the MIT-BIH arrhythmia database, which selects five different types of arrhythmias from the ECG waveform to estimate the proposed model. Various ECG arrhythmia categories, including Normal (NOR), Right Bundle Branch Block (RBBB), Premature Ventricular Contraction (PVC), Atrial Premature Beat (APB), and Left Bundle Branch Block (LBBB) have been identified. For performance analysis, the metrics such as precision, accuracy, F1-score, error rate, sensitivity, false positive rate, specificity, Mathew coefficient, Kappa coefficient, and outcomes are included and compared with the traditional methods to validate the effectiveness of the implemented scheme. The proposed scheme has achieved an overall accuracy rate of approximately 97.19 % compared to the traditional deep learning models like CNN (89.87 %), FTBO (85 %), and Capsule Network (97.0 %). The comparison results indicate that the proposed hybrid model outperforms these traditional models. © 2025 Elsevier Ltd

关键词： Electrocardiograms

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Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization

arXiv

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arXiv 2025年

作者： Wang, Shiyu Avagyan, Mariam Shen, Yihan Lamy, Arnaud Wang, Tingran Márka, Szabolcs Márka, Zsuzsa Wright, John Department of Electrical Engineering Columbia University New York United States Data Science Institute Columbia University New York United States Department of Computer Science Columbia University New York United States Department of Physics Columbia University New York United States Columbia Astrophysics Laboratory Columbia University New York United States Department of Applied Physics and Applied Mathematics Columbia University New York United States

Learned denoisers play a fundamental role in various signal generation (e.g., diffusion models) and reconstruction (e.g., compressed sensing) architectures, whose success derives from their ability to leverage low-dimensional structure in data. Existing denoising methods, however, either rely on local approximations that require a linear scan of the entire dataset or treat denoising as generic function approximation problems, often sacrificing efficiency and interpretability. We consider the problem of efficiently denoising a new noisy data point sampled from an unknown ddimensional manifold M ∈ D, using only noisy samples. This work proposes a framework for test-time efficient manifold denoising, by framing the concept of "learning-to-denoise" as "learning-to-optimize". We have two technical innovations: (i) online learning methods which learn to optimize over the manifold of clean signals using only noisy data, effectively "growing" an optimizer one sample at a time. (ii) mixed-order methods which guarantee that the learned optimizers achieve global optimality, ensuring both efficiency and near-optimal denoising performance. We corroborate these claims with theoretical analyses of both the complexity and denoising performance of mixed-order traversal. Our experiments on scientific manifolds demonstrate significantly improved complexity-performance tradeoffs compared to nearest neighbor search, which underpins existing provable denoising approaches based on exhaustive search. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Drainability and Fillability of Polyominoes in Diverse Models of Global Control

arXiv

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arXiv 2025年

作者： Fekete, Sándor P. Kramer, Peter Reinhardt, Jan-Marc Rieck, Christian Scheffer, Christian Computer Science TU Braunschweig Germany Electrical Engineering and Computer Science Bochum University of Applied Sciences Germany Discrete Mathematics University of Kassel Germany

Tilt models offer intuitive and clean definitions of complex systems in which particles are influenced by global control commands. Despite a wide range of applications, there has been almost no theoretical investigation into the associated issues of filling and draining geometric environments. This is partly because a globally controlled system (i.e., passive matter) exhibits highly complex behavior that cannot be locally restricted. Thus, there is a strong need for theoretical studies that investigate these models both (1) in terms of relative power to each other, and (2) from a complexity theory perspective. In this work, we provide (1) general tools for comparing and contrasting different models of global control, and (2) both complexity and algorithmic results on filling and draining. Copyright © 2025, The Authors. All rights reserved.

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Multiaccuracy and Multicalibration via Proxy Groups

arXiv

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arXiv 2025年

作者： Bharti, Beepul Clemens-Sewall, Mary Versa Yi, Paul H. Sulam, Jeremias Johns Hopkins University United States Department of Biomedical Engineering Johns Hopkins University United States Department of Applied Mathematics & Statistics Johns Hopkins University United States St. Jude Children’s Research Hospital United States Department of Computer Science Johns Hopkins University United States

As the use of predictive machine learning algorithms increases in high-stakes decision-making, it is imperative that these algorithms are fair across sensitive groups. Unfortunately, measuring and enforcing fairness in real-world applications can be challenging due to missing or incomplete sensitive group data. Proxy-sensitive attributes have been proposed as a practical and effective solution in these settings, but only for parity-based fairness notions. Knowing how to evaluate and control for fairness with missing sensitive group data for newer and more flexible frameworks, such as multiaccuracy and multicalibration, remains unexplored. In this work, we address this gap by demonstrating that in the absence of sensitive group data, proxy-sensitive attributes can provably be used to derive actionable upper bounds on the true multiaccuracy and multicalibration, providing insights into a model’s potential worst-case fairness violations. Additionally, we show that adjusting models to satisfy multiaccuracy and multicalibration across proxy-sensitive attributes can significantly mitigate these violations for the true, but unknown, sensitive groups. Through several experiments on real-world datasets, we illustrate that approximate multiaccuracy and multicalibration can be achieved even when sensitive group information is incomplete or unavailable. © 2025, CC BY.

关键词： Sensitive data

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Towards Invariance to Node Identifiers in Graph Neural Networks

arXiv

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arXiv 2025年

作者： Bechler-Speicher, Maya Eliasof, Moshe Schönlieb, Carola-Bibiane Gilad-Bachrach, Ran Globerson, Amir Blavatnik School of Computer Science Tel-Aviv University Meta United States Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Bio-Medical Engineering Tel-Aviv University Edmond J. Safra Center for Bioinformatics Tel-Aviv University Google Research United States

Message-Passing Graph Neural Networks (GNNs) are known to have limited expressive power, due to their message passing structure. One mechanism for circumventing this limitation is to add unique node identifiers (IDs), which break the symmetries that underlie the expressivity limitation. In this work, we highlight a key limitation of the ID framework, and propose an approach for addressing it. We begin by observing that the final output of the GNN should clearly not depend on the specific IDs used. We then show that in practice this does not hold, and thus the learned network does not possess this desired structural property. Such invariance to node IDs may be enforced in several ways, and we discuss their theoretical properties. We then propose a novel regularization method that effectively enforces ID invariance to the network. Extensive evaluations on both real-world and synthetic tasks demonstrate that our approach significantly improves ID invariance and, in turn, often boosts generalization performance. © 2025, CC BY.

关键词： Graph neural networks

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An improved boundary element-free method (IBEFM) for two-dimensional potential problems

引用

Chinese Physics B 2009年第10期18卷 4065-4073页

作者：任红萍程玉民张武 School of Computer Engineering and Science Shanghai University Shanghai Institute of Applied Mathematics and Mechanics Shanghai University

The interpolating moving least-squares （IMLS） method is discussed first in this paper. And the formulae of the IMLS method obtained by Lancaster are revised. Then on the basis of the boundary element-free method （BEFM）, combining the boundary integral equation （BIE） method with the IMLS method, the improved boundary element-free method （IBEFM） for two-dimensional potential problems is presented, and the corresponding formulae of the IBEFM are obtained. In the BEFM, boundary conditions are applied directly, but the shape function in the MLS does not satisfy the property of the Kronecker ~ function. This is a problem of the BEFM, and must be solved theoretically. In the IMLS method, when the shape function satisfies the property of the Kronecker 5 function, then the boundary conditions, in the meshless method based on the IMLS method, can be applied directly. Then the IBEFM, based on the IMLS method, is a direct meshless boundary integral equation method in which the basic unknown quantity is the real solution of the nodal variables, and the boundary conditions can be applied directly and easily, thus it gives a greater computational precision. Some numerical examples are presented to demonstrate the method.

关键词： moving least-squares approximation interpolating moving least-squares method mesh- less method improved boundary element-free method potential problem

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

引用

International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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