检索结果-内蒙古大学图书馆

Analyzing X’s Web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Abdidizaji, Sina Baekey, Alexander Jayalath, Chathura Mantzaris, Alexander Garibay, Ozlem Ozmen Garibay, Ivan Department of Industrial Engineering University of Central Florida OrlandoFL United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Statistics and Data Science University of Central Florida OrlandoFL United States

The dissemination of news articles on social media platforms significantly impacts the public’s perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights into information flow but prove insufficient for identifying hidden influencers in large-scale social media networks such as X (previously known as Twitter). This study adopts and enhances a non-parametric framework based on Transfer Entropy to elucidate the influence relationships among X users. It further categorizes the distribution of influence exerted by these actors through the innovative use of multiplex network measures within a social media context, aiming to pinpoint influential actors during significant world events. The methodology was applied to three distinct events, and the findings revealed that actors in different events leveraged different types of news articles and influenced distinct sets of actors based on the news category. Notably, we found that actors disseminating trustworthy news articles to influence others occasionally resort to untrustworthy sources. However, the converse scenario, wherein actors predominantly using untrustworthy news types switch to trustworthy sources for influence, is less prevalent. This asymmetry suggests a discernible pattern in the strategic use of news articles for influence across social media networks, highlighting the nuanced roles of trustworthiness and popularity in the spread of information and influence. © 2024, CC BY.

关键词： Entropy

WELL-POSEDNESS OF THE ELECTRON MHD WITH PARTIAL RESISTIVITY

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Dai, Mimi Babaei, Hassan Department of Mathematics Statistics and Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Due to the singular nonlinear Hall term, the non-resistive electron magnetohydrodynamics (MHD) is not known to be locally well-posed in general. In this paper we consider the 221D electron MHD with either horizontal or vertical resistivity and show local well-posedness in Sobolev *** Codes 35Q35, 76B03, 76D09, 76E25, 76W05 Copyright © 2025, The Authors. All rights reserved.

关键词： Sobolev spaces

UACOF: A USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions 39

学校读者我要写书评

暂无评论

UACOF: A USV-AUV Collaboration Framework for Underwater Task...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Jingzehua Xie, Guanwen Ding, Yimian Zeng, Yongming Wang, Haoyu Zhang, Shuai MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Ocean College Zhejiang University China Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)157735897X

Ocean exploration requires effective collaboration between the unmanned surface vehicle (USV) and autonomous underwater vehicles (AUVs). We propose UACOF, a USV-AUV collaboration framework that enhances multi-AUV performance under extreme sea conditions. The framework includes high-precision multi-AUV location via USV path planning with Fisher information matrix optimization and reinforcement learning training for cooperative tasks. Experimental results show UACOF's superior feasibility, performance, coordination and robustness in extreme conditions. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Fisher information matrix

Generalizing Neural Wave Functions

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gao, Nicholas Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Recent neural network-based wave functions have achieved state-of-the-art accuracies in modeling ab-initio ground-state potential energy surface. However, these networks can only solve different spatial arrangements of the same set of atoms. To overcome this limitation, we present Graph-learned Orbital Embeddings (Globe), a neural network-based reparametrization method that can adapt neural wave functions to different molecules. Globe learns representations of local electronic structures that generalize across molecules via spatial message passing by connecting molecular orbitals to covalent bonds. Further, we propose a size-consistent wave function Ansatz, the Molecular Orbital Network (Moon), tailored to jointly solve Schrödinger equations of different molecules. In our experiments, we find Moon converging in 4.5 times fewer steps to similar accuracy as previous methods or to lower energies given the same time. Further, our analysis shows that Moon’s energy estimate scales additively with increased system sizes, unlike previous work where we observe divergence. In both computational chemistry and machine learning, we are the first to demonstrate that a single wave function can solve the Schrödinger equation of molecules with different atoms jointly. © 2023, CC BY.

关键词： Molecular orbitals

Efficient Chest X-Ray Investigation Using Firefly Algorithm Optimized Deep and Handcrafted Features 9th

学校读者我要写书评

暂无评论

Efficient Chest X-Ray Investigation Using Firefly Algorithm ...

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Kadry, Seifedine Al-Betar, Mohammed Azmi Yassine, Sahar Mohan, Ramya Arunmozhi, Rama Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India

ISBN: (纸本)9783031440830

Radiological imaging of the chest (X-ray) is a cost-effective, widely accepted method of examining the lungs and abnormalities. During this research, a clinically significant Convolutional-Neural-Network (CNN) framework will be proposed for the examination of lung abnormalities. The proposed scheme aims to achieve better detection accuracy from the selected chest X-ray data. The following stages are included in these techniques: (i) CNN segmentation of the lung section, (ii) Deep-feature extraction utilizing selected CNN schemes, (iii) Handcrafted-feature extraction, (iv) Optimizing features using Firefly Algorithms, and (v) Binary classification and cross-validation of fivefold cross-validation. By implementing the pre-trained VGG-UNet, this framework is capable of extracting lungs sections from X-ray images. Using this lung segment, handcrafted features such as Local Binary Patterns (LBP) and Pyramid Histograms of Oriented Gradients (PHOG) are obtained. DFs and HFs are obtained using the FA, and then a serial concatenation is performed in order to obtain a hybrid feature vector. This feature vector is used to classify X-ray images into healthy and diseased groups. For examination in this study, X-ray images of disease classes, such as tuberculosis, COVID19, pneumonia, lung masses, and effusions, are considered. Based on the experimental results of this study, >98% of disease detection accuracy was confirmed using the proposed scheme combined with the SoftMax classifier. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第3期8卷 670-679页

作者： Guancheng Wang Zhihao Hao Haisheng Li Bob Zhang PAMI Research Group Department of Computer and Information ScienceUniversity of MacaoTaipaMacaoChina China Industrial Control Systems Cyber Emergency Response Team BeijingChina Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijingChina

This study proposes a novel gradient‐based neural network model with an activated variable parameter,named as the activated variable parameter gradient‐based neural network(AVPGNN)model,to solve time‐varying constrained quadratic programming(TVCQP)*** with the existing models,the AVPGNN model has the following advantages:(1)avoids the matrix inverse,which can significantly reduce the computing complexity;(2)introduces the time‐derivative of the time‐varying param-eters in the TVCQP problem by adding an activated variable parameter,enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory;(3)adopts the activation function to accelerate the convergence *** solve the TVCQP problem with the AVPGNN model,the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker ***,a variable parameter with an activation function is employed to design the AVPGNN *** accuracy and convergence rate of the AVPGNN model are rigorously analysed in ***,numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed ***,to explore the feasibility of the AVPGNN model,appli-cations to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated.

关键词： computational intelligence mathematics computing optimisation

Fiducial inference for partially identified parameters with applications to instrumental variable models

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Cui, Yifan Hannig, Jan Center for Data Science Zhejiang University China Department of Statistics and Operations Research UNC-Chapel Hill United States

In the past two decades, there has been a fast-growing literature on fiducial inference since it was first proposed by R. A. Fisher in the 1930s. However, most of the fiducial inference based methods and related approaches have been developed for point-identified models, i.e., statistical models where the parameters of interest are uniquely determined by the observed data and the model's assumptions. In this paper, we propose a novel fiducial approach for partially identified statistical models. As a leading example, we consider the instrumental variable model with a variety of causal assumptions and estimands. The proposed methods are illustrated through extensive simulations and a data analysis evaluating the effect of consuming Vitamin A supplementation on reducing mortality rates. Copyright © 2025, The Authors. All rights reserved.

关键词： Uncertainty analysis

Climate Impact Prediction: Whale-Optimized Conv-XGBoost with Remote Sensing and Sociological data

学校读者我要写书评

暂无评论

Remote Sensing in Earth Systems sciences 2024年第4期7卷 443-456页

作者： Jayakarthik, R. Chandrashekhara K.T Sampath, O. Kumar, Deepak Biban, Laxmi Maroor, Jnaneshwar Pai Malluvalasa, S. N. Lakshmi Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Science and Engineering BMS Institute of Technology and Management Bangalore India Department of CSE RGM College of Engineering and Technology Nandyal India Department of Applied Sciences Manav Rachna International Institute of Research and Studies Faridabad India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to be University) Mangaluru Karnataka India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh India

Climate change poses significant challenges globally, impacting ecosystems, economies, and societies. Accurate prediction of climate impacts is crucial for developing effective mitigation strategies and informed policy decisions. Traditional predictive models often struggle to capture the complex interactions between environmental and socio-economic factors, necessitating advanced approaches that leverage diverse data sources and sophisticated algorithms. This study presents a robust predictive framework integrating convolutional neural networks (CNN) and XGBoost, optimized by the whale optimization algorithm (WOA). The proposed model leverages high-resolution satellite imagery and socio-economic data, combining spatial feature extraction with advanced machine learning techniques to enhance prediction accuracy and efficiency. We evaluate the model’s performance against baseline models and various optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). The integrated CNN and XGBoost model optimized by WOA significantly outperforms these benchmarks, achieving an accuracy of 98%, with substantial improvements in precision, recall, F1-score, and redundant phrases, and focuses directly on the core points to enhance clarity such as mean squared error (MSE), area under the curve (AUC), specificity, sensitivity, mean absolute error (MAE), and R². The results highlight the efficacy of the proposed approach in capturing complex environmental and socio-economic interactions, providing a powerful tool for climate impact assessment and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate Impact Prediction CNN Convolutional Neural Networks GA Genetic Algorithm Machine Learning Model Optimization Particle Swarm Optimization PSO Spatial Feature Extraction Whale Optimization Algorithm WOA XGBoost

Malicious Traffic Classification in WSN using Deep Learning Approaches 2

学校读者我要写书评

暂无评论

Malicious Traffic Classification in WSN using Deep Learning ...

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN