检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Lee, Jonathan E. Zhu, Min Xi, Ziqiao Wang, Kun Yuan, Yanhua O. Lu, Lu Department of Chemical and Environmental Engineering Yale University New HavenCT06511 United States Department of Statistics and Data Science Yale University New HavenCT06511 United States Department of Computer Science and Engineering University of California San DiegoCA92093 United States ExxonMobil Technology and Engineering Company AnnandaleNJ08801 United States

Geological carbon sequestration (GCS) involves injecting CO2 into subsurface geological formations for permanent storage. Numerical simulations could guide decisions in GCS projects by predicting CO2 migration pathways and the pressure distribution in storage formation. However, these simulations are often computationally expensive due to highly coupled physics and large spatial-temporal simulation domains. Surrogate modeling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. Among these, the Fourier neural operator (FNO) has been applied to three-dimensional synthetic subsurface models. Here, to further improve performance, we have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operator network (DeepONet). This new framework is twice as efficient as a nested FNO for training and has at least 80% lower GPU memory requirement due to its flexibility to treat temporal coordinates separately. These performance improvements are achieved without compromising prediction accuracy. In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond the training range has been thoroughly evaluated. Nested Fourier-DeepONet outperformed the nested FNO for extrapolation in time with more than 50% reduced error. It also exhibited good extrapolation accuracy beyond the training range in terms of reservoir properties, number of wells, and injection rate. © 2024, CC BY-NC-ND.

关键词： Extrapolation

来源：评论

学校读者我要写书评

暂无评论

SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

引用

arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing IoT Network Security with Deep Learning-Anomaly Detection Model

Revolutionizing IoT Network Security with Deep Learning-Anom...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Manish Vyas R Vijayaganth Jigyasha Chandhok Aviral Srivastava S. Arumugam Mohit Tiwari Computer Science Department College Name & Address: Vikrant Institute of Technology and Management Indore Artificial Intelligence and Data Science M.Kumarasamy College of Engineering Thalavapalayam Karur Computer Science & Engineering Tula’s Institute of Engineering and Management NAAC Grade A+ Accredited institute Dhoolkot Dehradun Uttarakhand Amity University Rajasthan Amity University Rajasthan NH 11 C kant kalwar Jaipur Computer Science and Applications Periyar Maniammai Institute of Science & Technology Periyar Nagar Vallam Thanjavur Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi

The term “Internet of Things” (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. The IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. These days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Rainfall-Induced Landslide Prediction using Graph-Based Convolutional Network Models

Rainfall-Induced Landslide Prediction using Graph-Based Conv...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： P. Rajasekar K. M. Devendraiah A Tamizhselvi Droupti Yadav A. Priyadharshini D. Suganthi Department of Data Science and Business System SRM Institute of Science and Technology India V. V. Sangha's Independent P U College Ballari India Department of Information Technology St. Joseph's College of Engineering Chennai India Department of Environmental Science Technology School of Basic Sciences CSJM University Kanpur India Department of Computer Science and Engineering (Cyber Security) Nandha Engineering College Erode India Saveetha College of Liberal Arts and Sciences SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Landslides are natural disasters that are devastating and unpredictable. Community notification and casualty reduction require early warning systems. Using data analytics and machine learning technology (MLT), IoT-enabled early warning models can monitor environmental variables and predict landslides. However, current models struggle to anticipate *** innovative strategy suggested by this work involves feature extraction, preprocessing, and model training. The first step to high-quality data is preparing the landslip grid points with Multicollinearity Analysis. Simulating salp chains improves feature selection during feature extraction with the Salp Swarm Algorithm (SSA). During training, a Graph-Based Convolutional Neural Network (G-CNN) is used for optimum predictions. The suggested strategy was more predictive than before, averaging 92.76% Due to its accuracy, the G-CNN outperforms graph-based methods and standard CNNs in handling complex environmental *** SSA-based feature extraction with G-CNN training improves landslip prediction accuracy, a promising early warning system development. This technique could improve natural catastrophe prediction and response by using IoT and ML advances.

关键词： Training Landslides Analytical models Accuracy Biological system modeling Disasters Predictive models Feature extraction Prediction algorithms Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Assessing the Unobserved: Enhancing Causal Inference in Sociology with Sensitivity Analysis

arXiv

引用

arXiv 2023年

作者： Lin, Cheng Peña, Jose M. Daoud, Adel Linköping University Linköping Sweden Linköping University Linköping Sweden The Division of Data Science and Artificial Intelligence The Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Explaining social events is a primary objective of applied data-driven sociology. To achieve that objective, many sociologists use statistical causal inference to identify causality using observational studies—research context where the analyst does not control the data generating process. However, it is often challenging in observation studies to satisfy the unmeasured confounding assumption, namely, that there is no lurking third variable affecting the causal relationship of interest. By reviewing articles in American Journal of Sociology and American Sociological Review, we find that scholars tend to use a variety of strategies to avoid using causal language directly by for example refraining to disclose their causal assumptions, denoting their study as causal, and find other verbs than "causal" to denote "cause and effect." That is the case, despite the goal of their studies being explicitly explanatory. While those strategies are likely employed to navigate the publication process (getting passed the peer review phase), we argue that those strategies are likely holding back applied research. In this article, we develop a framework enabling sociologists to employ a different strategy to enhance the quality of observational studies. Our framework builds on a surprisingly simple statistical approach, sensitivity analysis: a thought-experimental framework where the analyst imagines a lever, which they can pull for probing a variety of theoretically driven statistical magnitudes of posited unmeasured confounding which in turn distorts the causal effect of interest. By pulling that lever, the analyst can identify how strong an unmeasured confounder must be to wash away the estimated causal effect. Although each sensitivity analysis method requires its own assumptions, this sort of post-hoc analysis provides underutilized tools to bound causal quantities. Extending Lundberg et al, we develop a five-step approach to how applied sociological research can incorporate sen

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

On the constitutive behaviour of the inertial drag with radiative and dissipative heat and chemical reaction for Casson fluid flow over an extending surface through permeable medium

引用

Partial Differential Equations in Applied Mathematics 2025年 14卷

作者： Baithalu, Rupa Agbaje, Titilayo M Panda, Subhajit Mishra, S.R. Department of Mathematics Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar 751030 India School of Computer Science and Applied Mathematics University of the Witwatersrand Private Bag 3 Braamfontein Johannesburg 2050 South Africa Centre for Data Science Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar 751030 India

In advanced industrial and biomedical application combined with polymer processing, blood flow analysis, and food processing, the Casson fluid model emerged as a crucial framework for illustrating the transportation of non-Newtonian fluids. The proposed analysis investigates the constitutive behavior of the Darcy-Forchheimer inertial drag considering Casson fluid flow through an extending surface packed with Darcy medium. Also, favorable body forces like radiative and dissipative heat effects and chemical reactions are analyzed to carry out the synergistic role in the thermal and concentration distribution. The suitable assumptions of transformation rules are adopted to transform the governing flow phenomena into dimensionless forms. This leads to the appearance of certain factors involved in these governing equations. Further, coupled nonlinear sets of model problems are handled numerically employing the “spectral Quasi-linearization method”. The conformity of the attribute of convergence of the current strategy and the validation with the earlier investigation supported the particular cases. Further, the behaviour of the controlling factors on the flow profiles is depicted graphically. The results reveal the effective interaction of inertial drag and radiant heat on the velocity vis-a-vis temperature distributions. Moreover, the illustration of the enhanced chemical reaction influences the concentration profile with stronger reactions leading to sharper gradients. © 2025 The Author(s)

关键词： Casson fluid Chemical reaction Darcy-Forchheimer drag Shooting based numerical technique Thermal radiation

来源：评论

学校读者我要写书评

暂无评论

Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R Cibi. A S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Data & Analytics Visualizaation – Delivery Virtusa Consulting Service Private Limited Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.

关键词： Accuracy Cancer treatment Medical services Machine learning Digital twins Mirrors Artificial intelligence Wearable sensors Standards Cancer

来源：评论

学校读者我要写书评

暂无评论

Autonomous Decision-Making in Cyber-Physical Systems Using Double Deep Q-Learning and Multi-Agent Collaboration

Autonomous Decision-Making in Cyber-Physical Systems Using D...

引用

Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Divyabarathi P Anbarasan R. Vasanthi R B. Leena Akshya J M. Sundarrajan Mani Deepak Choudhry Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bharathidasan Engineering College Thirupathur Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The autonomous decision-making capabilities of cyber-physical systems (CPS) establish key elements that optimize the management of resources while distributing tasks and maximizing system operational efficiency. Deep Q-Learning (DQN) represents one approach along with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Multi-Agent Proximal Policy Optimization (PPO) that demonstrates capability in handling decision tasks. The existing models face multiple implementation challenges including inaccurate Q-value estimations weak multi-agent relationships and restricted scalability for dynamic systems. The inadequate performance of present methods for energy efficiency and latency alongside poor collaborative coordination led to the necessity of developing a new approach. The new framework DDQN-MAC takes double-deep Q-Learning and combines it with multi-agent collaboration technology to enhance decision-making abilities specific to challenging environments. The method solves overestimation bias through separate action selection and evaluation processes that use multi-agent collaboration for dynamic task distribution and resource control. The designed model demonstrates enhanced performance in multiple critical evaluation measures. Benchmarks indicate that DDQN-MAC attained a superior completion rate of 95.4% while demonstrating reduced latency performance at 12.5 ms versus the other methods DQN (18.7 ms) and Multi-Agent PPO (92.3%). The research provides fundamental groundwork to guide upcoming investigations in hybrid model development while advancing complex multi-agent system capabilities for enhanced autonomous navigational choices within variable operational environments.

关键词： Q-learning Decision making Collaboration Estimation Cyber-physical systems Dynamic scheduling Energy efficiency Resource management Vehicle dynamics Optimization

来源：评论

学校读者我要写书评

暂无评论

Improving Deep Learning MRI Reconstruction with Contrastive Learning Pretraining

Improving Deep Learning MRI Reconstruction with Contrastive ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Mevan Ekanayake Zhifeng Chen Mehrtash Harandi Gary Egan Zhaolin Chen Monash Biomedical Imaging Monash University Australia Department of Electrical and Computer Systems Engineering Monash University Australia Department of Data Science and AI Monash University Australia School of Psychological Sciences Monash University Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Image reconstruction in Magnetic Resonance Imaging (MRI) aims to generate a high-quality image of the underlying anatomy from a minimal amount of measurements acquired during the scanning process. Deep learning (DL) has recently become the state-of-the-art approach for MRI reconstruction, often employing deep neural networks that learn to transform undersampled measurements into high-quality images, typically in a data-driven supervised manner. In our work, we propose a self-supervised contrastive learning pretraining framework for MRI that enhances the latent space for downstream reconstruction. Our framework is based on the fundamental principle of mutual information maximization, and we attempt to maximize the mutual information between different views of the same scan in latent space using contrastive learning. The learned latent space representations are subsequently utilized for reconstruction, and our experiments on various DL reconstruction models demonstrate significant improvements in performance both qualitatively and quantitatively.

关键词： Deep learning Magnetic resonance imaging Biological system modeling Contrastive learning Transforms Artificial neural networks Biomedical measurement

来源：评论

学校读者我要写书评

暂无评论

PROTEIN DISCOVERY WITH DISCRETE WALK-JUMP SAMPLING

arXiv

引用

arXiv 2023年

作者： Frey, Nathan C. Berenberg, Daniel Zadorozhny, Karina Kleinhenz, Joseph Lafrance-Vanasse, Julien Hötzel, Isidro Wu, Yan Ra, Stephen Bonneau, Richard Cho, Kyunghyun Loukas, Andreas Gligorijević, Vladimir Saremi, Saeed Prescient Design Genentech United States Antibody Engineering Genentech United States Department of Computer Science New York University United States Center for Data Science New York University United States

We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our Discrete Walk-Jump Sampling formalism combines the contrastive divergence training of an energy-based model and improved sample quality of a score-based model, while simplifying training and sampling by requiring only a single noise level. We evaluate the robustness of our approach on generative modeling of antibody proteins and introduce the distributional conformity score to benchmark protein generative models. By optimizing and sampling from our models for the proposed distributional conformity score, 97-100% of generated samples are successfully expressed and purified and 70% of functional designs show equal or improved binding affinity compared to known functional antibodies on the first attempt in a single round of laboratory experiments. We also report the first demonstration of long-run fast-mixing MCMC chains where diverse antibody protein classes are visited in a single MCMC chain. © 2023, CC BY.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：