检索结果-内蒙古大学图书馆

Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units

引用

Multimedia Tools and Applications 2025年第15期84卷 14329-14358页

作者： Xia, Dawen Ao, Yuce Wei, Xiaoduo Li, Yunsong Chen, Yan Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Department of Automotive Engineering Guizhou Traffic Technician and Transportation College Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Accurate traffic flow prediction is essential to address traffic issues and assist traffic managers make informed decisions in intelligent transportation systems. Extracting potential features from traffic data is challenging due to the complex topology of urban road networks and the time-varying traffic flow. To capture the global spatiotemporal characteristics of traffic flow, we propose a novel model based on graph convolutional networks with a parallel attention network and stacked gated recurrent units (PAGCN-SGRU). First, the parallel attention (PA) network enhances the feature representation of global traffic road nodes and road segments. Then, the graph convolutional networks (GCN) are designed to extract spatial characteristics. Next, the stacked gate recurrent units (SGRU) are employed to capture temporal features. Finally, PAGCN-SGRU discovers global spatiotemporal features for traffic flow prediction. The experimental results demonstrate that the accuracy of PAGCN-SGRU under the SZ-dataset is improved by 9.76%, 72.54%, 5.76%, 16.07%, 2.07%, 1.82%, 3.35%, and 6.59%, respectively, compared to that of HA, ARIMA, SVR, GCN, T-GCN, A3T-GCN, ST-GCN, and DCRNN. In the Los-dataset, the accuracy values increase by 7.11%, 8.94%, 6.66%, 7.77%, 3.43%, 2.45%, 2.28%, and 4.09%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Credit Card Batch Processing in Banking System 2nd

Credit Card Batch Processing in Banking System

引用

2nd International Conference on Advances in Computing Research, ACR 2024

作者： Poudel, Samir Movinuddin Gutta, Sanjana Kommu, Revanth Kumar Upadhyay, Jiblal Hasan, Md Nahid Poudel, Khem Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9783031569494

Batch payments, involving a single transaction to multiple recipients via a single bank account, offer an efficient alternative to individual payments. This method condenses multiple payments into a single debit entry on bank statements, accelerating payment processing and enhancing business efficiency. Utilizing bank wire transfers is a prevalent means of initiating batch payments. In contrast to real-time processing, where transactions are handled immediately, batch processing entails aggregating authorized credit card transactions by merchants for submission to their credit card processors at the close of each business day or at scheduled intervals [1]. Merchant-authorized credit card transactions are compiled and sent to customers’ banks, seeking authorization. Upon approval, funds are transferred to the business’s bank account. Our objective is to identify fraudulent credit card transactions to safeguard credit card customers from erroneous charges for unauthorized purchases. The model employed aims to be both swift and adept at detecting anomalies, swiftly classifying potentially fraudulent transactions. The study involved the evaluation of multiple classifiers such as Gradient-Boosted Tree (GBT) and Random Forest, revealing that while the GBT classifier exhibited exceptional precision, the Random Forest classifier emerged as the preferred choice for our dataset. The selection was based on practical considerations, including efficiency and ease of implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Monte Carlo study of uncertainty propagation in Euler rotational kinematic equation 17

Monte Carlo study of uncertainty propagation in Euler rotati...

引用

17th Siam Physics Congress: Carbon Neutrality, SPC 2022

作者： Nutsathaporn, C. Chomkokard, S. Wongkokua, W. Ruengittinun, S. Jinuntuya, N. Metrology Program Department of Physics Faculty of Science Kasetsart University Chatuchak Bangkok10900 Thailand Department of Computer Science Faculty of Science Kasetsart University Chatuchak Bangkok10900 Thailand

We perform a Monte Carlo simulation to study the propagation of uncertainty in Euler rotational kinematic equation. The rate gyro data from an IMU sensor are inserted into the Euler rotational kinematic differential equation to calculate the sensor orientation, which is specified by its Euler angles. The uncertainty of sensor orientation is determined by simulating the variation of rate gyro input due to its uncertainty obtained from the calibration process. We find that the uncertainties of the Euler angles grow with time. Their values are proportional to the square root of time elapse. We find also that the uncertainties are linearly proportional to the uncertainties of rate gyro data and the square root of sampling time. These results interestingly resemble the behaviour of the Wiener process, with the step size of random walk depends on the time step. We can use these results as a criterion to choose a suitable IMU due to the characteristic of the integrated rate gyro sensor, especially in the application like the vehicle tracking or posture warning for elderly/disable people, where the precise sensor orientation is needed for a long period of operation. © Published under licence by IOP Publishing Ltd.

关键词： Differential equations

来源：评论

学校读者我要写书评

暂无评论

Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

来源：评论

学校读者我要写书评

暂无评论

Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization 38

Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Com...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Zhuanghua Luo, Luo Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore CNRS@CREATE Ltd. 1 Create Way #08-01 CREATE Tower 138602 Singapore School of Data Science Fudan University Shanghai Key Laboratory for Contemporary Applied Mathematics China Department of Computer Science National University of Singapore Singapore

Stochastic compositional optimization (SCO) problems are popular in many real-world applications, including risk management, reinforcement learning, and meta-learning. However, most of the previous methods for SCO require the smoothness assumption on both the outer and inner functions, which limits their applications to a wider range of problems. In this paper, we study the SCO problem in that both the outer and inner functions are Lipschitz continuous but possibly nonconvex and nonsmooth. In particular, we propose gradient-free stochastic methods for finding the (δ, ϵ)-Goldstein stationary points of such problems with non-asymptotic convergence rates. Our results also lead to an improved convergence rate for the convex nonsmooth SCO problem. Furthermore, we conduct numerical experiments to demonstrate the effectiveness of the proposed methods. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat

引用

IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-16页

作者： Hsu, Chih-Chung Jian, Chih-Yu Tu, Eng-Shen Lee, Chia-Ming Chen, Guan-Lin National Cheng Kung University Institute of Data Science The Department of Statistics The Miin Wu School of Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science Tainan70101 Taiwan MobileDrive Artificial Intelligence R&d Department New Taipei City23679 Taiwan

This article addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a real-time compressed sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring RTCS. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems. © 1980-2012 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Augmented Reality and Artificial Intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

引用

3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

PROTEIN DISCOVERY WITH DISCRETE WALK-JUMP SAMPLING 12

PROTEIN DISCOVERY WITH DISCRETE WALK-JUMP SAMPLING

引用

12th International Conference on Learning Representations, ICLR 2024

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 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. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Antibodies

来源：评论

学校读者我要写书评

暂无评论

Reliable Entropy-induced Anchor Learning for Incomplete Multi-view Subspace Clustering

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5293-5306页

作者： Shen, Qiangqiang Guo, Zihou Wang, Hanzhang Xu, Yanhui Chen, Yongyong Wang, Shiqi Liang, Yongsheng City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong School of Science Guangdong Shenzhen518055 China Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China School of Computer Science and Technology Guangdong Shenzhen518055 China School of Electronics and Information Engineering Guangdong Shenzhen518055 China Shenzhen Technology University College of Big Data and Internet Guangdong Shenzhen518118 China

Under large-scale data with missing views, fast incomplete multi-view clustering (IMVC) with anchor learning is of critical importance due to its linear complexity O(n). However, existing anchor-based methods only explore the column orthogonality of anchor points, where their arbitrary column orthogonal basis vectors have weak constraint relationships with real samples and significant deviations from more representative anchors, thereby impeding the precise representation of sample similarities. To solve this issue, we propose a Reliable Entropy-induced anchor learning for incomplete Multi-view subspace Clustering (REMC), which performs an entropy approximation term to learn more representative anchors, and we prove that the information entropy minimization can be relaxed into the 2, 1-norm paradigm. Specifically, the proposed REMC first integrates anchor learning and subspace clustering to produce multiple view-specific bipartite graphs and capture the high-order correlations by imposing these bipartite graphs with the tensor nuclear norm. Then, we fuse all the view-specific bipartite graphs to build a consensus bipartite graph with entropy approximation regularization, and hence the proposed REMC can produce a more discriminative similarity graph, preserving each non-zero element in its column close to 1, while the other elements are approaching 0. Besides, an efficient algorithm is designed to solve the proposed REMC. Numerous results show the superior performance of our method on both the complete and incomplete data. © 1991-2012 IEEE.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

CLERC: An Intelligent Sign Language Translator for Improved Accessibility using Machine Learning 2

CLERC: An Intelligent Sign Language Translator for Improved ...

引用

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

作者： Vijayaganth, V. Santhosh Kumar, S. Davis, L. Pranava Darisan, S.V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350369960

Clerc's system is a modern smart language interpreter using advanced methods like artificial intelligence, imagery recognition, and natural languages translation. The Clergs System includes these are just some of tools used. The system is able to do this by turning gestures in sign language into verbal and written outputs smoothly thus helping people with hearing impairment to communicate easily in real time. In this research, an object recognition SSD MobileNets and FDN feature extraction models are used for accurate gesture detection. A machine-learning model can also be used to predict accurate translations. The efficacy of the Clerc system has been tested and validated to the utmost extent in different settings such as public places, schools, hospitals amongst others. This research seeks to explore possibilities of improving the system's precision, efficacy, scalability, and use with regards to the practical application. © 2023 IEEE.

关键词： Clerc system computer vision FPN-lite feature extractor scalability SSD Mobile Net Object recognition model

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：