检索结果-内蒙古大学图书馆

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Srinivasa Gupta B. Valarmathi Ishaan Hiwanj Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Predicting water quality is essential to preserving human health and environmental sustain ability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72 %, 83 %, 69%, 63 %, 66%, 71 %, 63 %, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models - Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest - evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights.

关键词： Support vector machines Adaptation models Accuracy Scalability Water quality Boosting Reliability Random forests Long short term memory Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

Stochastic Geometry Based Semantic Performance Analysis For Text Semantic Communication

引用

IEEE Transactions on Communications 2025年

作者： Zhu, Heng Zhu, Kun Zhang, Yang Niyato, Dusit Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Semantic communication has recently garnered substantial attention due to its potential to alleviate bandwidth constraints and improve network capacity. Nonetheless, existing studies primarily concentrate on network architecture and overlook the communication performance analysis. Therefore, this paper seeks to derive semantic-oriented error probability. Specifically, we develop a novel text semantic communication framework that comprises distinct semantic and physical layers. In the semantic layer, we employ latent Dirichlet allocation (LDA) to extract text topics and evaluate the topic distribution. Given an expected transmission accuracy, we propose a dichotomy to determine the minimal number of topics. These acquired topics, along with their respective distributions, are defined as the text semantic features. In the physical layer, the semantic features are encoded into a binary sequence and modulated with conventional methods. The relationship between the semantic and physical layers is uncover by associating coding of the semantic features with the symbol error probability (SEP). Considering a scenario wherein base stations (BSs) following a specific Poisson point process (PPP), we derive the approximate SEP and semantic inference error probability (SIEP) for multiple coding strategies. Simulation results show that the proposed text semantic communication network enables effective text transmission and the derived error probability accurately reflect the performance of an actual communication system. © 1972-2012 IEEE.

关键词： Latent Dirichlet allocation Semantic communication Semantic inference error probability Stochastic geometry

来源：评论

学校读者我要写书评

暂无评论

FedPPO: Reinforcement Learning-Based Client Selection for Federated Learning With Heterogeneous Data

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhao, Zheyu Li, Anran Li, Ruidong Yang, Lei Xu, Xiaohua University of Science and Technology of China School of Computer Science and Technology Hefei Anhui230026 China Kanazawa University Institute of Natural Sciences Ishikawa9201192 Japan South China University of Technology School of Software Engineering Guangzhou510006 China

Federated Learning (FL) enables multiple data owners to jointly train a machine learning model, which can improve joint environmental cognitive capability without disclosing their private local data. In FL systems, local clients are often heterogeneous, characterized by different data distribution forms and varying levels of noise patterns. This poses challenges to the convergence and test accuracy of the learned global model. Existing client selection methods failed to effectively address the data heterogeneity problem while considering the impacts of correlations between clients. In this work, we propose an efficient and adaptive client selection scheme with the Federated Proximal Policy Optimization (FedPPO). It utilizes a noisy client filtering method that leverages the principal component analysis and hierarchical clustering to reduce the action search space and accelerate the convergence of Deep Reinforcement Learning (DRL). It then evaluates each client's contribution by jointly using the accuracy of global model and local models and take the contribution score as the reward. Finally, it leverages a DRL algorithm, PPO, to adaptively select clients with high contributions to participate in training. Theoretical analysis shows that it is guaranteed to converge in an efficient manner. Experimental results on four real-world datasets show that FedPPO significantly outperforms existing approaches of constructing global models with faster convergence speed and higher test accuracy. © 2015 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?

arXiv

引用

arXiv 2025年

作者： Liang, Qingyuan Zhang, Zhao Sun, Zeyu Lin, Zheng Luo, Qi Xiao, Yueyi Chen, Yizhou Zhang, Yuqun Zhang, Haotian Zhang, Lu Chen, Bin Xiong, Yingfei School of Computer Science Peking University China Kuaishou Technology China Institute of Software Chinese Academy of Sciences China Department of Computer Science and Engineering Southern University of Science and Technology China

Grammar serves as a cornerstone in programming languages and software engineering, providing frameworks to define the syntactic space and program structure. Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. However, as language models scale to the billion level or beyond, syntax-level errors become rare, making it unclear whether grammar information still provides performance benefits. To explore this, we develop a series of billion-scale GrammarCoder models, incorporating grammar rules in the code generation process. Experiments on HumanEval (+) and MBPP (+) demonstrate a notable improvement in code generation accuracy. Further analysis shows that grammar-based representations enhance LLMs’ ability to discern subtle code differences, reducing semantic errors caused by minor variations. These findings suggest that grammar-based code representations remain valuable even in billion-scale models, not only by maintaining syntax correctness but also by improving semantic differentiation.1 © 2025, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Q-Learning enabled Ant Colony (Q-ANT) based Navigation Optimization Framework for UAVs

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Wei, Xing Li, Ning Yang, Hua Guilin University of Aerospace Technology School of Computer Science and Engineering Guilin China Guilin University of Electric Technology School of Software Engineering school of Computer Science and Information Security Guilin China Huanghuai University School of Computer and Artificial Intelligence Henan China

Flying ad-hoc networks (FANETs) composed of unmanned aerial vehicles (UAVs) which are serving in both military as well as civilian domains. The environmental changes and alterations in network topology present considerable challenges to communication among drone swarms. Consequently, there is an imperative need to investigate a high-performance routing scheme for UAV based FANETs. In this study, a routing scheme is introduced that employs the reinforcement Q-learning algorithm to enhance the ant colony optimization routing protocol (Q-ANT). The primary innovation of this scheme lies in the implementation of a neighborhood link selection mechanism that incorporates link residual time and link signal strength. This approach addresses the inadequacy of relying solely on a single indicator for assessing link residual survival time and expedites the convergence velocity of the ant colony routing algorithm. Q-ANT possesses the capability to optimize its routing strategy in accordance with the prevailing network environment by utilizing the Q-learning algorithm to attain the ideal option strategy in response to environmental fluctuations. The proposed scheme is implemented within the NS3 simulation environment and scenario-based simulation experiments have been conducted on three critical network metrics: network lifetime, end-to-end delay, and packet delivery rate. The experimental results exhibit the superiority of Q-ANT in terms of minimal energy consumption, minimal latency, and an enhanced packet delivery rate to the existing approaches such as FAnt-HocNet and Q-learning based QRF. © 1975-2011 IEEE.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Optimization-Inspired Graph Neural Network for Cellular Network Optimization

引用

IEEE Transactions on Mobile Computing 2025年

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling

arXiv

引用

arXiv 2025年

作者： Wu, Bingjie Yu, Zitong Xie, Yiping Liu, Wei Luo, Chaoqi Liu, Yong Goh, Rick Siow Mong Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China

Remote Photoplethysmography (rPPG) is a promising technique to monitor physiological signals such as heart rate from facial videos. However, the labeled facial videos in this research are challenging to collect. Current rPPG research is mainly based on several small public datasets collected in simple environments, which limits the generalization and scale of the AI models. Semi-supervised methods that leverage a small amount of labeled data and abundant unlabeled data can fill this gap for rPPG learning. In this study, a novel semi-supervised learning method named Semi-rPPG that combines curriculum pseudo-labeling and consistency regularization is proposed to extract intrinsic physiological features from unlabelled data without impairing the model from noises. Specifically, a curriculum pseudo-labeling strategy with signal-to-noise ratio (SNR) criteria is proposed to annotate the unlabelled data while adaptively filtering out the low-quality unlabelled data. Besides, a novel consistency regularization term for quasi-periodic signals is proposed through weak and strong augmented clips. To benefit the research on semi-supervised rPPG measurement, we establish a novel semi-supervised benchmark for rPPG learning through intra-dataset and cross-dataset evaluation on four public datasets. The proposed Semi-rPPG method achieves the best results compared with three classical semi-supervised methods under different protocols. Ablation studies are conducted to prove the effectiveness of the proposed methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Adaptive Graph Structure Learning Neural Rough Differential Equations for Multivariate Time Series Forecasting

引用

IEEE Transactions on Big Data 2025年

作者： Su, Yuming Ma, Tinghuai Rong, Huan Wahab, Mohamed Magdy Abdel Nanjing University of Information Science & Technology The School of Cyber Science and Engineering School of Computer Science Nanjing210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China Nanjing University of Information Science & Technology School of Software Nanjing210044 China Nanjing University of Information Science & Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Cairo University Faculty of Science Cario 12613 Egypt

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex correlations among variables in time series, and leveraging graph neural networks and recurrent neural networks for temporal aggregation and spatial propagation stage, has shown promise. However, traditional methods' graph structure node learning and discrete neural architecture are not sensitive to issues such as sudden changes, time variance, and irregular sampling often found in real-world data. To address these challenges, we propose a method called Adaptive Graph structure Learning neural Rough Differential Equations (AGLRDE). Specifically, we combine dynamic and static graph structure learning to adaptively generate a more robust graph representation. Then we employ a spatio-temporal encoderdecoder based on Neural Rough Differential Equations (Neural RDE) to model spatio-temporal dependencies. Additionally, we introduce a path reconstruction loss to constrain the path generation stage. We conduct experiments on six benchmark datasets, demonstrating that our proposed method outperforms existing state-of-the-art methods. The results show that AGLRDE effectively handles aforementioned challenges, significantly improving the accuracy of multivariate time series forecasting. © 2015 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images

arXiv

引用

arXiv 2025年

作者： Fang, Sheng Li, Kaiyu Li, Zhe Zhao, Jianli Zhang, Xingli College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China

Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical remote sensing

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Feature Extraction and Fusion for Longitudinal Alzheimer’s Disease Diagnosis 17th

Spatiotemporal Feature Extraction and Fusion for Longitudina...

引用

17th International Conference on Brain Informatics, BI 2024

作者： Guan, Zhenghua Yang, Peng Lei, Haijun Yang, Bao Song, Xuegang Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. The brain structure of AD patients progressively atrophies over time. Longitudinal data can capture the changes in imaging over time, providing important information for early detection and diagnosis of the disease, which is not achievable with images from a single time point. We propose a novel longitudinal AD diagnosis model that leverages deformation field data obtained by registering images from adjacent time points. Each pixel in the deformation field represents the displacement vector of the corresponding pixel between two scans, reflecting information about brain tissue atrophy. By effectively extracting both temporal and spatial features from the longitudinal data, our model enhances early AD diagnosis. The input data for the model consists of processed sMRI images and deformation fields calculated between each pair of time points in the longitudinal sMRI data. The model is composed of three modules: Convolutional Long Short-Term Memory (CLSTM), Gate Fusion Long Short-Term Memory (GFLSTM), and an attention module. CLSTM extracts temporal features while preserving spatial features by maintaining the 3D shape of the features, GFLSTM achieves spatiotemporal feature fusion within a cascaded network, and the attention module further enhances feature representation. Our proposed method was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and experiments demonstrate that it outperforms several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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：