检索结果-内蒙古大学图书馆

International Conference on data Engineering

作者： Yudong Niu Yuchen Li Panagiotis Karras Yanhao Wang Zhao Li School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Copenhagen Copenhagen Denmark School of Data Science and Engineering East China Normal University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of hierarchically structured communities. We first propose a novel compressed COD evaluation approach to accelerate the influence estimation by eliminating redundant computations for overlapping communities. Then, we further devise a local hierarchical reclustering method to alleviate the skewness of hierarchical communities generated by global clustering for a specific query attribute. Extensive experiments confirm the effectiveness and efficiency of our solutions to COD: they find characteristic communities better than existing community search methods by several quality measures and achieve up to 25 x speedups against well-crafted baselines.

关键词： Estimation Search problems data engineering

来源：评论

学校读者我要写书评

暂无评论

Testing of Emerging Wireless Sensor Networks Using Radar Signals With Machine Learning Algorithms

IEEE Journal of Selected Areas in Sensors

引用

IEEE Journal of Selected Areas in Sensors 2024年 1卷 49-59页

作者： Shitharth Selvarajan Hariprasath Manoharan Adil O. Khadidos Alaa O. Khadidos School of Built Environment Engineering and Computing Leeds Beckett University Leeds U.K. Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia

In this article, machine learning methods are used to assess how well wireless sensor networks transmit and receive radar signals. Measurements are done with labeled and unlabeled datasets where output functions are modified in relation to transmitted input in order to test the transceiver of radar signals. The main contribution in the proposed method is to focus on the possibility of choosing a free space model that transmits the radar signals in wireless sensor networks without any interruptions. Hence, for such type of transmissions, reference time period is selected in order to perform radar signal classification, and at the same time, separation of unnecessary interruptions is reduced using clustering procedures. Since the radar signals can be monitored with automatic transmission techniques, the outcomes are combined with supervised, unsupervised, and reinforcement learning models to increase the effect of transmissions. Therefore, the objective functions are designed with three scenarios where reinforcement learning proves to provide adequate connections for radar signals to all wireless sensor networks at reduced error of 0.3%. In addition, with reinforcement learning, the distance of radar signal transmission is maximized to a level greater than 75% at minimized noise ratio of 0.8%.

关键词： Radar Wireless sensor networks Sensors Spaceborne radar Security Radar measurements Mathematical models

来源：评论

学校读者我要写书评

暂无评论

Prediction of Loan Approval in Banks Using Machine Learning Algorithm

Prediction of Loan Approval in Banks Using Machine Learning ...

引用

Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy P A Manoharan J. Anvar Shathik A. Pandiaraj N. Ramshankar Naveen G Department of Computing Technologies SRM Institute of Science and Technology Chennai Chennai Institute of Technology Chennai AIML and Data science Anjuman Institute of Technology and Management Karnataka India Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Normally, the banking sector gains profit from different kinds of sources, but the main factor in their profitable growth is giving loans to their customers. A bank's profit or loss is determined by the quantity of the loan. A huge number of people are taking loans from banks and not clearing their dues. So there is a tedious process for banks to identify them and collect them. This in turn leads to following up on a complex verification process and issuing loans to their customers. Customer satisfaction is mandatory in every field, but in the banking sector, it is essential to identify genuine applicants and satisfy their needs, as well as to keep bankers free from non-payable customers. The manual method of identifying the right person and providing a loan to them may cause misconceptions. Several studies in this field suggest various kinds of approaches to identifying applicant status. Therefore, the use of machine learning (ML) approaches in the banking sector to resolve difficulties faced by bankers is becoming more popular. This reduces the problems faced by bankers and makes their applicants satisfactory. The time taken for this process is too long. It is essential to select the best ML approach from the different kinds of classifier algorithms available. The attributes chosen for this study include the applicant's education, type of work, applicant income, loan amount, co-applicant income, credit history, and property area. The proposed study reviews ML approaches such as K-nearest Neighboring (KNN), Logistic Regression, Decision Tree and Support vector Mechanism (SVM). Among those, KNN outperforms the other three algorithms, and the efficiency obtained by KNN is 87.50%, Logistic Regression is 83.512%, Decision Tree is 77.09%, and Random forest (RF) is 74.49%.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Banking Nearest neighbor methods Vectors Sustainable development Recommender systems Testing

来源：评论

学校读者我要写书评

暂无评论

FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

引用

IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

来源：评论

学校读者我要写书评

暂无评论

Rethinking the visual cues in audio-visual speaker extraction

arXiv

引用

arXiv 2023年

作者： Li, Junjie Ge, Meng Pan, Zexu Cao, Rui Wang, Longbiao Dang, Jianwu Zhang, Shiliang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China

The Audio-Visual Speaker Extraction (AVSE) algorithm employs parallel video recording to leverage two visual cues, namely speaker identity and synchronization, to enhance performance compared to audio-only algorithms. However, the visual front-end in AVSE is often derived from a pre-trained model or end-to-end trained, making it unclear which visual cue contributes more to the speaker extraction performance. This raises the question of how to better utilize visual cues. To address this issue, we propose two training strategies that decouple the learning of the two visual cues. Our experimental results demonstrate that both visual cues are useful, with the synchronization cue having a higher impact. We introduce a more explainable model, the Decoupled Audio-Visual Speaker Extraction (DAVSE) model, which leverages both visual cues. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised meibomian gland segmentation via mutual consistency constraints and uncertainty rectification

Semi-supervised meibomian gland segmentation via mutual cons...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lin, Jiawen Lu, Feng Li, Li Lin, Lingjie Li, Dongqi Borchert, Glen M. Huang, Jingshan Fuzhou University College of Computer and Data Science Fuzhou China Fujian Provincial Hospital South Branch Department of Ophthalmology Fuzhou China University of California Irvine Department of Economics School of Information and Computer Sciences Irvine United States University of South Alabama College of Medicine Department of Pharmacology Mobile United States University of South Alabama School of Computing & College of Medicine Mobile United States

ISBN: (纸本)9798350337488

To solve the label scarcity of meibomian gland segmentation in infrared meibography images, a novel framework for semi-supervised meibomian gland segmentation is firstly presented in this paper. Extra mutual feature consistency constraint is added along with the cross pseudo supervision , guiding the model more robustness and discriminative. Meanwhile, cross uncertainty rectification is introduced to avoid noisy labels, further improving the pseudo supervision. Experimental results on an internal dataset reveals that our method yields significant performances using only 10% of the labeled data compared to the fully supervised segmentation, and outperforms the state-of-the art semi-supervised segmentation methods. Combination of mutual consistency regularization and cross uncertainty rectifi-cation guides model to distinguish glands from background well with limited labeled data. © 2023 IEEE.

关键词： meibomian gland dysfunction meibomian gland segmentation mutual consistency constraint semi-supervised learning uncertainty rectification

来源：评论

学校读者我要写书评

暂无评论

Emoji Anticipation and Prediction Using Deep Neural Network Model

Emoji Anticipation and Prediction Using Deep Neural Network ...

引用

International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： J. Shobana S. Amudha Siva Kumar A Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu(DT) Tamilnadu India

ISBN: (纸本)9781665462761

Emojis are tiny pictorial depictions of feelings or objects that are frequently used in text messaging to enhance intercultural dialogue. Emojis are increasingly being used by users of social media sites like Twitter and Instagram in their text messages to express broader emotions that are frequently challenging to express in words alone. Combining text and emojis to express emotion has become a crucial part of how people communicate in the twenty-first century. In order to ascertain the connection between these text messages and the emojis used in them, a problem statement has been developed. In this study, we offer a technique for anticipating several emojis for a given text-based tweet message. Three modules make up the suggested model. The given text data is preprocessed in the first module, the model on which the data is trained is in the second, and a multi-class classifier is used in the third module to predict the emojis that the given text would evoke. This proposed deep neural network model aims to understand the underlying semantics of a text sentence using natural language processing techniques in order to predict appropriate emojis. Experiments performed utilizing a sizable data collection of Twitter-sourced real-world emoji usage.

关键词： Deep learning Support vector machines Social networking (online) Neural networks Blogs Semantics Predictive models

来源：评论

学校读者我要写书评

暂无评论

Weighted Squared Volume Minimization (WSVM) for Generating Uniform Tetrahedral Meshes

arXiv

引用

arXiv 2024年

作者： Yu, Kaixin Wang, Yifu Song, Peng Meng, Xiangqiao He, Ying Chen, Jianjun The School of Aeronautics and Astronautics Zhejiang University Hangzhou China The College of Computing and Data Science Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Computing The Hong Kong Polytechnic University Hong Kong

This paper presents a new algorithm, Weighted Squared Volume Minimization (WSVM), for generating high-quality tetrahedral meshes from closed triangle meshes. Drawing inspiration from the principle of minimal surfaces that minimize squared surface area, WSVM employs a new energy function integrating weighted squared volumes for tetrahedral elements. When minimized with constant weights, this energy promotes uniform volumes among the tetrahedra. Adjusting the weights to account for local geometry further achieves uniform dihedral angles within the mesh. The algorithm begins with an initial tetrahedral mesh generated via Delaunay tetrahedralization and proceeds by sequentially minimizing volume-oriented and then dihedral angle-oriented energies. At each stage, it alternates between optimizing vertex positions and refining mesh connectivity through the iterative process. The algorithm operates fully automatically and requires no parameter tuning. Evaluations on a variety of 3D models demonstrate that WSVM consistently produces tetrahedral meshes of higher quality, with fewer slivers and enhanced uniformity compared to existing methods. Check out further details at the project webpage: https://***/***/. © 2024, CC BY.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

ARE YOU SURE? ENHANCING MULTIMODAL PRE-TRAINING WITH MISSING MODALITIES THROUGH UNCERTAINTY ESTIMATION

arXiv

引用

arXiv 2025年

作者： Nguyen, Duy A. Do, Quan Huu Doan, Khoa D. Do, Minh N. Siebel School of Computing and Data Science University of Illinois Urbana-ChampaignIL United States College of Engineering and Computer Science VinUniversity Hanoi Viet Nam Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States

Multimodal learning has demonstrated incredible successes by integrating diverse data sources, yet it often relies on the availability of all modalities - an assumption that rarely holds in real-world applications. Pretrained multimodal models, while effective, struggle when confronted with small-scale and incomplete datasets (i.e., missing modalities), limiting their practical applicability. Previous studies on reconstructing missing modalities have overlooked the reconstruction’s potential unreliability, which could compromise the quality of the final outputs. We present SURE (Scalable Uncertainty and Reconstruction Estimation), a novel framework that extends the capabilities of pretrained multimodal models by introducing latent space reconstruction and uncertainty estimation for both reconstructed modalities and downstream tasks. Our method is architecture-agnostic, reconstructs missing modalities, and delivers reliable uncertainty estimates, improving both interpretability and performance. SURE introduces a unique Pearson Correlation-based loss and applies statistical error propagation in deep networks for the first time, allowing precise quantification of uncertainties from missing data and model predictions. Extensive experiments across tasks such as sentiment analysis, genre classification, and action recognition show that SURE consistently achieves state-of-the-art performance, ensuring robust predictions even in the presence of incomplete data. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

引用

Innovations in Communications, Electrical and Computer Engineering (ICICEC), International Conference on

作者： L. Ramalingam Raveendra N Amarnath V. Arun N. C. Sendhilkumar Rupavathy Ravi C. Srinivasan Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Mphasis Corporation Melissa Texas USA Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Indu College of Engineering and Technology Hyderabad India Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions.

关键词： Temperature measurement Temperature sensors Accuracy Medical services Wounds Predictive models Prediction algorithms Safety Sensors Monitoring

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：