检索结果-内蒙古大学图书馆

DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

引用

International Journal of Cognitive Computing in Engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

PRAP-PIM:A weight pattern reusing aware pruning method for ReRAM-based PIM DNN accelerators

引用

High-Confidence Computing 2023年第2期3卷 50-59页

作者： Zhaoyan Shen Jinhao Wu Xikun Jiang Yuhao Zhang Lei Ju Zhiping Jia School of Computer Science and Technology Shandong UniversityQindao 266237China School of Cyber Science and Technology Shandong UniversityQindao 266237China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Resistive Random-Access Memory(ReRAM)based Processing-in-Memory(PIM)frameworks are proposed to accelerate the working process of DNN models by eliminating the data movement between the computing and memory *** further mitigate the space and energy consumption,DNN model weight sparsity and weight pattern repetition are exploited to optimize these ReRAM-based ***,most of these works only focus on one aspect of this software/hardware codesign framework and optimize them individually,which makes the design far from *** this paper,we propose PRAP-PIM,which jointly exploits the weight sparsity and weight pattern repetition by using a weight pattern reusing aware pruning *** relaxing the weight pattern reusing precondition,we propose a similarity-based weight pattern reusing method that can achieve a higher weight pattern reusing *** results show that PRAP-PIM achieves 1.64×performance improvement and 1.51×energy efficiency improvement in popular deep learning benchmarks,compared with the state-of-the-art ReRAM-based DNN accelerators.

关键词： Resistive Random-Access Memory Processing-in-Memory Deep neural network Model compression

来源：评论

学校读者我要写书评

暂无评论

A Beam-Steering Broadband Microstrip Antenna with High Isolation

引用

Chinese Journal of Electronics 2023年第2期32卷 325-333页

作者： JIANG Zhaoneng SHA Yongxin NIE Liying XUAN Xiaofeng School of Computer and Information Hefei University of Technology School of Electronic Science and Applied Physics Hefei University of Technology

In this paper, a 4.2–7.2 GHz(52.6%)beam-steering microstrip antenna was proposed. The proposed antenna consists of three tapered slots and *** three radiation directions of the antenna on the plane are independent of each other, and the three feeds correspond to the three radiation structures. Symmetry isolation trenches are introduced to improve isolation between different ports. Radiation pattern simulation and measurement show horizontal beam steering at the sampled frequencies of 4.2, 5, 6, and 7.2 GHz. The results shows that the overlapped beam of the three ports in the Eplane and H-plane can cover more than 200 degrees and 60degrees, respectively. Apart from the capability of beamsteering, high isolation(more than 28 d B) of the proposed antenna in the operating band is obtained.

关键词： Antenna measurements Mutual coupling Slot antennas Microstrip antennas Antenna feeds Frequency measurement Broadband antennas

来源：评论

学校读者我要写书评

暂无评论

Breadth Learning and Network Embedding Techniques for Sensitive Information Detection in Social Media

引用

Informatica (Slovenia) 2024年第19期48卷 73-88页

作者： Luo, Jianping Zhu, Rongshen School of Arts and Science Chengdu College University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science Chengdu College University of Electronic Science and Technology of China Chengdu611731 China

Social media platforms have brought tremendous changes to the way society communicates and transmits information. However, some extreme individuals post sensitive information such as false, malicious attacks, terrorism, or negative comments. To address this rapidly evolving network security challenge, this study introduces breadth learning and network embedding techniques from machine learning to establish a new method for information extraction and auditing. This method adopts a meta path random walk form to extract and filter effective information from multiple heterogeneous social networks. A network embedding technique based on breadth learning is proposed to transform each node into a low-dimensional feature space. Finally, a fusion function is designed to quantify the correlation between sensitive users on social platforms. The relationship between users and sensitive items is predicted by the correlation score, and then the sensitive information publisher is screened. Finally, the sensitive information released by the sensitive information publisher is effectively locked after it is identified. As a result, a sensitive information extraction and review model based on breadth learning and network embedding is constructed. The results showed that the research model exhibited a high degree of accuracy (97.3%) in mixed datasets on social platforms, outperforming other models designed for the review of sensitive information. Moreover, the model demonstrated superior performance on social platforms with larger data volumes. Accuracy represents the percentage of correct identification of sensitive information, indicating that the research model can more accurately identify sensitive information and its publishers. The research results have significant implications for the network security governance of open large-scale social media platforms. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Network embeddings

来源：评论

学校读者我要写书评

暂无评论

An Approach for Privacy-Aware Mobile App Package Recommendation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6240-6252页

作者： Liu, Shanpeng Cao, Buqing Liu, Jianxun Kang, Guosheng Shi, Min Li, Xiong Fletcher, Kenneth K. Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University of Technology School of Computer Zhuzhou412007 China Harvard University Harvard Medical School BostonMA02138 United States School of Computer Science and Engineering Chengdu611731 China University of Massachusetts School of Computer Science BostonMA02125 United States

With the prosperity of the mobile Internet, the abundance of data makes it difficult for users to choose their favorite app. Thus, mobile app recommendation as an emerging topic attracts lots of attention. However, existing methods for app recommendation rarely consider recommendation accuracy under the privacy representation of user preferences. To address this problem, we propose a privacy-aware app package recommendation method named APP-Rec. Specifically, in this method: 1) treat an app and its associated heterogeneous entities (APP-Rec considers not only the apps themselves but also a variety of related factors-collectively referred to as heterogeneous entities, such as app category and app neighbors) as an app package and extract comprehensive features from the app package using an intrapackage attention network and an interpackage attention network to improve app recommendation;and 2) design a privacy module utilizing Laplace noise to achieve privacy preservation of user preferences. Experimental results show that APP-Rec outperforms the state-of-the-art methods in terms of area under the curve (AUC). Moreover, the privacy preservation of user preferences in APP-Rec is proved by theoretical analysis and experimental results. © 2024 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination

引用

computers, Materials & Continua 2023年第9期76卷 3325-3343页

作者： Peng Liu Qian He Baokang Zhao Biao Guo Zhongyi Zhai School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Computer Science National University of Defense TechnologyChangsha410073China

Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in ***,data privacy is potentially risky when data is outsourced to cloud servers or edge *** data encryption ensures data confidentiality,it can impede data sharing and ***-based searchable encryption(ABSE)is proposed as an effective technique for enhancing data security and ***,ABSE has its limitations,such as single attribute authorization failure,privacy leakage during the search process,and high decryption *** paper presents a novel approach called the blockchain-assisted efficientmulti-authority attribute-based searchable encryption scheme(BEM-ABSE)for cloudedge collaboration scenarios to address these ***-ABSE leverages a consortium blockchain to replace the central authentication center for global public parameter *** incorporates smart contracts to facilitate reliable and fair ciphertext keyword search and decryption result *** minimize the computing burden on resource-constrained devices,BEM-ABSE adopts an online/offline hybrid mechanism during the encryption process and a verifiable edge-assisted decryption *** ensures both low computation cost and reliable *** analysis conducted under the random oracle model demonstrates that BEM-ABSE is resistant to indistinguishable chosen keyword attacks(IND-CKA)and indistinguishable chosen plaintext attacks(INDCPA).Theoretical analysis and simulation results confirm that BEM-ABSE significantly improves computational efficiency compared to existing solutions.

关键词： Attribute-based encryption search encryption blockchain multi-authority cloud-edge

来源：评论

学校读者我要写书评

暂无评论

Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

引用

Soft Computing 2025年第4期29卷 2003-2013页

作者： Li, Te Chen, Liqiong Sun, Huaiying Hou, Mengxia Lei, Yunjie Zhi, Kaiwen School of Computer and Engineering East China University of Science and Technology Meilong road Shanghai200237 China School of Computer Science and Information Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China School of Chemistry and Environmental Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China

With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the temporal attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes an LSTM-DNN model based on the novel bidirectional Dynamic Time Warping (DTW) algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a novel bidirectional DTW algorithm to calculate the similarity between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" and "exploration" of information, and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs an Long Short-Term Memory-Deep Neural Networks (LSTM-DNN) to dynamically obtain user interests and preferences, and proposes a new metric Cumulative Self-System Diversity (CSSD) to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Joint reasoning-based embedded multi-hop KGQA

引用

Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 457-469页

作者： Xu, Tongzhao Tohti, Turdi Liang, Yi Zuo, Zicheng Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China

Existing multi-hop knowledge graph question answering (KGQA) methods, which attempt to mitigate knowledge graph (KG) sparsity by introducing external text repositories instead of leveraging the question-answer information itself, ignore the semantic gap between the question modality and the knowledge graph modality as well as the role played by neighboring entities in the best answer selection. To address the above problems, we propose a Joint Reasoning-based Embedded Multi-hop KGQA (JREM-KGQA) method, which addresses these issues through three key innovations: 1) Early Joint Embedding. We construct a Question Answering-Knowledge Graph-Collaborative Work Diagram (QA-KG-CWD) and train the diagram using a knowledge graph embedding (KGE) model. This not only alleviates the knowledge graph sparsity but also effectively enhances the model's long-path reasoning ability. 2) Semantic Fusion Module. We narrowed the semantic gap between the question modality and the knowledge graph modality through the semantic fusion module to achieve more effective reasoning. 3) Node Relevance Scoring. We employ three node relevance scoring strategies to ensure that the best answer is selected from the huge knowledge graph. We evaluated our model on MetaQA as well as PQL datasets and compared it with other methods. The results demonstrate that our proposed model outperforms existing methods in terms of long-path reasoning ability, effective mitigation of knowledge graph sparsity, and overall performance. We have made our models source code available at github: https://***/feixiongfeixiong/JREM-KGQA. © 2024 - IOS Press. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

引用

Neural Computing and Applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

引用

Light(science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

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：