检索结果-内蒙古大学图书馆

MCFusion: Frequency Domain Characteristics Enhancement and Feature Compensation Fusion Network for RGB-T Object Detection

引用

IEEE Sensors Journal 2025年第11期25卷 20880-20893页

作者： Gao, Yinbo Liao, Zhuhua Liu, Yizhi Yi, Aiping Zhang, Guoqiang Hunan University of Science and Technology China Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China Hainan Normal University Department of Information Science and Technology Haikou China

The multi-modal object detection technology based on visible-thermal vision sensors has drawn significant attention as it is capable of achieving reliable object detection in complex scenes with challenging lighting conditions such as low light or backlight. However, there has been a lack of focus on the frequency domain feature information of the visible and thermal modalities themselves, as well as the complementarity of cross-modal features. Furthermore, current visible and thermal feature fusion methods only utilize feature information from the current layer, neglecting context information. Therefore, this paper proposes a novel network framework for enhancing frequency domain Characteristics and fusing cross-layer and cross-modal features to compensate for these limitations. This framework introduces two key modules: the Frequency Domain Characteristics Enhancement (FCE) module and the Cross-Layer and Cross-Modal Feature Compensation Fusion (CCF) module. The FCE module consists of two sub-modules. Reduce high-frequency information loss(FCE-RHFL) module operates on the visible modality to reduce high-frequency information loss, utilizing methods such as high-frequency masking and frequency recombination. Meanwhile, Enhance high-frequency information representation(FCE-EHFR) module enhances high-frequency information representation for the thermal modality through convolutions with different kernels and frequency domain enhancement techniques. In the CCF module, cross-modal and cross-layer feature compensation methods are employed to compensate for differences in modalities, followed by capture complementary information across modalities using a query-guided cross-attention mechanism. Finally, we conduct experimental comparisons on the KAIST and FLIR datasets, and the experimental results demonstrate that our method has excellent performance and robust detection results. © 2001-2012 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

引用

computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Review on use of Immersive Technologies in Tourism

Review on use of Immersive Technologies in Tourism

引用

2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Kulkarni, Simantinee Kulkarni, Anagha Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Cummins College of Engineering for Women Department of Information Technology Pune India

ISBN: (纸本)9798331508432

Nowadays immersive technologies play a very important role in different industries like automotive, manufacturing, marketing and advertising, travel, education, health care etc. Augmented Reality, Virtual Reality and Mixed Reality are the types of immersive technologies. Immersive technologies will have a very bright market soon. This paper firstly gives you a brief idea about immersive technologies. Immersive technologies have different use cases in different domains. Tourism is one of the important use cases of Immersive technology. People can visit different historical places on their tour. Historical places have a tremendous amount of knowledge. By using immersive technologies, we can make visits to historical places more interesting and livable. So, people can enjoy their tour after visiting such places. Tourists will get proper guidance using these technologies. This paper also reviews previous research work that has been conducted for historical places outside India. This paper discusses different types of tracking techniques, devices and software used for immersive technologies and how to model a virtual environment. © 2024 IEEE.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Smart Retail: Utilizing Machine Learning for Demand Prediction, Price Strategy, and Inventory Management 16

Smart Retail: Utilizing Machine Learning for Demand Predicti...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Singhal, Krish Singh, Vaibhav Kaul, Amrita Computer Science Engineering Jaypee Institute of Information Technology Noida India Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798331505264

This research demonstrates the ability of Artificial Intelligence (AI) to enhance real-time demand forecasting, price prediction, and inventory management by analyzing key factors such as competitor prices, customer preferences, and stock levels. The Random Forest Regressor (RFR) model has been implemented to achieve a Mean Squared Error (MSE) of 8.15% for demand forecasting and 1.11% for price prediction, highlighting its accuracy. These AI-driven insights allow businesses to make smarter, faster decisions, optimize pricing strategies, and adjust inventory levels to meet market demands. Additionally, the model helps avoid surplus or deficit, ensuring seamless inventory flow, reducing operational costs, and improving overall profitability. AI integration into retail processes allows companies to respond dynamically to real-time changes and maintain a competitive edge in a fast-paced market environment. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Data Secure Storage Mechanism for IIoT Based on Blockchain

引用

computers, Materials & Continua 2024年第3期78卷 4029-4048页

作者： Jin Wang Guoshu Huang R.Simon Sherratt Ding Huang Jia Ni School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Nanjing University of Information Science and TechnologyNanjing210044China

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of *** technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the *** the traditional blockchain,data is stored in a Merkle *** data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based ***,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of *** solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC ***,this paper uses PVC instead of the Merkle tree to store big data generated by *** can improve the efficiency of traditional VC in the process of commitment and ***,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of *** mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

关键词： Blockchain IIoT data storage cryptographic commitment

来源：评论

学校读者我要写书评

暂无评论

Secure blockchain bidding auction protocol against malicious adversaries

引用

High-Confidence Computing 2024年第3期4卷 58-63页

作者： Xiaobing Dai Jiajing Liu Xin Liu Xiaofen Tu Ruexue Wang Department of Computer and Information Engineering Baotou Vocational&Technical CollegeBaotou 014010China Department of Computer Tianjin Ren’ai CollegeTianjin 301636China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In recent years,with the development of blockchain,electronic bidding auction has received more and more *** at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third *** protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve *** can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary *** security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is *** efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

关键词： Blockchain Secure multi-party computation Electronic bidding auction Malicious model Real/ideal model paradigm

来源：评论

学校读者我要写书评

暂无评论

Analysis of Employee Attrition using Statistical and Machine Learning Approaches 1st

Analysis of Employee Attrition using Statistical and Machine...

引用

1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Singh, Akash Kumar Thakral, Prateek Department of Computer Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

ISBN: (纸本)9783031487804

Attrition means exit of employees from an organization due to any reason. The exit may be voluntary or involuntary in nature. Increased rate of employee attrition leads to loss of an organization’s resources in terms of time, cost, customer satisfaction as well as talent acquisition team’s efforts. The pattern of statistical approaches used in this paper to study the causes of attrition provides insights for the decision makers of organizations about the possible constraints due to which employees may think of leaving. This paper analyses an employee dataset and proposes a machine learning based model with an F1 Score of 0.99 and accuracy of 98.63% that can be used and deployed by the companies on their datasets to predict attrition. The timely prediction of attrition using this method will help the companies to prevent the possible attrition of employees and will help in retaining them for good. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review

引用

Recent Patents on engineering 2024年第9期18卷 63-77页

作者： Singh, Richa Srivastava, Nidhi Kumar, Ashwani Amity Institute of Information Technology Amity University Lucknow Campus Uttar Pradesh Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including national security, the storage of private data, social welfare, economic concerns, and more. As a result, many strategies and approaches for this goal have been developed over time, making the anomaly detection domain a large research subject. The primary concern of this patent study is to review the most crucial elements relating to anomaly detection, including an overview of background analysis and a core study on the most important approaches, procedures, and systems in the field. To make the structure of this survey easier to understand, the domain of anomaly detection was examined along with five dimensions: Detection methods in network traffic, objectives of the patent paper, various datasets used, accuracy, and open issues/gaps. The gap which has been identified after the survey can be extended as a future scope might be helpful for the researcher. © 2024 Bentham Science Publishers.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Inductive Lottery Ticket Learning for Graph Neural Networks

引用

Journal of computer Science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

引用

清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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：