检索结果-内蒙古大学图书馆

Multimodal Wearable System With Dual-Frequency Enhancement Network for Risk Recognition

IEEE Internet of Things Journal 2025年第11期12卷 17634-17648页

作者： Yu, Feng Liu, Jiajie Yu, Hanchen Cheng, Wentao Liu, Li Jiang, Minghua Wuhan Textile University School of Computer Science and Artificial Intelligence Hubei Wuhan430200 China Engineering Research Center of Hubei Province for Clothing Information Hubei Wuhan430200 China Wuhan Textile University School of Electronics and Electrical Engineering Hubei Wuhan430200 China

Smart wearable systems can monitor users’ physiological data in real time, detect anomalies promptly through risk recognition technologies, provide early warnings, and assist users in taking preventive measures. However, single modal information is difficult to accurately recognize the behavioral state, expression state, and environmental conditions. Furthermore, multimodal data are often affected by noise and interference, complicating the accurate identification of risky behaviors. To address these challenges, we propose a smart wearable system based on the dual-frequency enhancement network (DFENet): 1) the multimodal sensor system is designed to combine behavioral recognition, expression recognition, and environmental recognition for comprehensive monitoring and recognition of multidimensional risk factors in complex scenarios;2) the DFENet is proposed to overcome challenges in feature extraction and accurate classification in complex environments;and 3) the behavioral recognition dataset and the expression recognition dataset are built to verify the effectiveness of the designed smart wearable system. Experimental results indicate that the proposed system can real-time achieve risk recognition across physical activity, expression state, and environmental conditions, and the proposed DFENet achieves excellent performance in accuracy, parameters, and floating-point operations (FLOPs) metrics on the three datasets. © 2014 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

A hybrid blockchain and machine learning approach for intrusion detection system in Industrial Internet of Things

引用

Alexandria engineering Journal 2025年 127卷 619-627页

作者： Song, Wu Zhu, Xiangyuan Ren, Sheng Tan, Wenxue Peng, Yibo School of Computer and Electrical Engineering Hunan University of Arts and Science Hunan Changde415000 China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Hunan Changsha410138 China

The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, which enables manufacturing to be automated and data collected in real-time. Edge IoT devices are subject to cybersecurity threats and unauthorised access. Decentralisation and resource limitations of IIoT often prevent traditional security mechanisms from addressing these threats. Intrusion detection systems (IDSs), which are used to detect intrusions in IIoT environments, are presented in this paper as hybrid machine learning-blockchain approaches. Blockchain technology ensures data integrity, secures communication, and prevents unauthorised modifications through the proposed system. To reduce false positives and improve threat detection accuracy, XGBoost is able to reduce the number of false positives. Using the BOT-IoT dataset, the model is demonstrated to be superior to conventional intrusion detection systems. This approach ensures enhanced security and trustworthiness of IIoT networks by offering a scalable, efficient, and secure solution. © 2025

关键词： Industrial internet of things (IIoT)

来源：评论

学校读者我要写书评

暂无评论

A 10-Approximation of the π2-MST 39

A 10-Approximation of the π2-MST

引用

39th International Symposium on Theoretical Aspects of computer science, STACS 2022

作者： Biniaz, Ahmad Daliri, Majid Moradpour, Amir Hossein School of Computer Science University of Windsor Canada School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (纸本)9783959772228

Bounded-angle spanning trees of points in the plane have received considerable attention in the context of wireless networks with directional antennas. For a point set P in the plane and an angle α, an α-spanning tree (α-ST) is a spanning tree of the complete Euclidean graph on P with the property that all edges incident to each point p ∈ P lie in a wedge of angle α centered at p. The α-minimum spanning tree (α-MST) problem asks for an α-ST of minimum total edge length. The seminal work of Anscher and Katz (ICALP 2014) shows the NP-hardness of the α-MST problem for α = 23π , π and presents approximation algorithms for α = π2 , 3 , π. 2π In this paper we study the α-MST problem for α = π2 which is also known to be NP-hard. We present a 10-approximation algorithm for this problem. This improves the previous best known approximation ratio of 16. © Ahmad Biniaz, Majid Daliri, and Amir Hossein Moradpour.

关键词： Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第4期35卷 5355-5366页

作者： Du, Hao Wang, Jiazheng Liu, Min Wang, Yaonan Meijering, Erik Hunan University College of Electrical and Information Engineering The Intl. Scientific and Technological Innovation Cooperation Base for Biomedical Image Processing Changsha Hunan410082 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar shapes as well as colors between lesions and surrounding tissues, which seriously affects the improvement of segmentation accuracy. In this article, a novel method called Swin Pyramid Aggregation network (SwinPA-Net) is proposed by combining two designed modules with Swin Transformer to learn more powerful and robust features. The two modules, named dense multiplicative connection (DMC) module and local pyramid attention (LPA) module, are proposed to aggregate the multiscale context information of medical images. The DMC module cascades the multiscale semantic feature information through dense multiplicative feature fusion, which minimizes the interference of shallow background noise to improve the feature expression and solves the problem of excessive variation in lesion size and type. Moreover, the LPA module guides the network to focus on the region of interest by merging the global attention and the local attention, which helps to solve similar problems. The proposed network is evaluated on two public benchmark datasets for polyp segmentation task and skin lesion segmentation task as well as a clinical private dataset for laparoscopic image segmentation task. Compared with existing state-of-the-art (SOTA) methods, the SwinPA-Net achieves the most advanced performance and can outperform the second-best method on the mean Dice score by 1.68%, 0.8%, and 1.2% on the three tasks, respectively. © 2012 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Edge Detection Algorithm for Urban Road Images Based on Deep Learning 3

Edge Detection Algorithm for Urban Road Images Based on Deep...

引用

3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Zhou, Mo Zhang, Li School Of Electrical And Electronic Engineering Wuhan Institute Of Shipbuilding Technology Wuhan China School Of Computer Science Wuhan Qingchuan University Wuhan China

ISBN: (纸本)9798350318609

Conventional algorithms demonstrate poor performance in complex urban road settings. Factors like noise, shadows, and changes in the composition of the road surface in photos can erode the accuracy of traditional algorithms, making them less reliable in actual city settings. The accuracy of edge detection in photographs of urban roads is improved by the incorporation of an effective neural network. This enhancement makes it possible for the algorithm to more accurately capture the intricate details and geometric structure of highways. In order to preserve the original image quality, this study preprocesses a sizable amount of urban road image data. The establishment of an efficient neural network involves constructing a network with an Encoder for image downsampling. The bottleneck comprises multiple residual blocks, and an Encoder is employed for image upsampling. © 2024 IEEE.

关键词： Edge detection

来源：评论

学校读者我要写书评

暂无评论

REES AAEE special issue on engineering education research capability development: introduction by guest editors

引用

Australasian Journal of engineering Education 2023年第1期28卷 2-7页

作者： Dart, Sarah Seniuk Cicek, Jillian Sohoni, Sohum Learning and Teaching Unit & Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Centre for Engineering Professional Practice and Engineering Education University of Manitoba Winnipeg Canada Department of Electrical Engineering and Computer Science Milwaukee School of Engineering Milwaukee United States

Since the late 20th century, engineering Education Research (EER) has been expanding globally as a field, although its identity varies across institutions and countries around the world. This diversity in how EER is experienced across contexts impacts how capability is developed, including identities, knowledge, practices, agendas, funding, and pathways. The theme of ‘engineering Education Research Capability Development’ was explored in papers, presentations, and workshops throughout the jointly held 2021 Research in engineering Education Symposium and Australasian Association for engineering Education conference. This special issue presents a continuation of that theme. The papers collectively contribute to expanding global understanding of the field, with implications for future capacity building efforts in engineering education practice and research. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： engineering education

来源：评论

学校读者我要写书评

暂无评论

Pedestrian wind flow prediction using spatial-frequency generative adversarial network

引用

Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

来源：评论

学校读者我要写书评

暂无评论

A hybrid optimization approach for securing cloud-based e-health systems

引用

Multimedia Tools and Applications 2025年第16期84卷 16525-16560页

作者： Altherwi, Abdulhadi Ahmad, Mohammad Tauheed Alam, Md. Moddassir Mirza, Hidayath Sultana, Nazia Pasha, Amjad Ali Sultana, Nishat Khan, Asif Irshad Alam, Md Mottahir Azim, Rezaul Department of Industrial Engineering College of Engineering and Computer Science Jazan University PO Box. 706 Jazan45142 Saudi Arabia College of Medicine King Khalid University Abha Saudi Arabia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering and Computer Science Jazan University Jazan45142 Saudi Arabia Government Medical College Siddipet Ensanpalli Siddipet District Telangana502114 India Aerospace Engineering Department King Abdulaziz University Jeddah21589 Saudi Arabia Department of Business Administration Applied College Jazan University P. O. Box 706 Jazan45142 Saudi Arabia Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz Jeddah21589 Saudi Arabia Department of Physics University of Chittagong Chattogram4331 Bangladesh

In modern healthcare, cloud-based e-health technology offers substantial benefits but faces significant security challenges. Sensitive patient data is vulnerable to cyber threats during transmission and storage, potentially enabling unethical collaboration between healthcare professionals and cloud storage providers. This paper aims to develop a robust security framework that safeguards patient privacy and prevents unauthorized access to sensitive medical information in remote patient health monitoring systems. The study presents an Artificial Hybrid Optimization with an Attribute-Based Encryption (AHO-ABE) model. The model leverages cryptographic techniques and hybrid optimization to enhance data security. Specifically, whale optimization (WO) is used for efficient key generation, while sheep flock optimization (SFO) is employed for continuous network surveillance. Experimental results demonstrate that the AHO-ABE model significantly enhances data security, outperforming existing RPM-MEC, AID, AI-HCS, and IoT-HMS approaches. The model exhibits superior resilience against unauthorized access and data breaches, achieving a 5 ms execution time, 850 kbps throughput, 0.121 mJ energy usage, and 4 s latency. Combining cryptographic techniques with hybrid optimization algorithms, the AHO-ABE model provides a robust framework for safeguarding sensitive medical information. This approach ensures the integrity and confidentiality of patient data in remote healthcare settings, offering an effective solution against high-performance cyber attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Detection of Anomaly Detection with high dimensional data using volume and velocity aspects 5

Detection of Anomaly Detection with high dimensional data us...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Chakraborty, Koushik Tata, Ravi Kumar Mahajan, Rashima Dudeja, Deepak Ramkumar, R. Gupta, Upasana Adamas University Kolkata India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India Department of Computer Science and Engineering MMEC MMDU Mullana Ambala India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Tamilnadu Tiruchirapalli India Department of Computer Science & Engineering BBD University Lucknow India

ISBN: (纸本)9798400709418

Now a days detection of various anomalies finds all the unexpected events in the computing information over the internet are defined as anomalous events or inconsistency events. While detecting the various inconsistencies will help in getting the useful computing information system. Inconsistency type locations and exception investigation is the way of information mining prepared for recognizing information focuses that drop exterior anomaly. While doing so a standard is set up to anticipated design in a dataset. This location preparation is crucial since peculiarities like these are frequently a marker of bizarre behavior, such as potential false endeavors, security breaches, or cyber security interruptions. Organizations use information mining methods to identify inconsistencies and screen information points inputs and outputs moving over their IT framework and frameworks. On a very basic level, peculiarity discovery makes a difference organization address security spill, dodge information introduction, and set cautions against unordinary behavior that goes astray from the built-up design. This research paper deals with various techniques and ways for several anomalies inside the information. The finding of the research is how to detect the anomalies. © 2023 ACM.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

NVMS-Net: A Novel Constrained Noise-View Multiscale Network for Detecting General Image Processing Based Manipulations

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1233-1247页

作者： Singh, Gurinder Rana, Kapil Goyal, Puneet Kumar, Sathish Cleveland State University Electrical Engineering and Computer Science ClevelandOH44115 United States Indian Institute of Technology Ropar Punjab Rupnagar140001 India Thapar Institute of Engineering and Technology Punjab Patiala147004 India NIMS University Rajasthan Jaipur303121 India

Ensuring the authenticity and integrity of digital images is a major concern in multimedia forensics, driving research on universal schemes for detecting diverse image manipulations (or processing operations). Although some prior works have addressed general-purpose image manipulation detection, they have been evaluated under constrained environments. Developing an approach that effectively identifies multiple manipulations in real-world scenarios remains a challenge. To address this issue, in this article, we have designed a novel constrained noise-view based multiscale network (NVMS-Net) that jointly exploits constrained noise-view and multiscale feature learning for multiple image processing operation detection. Our NVMS-Net includes four stages: constrained noise-view, generalizable feature learning, multiscale feature learning, and classification. First, a noise extraction layer (NEL) is employed to suppress the image content information for the extraction of noise features. The statistical modeling of these noise features is further improved by statistical modeling layer (SML) to achieve a better noise-view of image tampering artifacts. Then, we use multiple convolution-batch normalization-ReLU (CBR) blocks to learn generalizable noise features that are sensitive to image manipulations. Further, a multiscale aggregation module (MSAM) is proposed that leverages image tampering features at multiple scales through the aggregation of low and high-level features. The extensive experimental results show that the NVMS-Net consistently outperforms the existing approaches on different dataset settings. Importantly, NVMS-Net shows better performance in real-world scenarios, particularly against antiforensic techniques and adversarial attacks. The proposed NVMS-Net provides an overall accuracies of 98.21% and 98.60% on BOSSBase and Dresden datasets, respectively. © 2020 IEEE.

关键词： Adversarial machine 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：