检索结果-内蒙古大学图书馆

27th international conference on Advanced Communications technology, ICACT 2025

作者： Sakano, Toshikazu Ojetunde, Babatunde Suzuki, Masahiro Temma, Katsuhiro Nakamura, Akira Fukada, Toshiaki Wave Engineering Laboratories Advanced Telecommunications Research Institute International 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto619-0288 Japan ATR-Trek Co. Ltd. 6-1-1-18F Nishinakajima Yodogawa-ku Osaka-shi Osaka532-0011 Japan

ISBN: (纸本)9791188428137

With the worldwide increase in disaster occurrences and their scale of damage, a resilient information and Communication technology (ICT) system that remains available in disaster-affected areas despite Internet, telecom, or mobile service disruptions is strongly desired. In particular, disaster first responders who rush to disaster-stricken areas to carry out investigations, search and rescue operations for people, require tools to collect, share, report, and record disaster information. This paper proposes a portable and elastic edge computing network to support the activities of disaster first responders. The portability and elasticity features enable us to adaptively apply the proposed edge computing network in various disaster situations. In the edge computing node, Artificial Intelligence (AI) functions, such as automatic speed recognition, are equipped to offer handsfree operation and on-site intelligent image processing for first responders. We developed a pilot model of an edge computing node and confirmed the requirements to include AI functionality through experimental evaluation. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

A Novel Federated Learning Framework for Healthcare Applications Using Wearable Devices 4

A Novel Federated Learning Framework for Healthcare Applicat...

引用

4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Natarajan, Rajesh Krishna, Sujatha Ranjith, Christodoss Prasanna University of Technology and Applied Sciences Al-Aqr 324 College of Computing and Information Sciences Information Technology Department Shinas Oman Messiah University Computer Science Department MechanicsburgPA United States

ISBN: (纸本)9798331518882

A key necessity in managing signals for processing and generating useful information in the healthcare domain is the use of many algorithms. Traditional computing methods have been successful but may not be effective in the future as secure privacy matters. This has led to the increasing popularity of machine learning technologies. However, we present a novel device for devices and sensors for federated learning (FL), which is aimed at establishing a new, safer framework. This pattern uses machine learning with a lot of emphasis on the availability of the devices. In such a case, models for the detection, monitoring, and management of diseases can be created. The FL approach is also believed to be applicable to several healthcare contexts. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Secure and privacy-preserving federated learning-based resource allocation for next generation networks

Secure and privacy-preserving federated learning-based resou...

引用

international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Dandoush, Abdulhalim Gouissem, Ala Ciftler, Bekir Sait Division of Information Technology College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar ESME Sduria/L2TI at Sorbonne University Paris Nord France Division of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781032803722

This paper surveys recent research on federated learning-based resource allocation for next-generation networks in order to identify research gaps and potential future directions. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in the literature are then reviewed. The strengths and limitations of these solutions are analyzed, and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., game theory or optimization techniques with federated learning) and developing new models and algorithms for AI-based resource allocation that address specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field. © 2025 The Author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT) 2nd

Towards a serverless computing and edge-intelligence archite...

引用

2nd international conference on Next-Generation computing and information Systems, ICNGCIS 2023

作者： Gupta, Ankur Gupta, Surbhi Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

Serverless computing has emerged as a compelling paradigm for the deployment of applications at the edge facilitating performant and lightweight services. Services deployed at the edge typically consume lesser compute and storage resources while offering relatively lower latency. Hence, a serverless architecture is a natural fit for the requirements of the Personal-Internet-of-Things (PIoT), an emerging sub-domain of the IoT in which data acquisition, analysis and computation is performed in the context of an individual user and her environment. For such environments privacy preservation, resource-light models, fast detection of emergency situations and low response times for services is paramount. In addition users demand hyper-personalized service delivery requiring embedded intelligence for real-time interventions. This paper proposes a serverless computing architecture with embedded intelligence at the edge for the PIoT which effectively meets the stated requirements. A proof-of-concept using AWS Lambda and tiny AI is implemented and initial results presented, establishing the effectiveness of the proposed framework. © 2025 The Author(s).

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Partial Distributed Deep Learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

引用

8th international conference on Electronic information technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. The latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Unified resource management platform based on the Netty framework 24

Unified resource management platform based on the Netty fram...

引用

5th international conference on Computer Science and Management technology, ICCSMT 2024

作者： Zhou, Liyan Wang, Tianwen School of Information Engineering Jiangsu Maritime Institute Jiangsu Nanjing China School of Naval Architecture & Intelligent Manufacturing Jiangsu Maritime Institute Jiangsu Nanjing China

ISBN: (纸本)9798400709999

With the advancement of formalization in China, e-government has realized the electronization of government affairs and improved the digital ***, the lack of unified resource management norms leads to the separate business system, complex management and prominent information *** order to solve this, this paper proposes unified management of resources and provides unified services, aiming at unified management of user, application and authorization information, and realizes unified data management and services through unified resource coding, storage design and resource *** system adopts Netty framework, Redis cache technology and database architecture to optimize the performance, adapt to the localization environment, improve the connection speed, reduce the latency and database read and write *** advantage is with the application system depth integration, can realize the unified coding of information resources, management and use, reduce repeated work, to ensure that the data only, consistent and the latest, for data authorization, login information research provide theory and practice reference, to the current network environment under the realization of trust service has certain reference value. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Netty framework

来源：评论

学校读者我要写书评

暂无评论

Cloud computing Resource Prediction Based on Adaptive Selection of Multiple Machine Learning Methods 24

Cloud Computing Resource Prediction Based on Adaptive Select...

引用

8th international conference on Electronic information technology and Computer Engineering, EITCE 2024

作者： Huang, Chao Wang, Xiaogang Yang, Zhihe Xiao, Dun School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In modern cloud computing environments, accurate prediction of resource demand is critical to optimize resource allocation and reduce costs. Existing methods are often inadequate in dealing with variable workloads. In this research, we introduce an innovative combined multiple machine learning prediction approach to enhance prediction accuracy and stability. First, a comprehensive dataset is built by segmenting historical resource utilization and extracting feature vectors using dynamic variable windows. The variable window automatically adjusts its size according to the volatility, thus saving resources. Second, a number of different algorithms are used for forecasting, and the optimal method is selected to capture resource utilisation patterns and improve forecasting accuracy. Finally, the system calls the optimal algorithm from the adaptive selector for prediction based on the feature vectors. The experimental results show that the method performs well under various workloads and scenarios and significantly outperforms the traditional method, providing a theoretical basis and practical guidance for improving the efficiency and performance of cloud computing resource management. © 2024 Copyright held by the owner/author(s).

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Building a safe and secure metaverse 2nd

Building a safe and secure metaverse

引用

2nd international conference on Next-Generation computing and information Systems, ICNGCIS 2023

作者： Gupta, Ankur Model Institute of Engineering and Technology Jammu India

ISBN: (纸本)9781032738659

The metaverse is an exciting domain representing an amalgamation of advances in computing and communications along with immersive technologies (AR/VR/XR). It is therefore intuitive for large corporations, research organizations and businesses cutting across industry verticals to adopt a metaverse-centric approach and explore diverse use-cases catering to business/individual needs. However, the transition from traditional applications to metaverse-native applications is not expected to be seamless. Privacy, safety and security concerns exist in the early versions of the metaverse with several flaws endangering individual user safety being highlighted. Plus, there is increased scrutiny on the illeffects of social media, gaming and immersive environments by Governments and regulatory agencies. Thus, it is increasingly clear that the final iteration of the metaverse will need to address the concerns of individual users while navigating a complex legal and regulatory landscape. This keynote examines the different aspects and challenges which the future metaverse will need to address. A set of "first principles" are put forth towards building an inclusive, safe and secure metaverse. © 2025 The Author(s).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep learning based multiple regression analysis approach for designing medical image recognition system 2nd

Deep learning based multiple regression analysis approach fo...

引用

2nd international conference on Next-Generation computing and information Systems, ICNGCIS 2023

作者： Singh, Shweta Jagadish, A.N. Vyas, Bhuman Wasim Bhatt, Mohammed Sameen, Aws Zuhair Degadwala, Sheshang Electronics and Communication Department IES College of Technology Bhopal India Department of Computer Science Bapuji Institute of Engineering and Technology Karnataka Davanagere India Credit Acceptance Canton United States Model Institute of Engineering and Technology J&K Jammu India College of Medical Techniques Al-Farahidi University Baghdad Iraq Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

Machine learning models are optimization models that make it possible to gather data, evaluate it, and provide the experts and management with the reports they need to make the best decisions. This study uses quantitative research, as it seeks to gather information and analyse it with statistical models such as regression analysis and analysis of variance. Researchers want to use statistical techniques such as descriptive analysis, regression analysis, and ANOVA (analysis of variance) to analyse the hypothesis. The purposive sample technique is used by the authors to choose respondents who work in the healthcare sector. A non-probability sampling technique called purpose sampling is used. The R squared value is 0.744, or 74.4 percent, according to the analysis. According to the investigation, there is a strong correlation between improved picture quality and ML-based techniques in digital image recognition. © 2025 The Author(s).

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

引用

IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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：