检索结果-南通市图书馆

Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

Digital Communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su nawaf alharbe Shuxin Feng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied College Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things Crowdsensing

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models

引用

Computer Systems Science & Engineering 2023年第4期45卷 395-408页

作者： Mohamed Ali Rakrouki nawaf alharbe Mashael Khayyat Abeer Aljohani Applied College Taibah UniversityMedina42353Saudi Arabia College of Computer Science and Engineering University of JeddahJeddah21959Saudi Arabia

Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text ***,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and ***,this paper proposes an improved extractive text summarization algorithm based on both topic and graph *** methodology of this work consists of two ***,the well-known TextRank algorithm is analyzed and its shortcomings are ***,an improved method is proposed with a new computational model of sentence *** experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization ***,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization *** experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE *** is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

关键词： Natural language processing text summarization graph model topic model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Minimizing Total Tardiness in a Two-Machine Flowshop Scheduling Problem with Availability Constraints

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 1119-1134页

作者： Mohamed Ali Rakrouki Abeer Aljohani nawaf alharbe Abdelaziz Berrais Talel Ladhari Applied College Taibah UniversitySaudi Arabia University of Tunis TunisTunisia

In this paper,we consider the problem of minimizing the total tardiness in a deterministic two-machine permutationflowshop scheduling problem subject to release dates of jobs and known unavailability periods of *** theoretical and practical importance of minimizing tardiness inflowshop scheduling environment has motivated us to investigate and solve this interested two-machine scheduling *** that solve this important optimality criterion inflowshop environment are mainly *** fact,despite the N P-hardnessin the strong sense of the studied problem,to the best of our knowledge there are no approximate algorithms(constructive heuristics or metaheuristics)or an algorithm with worst case behavior bounds proposed to solve this ***,the design of new promising algorithms is *** developfive metaheuristics for the problem under *** metaheuristics are:the Particle Swarm Optimization(PSO),the Differential Evolution(DE),the Genetic Algorithm(GA),the Ant Colony Optimization(ACO)and the Imperialist Competitive Algorithm(ICA).All the proposed metaheuristics are population-based *** metaheuristics have been improved by integrating different local search procedures in order to provide more satisfactory,especially in term of quality *** experiments carried out on a large set of randomly generated instances provide evidence that the Imperialist Competitive Algorithm(ICA)records the best performances.

关键词： Optimization machine scheduling flowshop evolutionary algorithms

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Hybrid Forecasting Framework Based on Support Vector Regression with a Modified Genetic Algorithm and a Random Forest for Traffic Flow Prediction

引用

Tsinghua Science and Technology 2018年第4期23卷 479-492页

作者： Lizong Zhang nawaf R alharbe Guangchun Luo Zhiyuan Yao Ying Li School of Computer Science and Engineering University of Electronic Sciences and Technology of China College of Community Taibah University Al-Madinah Saudi Arabia

The ability to perform short-term traffic flow forecasting is a crucial component of intelligent transportation systems. However, accurate and reliable traffic flow forecasting is still a significant issue due to the complexity and variability of real traffic systems. To improve the accuracy of short-term traffic flow forecasting, this paper presents a novel hybrid prediction framework based on Support Vector Regression （SVR） that uses a Random Forest （RF） to select the most informative feature subset and an enhanced Genetic Algorithm （GA） with chaotic characteristics to identify the optimal forecasting model parameters. The framework is evaluated with real-world traffic data collected from eight sensors located near the 1-605 interstate highway in California. Results show that the proposed RF- CGASVR model achieves better performance than other methods.

关键词： traffic flow forecasting feature selection parameter optimization genetic algorithm machine learning

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

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：