检索结果-南通市图书馆

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on remote Sensing Imaging

Computers, Materials & Continua 2023年第5期75卷 3993-4008页

作者： Sarkar Hasan Ahmed Adel Al-zebari rizgar r.zebari Subhi r.M.Zeebaree Computer Networks Department Sulaimani Polytechnic UniversitySulaimaniIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

remote sensing(rS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related *** has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic *** article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-resolution remote Sensing Images(ODLTCP-HrrSI)to resolve these *** presented ODLTCP-HrrSI technique majorly aims to forecast the critical traffic in smart *** attain this,the presented ODLTCP-HrrSI model performs two major *** the initial stage,the ODLTCP-HrrSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic ***,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)*** experimental outcomes demonstrate the enhanced performance of the ODLTCP-HrrSI technique over recent approaches with maximum accuracy of 98.23%.

关键词： remote sensing images traffic prediction deep learning smart cities intelligent transportation systems

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

Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

引用

Computer Systems Science & Engineering 2023年第11期47卷 1651-1664页

作者： Yousif Sufyan Jghef Mohammed Jasim Mohammed Jasim Subhi r.M.Zeebaree rizgar r.zebari Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Engineering College Al-Kitab UniversityKirkukIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

Wireless Sensor Networks(WSNs)gather data in physical environments,which is some *** ubiquitous sensors face several challenges responsible for corrupting them(mostly sensor failure and intrusions in external agents).WSNs were disposed to error,and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely *** learning(ML)was extremely utilized for detecting faults in ***,this study proposes a billiards optimization algorithm with modified deep learning for fault detection(BIOMDL-FD)in *** BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network *** do so,the presented BIOMDL-FD technique uses the attention-based bidirectional long short-term memory(ABLSTM)method for fault *** the ABLSTM model,the attention mechanism enables us to learn the relationships between the inputs and modify the probability to give more attention to essential *** the same time,the BIO algorithm is employed for optimal hyperparameter tuning of the ABLSTM model,which is stimulated by billiard games,showing the novelty of the *** analyses are made to affirm the enhanced fault detection outcomes of the BIOMDL-FD *** simulation results demonstrate the improvement of the BIOMDL-FD technique over other models with a maximum classification accuracy of 99.37%.

关键词： Wireless sensor network fault detection reliability deep learning metaheuristics

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

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

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

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：