针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了...针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
文摘针对传统面部识别技术中存在的诸多问题,如网络模型对关键通道特征的关注不足、参数量过大以及识别准确率不高等,本文提出了一种基于改进Visual Geometry Group 19(VGG19)模型的全新方案.该方案融合了U-Net网络架构的设计理念,并引入了改进的SE Attention模块,以期提高模型的收敛速度和对面部细节的关注程度.在保持VGG19深层特征提取能力的基础上,通过特定设计的卷积层和跳跃连接,实现了对特征的高效融合与优化.经过改进的VGG19模型,不仅能更好地提取面部特征,还能在保证准确率的前提下,降低模型参数,提高运算效率.为了验证改进模型的效果,利用FER2013数据集和CK+两个数据集对本文提出的模型进行了测试.实验结果显示,改进后的VGG19网络在表情识别的准确率上分别取得了1.58%和4.04%的提升.这一结果充分证明了本文提出的方法在解决传统面部识别问题方面的优越性,也为面部识别技术的进一步发展提供了新的思路.
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
