The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence...The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.展开更多
利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,...利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,对检测方法的适用性提出了更高的要求。针对以上问题,本文提出基于改进PatchSVDD (Patch-level Support Vector Data Description)模型的田间异常区域检测方法,该方法仅使用田间正常区域的标注信息,即可对田间异常区域进行检测和定位。首先,改进方法引入不相邻图像块之间的边界损失函数,从而提升了正常与异常样本边界的判别性,改进了检测的鲁棒性;第二,引入外部记忆组件,通过压缩存储正常样本特征,从而在保证检测精度的基础上有效减少了测试阶段的时间和空间消耗;第三,构建了包含杂草簇、种植缺失、障碍物、双倍种植和积水共5个异常类型的田间异常数据集。本文方法在平均检测AUC(Area Under Curve)值和平均定位AUC值上分别达到了96.9%和94.6%,相比于原算法分别提升1.2%和1.6%,从而验证了方法的有效性。展开更多
Accurately diagnosing skin lesion disease is a challenging task.Although present methods often use the multi-branch structure to get more clues,the rigescent methods of cropping zone and fusing branch results fail to ...Accurately diagnosing skin lesion disease is a challenging task.Although present methods often use the multi-branch structure to get more clues,the rigescent methods of cropping zone and fusing branch results fail to handle the instability of the disease zone and the difference in branch results,which leads to improper cropping and degrades Deep Convolutional Neural Networks(DCNN)’s performance.To address these problems,we propose a Multi-scale DCNN with Dynamic weight and Part cross-entropy loss model(namely MDP-DCNN)to bootstrap skin lesion diagnosis.Inspired by the object detection method,the multi-scale structure adjusts the cropping position based on the Gradient-weighted Class Activation Mapping(Grad-CAM)center.It enables the model to adapt to the disease zone variety in position and size.The dynamic weight structure alleviates the negative influence of branch differences by comparing the grey-cropped zone and its CAM.Moreover,we also propose the part cross-entropy loss to deal with the over-fitting problem.This optimizes the non-targeted label to decrease the influence on other labels’stability when the prediction is wrong.We conduct our model on the ISIC-2017 and ISIC-2018 datasets.Experiments demonstrate that MDP-DCNN achieves excellent results in skin lesion classification without external data.Multi-scale DCNN with dynamic weight and part loss function verifies its advantages in enhancing diagnosis accuracy.展开更多
文摘The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network;Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.
文摘利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,对检测方法的适用性提出了更高的要求。针对以上问题,本文提出基于改进PatchSVDD (Patch-level Support Vector Data Description)模型的田间异常区域检测方法,该方法仅使用田间正常区域的标注信息,即可对田间异常区域进行检测和定位。首先,改进方法引入不相邻图像块之间的边界损失函数,从而提升了正常与异常样本边界的判别性,改进了检测的鲁棒性;第二,引入外部记忆组件,通过压缩存储正常样本特征,从而在保证检测精度的基础上有效减少了测试阶段的时间和空间消耗;第三,构建了包含杂草簇、种植缺失、障碍物、双倍种植和积水共5个异常类型的田间异常数据集。本文方法在平均检测AUC(Area Under Curve)值和平均定位AUC值上分别达到了96.9%和94.6%,相比于原算法分别提升1.2%和1.6%,从而验证了方法的有效性。
基金supported by the scholarship from China Scholarship Council(CSC)(No.CSC N°201806280502).
文摘Accurately diagnosing skin lesion disease is a challenging task.Although present methods often use the multi-branch structure to get more clues,the rigescent methods of cropping zone and fusing branch results fail to handle the instability of the disease zone and the difference in branch results,which leads to improper cropping and degrades Deep Convolutional Neural Networks(DCNN)’s performance.To address these problems,we propose a Multi-scale DCNN with Dynamic weight and Part cross-entropy loss model(namely MDP-DCNN)to bootstrap skin lesion diagnosis.Inspired by the object detection method,the multi-scale structure adjusts the cropping position based on the Gradient-weighted Class Activation Mapping(Grad-CAM)center.It enables the model to adapt to the disease zone variety in position and size.The dynamic weight structure alleviates the negative influence of branch differences by comparing the grey-cropped zone and its CAM.Moreover,we also propose the part cross-entropy loss to deal with the over-fitting problem.This optimizes the non-targeted label to decrease the influence on other labels’stability when the prediction is wrong.We conduct our model on the ISIC-2017 and ISIC-2018 datasets.Experiments demonstrate that MDP-DCNN achieves excellent results in skin lesion classification without external data.Multi-scale DCNN with dynamic weight and part loss function verifies its advantages in enhancing diagnosis accuracy.
