Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation

Research on eye detection and segmentation is even more important with mask-wearing measures implemented during the COVID-19 pandemic. Thus, it is necessary to build an eye image detection and segmentation dataset (EIMDSD), including labels for detecting and segmenting. In this study, we established a dataset to reduce elaboration for chipping eye images and denoting labels. An improved DeepLabv3+ network architecture (IDLN) was also proposed for applying it to the benchmark segmentation datasets. The IDLN was modified by cascading convolutional block attention modules (CBAM) with MobileNetV2. Experiments were carried out to verify the effectiveness of the EIMDSD dataset in human eye image detection and segmentation with different deep learning models. The result shows that the IDLN model achieves the appropriate segmentation accuracy for both eye images, while the UNet and ISANet models show the best results for the left eye data and the right eye data among the tested models.

Intrusion detection by machine learning for multimedia platform.

The multimedia service company, Netflix, increased the number of new subscribers during the Coronavirus pandemic age. Intrusion detection systems for multimedia platforms can prevent the platform from network attacks. An intelligent intrusion detection system is proposed for the security IP Multimedia Subsystem (IMS) based on machine learning technology. For increasing the accuracy of the classifiers, it is vital to select the critical features to construct the intrusion detection system. Two-class classifiers, including the Decision Tree, Support Vector Machine, and Naive Bayesian, are selected to evaluate intrusion detection accuracy. According to the three classifiers' accuracy values, the most critical features are selected based on the features' ranking orders. Six critical features are selected:Service, dst_host_same_srv_rate, Flag, Protocol Type, Dst_host_rerror_rate, and Count. Numerical comparison with state_of_the_art shows that critical features improve intrusion detection accuracy, which can be better than the deep learning method.