The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

Existing lightweight salient object detection (SOD) methods aim to solve the problem of high computational costs that is prevalent with heavyweight methods. However, compared with heavyweight methods, the detection accuracy of lightweight methods is greatly reduced while real-time performance is not significantly improved. Therefore, we aim to establish a trade off between computational cost and detection performance by improving the network efficiency. We propose a fast and extremely lightweight end-to-end wavelet neural network (ELWNet) for real-time salient object detection. ELWNet can achieve salient object detection and segmentation at approximately 70FPS (GPU), 19FPS (CPU) with 76K parameters and 0.38G FLOPs. We introduce wavelet transform theory into a neural network, proposing a wavelet transform module (WTM), a wavelet transform fusion module (WTFM), a novel feature residual mechanism, and construct an efficient architecture. The wavelet transform theory is integrated into the neural network to realize the interaction between the features in the frequency and the time domain. Meanwhile, ELWNet does not rely on a pre-trained model, which significantly reduces redundant features. We validate the performance of ELWNet using five well-known datasets, and demonstrate state-of-the-art performance compared with 24 other SOD models in terms of being lightweight, detection accuracy and real-time capabilities. Our method maintains high detection performance while reducing the number of model parameters by approximately 99% compared with heavyweight methods.

Cross-view geo-localization refers to recognizing images of the same geographic target obtained from different platforms (such as drone-view, satellite-view and ground-view). However, cross-view geo-localization is challenging as image capture using different platforms coupled with extreme viewpoint variations can cause significant changes to the visual image content. Existing methods mainly focus on mining the fine-grained features or the contextual information in neighboring areas, but ignore the complete information of the entire image and the association of contextual information of adjacent regions. Therefore, a multi-branch joint representation learning network model based on information fusion strategies is proposed to solve this cross-view geo-localization problem. Firstly, we obtain feature information from the image through global information fusion branch and local information fusion branch to help the network learn the discernable information in the different images. In addition, a local-guided-global information fusion branch is introduced to make local information assist global features to enhance the learning of potential information in the images. Secondly, we introduced different information fusion strategies in each branch to increase the extraction of contextual information through expanding the global receptive field, thus improving the performance of the model. Finally, a series of experiments is carried out on four prevailing benchmark datasets, namely University-1652, SUES-200, CVUAS and CVACT datasets. The quantitative comparisons from the experiments clearly indicate that the proposed network framework has great performance. For example, compared with some state-of-the-art methods, the quantitative improvements of the R@1 and AP on the University-1652 datasets are 1.91%, 2.18% and 1.55%, 2.99% in both tasks, respectively.

Traditional visual place recognition (VPR) methods generally use frame-based cameras, which will easily fail due to rapid illumination changes or fast motion. To overcome this, we propose an end-to-end VPR network using event cameras, which can achieve good recognition performance in challenging environments (e.g., large-scale driving scenes). The key idea of the proposed algorithm is first to characterize the event streams with the EST voxel grid representation, then extract features using a deep residual network, and, finally, aggregate features using an improved VLAD network to realize end-to-end VPR using event streams. To verify the effectiveness of the proposed algorithm, on the event-based driving datasets (MVSEC, DDD17, and Brisbane-Event-VPR) and the synthetic event datasets (Oxford RobotCar and CARLA), we analyze the performance of our proposed method on large-scale driving sequences, including cross-weather, cross-season, and illumination changing scenes, and then, we compare the proposed method with the state-of-the-art event-based VPR method (Ensemble-Event-VPR) to prove its advantages. Experimental results show that the performance of the proposed method is better than that of the event-based ensemble scheme in challenging scenarios. To the best of our knowledge, for the VPR task, this is the first end-to-end weakly supervised deep network architecture that directly processes event stream data.

Cross-view geo-localization refers to the task of matching the same geographic target using images obtained from different platforms, such as drone-view and satellite-view. However, the view angle of images obtained through different platforms will vary greatly, which can bring great challenges to the cross-view geo-localization task. Therefore, we propose a multilevel feedback joint representation learning network based on adaptive area elimination to solve the cross-view geo-localization problem. In our network model, we first process the extracted global features to obtain part-level and patch-level features. We then utilize these features as feedback to the global features to extract the contextual information in the global features and improve the robustness of the extracted features. In addition, as images obtained from different platforms differ, there will always be some interference when matching images. Therefore, we introduce an adaptive area elimination strategy to erase the interference information in the global features and assist the model in obtaining crucial information. On this basis, the feature correlation loss function is designed to constrain learning when using global feature information, thereby eliminating possible interference, which can improve the network model performance. Finally, a series of experiments is carried out using two well-known benchmarks, namely, University-1652 and SUES-200, and the experimental results show that the proposed network model achieves competitive results, thereby demonstrating the effectiveness of the proposed model.