Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC–ResNet) model for fine-grained disease identification in the system. MDFC–ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC–ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.

Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications.

Steganography has long been regarded as a tool used for illicit and destructive purposes such as crime and warfare. Currently, digital tools are widely available to ordinary computer users also. Steganography software allows both illicit and legitimate users to hide messages so that they will not be detected in transit. This article provides a brief history of steganography, discusses the current status in the computer age, and relates this to forensic, security, and legal issues. The paper concludes with recommendations for digital forensics investigators, IT staff, individual users, and other stakeholders.

Aspect based sentiment analysis (ABSA) has been an important field due to its practicality that can dig out the sentiment polarities of different aspects in a sentence. For a sentence, "The service is nice while the food is bad", ABSA can obtain positive with aspect service and negative with aspect food. With this feature, ABSA related services can help people understand product reviews and community status in a more convenient way. However, we have analyzed deploying ABSA models in cloud computing is not a good choice due to the network resource consumption, central server load and the security consideration. We develop a system called aspect based hierarchical system (ABHS) whose basis is edge computing. ABHS can mitigate mentioned disadvantages of cloud computing and effectively provide services like recognizing the aspects in reviews, figuring out the sentiment polarity of each aspect in a review as well as generating the overall sentiment polarity of an aspect in all reviews. We also propose a scheme to deploy ABHS in edge computing, which can take the advantages of hierarchy, distribution, low latency, and heterogeneity. Experiments show our ABHS can effectively recognize the aspects and obtain useful results.