The idea behind layered design is the foundation of the Internet of Things. Each tier uses a variety of technologies for capacity, preparation, and information transmission. With regard to the risks and vulnerabilities related to IoT-enabled devices, as well as potential assurance mechanisms in light of equipment constraints and novel information transfer techniques, this study attempts to analyze the current architecture of the Internet of Things. After that, we talk about IoT architecture and applications. The following is a list of effective real-time IoT applications that are now in use: Self-driving cars, traffic management systems, smart grids, logistic management hierarchies, environment monitoring, building safety applications, and many more are examples of emerging technologies. Specifically, we highlight the latest improvements and challenges associated with IoT security. Finally, we discuss the state-of-the-art research and its conclusions, as well as the outstanding problems for future IoT security research.

Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fields. The application of machine learning, and block-chain techniques into medical image retrieval, classification and auxiliary diagnosis has become one of the research hotspots at present. Brain tumor is one of the major diseases threatening human life. The number of deaths caused by these diseases is increasing dramatically every year in the world. Aiming at the classification problem of brain CT images in healthcare. We propose a Mask RCNN with attention mechanism method in this research. First, the ResNet-10 is utilized as the backbone model to extract local features of the input brain CT images. In the partial residual module, the standard convolution is substituted by deformable convolution. Then, the spatial attention mechanism and the channel attention mechanism are connected in parallel. The deformable convolution is embedded to the two modules to extract global features. Finally, the loss function is improved to further optimize the precision of target edge segmentation in the Mask RCNN branch. Finally, we make experiments on public brain CT data set, the results show that the proposed image classification fragrance can effectively refine the edge features, increase the degree of separation between target and background, and improve the classification effect.

To solve the problem of pesticide misuse in judging common wheat diseases, we propose a wheat disease identification scheme combining the VGG convolutional neural network model with WeChat mini program technology. In the model training process, we continuously adjust the structure of the convolutional neural network VGG-16 to realize the real-time and accurate identification model of wheat disease. Specifically, the model parameters are optimized by locally adjusting the convolution layer of the VGG-16 network to achieve accurate maximization. Through the verification, the best accuracy rate of the wheat disease identification model is 85.1%. The mini program is compiled by the WeChat developer tool, which is developed based on WXML, WXSS and JavaScript. After building the wheat disease identification model, it is deployed on a cloud server that works continuously for working 24 h a day. In addition, the mini program posts HTTPS requests as the function of wheat disease identification. The implementation of this scheme can help users identify different types of wheat diseases and provide corresponding solutions according to the results, which is of great significance in underdeveloped agricultural areas.

Object detection is an important computer vision task, which is developed from image classification task. The difference is that it is no longer only to classify a single type of object in an image, but to complete the classification and positioning of multiple objects that may exist in an image at the same time. Classification refers to assigning category labels to the object, and positioning refers to determining the vertex coordinates of the peripheral rectangular box of the object. Therefore, object detection is more challenging and has broader application prospects, such as automatic driving, face recognition, pedestrian detection, medical detection etc,. Object detection can also be used as the research basis for more complex computer vision task such as image segmentation, image description, object tracking and action recognition. In traditional object detection, the feature utilization rate is low and it is easy to be affected by other environmental factors. Hence, this paper proposes a multimodal deep learning-based feature fusion for object detection in remote sensing images. In the new model, cascade RCNN is the backbone network. Parallel cascade RCNN network is utilized for feature fusion to enhance feature expression ability. In order to solve the problem of different segmentation shapes and sizes, the central part of the network adopts multi-coefficient cascaded hollow convolution to obtain multi-receptive field features without using pooling mode and preserving image information. Meanwhile, an improved selfattention combined receptive field strategy is used to obtain both low-level features with marginal details and high-level features with global semantics. Finally, we conduct experiments on DOTA set including ablation experiments and comparison experiments. The experimental results show that the mean Average Precision (mAP) and other indexes have been greatly improved, and its performance is better than the state-of-the-art detection algorithms. It has a good application prospect in the remote sensing image object detection task.

To solve the problem that the detection effect of crop pests and diseases is not ideal due to the complicated image background and the interference of irrelevant factors, this paper proposes a novel crop pests and diseases detection based on fuzzy neural network and multilevel feature fusion in remote sensing images. Firstly, the model is based on YOLOv5 and extracts the semantic level information of different depth features from the convolutional neural network, and then combines the weight aggregation module to learn the weight of each layer feature adaptively. Then the learned weights are loaded to the segmentation graphs obtained by sampling on each feature layer to obtain the final segmentation results. In this model, a fuzzy learning module is added to the skip connection part to remove noise features and alleviate the uncertainty between classes. The traditional cross entropy loss involves activating the output value with the Softmax function and calculating a weighted cross entropy loss with the label. If the weight of the cross entropy loss term is not adjusted, the model will tend to update the weight related to the background, which makes it difficult to deal with the category imbalance in remote sensing images. Therefore, we use focus loss to alleviate the problem of class imbalance in images. The results on public data sets show that the accuracy rate of the proposed model in this paper is over 95%, the recall rate is over 85%, and the average accuracy is 91.2%. In terms of F1, compared with other advanced methods, the presented method has achieved improvements of 6.6%, 8.5%, and 7.7% respectively. It shows that the new model has strong robustness and generalization for crop pest detection.