Internet of Things (IoT) devices promote the rapid development of cloud storage outsourcing data service, which is favored by more and more terminal users. Therefore, how to ensure the integrity verification of user data in cloud server has become a hot issue that needs to be solved urgently. For resource-constrained users, current cloud data audit scheme has the problems such as complex computation, high cost and low efficiency. To solve these problems, an efficient dynamic data audit scheme for resource-constrained users was proposed. First, a new data structure was proposed based on Novel Counting Bloom Filter (NCBF) and Multi-Merkle Hash Tree (M-MHT) to support dynamic audit, namely NCBF-M-MHT. In this data structure, the NCBF structure was able to realize the dynamic updating request of data within O(1) time, thereby ensuring the efficiency of audit. And the root node of M-MHT structure performed signing by user authentication to ensure the security of data. Then, different allocation methods were adopted for different audit entities, and the data evidence and label evidence were used to verify the correctness and integrity of data. Experimental results show that compared with the audit scheme based on Dynamic Hash Table (DHT), the audit scheme based on Merkle Hash Tree (MHT) and the audit scheme based on Location Array-Doubly Linked Info Table (LA-DLIT), the time cost of the proposed scheme in the audit verification phase is reduced by 45.40%, 23.71% and 13.85%, and the time cost in the dynamic update phase is reduced by 43.33%, 27.50% and 17.58% respectively.

Grape quality inspection needs the identification of multiple categories of grapes, and there are many scenes such as depth of field changes and multiple strings in the grape images. Grape recognition is ineffective due to the limitations of single pretreatment method. The research objects were 15 kinds of natural scene grape images collected in the greenhouse, and the corresponding image dataset Vitis-15 was established. Aiming at the large intra-class differences and small inter-class of differences grape images, a multi-scale grape image recognition method based on Convolutional Neural Network (CNN) was proposed. Firstly, the data in Vitis-15 dataset were pre-processed by three methods, including the image rotating based data augmentation method, central cropping based multi-scale image method and data fusion method of the above two. Then, transfer learning method and convolution neural network method were adopted to realiize the classification and recognition. The Inception V3 network model pre-trained on ImageNet was selected for transfer learning, and three types of models-AlexNet, ResNet and Inception V3 were selected for convolution neural network. The multi-scale image data fusion classification model MS-EAlexNet was proposed, which was suitable for Vitis-15. Experimental results show that with the same learning rate on the same test dataset, compared with the augmentation and multi-scale image method, the data fusion method improves nearly 1% testing accuracy on MS-EAlexNet model with 99.92% accuracy, meanwhile the proposed method has higher efficiency in classifying small sample datasets.

Concerning the problem that how to initialize the weights of deep neural networks, which resulted in poor solutions with low generalization for spam filtering, a classification method of Deep Belief Net (DBN) was proposed based on the fact that the existing spam classifications are shallow learning methods. The DBN was pre-trained with the greedy layer-wise unsupervised algorithm, which achieved the initialization of the network. The experiments were conducted on three datesets named LinsSpam, SpamAssassin and Enron1. It is shown that compared with Support Vector Machines (SVM) which is the state-of-the-art method for spam filtering in terms of classification performance, the spam filtering using DBN is feasible, and can get better accuracy and recall.

The thorough research on ant colony algorithm points out that the ant colony algorithm has superiority in solving large nonlinear optimization problem. Through careful analysis of the deficiencies that genetic algorithm and particle swarm algorithm solve the problem of vehicle dispatching system, based on the advantage of ant colony algorithm and the own characteristics of vehicle dispatching system, the basic ant colony algorithm was improved in the paper, and the algorithm framework was created. Based on the linear programming theory, the article established mathematical model and operation objectives and constraints for vehicle dispatching system, and got the optimal solution of vehicle dispatching system problem with the improved ant colony algorithm. According to the optimal solution and the dispatching criterion real-time scheduling was achieved. The article used Java language to write a simulation program for comparing the improved particle swarm optimization algorithm and ant colony algorithm. Through the comparison, it is found a result that the improved ant colony algorithm is correct and effective to solve the vehicle dispatching optimization problem.

Comprehensive and accurate annotation of Deep Web data is the key technology to Deep Web data integration, but the existing methods of Deep Web data annotation are unavailable to effectively solve the problem. Therefore, an approach of Deep Web data annotation based on result schema was proposed. The paper, through analyzing Deep Web result pages and extracting structured data, completed data pretreatment work, then though establishing the correct semantic mapping relation between integrated result schema and staying annotation data, achieved correct annotation of Deep Web data. The experimental results over four real areas show that the proposed method can efficiently annotate Deep Web data.