Aiming at the problems of feedforward Convolutional Neural Network(CNN), such as small receptive field, insufficient context information acquirement and that only shallow features can be extracted by the feature extraction convolutional layer of the network, an improved remote sensing image fusion algorithm based on channel attention feedback network was proposed. Firstly, the detail features of PANchromatic(PAN) images and the spectral features of Low-resolution MultiSpectral(LMS) images were initially extracted through two convolutional layers. Secondly, the extracted features were combined with the deep features fed back from the network and inputted to the channel attention mechanism module to obtain the initially refined features. Thirdly, the deep features with stronger characterization capability were generated by feedback module. Finally, High-resolution MultiSpectral(HMS) images were obtained by putting the generated deep features into the reconstruction layer with deconvolution. Experimental results on three different satellite image datasets show that the proposed algorithm can well extract the detail features of PAN images and the spectral features of LMS images, and the HMS images recovered by this algorithm are clearer subjectively and better than the comparison algorithms objectively; at the same time, the Root Mean Square Error(RMSE) index of the proposed method is more than 50% lower than that the traditional methods, and more than 10% lower than that the feedforward convolutional network methods.

The current study of instance-transfer learning suffers from the mismatch between the granularities of data from multi-source heterogeneous domains. A Transfer Sparse unsupervised Hierarchical Probabilistic Self-Organizing Graph (TSHiPSOG) method based on the framework of Hierarchical Probabilistic Self-Organizing Graph (HiPSOG) method in the single domain was proposed. Firstly, representation vectors with different granularities were extracted from source and target domains by using hierarchical self-organizing model based on a probabilistic mixture of multivariate Gaussian component; and the sparse graph probabilistic criterion was used to control the growth of the model. Secondly, the most similar representation vector of the target domain data was searched in the rich-information source domain by using the Maximum Information Coefficient (MIC). Then, the data in the target domain was classified using labels of similar representation vectors in the source domain. Finally, the experimental results on the international universal 20 Newsgroups dataset and the spam detection dataset show that the proposed method improves the average classifying accuracy of target domain using the information from source domain by 15.26% and 9.05%. Moreover, the approach improves the average classifying accuracy with mining different granularity representation vectors by 4.48% and 4.13%.

The existing privacy preservation methods in location-based services only focus on the protection of user location and identification information. It produces the truth of sensitive homogeneity attack when the queries in a cloaking set are sensitive information. To solve this problem, a personalized (k,p)-sensitive anonymization model was presented. On the basis of this model, a pruning tree-based cloaking algorithm called PTreeCA was proposed. The tree-type index in the spatial database has two features. The one is that mobile users are roughly partitioned into different groups according to the locations of mobile users; the other one is that the aggregated information can be stored in the intermediate nodes. By utilizing the two features, PTreeCA could find the cloaking set from the leaf node where the query user is and its sibling nodes, which are benefit for improving efficiency of the anonymization algorithm. The efficiency and effectiveness of PTreeCA are validated by a series of designed experiments on the simulated and real data sets. The average success rate is 100%, and the average cloaking time is only about 4ms. The experimental results show that PTreeCA is effective in terms of success rate, cloaking time, and the anonymization cost when the privacy requirements levels are low or medium.

To solve high energy and time delay cost problems caused by wormhole detection in Ad Hoc networks, a light-weighted wormhole detection method, using less time delay and energy, was proposed. The method used the neighbor number of routing nodes to get a set of abnormal nodes and then detect the presence of a wormhole by using the neighbor information of abnormal node when routing process was completed. The simulation results show that the proposed method can detect wormhole with less number of routing query. Compared with the DeWorm (Detect Wormhole) method and the E2SIW (Energy Efficient Scheme Immune to Wormhole attacks) method, it effectively reduces the time delay cost and energy cost.

In the field of cloud computing, it is a key problem that how task schedules. This paper presented an artificial immune algorithm for dynamic task scheduling on cloud computing platform. Firstly, the algorithm used the queuing theory to determine the conditions of cloud computing platform to maintain steady-state, and provided the basic data for the following algorithm. Then, this paper used the clone selection algorithm to search out the approximate optimal configuration which calculated resources for different virtual machines of different nodes in the cluster. Finally, proper load balancing processing algorithm joined with immune theory improved the antibody genes. The results of simulation experiment show that, this algorithm can effectively improve the convergence speed and accuracy, search reasonable allocation quickly and improve the cluster resource utilization.

The problem of birth defects is one of the most important public health problems in the world, and the application of data mining method to improve the diagnostic accuracy for birth defects is a hot medical research issue. The authors proposed two emerging patterns for birth defects feature extraction: the defection contrast to normal and the normal contrast to defection. The Birth Defects Detection based on Emerging Patterns (BDD-EP) algorithm was implemented through combining the proposed patterns with C4.5 decision tree. The extensive experimental results show that the detection accuracy of BDD-EP is as high as 90.1%, the F-measure of normal samples is 93.9%, and the F-measure of defect samples is 741%. Compared with other famous classical classification algorithms, BDD-EP algorithm can get better results.

Updated solution in parallel implementation of discovery of association rules was studied,and IDD algorithm based on DD algorithm was introduced.After that,HD algorithm,based on IDD and CD algorithms was proposed to solve the problem of distributing the candidate item sets among processors effectively.The last part was the complexity analysis of the IDD and HD algorithms.