Focusing on the problem of poor memory in long sentences and the lack of core words' influence in semantic relation extraction, an Attention based bidirectional Neural Turing Machine (Ab-NTM) model was proposed. Instead of a Recurrent Neural Network (RNN), a Neural Turing Machine (NTM) was used firstly, and a Long Short-Term Memory (LSTM) network was acted as a controller, which contained larger and non-interfering storage, and it could hold longer memories than the RNN. Secondly, an attention layer was used to organize the context information on the word level so that the model could pay attention to the core words in sentences. Finally, the labels were gotten through the classifier. Experiments on the SemEval-2010 Task 8 dataset show that the proposed model outperforms most state-of-the-art methods with an 86.2% F1-score.

Micro-blog has become an important platform for information sharing. Meanwhile, it is also one of the main ways for spreading of different misinformation. In order to detect the micro-blog misinformation quickly and effectively, a method based on Gradient Boost Decision Tree (GBDT) was proposed. Firstly, classification features of content, user properties, information dissemination and time characteristic were extracted from the comments of micro-blog. Then an identification model based on GBDT algorithm was proposed to detect misinformation. Finally, two real micro-blog datasets were used to verify the efficiency and effectiveness of the model. The experimental results show that the proposed model can effectively improve the accuracy of micro-blog misinformation detection.

In Content-Based Image Retrieval (CBIR), the low-level visual features are not consistent with the high-level semantic features captured by human, and it is difficult to reflect the similarity of images by traditional distance measurements. To solve these problems, an image retrieval algorithm based on Convolutional Neural Network (CNN) and manifold ranking was proposed. Firstly, the image dataset was put into CNN, image features were extracted through the fully connected layers of the network after supervised learning; secondly, the image features were normalized and then Efficient Manifold Ranking (EMR) algorithm was used to return the ranked scores for query images; finally, the most similar images were returned to users according to the scores. In corel dataset, the mean Average Precision (mAP) of deep image feature was 53.74% higher than that of the scene descriptor features, and the mAP of efficient manifold ranking was 18.34% higher than that of the cosine distance. The experimental results show that the proposed algorithm can effectively improve the accuracy of image retrieval.

Samples are required to meet the manifold assumption in Spectral Embedded Clustering (SEC) algorithm, and class labels of samples can always be embedded in a linear space, which provides a new idea for spectral clustering of linearly separable data, but the linear mapping function used by the spectral embedded clustering algorithm is not available to process the nonlinear high-dimensional data. To solve this problem, this paper cored the linear mapping function, built a Spectral Embedded Clustering based on Kernel function (KSEC) model. This model can solve the problem that the linear mapping function can't deal with nonlinear data, as well as it can achieve kernel's dimension reduction synchronously. The experimental results on real data sets show that the improved algorithm can improve the clustering accuracy by 13.11% averagely, and the highest 31.62%, especially for high-dimensional data clustering accuracy can be increased by 16.53% on average. And the sensitive experiments on algorithm to parameters show the stability of the improved algorithm, so compared with traditional spectral clustering algorithms, higher accuracy and better clustering performance are obtained. And the method can be used for such complex image processing field as remote sensing image.

In this paper, the grey constrained nonlinear programming problems were investigated. With the help of mean, this paper firstly transformed the original grey optimization problem into a determinate constrained nonlinear programming problem. Then, based on the estimation of distribution algorithm, a stochastic search method was developed to solve the determinate constrained nonlinear programming problem. The key technique of the proposed method was explained in detail and the steps of the proposed method were described concretely. Finally, the elementary numerical examples show the proposed stochastic search method is feasible and effective.

The current network relationship analysis mainly studies the association relationship or group relations between users. Due to the variety of characteristic relation between users in mobile communication network, the relationship between the users and the groups are also diverse. On the basis of the specific groups with certain communication correlation and location similarity in the mobile communication network, the position prediction was introduced to the correlation measurement of position item, the location trajectory correlation measurement criterion was established, and an association user mining algorithm was proposed. The experimental result indicates that the proposed method can achieve the measuring of the relationship between users and the groups, and discover potential users associated with specific groups.

When the dark channel image dehazing algorithms deal with the bright region without satisfying the dark channel fog priori condition, the estimated transmission is relatively small, and it leads to large deviation from the original image in terms of color, smoothness and texture. Therefore, a feedback regulation mechanism of the dark channel dehazing was proposed. First, removed haze using dark channel prior algorithm and gave the feedback difference of the texture smoothness of haze-free image and the original image, segmented the bright region by using Fuzzy C-Means (FCM) algorithm, and then used the Gaussian function to adjust the transmission of the bright region, made it closer to the actual transmission. Finally, the article got haze-free image by using the adjusted transmission. The experimental results show that the proposed algorithm can effectively deal with the bright region which does not meet the assumptions of dark channel. It also makes the dehazed image's color more accord with the real scene, and its visual effect is also better. This method can improve the robustness of outdoor surveillance system.

The traditional three-decision-making model relies on the experience of experts to set the threshold, thus impeding the wide application of three-decision-making model in many fields. To minimize the decision-making risk, a computational model of the risk-loss was built, and a new classification algorithm which needs no priori knowledge was given. The algorithm used model conditions to determine the range of parameters value which minimized the risk-loss, then divided the range into several equal grids, got the smallest range of parameters through searching these grids, and the smallest range was the optimal threshold. At last, a three-decision-making classifier was built by using the threshold, and then this classifier and Bayesian classifier were applied to part of UCI data sets. The comparison shows that the performance of three-decision-making classifier is superior, which shows the effectiveness of the algorithm.

In this paper, a sort of optimal inventory model with postponement strategy i.e. bilevel integer programming problem was researched. Bilevel integer programming problem was proved to be equivalent to constrained integer programming problem, and could be transformed to integer programming problem without constraint via penalty function. A genetic algorithm was proposed to solve this problem. The numerical simulation results show that the proposed model can improve the inventory benefits of overall supply chain，and reasonably adjust the optimal inventory of all branch points.