Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval. Users of annotated images can locate images they want to search by providing keywords. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Users searching for images are usually only interested in the salient areas. We propose an algorithm that integrates a visual attention mechanism with image annotation. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. When the image is annotated, words relating to the salient region are given first. The support vector machine uses particle swarm optimization to annotate the images automatically. Experimental results show the effectiveness of the proposed algorithm.

With the progress of network technology, there are more and more digital images of the internet. But most images are not semantically marked, which makes it difficult to retrieve and use. In this paper, a new algorithm is proposed to automatically annotate images based on particle swarm optimization (PSO) and support vector clustering (SVC). The algorithm includes two stages: firstly, PSO algorithm is used to optimize SVC; secondly, the trained SVC algorithm is used to annotate the image automatically. In the experiment, three datasets are used to evaluate the algorithm, and the results show the effectiveness of the algorithm.

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As online social networking platforms change the ways and means of people communicating, accurate link prediction among a massive pool of users has become a difficult problem. The problem arises in many applications, such as friend recommendation, news feedback, and product recommendation. In this paper, we propose a novel algorithm to solve this problem. The existing online social network link prediction algorithms have some deficiencies in link prediction accuracy because they cannot make full use of information or capture all the features. From an unconventional perspective, this paper formulates the link prediction problem as a matrix denoising problem. We first propose an unsupervised marginalized denoising model (USMDM) and explain in detail its effectiveness. The core of the USMDM lies with a mapping function that is capable of identifying patterns in a vast amount of user information and also understands the topological structure of social networks. The mapping function projects the observed matrix onto a target matrix. To improve efficiency and prevent overfitting, a low-rank matrix is used to replace the original matrix in the learning process. Using the weak law of large number, the function can be learned on limited datasets. To illustrate the effectiveness of the proposed algorithm, experiments are conducted on four real social networks, and the results demonstrate the effectiveness of the model.INDEX TERMS Social networks, link prediction, matrix denoising, weak law of large number.

Text clustering is one of the key research areas in data mining. k-medoids algorithm is a classical division algorithm, and can solve the problem of isolated points, But it often converges to local optimum. This article presents a improved social evolutionary programming(K-medoids Social Evolutionary Programming,KSEP). The algorithm is the k-medoids algorithm as the main cognitive reasoning algorithm. and Improved to learning of Paradigm、Optimal paradigm strengthening and attenuation and Cognitive agent betrayal of paradigm. This algorithm will increase the diversity of species group and enhance the optimization capability of social evolutionary programming, thus improve the accuracy of clustering and the capacity of acquiring isolated points.