Nowadays, the collaborative filtering techniques have demonstrated an excellent performance in the top-N recommendation. However conventional methods in similarity measurement are insufficient when the condition of data sparsity and cold start occur, which leads to a poor accuracy in prediction. In order to concur the limitation, a collaborative filtering algorithm of calculating similarity based on item rating and attributes is proposed. Firstly, we calculate the similarity of item attributes, then calculate the similarity of the project according to the user rating of the project. Meanwhile, a weighted control coefficient is proposed to combine the similarity between item attributes and rating of items, which contribute to obtain nearest neighbors. Experiments have shown that our algorithm has major potential in solving the problem of cold start, therefore improving the precision of the recommendation system.

Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.

When the traditional collaborative filtering algorithm faces high sparse data, its precision and quality of recommendation become unsatisfied. With the development of social networks, it is possible to selectively fill the missing value in the user-item matrix by using the friendship or trust relationship information of social networks. According to the memory-based collaborative filtering algorithm, in the paper, the two steps which are similarity calculation and user rating prediction are taken into account. Besides, this paper has filled appropriately the missing value and improved memory-based collaborative filtering recommendation algorithms to integrate the social relations. The experiment on the Epinions dataset shows that the improved algorithm can effectively alleviate the sparsity problem of user rating data and perform better than other classic algorithms in RMSE and MAP evaluation metrics.

When medical images transmitted and stored in hospitals, it require strict security, confidentiality and integrity. However, the transmission of wireless and wired networks has made the medical information vulnerable to attacks like tampering, hacking etc. And the ROI of medical image is unable to tolerate significant changes. In order to dealing these problems, we have proposed an algorithm that introducing the digital watermarking technology to increase the security of medical images. The scheme uses a part of sign sequence of DCT coefficients as the feature vector of images. It can avoid the sophisticated process of finding the Region of Interest (ROI) of medical images. At the same time, the watermarking image is encrypted by Logistic Map to enhance its confidentiality. The experimental results show that the scheme has strong robustness against common attacks and geometric attacks. Moreover, compared with the existing medical watermarking techniques, it can embed much more data, less complexity and make embed multi watermarks realized.