Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation.

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment.

Intelligent technique for knowledge reuse of dental medical records based on case-based reasoning.

With the rapid development of both information technology and the management of modern medical regulation, the generation of medical records tends to be increasingly intelligent. In this paper, Case-Based Reasoning is applied to the process of generating records of dental cases. Based on the analysis of the features of dental records, a case base is constructed. A mixed case retrieval method (FAIES) is proposed for the knowledge reuse of dental records by adopting Fuzzy Mathematics, which improves similarity algorithm based on Euclidian-Lagrangian Distance, and PULL & PUSH weight adjustment strategy. Finally, an intelligent system of dental cases generation (CBR-DENT) is constructed. The effectiveness of the system, the efficiency of the retrieval method, the extent of adaptation and the adaptation efficiency are tested using the constructed case base. It is demonstrated that FAIES is very effective in terms of reducing the time of writing medical records and improving the efficiency and quality. FAIES is also proven to be an effective aid for diagnoses and provides a new idea for the management of medical records and its applications.