With the popularity of cloud computing, how to meet user's personalized and diverse requirements of service composition is a key problem that needs to be resolved. This paper proposes a cloud service composition method based on multi-granularity clustering, organizing services in the perspective of granularity to meet user's such requirements in service composition. Services disorder turn to be in order through multi-granularity clustering, which includes three steps: basic services clustering based on message semantic similarity computing, correlation mining and multigranularity services clustering. The experimental results demonstrate that by utilizing the proposed method, users' personalized and diverse requirements are satisfied and the efficiency of service composition is improved greatly.
Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys.
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