Facilitated by the SOA and new Web technologies, Service-Oriented Rich Clients (SORCs) compose various Web-delivered services in Web browser to create new applications. The SORCs support client-side data storage and manipulation and provide more features than traditional thin clients. However, the SORCs might suffer from data access issues, mainly due to both client-side incompatible data sources and server-side improper or even undesirable cache strategies. Addressing the data access issues, this paper proposes a data access framework for SORCs. The main contributions of this paper are as follows. First, the framework makes the SORCs accommodate heterogeneous local storage solutions and diverse Web browsers properly. The framework abstracts the underlying details of different local storages and selects the most proper data sources for current SORC in use. Secondly, the framework provides a cache mechanism, which supports client-side customized cache strategies. An adaptive technique for the strategies is also proposed to adjust cache strategies based on users?? historical actions to achieve better performance. 相似文献
The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (Fy) and undrained shear strength of the cohesive soil (Cu) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R2) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.
Applied Intelligence - Pharmaceutical drug combinations can effectively treat various medical conditions. However, some combinations can cause serious adverse drug reactions (ADR). Therefore,... 相似文献
Neural Computing and Applications - With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and... 相似文献