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Titlebook: Advances in Software Engineering; International Confer Tai-hoon Kim,Wai-Chi Fang,Kirk P. Arnett Conference proceedings 2009 Springer-Verlag

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樓主: energy
41#
發(fā)表于 2025-3-28 17:37:59 | 只看該作者
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發(fā)表于 2025-3-28 19:30:20 | 只看該作者
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發(fā)表于 2025-3-29 00:15:16 | 只看該作者
A Model-Driven Framework for Dynamic Web Application Development,b applications in an object-oriented manner. In terms of the Model-View-Control framework (MVC), the control layer has two responsibilities, one is to retrieve the data for the view layer, and the other is to control the navigational structure of the view layer. This makes the boundary between the c
44#
發(fā)表于 2025-3-29 05:06:52 | 只看該作者
Experience with MOF-Based Meta-modeling of Component-Based Systems,ications, including embedded ones. To allow comfortable and easy development, component systems have to provide a rather a big set of development supporting tools including at least a tool for composition and repository for storing and retrieving components. In this paper, we evaluate and present ad
45#
發(fā)表于 2025-3-29 09:28:10 | 只看該作者
Closely Spaced Multipath Channel Estimation in CDMA Networks Using Divided Difference Filter, Difference Filter (DDF). We consider the case of paths that are a fraction of chip apart, also knwon as closely spaced paths. Given the nonlinear dependency of the channel parameters on the received signals in multiuser/multipath scenarios, we show that the DDF achieves better performance than its
46#
發(fā)表于 2025-3-29 13:32:49 | 只看該作者
,Building “Bag of Conception” Model Based on DBpedia,ion systems are based on the “Bag ofWords” (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich text representation by means of manual intervention or
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發(fā)表于 2025-3-29 15:44:57 | 只看該作者
Study on the Performance Support Vector Machine by Parameter Optimized, machine learning techniques like support vector machines(SVM) and related large margin methods have been successfully applied for this task. Unfortunately, the high dimensionality of input feature vectors impacts on the classification speed. The kernel parameters setting for SVM in a training proce
48#
發(fā)表于 2025-3-29 22:45:37 | 只看該作者
49#
發(fā)表于 2025-3-30 02:00:25 | 只看該作者
Discovering Decision Tree Based Diabetes Prediction Model,s our tool to discover decision tree based diabetes prediction model from a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. Following the data mining process, our discussion will focus on the data preprocessing, including attribute ide
50#
發(fā)表于 2025-3-30 04:49:06 | 只看該作者
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