Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized

weighted in the form of matrices as; internal factor evaluation and external factor evaluation. Analytical hierarchy process and expert choice software were applied to weight the factors. At the end, by considering the socioeconomic and environmental issues, the strategy of using protective strategies

class of multiprocess programs. A correctness proof can be designed together with the program by a hierarchical process of stepwise refinement, making the method practical for larger programs. The resulting proofs tend to be natural formalizations of the informal proofs that are now used.

Hepatitis B virus infected patient

This paper examines the use of inductive learning to categorize natural language documents into predefined content categories. Categorization of text is of increasing importance in information retrieval and natural language processing systems. Previous research on automated text categorization has

a stepwise process for regeneration and revegetation of

to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From

The diagnoses of many diseases have become increasingly complex. Many results, obtained from tests with substantial imperfections, must be integrated into a diagnostic conclusion about the probability of disease in a given patient. A practical approach to this problem is to estimate the pretest probability of disease, and the sensitivity and specificity of different diagnostic tests. With this information, test results can be analyzed by sequential use of Bayes' theorem of conditional probability.

will depend on the formalism used, and may restrict in quite unexpected ways what we are able to formalise. In this paper we illustrate some limits to developing real world processes using existing formalisms, and we define a new notion of refinement, vertical refinement, which addresses some

Our understanding of the program derivation process has evolved to the point where it can be described in terms of a clearly defined sequence of steps. In this paper, we will identify those steps and show how they may be used to derive programs from formal specifications.