By Ed. by Jairo Gutiérrez.
The expanding enterprise use of instant and cellular applied sciences on a number of units has speeded up the necessity for a greater realizing of the applied sciences concerned. company facts Communications and Networking: A examine viewpoint addresses the major concerns for companies using information communications and the expanding value of networking applied sciences in enterprise. enterprise information Communications and Networking: A learn viewpoint covers a chain of technical advances within the box whereas highlighting their respective contributions to enterprise or organizational targets, and facilities at the problems with network-based purposes, mobility, instant networks, and community safety.
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Within the adult sites we find content ranging from the erotic to the pornographic, and within the non-adult sites we find health-based information, antipornographic and anti-AIDS sites, and so forth. Textual. and. Structural. Analysis. The selection of features used in a machine learning process is a key step which directly affects the performance of a classifier. Our study of the state of the art and manual collection of our test datasets helped us a lot to gain intuition on pornographic Web site characteristics and to understand discriminating features between pornographic Web pages and inoffensive ones.
In this chapter, we focus our attention on the use of skin color related visual content-based analysis along with textual and structural content-based analysis for improving automatic pornographic Web site classification and filtering. Unlike the most commercial filtering products which are mainly based on indicative keywords detection or manually collected black list checking, the originality of our work resides on the addition of structural and visual content-based analysis to the classical textual content-based analysis along with several major-data mining techniques for learning and classifying.
Each leaf node represents a class. In order to classify an unlabeled data sample, the classifier tests the attribute values of the sample against the decision tree. A path is traced from the root to a leaf node which holds the class predication for that sample. Let Ω be the population of samples to be classified. To each sample ϖ of Ω one can associate a particular attribute, namely its class label C. We say that C takes its value in the class of labels. For a problem of two classes c1, c2, one can thus write: C: Ω→Γ={c1, c2} ϖ→C(ϖ) For instance, c1 might be the label representing the class of pornographic Web sites while c2 the nonpornographic ones.