Rogue Access Points (RAPs) is one of the leading security threats in current network scenario, if not properly handled in time could lead from minor network faults to serious network failure. Most of the current solutions to detect rogue access points are not automated and are dependent on a specific wireless technology. In this paper we propose the integrated solution for detection and eliminate the rogue access points. Rogue detection algorithm is also proposed. This Methodology has the following properties: (1) it doesn’t require any specialized hardware; (2) the proposed algorithm detects and completely eliminates the RAPs from network; Our proposed solution is effective and low cost.
Content is a user-designed form of information, for example, observation, perception, or review. This type of information is more relevant to users, as they can relate it to their experience. The research problem is to identify the credibility and the percentage of credibility as well. Assessment of such content is important to convey the right understanding of the information. Different techniques are used for content analysis, such as voting the content, Machine Learning Techniques, and manual assessment to evaluate the content and the quality of information. In this research article, content analysis is performed by collecting the Movie Review dataset from Kaggle. Features are extracted and the most relevant features are shortlisted for experimentation. The effect of these features is analyzed by using base regression algorithms, such as Linear Regression, Lasso Regression, Ridge Regression, and Decision Tree. The contribution of the research is designing a heterogeneous ensemble regression algorithm for content credibility score assessment, which combines the above baseline methods. Moreover, these factors are also toned down to obtain the values closer to Gradient Descent minimum. Different forms of Error Loss, such as Mean Absolute Error, Mean Squared Error, LogCosh, Huber, and Jacobian, and the performance is optimized by introducing the balancing bias. The accuracy of the algorithm is compared with induvial regression algorithms and ensemble regression separately; this accuracy is 96.29%.
Develop an unsupervised learning framework for extracting popular product attributes from product description pages originated from different E-commerce Web sites. Unlike existing information extraction methods that do not consider the popularity of product attributes, in this proposed framework is able to not only detect popular product features from a collection of customer reviews but also map these popular features to the related product attributes. Building an intelligent E-commerce systems typically involves a component that can automatically extract product attribute information from a variety of product description pages in different E-commerce Web sites. Web information extraction methods such as wrappers are able to automatically extract product attributes from the Web content One novelty in this framework is that it can bridge the vocabulary gap between the text in product description pages and the text in customer reviews. Technically,in this framework developed a discriminative graphical model based on hidden Conditional Random Fields. As an unsupervised model, this framework can be easily applied to a variety of new domains and Web sites without the need of labelling training samples. E-commerce is proposed for enhancing the capability. Covered by electronic commerce surroundings, facing therefore voluminous new recent business model, it's obligatory to conduct the analysis to the electronic commerce pattern analysis method and like this is often useful in North American nation uncover the new electronic commerce pattern as provide the approach for electronic commerce pattern modernization to be conjointly helpful within the enterprise outline the particular electronic commerce strategy and therefore the implementation step. Initiated from this encouragement, during this paper proposes the innovative construct of the E-commerce recent agricultural product selling supported the massive web knowledge platform later the rapid development of rebuilding and opening up, China's agriculture has entered a new historical stage of development. Evaluate the growth mechanism of agricultural production enterprises from the angle of resource dynamic provide. In the e-commerce environment, the enterprise data and economic information are relatively concentrated, so the economical accounting system can instantly grasp the current activities of the economical data, and quickly generate economical information.
Software cost estimation is the important activity while the development of the software. Expenditure assessment is bit complex task as it can be affected by many factors. This factors aids in the calculating of maintenance cost of software. In this paper we have implemented the function point analysis and test point analysis in order to discover the maintenance cost. This is accomplished by using the various techniques to calculate the function point analysis and test point analysis. Along with the calculation of maintenance cost we have also presented the module to assess the reliability of the software from the context of white box testing. Software reliability growth models are aids to evaluate the reliability of the software. This paper presented the analysis of code based SRGM to estimate the reliability.
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