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With the rapid expansion of e-commerce over the decades, the growth of the user generated content in the form of reviews is enormous on the Web. A need to organize the e-commerce reviews arises to help users and organizations in making an informed decision about the products. Opinion mining systems based on machine learning approaches are used online to categorize the customer opinion into positive or negative reviews. Different from previous approaches that employed single rule based or statistical techniques, we propose a hybrid machine learning approach built under the framework of combination (ensemble) of classifiers with principal component analysis (PCA) as a feature reduction technique. This paper introduces two hybrid models, i.e. PCA with bagging and PCA with Bayesian boosting models for feature based opinion classification of product reviews. The results are compared with two individual classifier models based on statistical learning i.e. logistic regression (LR) and support vector machine (SVM). We found that hybrid methods do better in terms of four quality measures like misclassification rate, correctness, completeness and effectiveness in classifying the opinion into positive and negative. [ABSTRACT FROM AUTHOR]

Internet Voting ( i-Voting) can play a really vital role in the democracy of our life. This paper analyzes the various security issues involved in an i-voting like privacy, authentication, anonymous, uniqueness, accuracy and Uncoercibility. This paper also discusses about what we need to achieve the above requirements to implement an internet voting. This paper also discusses what are all the problems will occur while implementing an i-voting system and the solutions are also discussed. [ABSTRACT FROM AUTHOR]