In the context of e-commerce, the concept of security involves two areas: hard security and soft security. Hard security includes cryptography, information hiding and other standards, while soft security is associated with the methods which are based on trust. finding malevolent entities or malevolent agents are one of the most important concepts in e-commerce. Thus, sufficient attention should be paid to their security. Since the centralized security management architecture in e-commerce environment and cannot have enough effectiveness nor be implemented, with the aim of overcoming the limitations of centralized architectures, a distributed and dynamic algorithm is proposed to finding malevolent entities and improving security. This algorithm uses a distributed network. The results of this study indicate that the proposed algorithm is capable of finds malevolent entities and improves security in a fast and efficient manner.
Everyday many online product sales websites and specialized reviewing forums publish a massive volume of human-generated product reviews. People use these reviews as valuable free source of knowledge when decide to buy products. Therefore, an accurate automated system for distinguishing useful reviews from non-useful ones is of great importance. This article presents a new model for specifying the usefulness of comments using the textual features extracted from the reviews. Various types of features including emotion-related, linguistic and text-related features, valence, arousal, and dominance (VAD) values, review-length and polarity of comments are exploited in this study. Moreover, two new algorithms are presented: an improved evidential algorithm for emotion recognition, and an algorithm for extracting VAD values for each review. Finally, the usefulness of reviews is predicted using the mentioned features and an improved Dempster-Shafer score fusion algorithm. The proposed method is applied to review datasets of Books and Video Games of Amazon. The results show that combining the features associated with emotions, features of VAD, and text-related features improves the accuracy of predicting the usefulness of reviews. Also, in comparison with the original Dempster-Shafer method, the precision of the improved Dempster-Shafer algorithm for both datasets is 15% and 11% higher, respectively.
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