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@article{158797,
        author = {Madallapalli Sushanth and C.A.Daphnie Desona Clemency and NarreddyMurali Krishna Reddy},
        title = {Email Phishing message classification using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {684-688},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158797},
        abstract = {Vehicle reconciliation strategies have been involved a few times in spam channels to incorporate approaching/active messages, for example, spam and spam bunches. This technique expresses that each bunch contains little miniature groups, and each miniature group is dispersed. Notwithstanding, this thought ought not be trifled with, and the miniature group might have a lopsided dispersion. To build the respectability of the main strategy for appropriating the Internet class, we suggest supplanting the Euclidean space with a succession of models that incorporate into the miniature bunch connected with the circulation. Here, the Naïve Bayes (INB) classification has been carried out to carry out miniature bunches across the line. While these INBs can decide the distance and limits of micro clusters, Euclidean space considers the overall worth of the group and misdirects the bigger micro cluster. In this report, Den Stream is upheld by a committed framework called INB Den Stream. To represent the presentation of INB-Den Stream, current techniques, like Den Stream, StreamKM ++, and CluStream, have been utilized in the Twitter chronicle, and their exhibition has been estimated as far as quality, honesty as a general rule, memory as a rule, F1 measurements., markers, and complex estimations. The relatively close outcomes show that our techniques outflank its rivals in the set figures.},
        keywords = {Spam Detection, Twitter, Car Training, Regular Forest, Certificate Tree, SVM.},
        month = {},
        }