A Chronological Method of Detecting Image Based Email
Mallikka Rajalingam1, M. Balamurugan2
1Mallikka Rajalingam, Research Officer, Department of Computer Science & Engineering, Bharathidasan University, Trichy, (Tamil Nadu). India.
2Dr. M. Balamurugan, Professor, Department of Computer Science and Engineering of Bharathidasan University, Trichy, (Tamil Nadu) India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 4579-4583 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3308078219/19©BEIESP | DOI: 10.35940/ijrte.B3308.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we present a Visual feature extraction using improvised SVM and KNN classifiers. The proposed method is an automatic, stable, quick response automatic segmentation, followed by feature extraction and classification to detect spam from the images and the text. The KNN classifier is used to extract features by predicting nearest neighbour while SVM, analyze the data for classification and regression. The hybrid-based Visual feature extraction and classification is elaborated wherein this work discuss the proposed approach which incorporated using improvised SVM and KNN classifier. Moreover, identified patterns via feature extraction method by means of a minimum number of features that are effective in discriminating pattern classes. With all the aforementioned concepts elaborated, the experimental set-up was elaborated with the experimental task, and the results of the character recognition component are further elucidated.
Index Terms: Detection, Image Spam Email, Recognition, and Segmentation.
Scope of the Article: Pattern Recognition