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This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.<\/jats:p>","DOI":"10.1007\/s40747-022-00741-6","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T08:02:56Z","timestamp":1650960176000},"page":"4897-4909","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1454-6090","authenticated-orcid":false,"given":"Mai A.","family":"Shaaban","sequence":"first","affiliation":[]},{"given":"Yasser F.","family":"Hassan","sequence":"additional","affiliation":[]},{"given":"Shawkat K.","family":"Guirguis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"key":"741_CR1","unstructured":"Grossbard J (2021) SMS Marketing Statistics 2021 For USA Businesses. https:\/\/www.smscomparison.com\/mass-text-messaging\/2021-statistics\/"},{"key":"741_CR2","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.cose.2017.12.006","volume":"73","author":"D Goel","year":"2018","unstructured":"Goel D, Jain A (2018) Mobile phishing attacks and defence mechanisms: state of art and open research challenges. 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