Αποτελέσματα Αναζήτησης
In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification.
3 Ιουν 2016 · We present a comprehensive review of the most effective content-based e-mail spam filtering techniques. We focus primarily on Machine Learning-based spam filters and their variants, and...
11 Μαΐ 2022 · PDF | Spam emails have been traditionally seen as just annoying and unsolicited emails containing advertisements, but they increasingly include scams,... | Find, read and cite all the...
16 Ιουν 2021 · Here, we propose a detection model based on the LSTM algorithm for identifying spam and non-spam emails using a dataset from Kaggle comprising a total of 5.572 entries.
12 Σεπ 2022 · This work describes how to detect spam emails using machine learning based on words, numbers, and characters in the emails’ content. We have used some prevalent machine learning models (Naive Bayes’, Neural Network, K-NN, Tree, Logistic Regression) and compared them to classify emails.
3 Φεβ 2022 · Kumar et al. discussed email spam detection using various ML algorithms. Their article explores ML methods and how to implement them on datasets. The optimal algorithm for email spam detection with the highest precision and accuracy is identified from various ML algorithms.
23 Ιουν 2008 · This paper analyzes to what extent Bayesian filtering techniques used to block email spam, can be applied to the problem of detecting and stopping mobile spam, and demonstrates that Bayesian filters can be effectively transferred from email to SMS spam.
