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WebMar 4, 2024 · This post aims to describe the application of Long Short-Term Memory (LSTM) Neural Networks (NNs) for spam detection. The library used to build the NN … WebApr 10, 2024 · Over the last decade, the Short Message Service (SMS) has become a primary communication channel. Nevertheless, its popularity has also given rise to the so-called SMS spam. These messages, i.e., spam, are annoying and potentially malicious by exposing SMS users to credential theft and data loss. To mitigate this persistent threat, we propose a …
WebWork has already started in the area of spam detection using Deep Learning algorithms by various researchers. Tzortzis have performed spam detection on five benchmark spam … WebMay 19, 2024 · lstm_model.fit (padded_sms_sequence, y, epochs = 5, validation_split=0.2, batch_size=16) Both training accuracy (0.9986) and validation accuracy (0.9839) imply that our model is very good at predicting spam and ham SMS. We can save our model and tokenizer for future uses as a pickle file.
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Extensive experiments are performed using LSTM for the spam detection on two datasets: SMS Spam Collection and Twitter Datasets already defined above. The … See more The experiments are conducted on two datasets—SMS spam collection dataset which is available in UCI repository. The second dataset for conducting … See more The parameters that helps in optimizing the LSTM network are defined below: 1. Word Vector Initialization The text needs to be converted into numeric form, … See more
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