Journal: PeerJ Computer Science
Article Title: A systematic literature review on spam content detection and classification
doi: 10.7717/peerj-cs.830
Figure Lengend Snippet: Existing research works on spam classification using deep learning.
Article Snippet: 3 , , Open source SpamBase dataset with 5,569 emails and Kaggle spam filter dataset , Fine-tuned BERT(Bidirectional Encoder Representations from Transformers) with Word2Vec approach , Spam detection efficiency is improved with the help of BERT word embedding approach , Need to utilize a large input sequence for better training of model. , Accuracy-0.98 F1-Score-0.98.
Techniques: Sequencing, Plasmid Preparation, Selection