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 works that employ various text feature extraction techniques.

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: Plasmid Preparation, Activity Assay

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 rule-based systems.

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:

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 machine 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:

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