## 2 Literature Review  
Table 2 provides a summary of the studies showing approaches, datasets, and performance metrics used in different studies. [PERSON] and [PERSON] [10] utilize BERT for spam detection and compare the performance with Deep Neural Network (Bidirectional LSTM), KNN, and NB. Spambase and Spamfilter (from Kaggle) are used, with accuracy and F-score as the performance measures. [PERSON] et al. [11] mutually compare several algorithms which include regression, xboost, SVM, random forest, word embedding, and LSTM. Five datasets are used, which are TrecSpam 2007, Enron, PU, Lingspam, and Basket (which is the combination of the four datasets). Accuracy, loss, precision, and recall are used as the performance metrics. [PERSON] et al. [15] develop the DeepSpamNet algorithm by combining LSTM and CNN. The proposed algorithm is compared with logistic regression (LR), Gaussian NB, KNN, decision tree, adaboost, random forest, and SVM. Four datasets, namely Lingspam, PU, Spam Assasin, and Enron, are used. The study employs accuracy, precision, recall, F-score, and G-mean as the performance measures. [PERSON] et al. [16] also evaluate the performance of SVR, KNN, NB, decision tree, random forest, adaboost, and bagging. However, only a single dataset is used, which is Spamcsv from Kaggle. Moreover, accuracy and precision are used as the performance metrics. [PERSON] et al. [17] utilize xboost for spam email detection. The xboost algorithm is compared with several algorithms, including SVM, rotation forest, MLP, decision tree (J48), and NB. Performance is evaluated using a single dataset called Spam_ham_dataset from Kaggle. Five performance measures are employed, which are accuracy, precision, recall, specificity, and F-score. [PERSON] et al. [12] employ NB, CNN, SVM, and LSTM for spam email detection in Urdu. A self-collected dataset named \"Urduemaildataset\" is used for evaluation. Four metrics, namely accuracy, precision, recall, and F-score, are used for performance evaluation. [PERSON] et al. [18] use a single dataset (SpamAssassin) to compare the performance of NN, Logistic regression, SVM, and NB. Performance is measured with respect to accuracy, precision, recall, and F-score. [PERSON] et al. [19] apply NB, SVM, decision tree, CNN, and LSTM for the spam detection problem. However, they only use accuracy as the performance measure while using a single dataset which consists of self-collected emails. [PERSON] et al. [20] employ NB and decision tree (J48) while using a single dataset from Kaggle. The performance metrics are accuracy, precision, recall, and F-score. [PERSON] [13] utilize several algorithms including CNN, LSTM, Random forest, SVM, NB, and decision tree while using only the Enron dataset. Three metrics, namely accuracy, recall, and precision are used for performance evaluation. [PERSON] et al. [21] also evaluate the performance of several algorithms, which include SVM, decision tree, logistic regression, and multinomial NB. A single dataset from Kaggle is used with accuracy, precision, recall, and F-score as the performance measures. [PERSON] et al. [28] mutually compare adaboost, random forest, decision tree (J48), SVM, bagging, and deep learning. Performance is evaluated with accuracy, precision, recall, and F1-score while using a single dataset called CSDM2010_SPAM. [PERSON] et al. [27] evaluate BERT using the Enron dataset. A single evaluation metric, namely the F-score, is used. [PERSON] et al. [1] propose an algorithm combining SVM and NB and call it SVM-NB. The proposed algorithm is compared with SVM and NB algorithms using the DATAMALL dataset. Recall and precision are used for performance evaluation. [PERSON] and [PERSON] [22] employ NB, J48 decision tree, and logistic tree classifier algorithms for spamdetection. They measure the performance of the three algorithms using accuracy and precision while utilizing a dataset of self-collected emails. [PERSON] and [PERSON] [14] implement NB, J48 decision tree, and deep neural network.