Author (s) | Techniques | Result | Remarks |
---|---|---|---|
Kandha Swamy et al. [36] | Multiple ML based algorithms: SVM, K-NN, J48 and Random Forest | 73.82% with J48 classifier and claimed 100% with K-NN | There is no adequate explanation is provided for the pre-processing procedure  that was performed on the dataset. |
Yuvraj et al. [37] | Random Forest, Decision Tree and Naïve Bayes classifier with data processing | Claimed 94% and 84% accuracies with Random Forest Classifier and Decision Tree | Not specified how the data was pre-processed, although  they did outline the Information Gain approach for feature selection, which was utilized to extract the important features. |
Sisodia et al. [38] | Decision Tree, Naïve Bayes and SVM approach with Data Pre-processing. | Reported highest accuracy of 76.30% with  Naïve Bayes | Experimentation was carried out with 10 fold cross-validation, and there was no more clear information on data processing. |
Olaniyi et al. [39] | Multi Layer Feed Forward Network (MLP-NN) | Reported 82% accuracy with MLP-NN | Before processing the data for classification, the authors normalized the dataset in order to get a stable numerical representation. |
Ashiquzzaman et al. [33] | Deep Neural Networks with MLP, GRNN, and RBF | Claimed an accuracy of 88.41% | The authors made a conscious decision not to pre-process the dataset because DNN is capable of filtering the data and acquiring the biases. |
Zhou et al. [40] | Enhanced Deep Neural Network | Reported an accuracy of 94.02% | Model is primarily designed with the help of a deep neural network’s hidden layers and it make use of dropout regula- -rization in order to avoid over-fitting. |
Yahyaoui et al. [41] | Convolutional Neural Network | Reported an accuracy of 76.81% | TThere is no adequate information on methodology and techniques. |
Naz et al. [42] | Decision Tree and Naive Bayes | 96.62% and 76.33 % Accuracies reported | The authors worked on different classifiers and reported accuracies in the range between 76% to 97%. |
Abdulhadi et al. [43] | Random Forest Classifier | Reported an accuracy of 82% | TThere is no adequate information on data pre-processing and methods. |
Abdollahi et al. [44] | Ada boost algorithm | Reported an accuracy of 92% | This study aimed for integraion of different data mining techniques and developed ensemble based training to improve the performance. |