Table 5.
Distribution of applied AI algorithms and their categorizations by frequencies.
| Row Labels | Frequency |
|---|---|
| Linear and nonlinear models | 17 |
| DT+ SVM+ KNN+EC | 1 |
| ECF-S + ECF-W | 1 |
| Elastic Net+ RF+ SVM | 1 |
| KRL | 2 |
| LR+ RF | 1 |
| LR+ RF+ SVM | 1 |
| MLP + RF | 1 |
| NN | 1 |
| RF | 2 |
| RF + SVM | 1 |
| RF+ ELNET + SVMs | 1 |
| RF+ NN | 1 |
| RF+ XGB +LR | 1 |
| SVM | 1 |
| SVM + DT | 1 |
| Deep learning model | 15 |
| AITL | 1 |
| CDSS | 1 |
| CNN | 5 |
| CNN+LSTM | 1 |
| Deep-Resp-Forest | 1 |
| DenseNet-121 | 1 |
| DL | 1 |
| MLP | 1 |
| NN | 3 |
| Linear model | 15 |
| CART | 1 |
| GloNetDRP | 1 |
| MEFS | 1 |
| NMTF | 1 |
| RF | 1 |
| SVM | 10 |
| Rule-based system | 6 |
| CDSS | 6 |
| Bayesian model | 3 |
| GBGFA | 1 |
| NB | 1 |
| NB + HNB | 1 |
| Nonlinear model | 3 |
| AF-UCS | 1 |
| DT | 1 |
| LASSO | 1 |
| Bayesian model + Linear and nonlinear models | 2 |
| NB+ BM | 1 |
| RF+ SVM+NB | 1 |
| NLP | 2 |
| NLP | 2 |
| Grand Total | 63 |