## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & **Contribution** \\ \hline
[98] & 2 & 2024 & ANN and NB & Literature & It proposes a hybrid method consisting of the artificial neural networks and Naive Bayes techniques used to obtain statistical parameters accuracy, recall, precision, and F-measure. The proposed model achieved an accuracy of 97.22%. \\ \hline
[99] & 55 & 2014 & SVM and kNN & IEC10 DB & It proposes the application of the k-Nearest Neighbor imputation method to predict missing DGA dataset values and use these datasets with the predicted values to classify faults using a support vector machine. \\ \hline
[100] & 59 & 2015 & CANN-PSO & Electrical Utility & It proposes a hybrid important fault prediction method consisting of artificial neural networks and various Particle Swarm Optimization techniques. The proposed model achieved an accuracy of 98.00%, \\ \hline
[101] & 17 & 2020 & Self-Paced Ensemble algorithm & IEC TC10 Database, Ministry of Electricity and Energy of Egypt, North China Grid and Zhejiang & It investigates and presents a fault diagnosis method based on a Self-Paced Ensemble using classification hardness to improve diagnosis accuracy. The investigated model achieved an accuracy of 95.10%. \\ \hline
[102] & 16 & 2018 & Fuzzy c-means clustering algorithm & China Southern Power Grid and IEC TC10 & It presents a fault detection model based on the fuzzy c-means clustering algorithm and the dimensionality reduction techniques Improved Principal Component Analysis. The presented model achieved an accuracy of 95.10%. \\ \hline
[103] & 4 & 2022 & Improved Gray Wolf Optimizer and LS-SVM & Not Specified & It proposes a fault diagnosis hybrid support vector machine model with improved gray wolf optimization optimal least squares-support vector machine to address convergence and reputation diversity shortcomings of traditional support vector machine algorithms. The proposed model achieved an accuracy of 95.89%. \\ \hline
[104] & 3 & 2021 & Improved Firefly Algorithm and SVM & IEC TC10 & It proposes a fault diagnosis technique based on Improved Firefly Algorithm–Linear Programming Boosting-Classification and Regression Tree hybrid combination. Individuals the weights of weak classifiers builds stronger ones and then optimizes them for computation. The proposed model achieved an accuracy of 91.27%. \\ \hline
[105] & 20 & 2018 & Parzen Window estimation & Industrial Transformers & It proposes a model to address shortcomings of Roger’s ratios, IEC ratios, and Daval triangles methods based on the Parzen Window Estimation. This model utilizes five gases to compute fault probability. The proposed model achieved an accuracy of 95%. \\ \hline
[106] & 56 & 2018 & GA-SVM & IEC TC10 and Local Power Utilities & It investigates the use of a hybrid feature selection technique combined with the generic algorithm and support vector machine. This is further backed by an adaptive synthetic technique and arctangent transformation technique to improve training set properties. \\ \hline
[107] & 34 & 2017 & RVM-ANFIS algorithm & IEC TC10 and Jinliang & It proposes a hybrid technique that combines the Relevant Vector Machine and the Adaptive Neural Fuzzy Inference System to address ambiguous data characteristics related to conventional DCA interpretation methods. The proposed model achieved an accuracy of 95. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._