Table 4.

The integrated machine-learning and deep-learning algorithms in iLearnPlus

Algorithm category	Algorithm	Reference
Conventional machine-learning algorithms	Random forest (RF)	(105)
	Decision tree (DecisionTree)	(106)
	Support vector machine (SVM)	(107)
	K-nearest neighbors (KNN)	(108)
	Logistic regression (LR)	(109)
	Gradient boosting decision tree (GBDT)	(110)
	Light gradient boosting machine (LightBGM)	(111)
	Extreme gradient boosting (XGBoost)	(102)
	Stochastic gradient descent (SGD)	(34)
	Naïve Bayes (NaïveBayes)	(112)
	Linear discriminant analysis (LDA)	(113)
	Quadratic discriminant analysis (QDA)	(113)
Ensemble-learning frameworks	Bagging (Bagging)	(114)
	Adaptive boosting (AdaBoost)	(115)
Deep-learning algorithms	Convolutional neural network (CNN)	(30)
	Attention based convolutional neural network (ABCNN)	(116)
	Recurrent neural network (RNN)	(117)
	Bidirectional recurrent neural network (BRNN)	(118,119)
	Residual network (ResNet)	(120)
	Auto-encoder (AE)	(121)
	Multilayer perceptron (MLP)	(122)