Table 1. Summary of the big data analytics in supervised learning mechanisms.

Paper	AI technique	Scalability	Efficiency	Precision	Privacy
Carcillo et al. (2018)	Ensemble classifier based on random forest algorithms	√	√	√	X
Kannan et al. (2019)	Support vector machine	X	√	√	X
Feng et al. (2019)	LSTM neural network	X	√	√	X
Zhu, Cheng & Wang (2017)	Random forests regression, and gradient boosting decision tree	X	√	√	X
Hammou, Lahcen & Mouline (2020)	Recurrent neural network	√	X	√	X
Tian et al. (2020)	LSTM neural network	X	√	√	X
Wang et al. (2016)	Convolutional neural network	√	√	√	X
Kaur, Sharma & Mittal (2018)	Various machine learning methods like naive bayes, support vector machine, and decision tree	X	X	√	√
Nair, Shetty & Shetty (2018)	Decision tree model	√	√	√	√
AlZubi (2020)	SVM-trained multilayer neural network	X	X	√	X
El-bana, Al-Kabbany & Sharkas (2020)	Convolutional neural networks with transfer learning	X	√	√	X
Ragab & Attallah (2020)	Convolutional neural network with transfer learning	X	X	√	X
Ahmed, Bukhari & Keshtkar (2021)	Convolutional neural network	X	X	√	X
Ahmed et al. ()	Neural network	X	X	√	X
Asencio-Cortés et al. (2018)	Regression algorithms with ensemble learning	√	X	√	X
Wang et al. (2017)	Differential evolution SVM classifier	X	√	√	X
Vu et al. (2020)	Deep learning	X	X	√	X
Huang et al. (2016)	Ensemble of extreme learning machines	√	√	√	X
Banchhor & Srinivasu (2020)	Naive bayes classifier improved by using CGWO	X	X	√	X