|
| S. no | References | Techniques | Drawbacks |
|
| 1. | Shukla et al. [10] | Supervised and unsupervised machine learning algorithms | It is unable to obtain exact data filtering parameters. |
| 2. | Chen et al. [11] | Attitude-behavior-context (ABC) theory | Its incomplete consumer statistics data might affect the performance. |
| 3. | Liu et al. [12] | Bit-based latent spatiotemporal approach | This modality performs poorly in terms of classification accuracy |
| 4. | VLN and Deeplakshmi [14] | Support vector machines (SVM) based on machine learning | The targeted classifications might overlap occasionally |
| 5. | Wang et al. [16] | Adaptable deconstruction approach | If there are nonlinear correlations in the data, the linear regression model performs poorly |
| 6. | Revati et al. [17] | Gaussian process regression | This modal cannot locate the grouped data. |
| 7. | Najman et al. [19] | Growing neural gas | Predicting behavior requires more time. |
| 8. | Chakladar et al. [20] | Long short-term memory- (LSTM-) based deep neural network model | LSTMs are susceptible to specific initializations of activation functions |
| 9. | Asiri et al. [23] | Multiclass random forest | It is sluggish and inefficient for prediction |
| 10. | Subroto and Christianis [24] | Multilayer perceptron | Tuning of features affects multilayer perceptrons |
| 11. | Chaubey et al. [27] | -nearest neighbors (KNN) | Inefficient in terms of computing |
| 12. | Sheoran and Kumar [28] | Theory of planned behavior (TPB) | It takes a lot of time and effort |
| 13. | Zhang and Wang [29] | Enhanced deep forest strategy | When splitting the trees, it employs the complete feature memory space. |
| 14. | Amasyali and El-Gohary [13] | Big data technology based on machine learning | Data preprocessing in machine learning gives low enhancement of data and less accuracy in prediction |
| 15. | Malik et al. [18] | Machine learning techniques and functional link neural networks | Disproportionate precision variations in resource utilization |
| 16. | Phyo et al. [22] | Machine learning algorithm and voting regressor model | The classification of data set is more complex by using this model |
| 17. | Jupalle et al. [26] | Machine learning algorithm | Massive data sets are needed for machine learning in order to train the data set. |
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