The research aims to determine how well innovative Artificial Neural Network (ANN) and Recurrent Neural Network architectures can classify students’ academic performance (RNN). Artificial Neural Networks (ANNs) have been suggested and compared to Recurrent Neural Networks (RNNs) for use in predicting students’ academic achievement using data sets. For the purpose of prediction, we use ANN with a sample size of 20, as well as RNN with a sample size of 20. In this research, we looked at the problem of predicting where a student could end up as a college sophomore. In this respect, the effectiveness of an Artificial Neural Network algorithm for enhancing students’ performance on work placements has been examined and forecasted. To estimate students’ chances of being placed successfully, a methodology is given that uses slope-assisted tree computation. Each group included a total of 72 participants in the study. On the student data set, the RNN achieves a success rate of 69.3 percent in terms of analysing academic achievement, whereas the innovative ANN achieves a success rate of 88.89 percent. Since ANN is trained using a different set of data, it outperforms RNN (RNN). New Artificial Neural Networks (ANNs) outperform Recurrent Neural Networks (RNNs) in terms of performance (RNN).

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