Using Stacked Denoising Autoencoder for the Student Dropout Prediction
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- Type
- article
- Published
- 2017-12-01
- Cited by
- 12
- References
- 0
- OpenAlex
- https://openalex.org/W2782041652
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:31252582
Keywords
Dropout (neural networks), Autoencoder, Artificial neural network, Computer science, Noise reduction
References
Cited by
- Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting
- Predicting and Reducing Dropout in Virtual Learning using Machine Learning Techniques: A Systematic Review
- Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China
- Interpretable Deep Learning for University Dropout Prediction
- A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification
- Hybridization of cluster-based LDA and ANN for student performance prediction and comments evaluation
- Building Graduate Salary Grading Prediction Model Based on Deep Learning
- Building Student Course Performance Prediction Model Based on Deep Learning
- Adaptive-CSSA: adaptive-chicken squirrel search algorithm driven deep belief network for student stress-level and drop out prediction with MapReduce framework
- Educational Data Mining: A Systematic Review on the Applications of Classical Methods and Deep Learning Until 2022
- EEG Classification Using Reinforcement Learning and Swarm-Based Deep Sparse Autoencoder
- Educational data mining: a 10-year review
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