… significantly capture more at-risk students (the highest average … , random forest, and deep
neural network—in the middle of the … neural network to predict student grade in each lesson …

… NEURAL NETWORK (DFFNN) After training the predictive … a deep learning technique called
Deep Feed Forward Neural … dropout in a MOOC: An evaluation of a deep neural network …

… a Deep Neural Network (DNN) framework for binary classification with two hidden layers, for
the early prediction of … The framework was evaluated by using different activation functions (…

… from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent
Neural Network (… weight to the ones that are related to the students’ last action. Then, to conduct …

… We proposed and applied a minimal recurrent neural network (minimalRNN) model to data
from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, …

… Therefore, the at-risk students in this study were defined as those who might get a final mark
… LSTM network is a subset of recurrent neural networks (RNNs), which are neural networks (…

… whether a deep learning model that has been trained using student data from one course
can be repurposed for other related courses. Deep neural networks are represented by a …

… , and future status prediction drive the learning algorithms to … birth to the use of recurrent
neural networks (RNNs), which … pressure prediction with deep recurrent neural networks,” in …

… -to-sequence vessel trajectory prediction models based on encoder–decoder recurrent
neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory …

… generative adversarial network based deep support … to deep learning. DSVM takes the
advantage in small dataset, as a key difference comparing with traditional deep neural network. …