Predicting student dropout is a challenging problem in the education sector. This due to an imbalance data, mainly because number of registered students always higher than students. Developing model without taking data issue into account may lead ungeneralized model. In this study, different balancing techniques were applied improve prediction accuracy minority class while maintaining satisfact...

Deep learning has become the state-of-art tool in many applications, but the evaluation and training of such models is very time-consuming and expensive. Dropout has been used in order to make the computations sparse (by not involving all units), as well as to regularize the models. In typical dropout, nodes are dropped uniformly at random. Our goal is to use reinforcement learning in order to ...

OBJECTIVE Premature discontinuation of therapy is a widespread problem that hampers the delivery of mental health treatment. A high degree of variability has been found among rates of premature treatment discontinuation, suggesting that rates may differ depending on potential moderators. In the current study, our aim was to identify demographic and interpersonal variables that moderate the asso...

Recently, nested dropout was proposed as a method for ordering representation units in autoencoders by their information content, without diminishing reconstruction cost (Rippel et al., 2014). However, it has only been applied to training fully-connected autoencoders in an unsupervised setting. We explore the impact of nested dropout on the convolutional layers in a CNN trained by backpropagati...

Student dropout occurs quite often in universities providing distance education. The scope of this research is to study whether the usage of machine learning techniques can be useful in dealing with this problem. Subsequently, an attempt was made to identifying the most appropriate learning algorithm for the prediction of students' dropout. A number of experiments have taken place with data pro...

We apply both a traditional 'dropout' approach and a pho-tometric redshift estimation technique to the Hubble Deep Field South data. We give a list of dropout selected z∼3 objects, and show their images. We then discuss our photometric redshift estimation technique, demonstrate both its effectiveness and the role played by near-IR data, and then apply it to HDF-S to obtain an estimated redshift...

Predicting college and school dropouts is a major problem in educational system and has complicated challenge due to data imbalance and multi dimensionality, which can affect the low performance of students. In this paper, we have collected different database from various colleges, among these 500 best real attributes are identified in order to identify the factor that affecting dropout student...

Limiting attrition (i.e., participant dropout before the conclusion of a study) is a major challenge faced by researchers when implementing clinical trials. Data from a smoking cessation trial for females (N = 246) were analyzed in order to identify baseline smoking-related, demographic and psychological characteristics affecting likelihood of early (i.e., before the quit attempt) and late (i.e...

Massive open online courses (MOOCs) have received wide publicity and many institutions have invested considerable effort in developing, promoting and delivering such courses. However, there are still many unresolved questions relating to MOOCs and their effectiveness. One of the major recurring issues raised in both academic literature and the popular press is the consistently high dropout rate...

Deep neural networks with their large number of parameters are highly flexible learning systems. The high flexibility in such networks brings with some serious problems such as overfitting, and regularization is used to address this problem. A currently popular and effective regularization technique for controlling the overfitting is dropout. Often, large data collections required for neural ne...