Predicting Depression Levels Using Social Networking

On figure 1C, we see the following behavior: after a trigger, the value is correctly updated in short-term memory and remains stable until C is updated (after 100 time steps) and

During training process, in order to make every forward unit responsible to the classification, for every forward unit there are temporary fully connected layers applied to train

In the study we performed depression detection task among 1020 users of Russian- speaking social network Vkontakte based on their text messages.. By analyzing Beck Depression

In Ironic Detection in the Arabic Text (IDAT) overview [14], the task is to detect the irony in Arabic tweets from different political issues and events in the middle east from

To classify the data of the two other samples, using an equal number of classes of training and control samples, as well as using groups of semantically similar classes

SSN 1 The CLNN model used the given 33 layers of spatial feature maps to predict the taxonomic ranks of species.. Every layer of each tiff image was first center-cropped to a size

The results of Ada Boost, LR, RF and SVM classifiers using BOW features and RNN classifier using Fasttext embedding on the given test set in terms of ERDE 5 , ERDE 50 ,

Early risk prediction on the Internet (eRisk), which started since 2017, is a task held in the Conference and Labs of the Evaluation Forum (CLEF) based on the consideration