Abstract:In the Thematic Apperception Test, a picture story exercise (TAT/PSE; Heckhausen, 1963), it is assumed that unconscious motives can be detected in the text someone is telling about pictures shown in the test. Therefore, this text is classified by trained experts regarding evaluation rules. We tried to automate this coding and used a recurrent neuronal network (RNN) because of the sequential input data. There are two different cell types to improve recurrent neural networks regarding long-term dependencies in s… Show more
“…Thus GRU is a slightly more streamlined variant that often offers comparable performance and is significantly faster to compute [ 18 ]. Although GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets [ 18 , 34 ], both variants of RNN have proven their effectiveness while producing the outcome. Fig.…”
Section: Deep Learning Techniques and Applicationsmentioning
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a
challenging
task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and
comprehensive view
on DL techniques including a
taxonomy
considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or
discriminative learning
, unsupervised or
generative learning
as well as
hybrid learning
and relevant others. We also summarize
real-world application areas
where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with
research directions
. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.
“…Thus GRU is a slightly more streamlined variant that often offers comparable performance and is significantly faster to compute [ 18 ]. Although GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets [ 18 , 34 ], both variants of RNN have proven their effectiveness while producing the outcome. Fig.…”
Section: Deep Learning Techniques and Applicationsmentioning
Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a
challenging
task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and
comprehensive view
on DL techniques including a
taxonomy
considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or
discriminative learning
, unsupervised or
generative learning
as well as
hybrid learning
and relevant others. We also summarize
real-world application areas
where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with
research directions
. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.
“…The main difference between LSTM and GRU cells is their number of gates and internal states, where LSTMs are more complex (two internal states and three gates) than GRUs (one internal state and two gates). While in some cases GRUs outperform LSTMs, there is no clear rule of when to use one or the other (Yazidi et al, 2020). Each RNN contains a FNN layer with a single node at its end, which is used to compute the predicted values from the hidden states of the last time step (h T ).…”
Abstract. Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature is and will be essential for effective integrated water management and the development of adaptation strategies to future global changes (e.g. climate change). This study tests the performance of six different machine-learning models: step-wise linear regression, random forest, eXtreme Gradient Boosting (XGBoost), feed-forward neural networks (FNNs), and two types of recurrent neural networks (RNNs). All models are applied using different data inputs for daily water temperature prediction in 10 Austrian catchments ranging from 200 to 96 000 km2 and exhibiting a wide range of physiographic characteristics. The evaluated input data sets include combinations of daily means of air temperature, runoff, precipitation and global radiation. Bayesian optimization is applied to optimize the hyperparameters of all applied machine-learning models. To make the results comparable to previous studies, two widely used benchmark models are applied additionally: linear regression and air2stream. With a mean root mean squared error (RMSE) of 0.55 ∘C, the tested models could significantly improve water temperature prediction compared to linear regression (1.55 ∘C) and air2stream (0.98 ∘C). In general, the results show a very similar performance of the tested machine-learning models, with a median RMSE difference of 0.08 ∘C between the models. From the six tested machine-learning models both FNNs and XGBoost performed best in 4 of the 10 catchments. RNNs are the best-performing models in the largest catchment, indicating that RNNs mainly perform well when processes with long-term dependencies are important. Furthermore, a wide range of performance was observed for different hyperparameter sets for the tested models, showing the importance of hyperparameter optimization. Especially the FNN model results showed an extremely large RMSE standard deviation of 1.60 ∘C due to the chosen hyperparameters. This study evaluates different sets of input variables, machine-learning models and training characteristics for daily stream water temperature prediction, acting as a basis for future development of regional multi-catchment water temperature prediction models. All preprocessing steps and models are implemented in the open-source R package wateRtemp to provide easy access to these modelling approaches and facilitate further research.
“…Other research has noted that GRU cells may be better for specificity, or finding true negatives, and focusing on less prevalent content, whereas LSTM cells are better for detecting true positives and focusing on highly prevalent content. (Gruber and Jockisch, 2020). Looking into the dev dataset, only 6% of the observations contain no toxic spans, but this model is not predicting whether an entire comment contains any toxic spans, it is predicting if each word is toxic.…”
Toxic language is often present in online forums, especially when politics and other polarizing topics arise, and can lead to people becoming discouraged from joining or continuing conversations. In this paper, I use data consisting of comments with the indices of toxic text labelled to train an RNN to determine which parts of the comments make them toxic, which could aid online moderators. I compare results using both the original dataset and an augmented set, as well as GRU versus LSTM RNN models.
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