RNNSpeech-to-textText-to-textText-to-speechThis paper presents the implementation of a speech-to-speech translator using python that can overcome the barrier of different languages. The user can speak in Marathi which will be taken as the input and output will be the translated speech in ...
The black line represents zero performance difference between the two experiments. We could see the performance in most of the algorithms increased with the downsampling except for the TCN in the smaller tap sizes. In fact, RNNs (LSTM, GRU, and QRNN) improved its performance more than 0.1 ...
Three different models were evaluated: Recurrent neural network (RNN) with Long short-term memory (LSTM), RNN with bidirectional LSTM, and Random Forest. Additionally, the performance was evaluated in terms of accuracy, precision, recall, F1-score, specificity, and sensitivity. The results showed ...
In our first model, RNN-LSTMs model has been used to capture the semantic and syntactic relationships between words of a sentence with help of word2vec. In the second model, one-dimensional convolutional neural networks were used to learn structure in paragraphs of words and the techniques ...
DepressionRNN (recurrent neural network)LSTM (long short-term memory)Random forestDepression is a prevalent mental health condition, and social media platforms have become valuable sources for understanding individuals' emotional well-being. In this study, we aimed to develop an accurate classification ...
In this paper, we propose and evaluate a soldering education system based on haptics. The experimental results show that Long Short-Term Memory (LSTM) performs better than Recurrent Neural Network (RNN) in detecting dangerous movements.Toyoshima, Kyohei...
This paper compared the performance of the recurrent neural network (RNN) and long short-term memory (LSTM) in the form of accuracy. The three-year energy generation dataset is split into train and test data. The train data uses three years of energy generation data from January 2019 to ...
The three DL models were a deep neural network (DNN), a long short-term memory (LSTM), and a mix of convolutional and recurrent neural networks (CNN-RNN-LSTM). The models performed similarly with a slight favor for the last one. Ullah et al. [7] proposed a convolutional neural network...
The LSTM was developed to address this problem. It is an RNN with substructures that help to manage the memory of the recurrent neural network. Figure 4 shows the cell of an LSTM. Figure 4. LSTM cell [29]. First, the LSTM must decide what information to disregard in the cell. This ...
motion. LSTM and gated recurrent unit (GRU) variants of RNNs are often used to mitigate the vanishing gradient problem and capture longer-term dependencies, but in our comparison of ML techniques, only LSTM has been considered due to the complexity and processing overhead of the GRU technique....