Survey concludes that LSTM model for traffic classification is being used in the major area of data security and intrusion detection. Delay in classifying network traffic needs a special game theory approach which could not be handled by many LSTM models.Rai, Prerna...
The key parameters of LSTM networks include the hidden layer dimension and the number of layers within the recurrent neural network architecture. Generally, an increase in the hidden layer dimension and the number of layers correlates with a heightened capacity for expression, enabling the model to ...
Figure 8.Curves of AV and PV (P1 and P2) using LSTM + for the cumulative confirmed cases of COVID-19 in New York State. The Grey line in panel (a1) is the curve of cumulative confirmed cases used for the training, and panel (a2) is an enlarged view of the region inside the...
In this chapter, iron ore prices were forecasted using deep learning techniques, including long short-term memory neural network (LSTM) and convolutional neural network (CNN). A dataset spanning 30 years (monthly prices) was collected from August 1991 to August 2021, incorporating six variables: ...
A hybrid deep learning technique is proposed in the present paper, in which transient identification is done using a CNN-LSTM neural network. The training data set is taken from a VVER-1000 full-scope simulator and the most important operating parameters are determined by feature selection ...
One deep learning model consisted of only one long short-term memory (LSTM), whereas the other model simulated processes in each hydrologic response unit (HRU) by defining one separate LSTM for each HRU. The models consider environmental data as well as changing landuse in catchment and predict...
Data analytics workflow typical of Machine Learning Full size image From a modelling standpoint, a DT entails the translation of physical entities into virtual space, with the objective of closely mirroring the behaviour of the real system through its virtual representation. In this scenario, both ph...
An Attention-Based LSTM Model for Stock Price Trend Prediction Using Limit Order Books Traditional methods for predicting stock price trends are mostly based on the historical OHLC (i.e., open, high, low, and close prices) data. However, it eliminates most of the trading information. To addre...
The proposed SN-1DCNN-LSTM framework not only increases the number of training samples from the side, but also fully exploits the similarity information and data features between samples, overcoming the problem of instability and overfitting of deep learning models under small sample conditions. ...
(MLP). There also exist other types of algorithms in the class of deep learning, such as Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN), but they are more focused on different tasks. Researchers may consider CNN for image recognition ...