I'm currently working on a project that involves combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) architectures using PSO .
I'm seeking more guidance on the best practices and considerations for effectively combining these techniques
Combining LSTM and CNN using PSO enhances sequence and image analysis. For instance, in stock price prediction, LSTM captures temporal patterns while CNN identifies visual features from financial charts. PSO optimizes their fusion by tuning hyperparameters like layer sizes and learning rates. This synergistic approach improves model accuracy by 10% compared to standalone methods. This hybrid architecture offers a dynamic toolset for tackling complex tasks where both sequential and spatial information play crucial roles.
Combining LSTM and CNN can improve both spatial and temporal features effectively. However, the challenge lies in optimally tuning the hyperparameters for such a hybrid architecture. PSO/Bayesian Optimization or any other method can be an effective technique, as it can search the hyperparameter space more efficiently than traditional methods like grid search.
Our recent paper extended the CNN-LSTM architecture by incorporating a Conditional Random Field (CRF) layer. Adding a CRF layer after CNN-LSTM will improve the temporal learning capacity of the neural network. The CRF layer acts as a sequence modeler that refines the LSTM output, making the model more effective for sequence-dependent tasks. We used Bayesian Optimization for hyperparameter tuning, you can replace BO with PSO.
you can see the method here:
Article EEG-Based Emotion Classification in Financial Trading Using ...
Hi Elmenawy, a simple integration go CNN and LSTM (CNN-LSTM network) can be prone to high prediction error as individual modules in a CNN-LSTM network handle spatial and temporal patterns separately to learn the input data. On the other hand, a Convolutional LSTM (ConvLSTM) neural network embeds convolutional structures inside the LSTM neural network to make predictions, which give lower prediction errors. You can check out the following article for details:
"Convolutional LSTM: A Deep Learning Approach to Predict Shoulder Joint Reaction Forces"
Article Convolutional LSTM: A Deep Learning Approach to Predict Shou...
Article Convolutional LSTM: A Deep Learning Approach to Predict Shou...
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