Table 4 Comparison of different deep learning methods

Convolutional neural networks (CNNs)

• CNNs excel at image classification, object detection, and segmentation. Convolution operations help them recognize image patterns

• CNNs can handle image noise, making them a good choice for noisy tasks

• CNNs are versatile computer vision tools for image processing

• Training CNNs requires large datasets, which may deter some users

• CNNs are computationally expensive to train and deploy, which limits some applications

Long short-term memory (LSTM)

• LSTM networks can learn long-range dependencies, making them suitable for natural language processing and speech recognition

• Robust to noise: LSTM networks are good for noisy data tasks

• LSTM networks are versatile ML tools

• Training LSTM networks require large datasets, which may deter some users

• LSTM networks are computationally expensive to train and deploy, which may limit their use

• Sometimes wrong: LSTM networks are not ideal for all tasks. For tasks with poorly defined data or nonlinear data relationships, they may not be as effective as other models

Recurrent convolutional neural networks (RCNNs)

• RCNNs can be used for a variety of tasks, including:

• Natural language processing: RCNNs can identify sentiment and entities in text

• Speech recognition: RCNNs can transcribe audio and identify speakers

• Time series analysis: RCNNs can predict time series values and identify patterns

• RCNNs are versatile because they can handle spatial and temporal relationships

• RCNNs can learn long-range dependencies, making them suitable for natural language processing and speech recognition

• Robust to noise: RCNNs are good for noisy data tasks

• RCNNs are versatile ML tools