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Image Search Results


Custom convolutional neural network methodology. (Reprinted with permission from Deepfake Faces Dataset by Dagnelies, https://www.kaggle.com/datasets/dagnelies/deepfake-faces ).

Journal: Frontiers in Artificial Intelligence

Article Title: Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks

doi: 10.3389/frai.2026.1737761

Figure Lengend Snippet: Custom convolutional neural network methodology. (Reprinted with permission from Deepfake Faces Dataset by Dagnelies, https://www.kaggle.com/datasets/dagnelies/deepfake-faces ).

Article Snippet: The CNN model handles inputs of size (224, 224, 3) using the Kaggle platform.

Techniques:

CNN model - training accuracy vs. validation accuracy and training loss vs. validation loss.

Journal: Frontiers in Artificial Intelligence

Article Title: Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks

doi: 10.3389/frai.2026.1737761

Figure Lengend Snippet: CNN model - training accuracy vs. validation accuracy and training loss vs. validation loss.

Article Snippet: The CNN model handles inputs of size (224, 224, 3) using the Kaggle platform.

Techniques: Biomarker Discovery

Flowchart of the SVMD-CNN-BiLSTM-A model.

Journal: PLOS One

Article Title: A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm

doi: 10.1371/journal.pone.0337103

Figure Lengend Snippet: Flowchart of the SVMD-CNN-BiLSTM-A model.

Article Snippet: The BiLSTM-Attention-CNN model leverages BiLSTM units, an attention mechanism for feature prioritization, and a CNN component to capture nonlinear dynamics and temporal dependencies in daily price data for corn and wheat from the Chicago Board of Trade (CBOT), evaluated against ten benchmark configurations.

Techniques:

Structure of BiLSTM network.

Journal: PLOS One

Article Title: A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm

doi: 10.1371/journal.pone.0337103

Figure Lengend Snippet: Structure of BiLSTM network.

Article Snippet: The BiLSTM-Attention-CNN model leverages BiLSTM units, an attention mechanism for feature prioritization, and a CNN component to capture nonlinear dynamics and temporal dependencies in daily price data for corn and wheat from the Chicago Board of Trade (CBOT), evaluated against ten benchmark configurations.

Techniques:

Scatter plots of actual and forecasted values of different optimization algorithms for BiLSTM models in corn and wheat datasets.

Journal: PLOS One

Article Title: A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm

doi: 10.1371/journal.pone.0337103

Figure Lengend Snippet: Scatter plots of actual and forecasted values of different optimization algorithms for BiLSTM models in corn and wheat datasets.

Article Snippet: The BiLSTM-Attention-CNN model leverages BiLSTM units, an attention mechanism for feature prioritization, and a CNN component to capture nonlinear dynamics and temporal dependencies in daily price data for corn and wheat from the Chicago Board of Trade (CBOT), evaluated against ten benchmark configurations.

Techniques: