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Image Search Results
Journal: International Journal of Environmental Research and Public Health
Article Title: Epileptic Seizures Detection Using Deep Learning Techniques: A Review
doi: 10.3390/ijerph18115780
Figure Lengend Snippet: Summary of related works done using 2D-CNNs.
Article Snippet: [ ] , 2D-CNN with
Techniques:
Journal: Sensors (Basel, Switzerland)
Article Title: A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems
doi: 10.3390/s23010001
Figure Lengend Snippet: Supervised learning used in the research from the selected literature. If the record contains a certain value for Software, then it is actually a representative of a hybrid approach in which some of the data-driven techniques are implemented. For clarity, all acronyms are provided in the Abbreviations list.
Article Snippet: AHU ,
Techniques: Software
Journal: Sensors (Basel, Switzerland)
Article Title: A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems
doi: 10.3390/s23010001
Figure Lengend Snippet: Brief summary of related work addressing the hybrid approach in HVAC FDD. For clarity, all acronyms are provided in the Abbreviations list.
Article Snippet: AHU ,
Techniques: Software
Journal: iScience
Article Title: Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring
doi: 10.1016/j.isci.2023.107083
Figure Lengend Snippet: Inlustraions of 2D CNN and GBDT models (A) Schematic overview of the 2D CNN model. Four convolutional layers and four max pooling layers are included in the 2D CNN model. Here, the kernel size of each convolutional layer is set to be three and the pooling size is two. (B) Illustration of GBDT model with N gradient boosting trees.
Article Snippet: Codes for K-mean ++, 2D CNN, 3D CNN and
Techniques:
Journal: iScience
Article Title: Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring
doi: 10.1016/j.isci.2023.107083
Figure Lengend Snippet: Comparison, testing, and training of 3D CNN (A) Bar graph showing comparison of predictive performance of the 3D CNN, 2D CNN, and GBDT algorithms via analysis of root mean squared error (RMSE) and mean absolute error (MAE) (∗p < 0.05, ∗∗p < 0.05, the Student’s t test). (B) Bar graph showing comparison of Pearson correlation coefficient (PCC) values between 3D CNN, 2D CNN, and GBDT algorithms. (C) Loss graph of ten training rounds (iterations) comparing the 3D CNN and 2D CNN for both dataset 1 and dataset 2. (D) Intraclass correlation coefficient analysis of 3D CNN’s predictive accuracy on a range of images with 0 to 70μCi radioactivity (∗p = 0.001, ∗∗p = 0.001, the F test).
Article Snippet: Codes for K-mean ++, 2D CNN, 3D CNN and
Techniques: Comparison, Radioactivity
Journal: iScience
Article Title: Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring
doi: 10.1016/j.isci.2023.107083
Figure Lengend Snippet:
Article Snippet: Codes for K-mean ++, 2D CNN, 3D CNN and
Techniques: Recombinant, Software
Journal: Computational Intelligence and Neuroscience
Article Title: EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
doi: 10.1155/2022/6486570
Figure Lengend Snippet: A review of recent research that applied the CNN-RNNs model for seizure prediction with their corresponding accuracies.
Article Snippet: Roy et al. [ ] , Sigmoid,
Techniques: Selection