Sequencing:Article Title: Recurrent neural network-based variant pathogenicity classifier
Article Snippet: Number of Kernels, Window Atrous Acti- Layer Type size Shape rate vation Input Sequence Convolution 40, 1 (L, 40) 1 Linear (layer 1a) 1D Input PSSM Convolution 40, 1 (L, 40) 1 Linear (layer 1b) 1D Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 40, 5 (L, 40) 1 Linear 1D Layer 3 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 4 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 5 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 6 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 7 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 8 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 9 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 10 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 11 Convolution 40, 5 (L, 40) 1 ReLU 1D Merge Merge layer — (L, 40) — — activations 5, 8 and 11. mode = ‘sum’ Layer 12 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 13 Convolution 40, 5 (L, 40) 1 ReLU 1D Output layer Convolution 1, 3 (L, 3) — Softmax 1D The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Window Atrous Acti- Layer Type size Shape rate vation Input Sequence Convolution 40, 1 (L, 40) 1 Linear (layer 1a) 1D Input PSSM Convolution 40, 1 (L, 40) 1 Linear (layer 1b) 1D Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 40, 5 (L, 40) 1 Linear 1D Layer 3 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 4 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 5 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 6 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 7 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 8 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 9 Convolution 40, 5 (L, 40) 2 ReLU 1D Layer 10 Convolution 40, 5 (L, 40) 2 ReLU 1D Layer 11 Convolution 40, 5 (L, 40) 2 ReLU 1D Merge Merge layer — (L, 40) — — activations 5, 8 and 11. mode = ‘sum’ Layer 12 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 13 Convolution 40, 5 (L, 40) 1 ReLU 1D Output layer Convolution 1, 3 (L, 3) — Softmax 1D The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities. ..
Article Title: Semi-supervised learning for training an ensemble of deep convolutional neural networks
Article Snippet: .. Number of Kernels, Window Atrous Activa- Layer Type size Shape rate tion Input Convolution 1D 40, 1 (L, 40) 1 Linear Sequence (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 1 ReLU Merge Merge - layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Window Atrous Activa- Layer Type size Shape rate tion Input Convolution 1D 40, 1 (L, 40) 1 Linear Sequence (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 2 ReLU Merge Merge - layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities.
Article Title: Deep convolutional neural networks for variant classification
Article Snippet: .. Number of Kernels, Layer Type Window size Shape Atrous rate Activation Input Sequence Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 1 ReLU Merge Merge-layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Layer Type Window size Shape Atrous rate Activation Input Sequence Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 2 ReLU Merge Merge-layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities.
Residue:Article Title: Recurrent neural network-based variant pathogenicity classifier
Article Snippet: Number of Kernels, Window Atrous Acti- Layer Type size Shape rate vation Input Sequence Convolution 40, 1 (L, 40) 1 Linear (layer 1a) 1D Input PSSM Convolution 40, 1 (L, 40) 1 Linear (layer 1b) 1D Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 40, 5 (L, 40) 1 Linear 1D Layer 3 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 4 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 5 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 6 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 7 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 8 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 9 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 10 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 11 Convolution 40, 5 (L, 40) 1 ReLU 1D Merge Merge layer — (L, 40) — — activations 5, 8 and 11. mode = ‘sum’ Layer 12 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 13 Convolution 40, 5 (L, 40) 1 ReLU 1D Output layer Convolution 1, 3 (L, 3) — Softmax 1D The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Window Atrous Acti- Layer Type size Shape rate vation Input Sequence Convolution 40, 1 (L, 40) 1 Linear (layer 1a) 1D Input PSSM Convolution 40, 1 (L, 40) 1 Linear (layer 1b) 1D Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 40, 5 (L, 40) 1 Linear 1D Layer 3 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 4 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 5 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 6 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 7 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 8 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 9 Convolution 40, 5 (L, 40) 2 ReLU 1D Layer 10 Convolution 40, 5 (L, 40) 2 ReLU 1D Layer 11 Convolution 40, 5 (L, 40) 2 ReLU 1D Merge Merge layer — (L, 40) — — activations 5, 8 and 11. mode = ‘sum’ Layer 12 Convolution 40, 5 (L, 40) 1 ReLU 1D Layer 13 Convolution 40, 5 (L, 40) 1 ReLU 1D Output layer Convolution 1, 3 (L, 3) — Softmax 1D The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities. ..
other:Article Title: Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.
Article Snippet: Layer Position Layer Type Output Channel Size a 1st layer 1-D convolution 32 2nd layer ReLU 32 3rd layer Max pooling (pool size: 2) 32 4th layer Dropout 32 5th layer 1-D convolution 64 6th layer ReLU 64 7th layer Max pooling (pool size: 2) 64 8th layer Dropout 64 9th layer 1-D convolution 128 10th layer ReLU 128 11th layer 1-D convolution 64 12th layer ReLU 64 13th layer Upsampling (size: 2) 64 14th layer Dropout 64 15th layer 1-D convolution 32 16th layer ReLU 32 17th layer Upsampling (size: 2) 32 18th layer Dropout 32 19th layer 1-D convolution 3 20th layer Softmax 3 b 1st layer Residual 1D-convolution block 32 2nd layer ReLU 32 3rd layer Max pooling (pool size: 2) 32 4th layer Residual 1D-convolution block 64 5th layer ReLU 64 6th layer Max pooling (pool size: 2) 64 7th layer Residual 1D-convolution block 128 8th layer ReLU 128 9th layer Residual 1D-convolution block 64 10th layer ReLU 64 11th layer Upsampling (size: 2) 64 12th layer Residual 1D-convolution block 32 13th layer ReLU 32 14th layer Upsampling (size: 2) 32 15th layer 1-D convolution 2 16th layer Softmax 2 Journal of Human Genetics Figure 2a, b, and c illustrate the impact of missing inputs on the performance of IMPUTE5, Minimac3, and RNN-IMP, respectively.
Article Title: Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.
Article Snippet: Second, the output (YAS) from the AS block continued to compl te the convolution oper tion and performed three 1D convolutions (Convolution 1/Convolutio 2/Convolu‐ tion 3) blocks (the number of kernels, kernel size, and strides were set to 16/64/128, 3/3/3, and 1/1/1, respectively), and one flatten layer, and the last part is output layer (Softmax).
Activation Assay:Article Title: Addressing data limitations in seizure prediction through transfer learning
Article Snippet: .. Layer Hyperparameters Output shape Input - 2560x19 Convolution 1D Filters = 128, Size = 3, Stride = 1, Pad = ’same’ 2560x128 Convolution 1D Filters = 128, Size = 3, Stride = 2, Pad = ’same’ 1280x128 Spatial dropout Rate = 20% 1280x128 Activation Swish function 1280x128 Batch normalisation - 1280x128 Convolution 1D Filters = 256, Size = 3, Stride = 1, Pad = ’same’ 1280x256 Convolution 1D Filters = 256, Size = 3, Stride = 2, Pad = ’same’ 640x256 Spatial dropout Rate = 20% 640x256 Activation Swish function 640x256 Batch normalisation - 640x256 Convolution 1D Filters = 512, Size = 3, Stride = 1, Pad = ’same’ 640x512 Convolution 1D Filters = 512, Size = 3, Stride = 2, Pad = ’same’ 320x512 Spatial dropout Rate = 20% 320x512 Activation Swish function 320x512 Batch normalisation - 320x512 Bidirectional LSTM Units = 64, Return sequences = False 128x1 Dropout Dropout rate = 20% 128x1 Fully connected Neurons = 2 2x1 Activation Softmax function 2x1 3 Results obtained for all approaches Table S7 contains seizure sensitivities (SSs), false positive rate per hour (FPR/h) values, and the output of the surrogate analysis for every patient for the standard approach and the transfer learning approach. ..
Article Title: DeepRespNet: A deep neural network for classification of respiratory sounds
Article Snippet: Respiratory sounds convey significant information about the pulmonary status.. This study proposes a deep learning-based framework to create an automatic, non-invasive, diagnostic method of categorizing pulmonary sounds.. A labelled database of pulmonary sounds has been collected using an electronic stethoscope and audio recording instrument.
Article Title: Deep convolutional neural networks for variant classification
Article Snippet: .. Number of Kernels, Layer Type Window size Shape Atrous rate Activation Input Sequence Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 1 ReLU Merge Merge-layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Layer Type Window size Shape Atrous rate Activation Input Sequence Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 2 ReLU Merge Merge-layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities.
Solvent:Article Title: Semi-supervised learning for training an ensemble of deep convolutional neural networks
Article Snippet: .. Number of Kernels, Window Atrous Activa- Layer Type size Shape rate tion Input Convolution 1D 40, 1 (L, 40) 1 Linear Sequence (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 1 ReLU Merge Merge - layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Window Atrous Activa- Layer Type size Shape rate tion Input Convolution 1D 40, 1 (L, 40) 1 Linear Sequence (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 2 ReLU Merge Merge - layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities.
Article Title: Deep convolutional neural networks for variant classification
Article Snippet: .. Number of Kernels, Layer Type Window size Shape Atrous rate Activation Input Sequence Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 1 ReLU Merge Merge-layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The details of the solvent accessibility model is shown in the table below, according to one implementation. .. Number of Kernels, Layer Type Window size Shape Atrous rate Activation Input Sequence Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1a) Input PSSM Convolution 1D 40, 1 (L, 40) 1 Linear (layer 1b) Merging Merge (mode = — (L, 80) — — Sequence + Concatenate) PSSM Layer 2 Convolution 1D 40, 5 (L, 40) 1 Linear Layer 3 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 4 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 5 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 6 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 7 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 8 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 9 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 10 Convolution 1D 40, 5 (L, 40) 2 ReLU Layer 11 Convolution 1D 40, 5 (L, 40) 2 ReLU Merge Merge-layer 5, — (L, 40) — — activations 8 and 11.mode = ‘sum’ Layer 12 Convolution 1D 40, 5 (L, 40) 1 ReLU Layer 13 Convolution 1D 40, 5 (L, 40) 1 ReLU Output layer Convolution 1D 1, 3 (L, 3) — Softmax The secondary structure class of a specific amino acid residue is determined by the largest predicted softmax probabilities.
Control:Article Title: DeepRespNet: A deep neural network for classification of respiratory sounds
Article Snippet: Respiratory sounds convey significant information about the pulmonary status.. This study proposes a deep learning-based framework to create an automatic, non-invasive, diagnostic method of categorizing pulmonary sounds.. A labelled database of pulmonary sounds has been collected using an electronic stethoscope and audio recording instrument.
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