“…The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.…”
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“…The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. …”
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“…Motivated by the latter, in this work, we propose a CNN approach called DeepPilot that takes camera images as input and predicts flight commands as output. …”
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“…We then fed the diffused image to a deep CNN to identify the complex and deep features for classification. …”
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“…We divided the dataset into training and testing, and after 10 k-fold validation, the reported accuracies of CNN and RNN are 87.11% and 86.52%, respectively. …”
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“…This article proposes a multimode medical image fusion with CNN and supervised learning, in order to solve the problem of practical medical diagnosis. …”
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“…The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. …”
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“…We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.…”
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“…In order to address the aforementioned limitations, this paper establishes a novelty prediction model based on convolutional recurrent neural network with the attention mechanism named as CNN-GRU-AM. There are four parts in the proposed CNN-GRU-AM model. …”
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“…This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. …”
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“…In practice, the accuracy of CNN-based sensors is highly dependent on the quality of the training datasets. …”
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“…In this paper, three categories of features (content features, uploader features and environment features) are proposed to construct a convolutional neural network (CNN) for misleading video detection. The experiment showed that all the three proposed categories of features play a vital role in detecting misleading videos. …”
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“…We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and F(1) scores for the classes S and V of 82.8% and 91.7%, respectively, which is far superior to other approaches.…”
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“…The images are separated into relevant patches of various sizes and stacked for use as inputs to CNN, which is then trained, tested, and validated. …”
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“…In this work, we present a matched filter (MF) interpretation of CNN classifiers accompanied by an experimental proof of concept using a carefully developed synthetic dataset. …”
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“…In order to build a robust classification algorithm for a calibration-less BCI system, we propose an end-to-end model that transforms the EEG signals into symmetric positive definite (SPD) matrices and captures the features of SPD matrices by using a CNN. To avoid the time-consuming calibration and ensure the application of the proposed model, we use the meta-transfer-learning (MTL) method to learn the essential features from different subjects. …”
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