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The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, the use of EEG signals for seizure detection has gained widespread academic attention. Aiming at the problem of overfitting deep learning models due to the small number of EEG signal data during epilepsy detection, this paper proposes an epilepsy detection method that combines data augmentation and deep learning. Methods: First, the Adversarial and Mixup Data Augmentation (AMDA) method is used to realize the data augmentation, which effectively enriches the number of training samples. To further improve the classification accuracy and robustness of epilepsy detection, this paper proposes a one-dimensional convolutional neural network and gated recurrent unit (AM-1D CNN-GRU) network model based on attention mechanism for epilepsy detection. Results and discussion: The experimental results show that the performance of epilepsy detection achieved by using augmented data is significantly improved, and the accuracy, sensitivity, and area under the subject's working characteristic curve are up to 96.06, 95.48%, and 0.9637, respectively. Compared with the nonaugmented data, all indicators are increased by more than 6.2%. Meanwhile, the detection performance was significantly improved compared with other epilepsy detection methods. The results of this research can provide a reference for the clinical application of epilepsy detection. [ABSTRACT FROM AUTHOR]

Plant diseases pose a significant menace to agricultural output and global food security. Detecting these diseases promptly and precisely is pivotal for effective disease control and optimization of crop yield. This survey paper presents a comprehensive analysis of recent advancements in plant disease detection leveraging deep learning and wavelet neural network approaches. Thirty notable papers are reviewed, emphasizing the diverse methodologies and datasets employed. The analyzed papers collectively showcase the efficacy of convolutional neural networks, wavelet transformations, and deep learning models in accurately identifying and classifying plant diseases. While conventional Convolutional Neural Networks (CNNs) have proven effective in image-based disease detection, they might encounter challenges in capturing frequency-domain nuances within plant images. Several papers emphasize specialized datasets such as Field Plant, Plant Village,and others, catering to real-world scenarios. Moreover, research indicates a continuous trend towards enhancing model performance, optimizing accuracy, and enabling real-time disease detection, promising a significant impact on agricultural practices. Precision, Recall, and F1-Score are essential performance metrics that can be used to conduct a thorough evaluation of the presented models. Additionally, the Confusion Matrix and Area Under the Precision-Recall Curve (AUC-PR) can be used to measure key parametric performance and real-world feasibility. [ABSTRACT FROM AUTHOR]