Search Results - "attention mechanisms"

Red cayenne pepper is a commodity that has important economic value in Indonesia, especially in West Java Province. Cayenne pepper often experiences significant price fluctuations which can cause inflation. In 2022 there will be the highest inflation in West Java in the last eight years due to incre...

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The Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) of...

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In recent years, UAV technology has developed rapidly and has been widely applied across various fields. However, as the adoption of civilian UAVs continues to grow, there has been a corresponding rise in the number of black flights by UAVs, which may cause criminal activities and privacy and securi...

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This study introduces the Channel-wise Attention with Relative Distance (CARD) model for NFT market prediction, addressing the unique challenges of NFT valuation through a novel deep learning architecture. Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates super...

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Multimodal emotion recognition technology leverages the power of deep learning to address advanced visual and emotional tasks. While generic deep networks can handle simple emotion recognition tasks, their generalization capability in complex and noisy environments, such as multi-scene outdoor setti...

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By combining graph neural networks and multiple attention mechanisms, a GNN-MAM (Graph neural network based on multiple attention mechanisms) model was developed, which utilizes the structural characteristics of graph neural networks to capture complex correlations and dynamic changes in financial d...

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Although recent deep-learning-based speech enhancement (SE) methods significantly outperform traditional approaches, their computational demands often scale proportionally with their performance. This scaling typically makes them impractical for deployment on data throughput-sensitive and resource-c...

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To address the significant shortcomings of traditional image retrieval models in feature extraction and recognition accuracy, this study proposes a new method based on the improved residual network and attention mechanism. By constructing convolutional neural networks, the research aims to achieve m...

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It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanis...

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This paper proposes a deeply integrated model called CAS-GNN, aiming to solve the collaborative path-planning problem for multi-agent vehicles operating in dynamic environments. Our proposed model integrates CAS-UNet and Graph Neural Network (GNN), and, by introducing a dynamic edge enhancement modu...

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Due to the problems of other existing models, such as the inability to extract the weights of the key parts of the text, the model classification is inaccurate, and it is difficult to adapt to the heavy work environment in the space text classification work. Therefore, based on the fusion of the BER...

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In recent years, deep learning has shown significant progress for image compression compared to traditional image compression methods. Although conventional standard-based methods are still used, they are limited in handling repetitive patterns and complex calculations, which can lead to image recon...

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Aiming at the problems of multiple data sources, multiple data modes, high data dimensions, large redundancy, small and unbalanced sample size, and difficulty in obtaining a large number of labeled data required for training, a deep bidirectional gated recurrent unit (DBGRU) electromagnetic behavior...

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The intricate and highly branched structure of retinal blood vessels, along with the fragility of fine vessels, makes segmentation a challenging task. To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. Specifically, the encoders...

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ObjectiveTo enhance the automatic detection precision of diabetic retinopathy (DR) lesions, this study introduces an improved YOLOv8 model specifically designed for the precise identification of DR lesions.MethodThis study integrated two attention mechanisms, convolutional exponential moving average...

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To address the limitations of convolutional neural networks in capturing global fault features, the high computational cost and overfitting risk of Transformer models in gear fault diagnosis, and the feature degradation under strong noise, this study proposes a novel convolution-Transformer&#x20...

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Although state-of-charge (SoC) forecasting has received considerable attention, long-term prediction remains a challenging task due to disrupted temporal dependencies and the neglect of battery signal characteristics. In this study, we propose a novel deep learning-based long-term SoC forecasting me...

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With the rapid development of e-commerce and the logistics industry, the importance of logistics packaging defect detection as a key link in product quality control is becoming increasingly prominent. However, existing target detection models often face the problems of difficulty in improving detect...

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In this paper, we propose a deep learning model based on dual-branch learning with a hybrid attention mechanism for meeting challenges in the underutilization of features in ophthalmic image diagnosis and the limited generalization ability of traditional single modal deep learning models when using...

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Autonomous mobile robots, equipped with multiple sensors, have been traditionally used to perform search, rescue and other tasks. A number of new scenarios for application of mobile robots have emerged in the last decade. These include automated logistics handling in warehouse-like environments (in...

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