In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, clas...
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ISBN:
(数字)9798350367300
ISBN:
(纸本)9798350367317
In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.
This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter o...
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The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of tho...
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ISBN:
(数字)9798331529376
ISBN:
(纸本)9798331529383
The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.
Segmentation plays a crucial role in computer-aided medical image diagnosis, as it enables the models to focus on the region of interest (ROI) and improve classification performance. However, medical image datasets of...
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In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided fi...
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Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often strug...
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ISBN:
(数字)9798331529024
ISBN:
(纸本)9798331529031
Multimodal Emotion Recognition in Conversation (ERC) is a task of predicting the emotion of each utterance in a conversation by utilizing both verbal and non-verbal modalities. However, existing approaches often struggle to bridge cross-modal gaps, resulting in misaligned features and frequent misclassification of minority emotions into semantically similar majority emotions. To address these challenges, we propose MERNet, a framework that employs cross-modal knowledge distillation and contrastive learning to align multimodal features and effectively distinguish subtle emotions in conversations. Our framework consists of two stages: 1) guiding non-verbal modalities with the text modality to transfer knowledge and align their features, and 2) applying contrastive learning with emotion labels as anchors to distinguish subtle differences between similar emotions and address the class imbalance problem. Experiments conducted on two benchmark datasets, IEMOCAP and MELD, demonstrate that our MERNet outperforms existing state-of-the-art models.
Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent ...
Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan.
With the continuous development of deep learning and artificial intelligence industry,more and more neural networks have been *** them,one typical network widely used is *** solves the problem of requiring multiple ge...
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With the continuous development of deep learning and artificial intelligence industry,more and more neural networks have been *** them,one typical network widely used is *** solves the problem of requiring multiple generators and discriminators for multiple domains and style *** this article,we will analyze and explore StarGAN v1 and StarGAN *** StarGAN's development history,advantages and disadvantages in the application,*** the same time,we will compare StarGAN with other neural networks such as CycleGAN and MSGAN through some discrimination criteria,and clearly demonstrate the influence of different networks on the generation of image data sets through quantitative and qualitative *** addition,we also found a representative code on some of the actual uses of StarGAN for hair replacement through online *** this code may be used to confuse the use of StarGAN v1 and v2,we will also improve this code in the article and put forward suggestions for *** this paper,we carried out the specific analysis of StarGAN's loss function based on the existing data and put forward some relevant *** taking the data we searched for StarGAN,it can be found that StarGAN could use as few resources as possible to achieve clearer and more varied results during the image generation task.
Due to its broad applications, remote sensing image captioning (RSIC) has gained popularity in recent years. However, it poses extra challenges for containing low-resolution images with highly structured semantic cont...
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ISBN:
(数字)9798350376340
ISBN:
(纸本)9798350376357
Due to its broad applications, remote sensing image captioning (RSIC) has gained popularity in recent years. However, it poses extra challenges for containing low-resolution images with highly structured semantic content. By incorporating image labeling and segmentation, this work develops an RSIC framework using a structured attention module that highlights important semantic components to maintain a geometric and structured shape. The quality and edge emphasis of UCM-captioned photographs are improved by upsampling them to 512×512 pixels. Using the Segment Anything Model (SAM) produces better image proposals, leading to higher accuracy than traditional techniques. A balanced output of large- and small-object masks is facilitated by SAM's promptability. The decoder can more easily learn a suitable statistical model using the model's spatial structure to provide an all-encompassing attention map. This work investigates the effects of multiple hyperparameters, including teacher forcing, the number of region proposals, and the impact of DSR and AVR loss factors. Overall, by combining image labeling and segmentation, this research improves remote sensing capabilities. It also shows how well the structured attention module and SAM work together to improve accuracy and consider different hyperparameter issues.
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