The prediction of carbon price can contribute to the reduction of carbon emissions. A carbon price forecasting model based on the data decomposition method, adaptive boosting (AdaBoost) algorithm, and Elman neural net...
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In the past decade or so, we have seen traffic congestion become a ginormous issue in the major cities of the world, the main reasons for the above being exponential growth in vehicle purchases, constant unplanned mai...
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Cultural heritage plays a significant role in shaping the identity of a region but recognizing cultural events poses a notable challenge in the field of computer vision. In this paper, a novel framework is proposed th...
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Crop disease identification is crucial for its role of maintaining crop yield and quality in precision agriculture. Traditional disease detection methods are often inefficient and error-prone. To address this issue, w...
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ISBN:
(纸本)9798350375084;9798350375077
Crop disease identification is crucial for its role of maintaining crop yield and quality in precision agriculture. Traditional disease detection methods are often inefficient and error-prone. To address this issue, we propose the ECA-ViT model, which combines Efficient Channel Attention (ECA) and Vision Transformers (ViT) to identify crop leaf diseases in cultivation environments. The ECA-ViT model uses adaptive 1D convolution kernels for rapid cross-channel interaction of local disease features in crop images, compensating for the Vision Transformer's lack of global context understanding and optim-izing the model. This balanced approach enhances feature learning while maintaining computational efficiency without increasing model complexity. We evaluated the ECA-ViT model using rice leaf disease datasets from real field settings. The results demonstrated that ECA-ViT outperformed existing deep lear-ning methods, achieving an accuracy of 95.41%.
Understanding how data separation (DS), visual separation (VS), and classifier performance (CP) are related to each other is important for applications in both machinelearning and information visualization. A recent ...
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ISBN:
(纸本)9783031667428;9783031667435
Understanding how data separation (DS), visual separation (VS), and classifier performance (CP) are related to each other is important for applications in both machinelearning and information visualization. A recent study showed that, for a specific machinelearning pipeline using a given multidimensional projection technique, high DS leads to high VS and next high CP. However, whether such correlations would stay the same (or not) when using other projection techniques was left open. We fill this gap by evaluating ten projection techniques in a pipeline that uses three contrastive learning methods (SimCLR, SupCon, and their combination) to produce latent spaces and next train and test classifiers for five image datasets of real-world application with human intestinal parasites. Our work identifies two classes of projection techniques - one leading to poor VS and next poor CS regardless of the available DS, and the other showing a good DS-VS-CP correlation. We argue that this last group of projections is a useful instrument in classifier engineering tasks.
The advancement of computer technology in the area of image generation opens up opportunities for the creation of art. However, owing to the complexity of artistic expression and the difficulty of data collection, gen...
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Under the development trend of economic globalization, machinelearning technology has been widely used in the world, and artificial neural networks have also been paid attention to by researchers. Among them, under t...
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Aiming at the accuracy requirement of automatic control of grinding machine, this paper combined with deep learning technology, designed a grinding machine acoustic emission data detection scheme based on GRU neural n...
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作者：
FarheenKumar, RajeevLab
School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi110 067 India
The concept of predictions has gained much attention over the last few years. Research on prediction based on experience is error-prone. Usually, a lot of data has been available with multiple variables, but not all f...
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learning classifiers using skewed or imbalanced datasets can occasionally lead to classification issues;this is a serious issue. In some cases, one class contains the majority of examples while the other, which is fre...
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