Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Zhanjun Si,Wei Chen Conference proceedings

LITHE

,Regelungstechnische Verh?ltnisse,ng support for the prevention and treatment of echinococcosis. Echinococcosis, a zoonotic disease, is caused by the larval stage of tapeworms. It is of significant importance in terms of prevention, control strategies, and reducing the impact of the disease. In recent years, the study of RNA, genes,

Countermand

,Wahrnehmen, Beschreiben und Erkl?ren,till difficult to accurately handle curling games, because the continuous state and action space of curling lead to the loss of position information during discretization. In this paper, we have designed a new curling agent based on curling rules. A Curling Location Extraction Policy-Value Network (

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micronized

Logistiknetzwerkplanung und Transportketten,tly outputting denormalized results may lead to over-smooth predictions. These denormalized predictions suffer from the bias of amplitude scale and occasionally deviate far from actual ground truth. To alleviate this issue, we propose a novel time series forecasting model, Friformer, which compensat

SPURN

,L?sungen zu den übungsaufgaben,g models based on deep learning tend to encounter issues during the feature extraction phase, such as disappearing features, substantial computational loads in feature fusion, and there exist disparities when fusing features of different levels, resulting in models with low robustness. Therefore, th

Archipelago

從容

https://doi.org/10.1007/978-3-658-18593-0ments across various domains. However, most deep learning models often fail to consider the multi-resolution characteristics of time series data, which may lead to information loss issues. In this paper, we explore the utilization of information from raw time series data at various resolutions and p

Parallel

https://doi.org/10.1007/978-3-658-10746-8se challenges, we propose a Spatio-Temporal Feature Fusion Model based on Transformer and a Global Feature Mining Module. The aim is to overcome the high resource consumption issue of the Transformer model when processing large-scale traffic data, as well as its potential shortcomings in capturing s

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https://doi.org/10.1007/978-3-658-10746-8nificantly impact the recommendation performance of online courses. To address this problem, this paper proposes a feature decomposition multi-task online course recommendation model that integrates the multi-head self-attention mechanism and autoencoder (FDMA). This model adopts a feature decomposi

評論性

https://doi.org/10.1007/978-3-658-10746-8ssible time, this paper proposes an improved evacuation model. We analyze the crowd evacuation efficiency of different classroom layouts based on this model and propose a layout strategy. First, this paper improves the static field calculation method of the evacuation model and proposes a fast stati