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

Transfusion

Spatio-temporal Fusion of Transformer and Global Feature Mining for Traffic Flow Predictiontegration of temporal and spatial correlations, and revealing the interconnections between global and local features. Through extensive experiments on five real-world traffic datasets, the research results demonstrate a significant improvement in prediction accuracy of our proposed method compared to existing models.

Preserve

Evaluating Effect of Classroom Interior Layouts on Crowd Evacuation Efficiency Using Improved Evacuaectiveness of the evacuation model. Finally, based on the evacuation test results of 10 classroom layouts, we evaluated the effectiveness of these 10 layouts and analyzed them and put forward classroom interior layout suggestions, such as expanding classroom exits and increasing classroom wall passages.

方便

杀死

垄断

The Degree of Symmetry of Fuzzy Relationsvided. Furthermore, for continuous t-norm, a symmetric fuzzy relation that closely approximates a given fuzzy relation is constructed. Some algorithm is designed to realize this approximation. Finally, similar results for some left-continuous t-norm are given.

方便

LegalGPT: Legal Chain of Thought for the Legal Large Language Model Multi-agent Frameworkabilities these models have demonstrated in complex reasoning and zero-shot learning (ZSL). By introducing a multi-intelligence framework based on Large Scale Legal Language Modeling, the research aims to improve the efficiency and performance of the models across a wide range of functions including

LARK

Leverage Diagnosis Intensity in Medication Recommendationses and personalize the proposed treatment. The majority of those systems prioritize medication recommendations based on the current diagnoses of the patient’s electronic health record (EHR) without considering diagnosis intensity. Thus, they fail to capture the intensity of the patient’s condition,

BROW

USST: Utilizing SimAM and SGA Techniques to Cassava Leaf Diseases Classification in Real Cultivationed, traditional detection methods hinder effective disease management. We introduce USST, a novel cassava leaf disease detection approach to address this challenge. USST employs a synergistic combination of two attention mechanisms for enhanced feature learning: optimized self-guided attention (SGA)

Monolithic

An Entity Alignment Model for Echinococcosis Knowledge Graphng 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,

Accolade