令人不愉快
发表于 2025-3-21 19:45:23
书目名称Neural Computing for Advanced Applications影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0669646<br><br> <br><br>书目名称Neural Computing for Advanced Applications读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0669646<br><br> <br><br>
健谈
发表于 2025-3-21 20:40:33
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缺陷
发表于 2025-3-22 01:57:07
978-981-97-7006-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
IRK
发表于 2025-3-22 07:59:32
Neural Computing for Advanced Applications978-981-97-7007-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
Hectic
发表于 2025-3-22 12:35:35
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finite
发表于 2025-3-22 16:28:27
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Mumble
发表于 2025-3-22 17:59:50
Temporal Knowledge Graph Link Prediction Using Synergized Large Language Models and Temporal Knowledcertain challenges. However, through collaboration, large language models and temporal knowledge graphs can complement each other, addressing their respective shortcomings. This collaborative approach aims to harness the potential feasibility and practical effectiveness of large language models as e
正式演说
发表于 2025-3-22 21:50:36
A New Multi-level Knowledge Retrieval Model for Task-Oriented Dialogueally retrieve knowledge and entire entity by utilizing dialogue context, while the correlations between dialogue context and entity attributes are overlook, leading suboptimal knowledge retrieval. Therefore, we introduce a Multi-Level knowledge retrieval model for Task-Oriented Dialogue (MLTOD) cons
delegate
发表于 2025-3-23 03:00:20
FPLGen: A Personalized Dialogue System Based on Feature Prompt Learninging characteristics that already belong to a certain person. However, utilizing personality information for personalized response generation remains a non-trivial task. The system must consider both the user’s conversation history and personality description, posing challenges for coherent model tra
hemoglobin
发表于 2025-3-23 06:01:20
Ensemble Learning with Feature Fusion for Well-Overflow Detectiononditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. Therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to opt