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Titlebook: Neural Computing for Advanced Applications; 5th International Co Haijun Zhang,Xianxian Li,Qian He Conference proceedings 2025 The Editor(s)

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發(fā)表于 2025-3-21 19:45:23 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Computing for Advanced Applications
副標題5th International Co
編輯Haijun Zhang,Xianxian Li,Qian He
視頻videohttp://file.papertrans.cn/670/669646/669646.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Neural Computing for Advanced Applications; 5th International Co Haijun Zhang,Xianxian Li,Qian He Conference proceedings 2025 The Editor(s)
描述.This book constitutes the refereed proceedings of the 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024, held in Guilin, China, during July 5–7, 2024...The 89 revised full papers presented in these proceedings were carefully reviewed and selected from 227 submissions. The papers are organized in the following topical sections:..Part I: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications;?Computer vision, and their engineering applications...Part II: Computational intelligence, nature-inspired optimizers, their engineering applications, and benchmarks...Part III: Natural language processing, knowledge graphs, recommender systems, multimodal Deep Learning, and their applications;?Fault diagnosis and forecasting, prognostic management, Time-series analysis, and cyber-physical system security..
出版日期Conference proceedings 2025
關鍵詞Neural networks; Machine learning algorithms; Computer vision; Data mining; Natural language processing;
版次1
doihttps://doi.org/10.1007/978-981-97-7007-6
isbn_softcover978-981-97-7006-9
isbn_ebook978-981-97-7007-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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978-981-97-7006-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Neural Computing for Advanced Applications978-981-97-7007-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
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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
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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
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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
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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
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