Research on the application of knowledge mapping and knowledge structure construction based on adaptive learning model

Zeng, Xiyin; Liu, Shouqiang

doi:10.1016/j.eswa.2024.123400

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.123400

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Research on the application of knowledge mapping and knowledge structure construction based on adaptive learning model

Xiyin Zeng,

Shouqiang Liu

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2024

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Cited by 2 publications

References 17 publications

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A Multimodal Deep Neural Network-based Financial Fraud Detection Model Via Collaborative Awareness of Semantic Analysis and Behavioral Modeling

2024

J CIRCUIT SYST COMP

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The monitoring and early warning of financial risks have become a crucial link in maintaining market stability and safeguarding the rights and interests of investors. Traditional financial risk monitoring methods often rely on a single data source or analysis model, making it challenging to comprehensively and accurately capture risk signals. Therefore, this paper proposes a novel financial risk monitoring model based on multimodal neural networks, which innovatively integrates multiple data sources, such as vision, language and audio, and utilizes their inherent correlations to enhance the accuracy of risk identification. First, by employing the Bidirectional Long Short-Term Memory Network (BiLSTM) structure and incorporating the self-attention mechanism, the semantic information of financial texts is deeply analyzed through the calculation of dynamic weight coefficients. Additionally, Option-based Hierarchical Reinforcement Learning (OHRL) is utilized to accurately model the behavior of market participants, capturing nuanced changes in their decision-making process. By integrating these two types of information, a comprehensive BiLSTM-OHRL model is formulated to evaluate the risk status of financial markets in a more comprehensive and accurate manner. The results demonstrate that the model performs impressively in financial risk monitoring, accurately capturing the emotional and behavioral characteristics of market participants, thereby enhancing the comprehensiveness and predictive capability of the monitoring model. It provides robust technical support for the stable operation of the financial market.

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A Multimodal Deep Neural Network-based Financial Fraud Detection Model Via Collaborative Awareness of Semantic Analysis and Behavioral Modeling

2024

J CIRCUIT SYST COMP

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FGeo-Parser: Autoformalization and Solution of Plane Geometric Problems

Zhu,

Zhang,

Huang

et al. 2024

Symmetry

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Automatic geometric problem-solving is an active and challenging subfield at the intersection of AI and mathematics, where geometric problem parsing plays a critical role. It involves converting geometric diagram and text into certain formal language. Due to the complexity of geometric shapes and the diversity of geometric relationships, geometric problem parsing demands that the parser exhibit cross-modal comprehension and reasoning capabilities. In this paper, we propose an enhanced geometric problem parsing method called FGeo-Parser, which converts problem diagrams and text into the formal language of the FormalGeo. It also supports reverse formalization to generate human-like solutions, reflecting the symmetry between parsing and generating. Specifically, diagram parser leverages the BLIP to generate the construction CDL and image CDL, while text parser employs the T5 to produce the text CDL and goal CDL where these neural networks are both based on a symmetric encoder–decoder architecture. With the assistance of a theorem predictor, these CDLs were automatically parsed and step-by-step reasoning was executed within FGPS. Finally, the reasoning process was input into a solution generator, which subsequently produced a human-like solution process. Additionally, we re-annotated problem diagrams and text based on the FormalGeo7K dataset. The formalization experiments on the new dataset achieved a match accuracy of 91.51% and a perfect accuracy of 56.47%, while the combination with the theorem predictor achieved a problem-solving accuracy of 63.45%.

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Research on the application of knowledge mapping and knowledge structure construction based on adaptive learning model

Cited by 2 publications

References 17 publications

A Multimodal Deep Neural Network-based Financial Fraud Detection Model Via Collaborative Awareness of Semantic Analysis and Behavioral Modeling

A Multimodal Deep Neural Network-based Financial Fraud Detection Model Via Collaborative Awareness of Semantic Analysis and Behavioral Modeling

FGeo-Parser: Autoformalization and Solution of Plane Geometric Problems

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