Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features

Yulita, Intan Nurma; Fanany, Mohamad Ivan; Arymurthy, A. M.

doi:10.1088/1742-6596/812/1/012113

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Conditional Random Field. The conditional random field (CRF) is a probabilistic model [12] used to label and split data with a sequence structure. It combines the properties of the maximum entropy model with the hidden Markov model, and it can describe long-distance dependence.…”

Section: Related Workmentioning

confidence: 99%

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

Feng

Hang

et al. 2022

Journal of Sensors

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There is a centralization of the core content in the text information of the new crown epidemic notification. This paper proposes a joint learning text information extraction method: TBR-NER (topic-based recognition named entity recognition) based on topic recognition and named entity recognition to predict the labeled risk areas and epidemic trajectory information in text information. Transfer learning and data augmentation are used to solve the problem of data scarcity caused by the initial local outbreak of the epidemic, and mutual understanding is achieved by topic self-labeling without introducing additional labeled data. Taking the epidemic cases in Hebei and Jilin provinces as examples, the reliability and effectiveness of the method are verified by five types of topic recognition and 15 types of entity information extraction. The experimental results show that, compared with the four existing NER methods, this method can achieve optimality faster through the mutual learning of each task at the early stage of training. The optimal accuracy in the independent test set can be improved by more than 20%, and the minimum loss value is significantly reduced. This also proves that the joint learning algorithm (TBR-NER) mentioned in this paper performs better in such tasks. The TBR-NER model has specific sociality and applicability and can help in epidemic prediction, prevention, and control.

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Section: Related Workmentioning

confidence: 99%

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

Feng

Hang

et al. 2022

Journal of Sensors

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Basic concepts and development of gesture recognition

Miao,

Li,

Liu

et al. 2024

Gesture Recognition

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Text Command Intelligent Understanding for Cybersecurity Testing

Yi,

Liu,

Jiang

et al. 2024

Electronics

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Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses.

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Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features

Cited by 3 publications

References 3 publications

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

TBR-NER: Research on COVID-19 Text Information Extraction Based on Joint Learning of Topic Recognition and Named Entity Recognition

Basic concepts and development of gesture recognition

Text Command Intelligent Understanding for Cybersecurity Testing

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