Character embedding-based Bi-LSTM for Zircon similarity calculation with clustering

Hu, Xiangben; Hu, Zhichen; Jiang, Jielin; Xue, Weiwei; Hu, Xiumian; Xu, Xueqing

doi:10.1007/s12145-022-00847-y

Cited by 3 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep analysis and knowledge discovery, especially with deep learning technologies, have made it possible to perform advanced analysis tasks on knowledge graphs, such as semantic search, powering recommendation systems, and predicting trends [174]. This deepens the understanding of complex relations between entities, enabling more accurate and personalized services.…”

Section: Prospects For Machine Learning In Knowledge Graphs Construct...mentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

show abstract

Section: Prospects For Machine Learning In Knowledge Graphs Construct...mentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

show abstract

“…Our method uses graph theory (He et al, 2022) and similarity calculation method (Hu et al, 2022) to calculate the relative stability of every node. A network with nodes and links 10.3389/fenrg.2022.1017932 is a directed graph, G(t) = (V(t), E(t)), which is called a neighbor relationship graph, where V(t) = {v 1 , v 2 , …, v n } represents the set of participating nodes, E(t) = {e 1 , e 2 , …, e m } represents the set of wireless links.…”

Section: Relative Stability (S R )mentioning

confidence: 99%

A cluster-based approach against wormhole attacks in MANETs among smart grid

2022

View full text Add to dashboard Cite

Mobile ad hoc networks in smart grid become more and more popular and significant. However, the deployment scenarios, the functionality requirements and the limited capabilities make them vulnerable to a large group of attacks, e.g., wormhole attacks. In this paper, a novel cluster-based scheme is proposed for the purpose of preventing wormhole attacks. Firstly, a clustering algorithm is proposed that employs a powerful analytical hierarchy process methodology to elect clusterheads. Afterwards, the elected clusterheads are required to implement the wormhole attacks prevention scheme which includes two phases, i.e., detection phase and location phase. By detection phase, the existence of wormhole attacks can be detected. By location phase, the wormhole nodes are able to be detected. Simulation results indicate the scheme in our paper can be used to prevent wormhole attacks in ad hoc networks efficiently.

show abstract

Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things

Mao,

Qian,

Lucas

2023

IEEE Access

View full text Add to dashboard Cite

This work aims to introduce Long Short-Term Memory (LSTM) under the Internet of Things (IoT) context to enhance the accuracy and granularity of sentiment analysis in animated online education texts. It employs a multimodal data collection approach and uses IoT technology to gather multimodal textual data from students engaged in animated online education. The data includes students' feedback texts, emotional texts, written texts, and verbal expressions during animated online education. Subsequently, a model named Information Block Bidirectional Long-Short term Memory (IB-BiLSTM) is designed and utilized to construct a sentiment classification model for animated online education texts. Experimental results demonstrate that the model achieves an accuracy of 93.92% and an F1-score of 90.34% for sentiment classification in animated online education texts and the loss function converges to around 0.14. This model effectively captures the emotional changes and evolution during students' learning process. Thus, the proposed model holds significant potential and practical significance for enhancing animated online education's personalization and emotional engagement. It provides valuable insights and guidance for the intelligent development of the education field.

show abstract

Character embedding-based Bi-LSTM for Zircon similarity calculation with clustering

Cited by 3 publications

References 29 publications

From data to insights: the application and challenges of knowledge graphs in intelligent audit

From data to insights: the application and challenges of knowledge graphs in intelligent audit

A cluster-based approach against wormhole attacks in MANETs among smart grid

Sentiment Analysis of Animated Online Education Texts Using Long Short-Term Memory Networks in the Context of the Internet of Things

Contact Info

Product

Resources

About