• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1720-1725
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• 2019

In this paper, we consider the efficiency of the scheme for for routing summary algorithms in discontiguous networks. Router than updating and transmitting the entire subnet information in the routing protocol, only the shortened update information is sent and the routing table is shortened to make the router resources more efficient and improve network stability and performance. However, if a discontiguous network is formed in the network design process, a problem arises due to the network contraction function and does not bring about the result of fundamental router efficiency. Using different major networks subnets one major network, causing problems in communication and routing information exchange if the configuration is incorrect. The algorithm proposed in this paper removes only the auto-summary algorithm from the existing algorithm, which increases the complexity and stability of the routing table and reduces the CPU utilization of network equipment from 16.5% to 6.5% Confirmed.

• Journal of Internet Computing and Services
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• v.25 no.3
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• pp.27-33
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• 2024

The environment of academic research is continuously changing due to the increase of information, which raises the need for an effective way to analyze and organize large amounts of documents. In this paper, we propose Performance Improvement of Topic Modeling using BART(Bidirectional and Auto-Regressive Transformers) based Document Summarization. The proposed method uses BART-based document summary model to extract the core content and improve topic modeling performance using LDA(Latent Dirichlet Allocation) algorithm. We suggest an approach to improve the performance and efficiency of LDA topic modeling through document summarization and validate it through experiments. The experimental results show that the BART-based model for summarizing article data captures the important information of the original articles with F1-Scores of 0.5819, 0.4384, and 0.5038 in Rouge-1, Rouge-2, and Rouge-L performance evaluations, respectively. In addition, topic modeling using summarized documents performs about 8.08% better than topic modeling using full text in the performance comparison using the Perplexity metric. This contributes to the reduction of data throughput and improvement of efficiency in the topic modeling process.