Improvement of Anthocyanin Stability in Butterfly Pea Flower Extract by Co-pigmentation with Catechin

Conference proceedings article

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Publication Details

Author list: Charurungsipong P., Tangduangdee C., Amornraksa S., Asavasanti S., Lin J.

Publisher: Hindawi

Publication year: 2020

Volume number: 141

ISBN: 9781728138213

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/record/display.uri?eid=2-s2.0-85078985383&origin=resultslist&sort=plf-f&src=s&st1=Charurungsipong+P.&sid=49686b81e6a12e562623ec2505905407&sot=b&sdt=b&sl=31&s=AUTHOR-NAME%28Charurungsipong+P.%29&relpos=1&citeCnt=6&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1

Languages: English-Great Britain (EN-GB)

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Abstract

Detection of anomalous events is very crucial for the maintenance and performance tuning in long-running distributed systems. System logs contain the complete information of system operation that can be used for describing the situations of the computing nodes. However, log messages are unstructured and difficult to utilize. In this work, we propose a novel anomaly detection framework in a Hadoop Distributed File System (HDFS) that transforms the log messages to structured data and automatically monitors the system operation logs using Convolutional Neural Networks (CNN). We evaluate the performance of anomaly detection in terms of precision, recall, and f-measure. The proposed framework can provide with precision = 94.76 ± 0.81%, recall = 99.53 ± 0.23%, and f-measure = 97.09 ± 0.49%. To apply the proposed framework in the practical application, we also concern about the training time and prediction productivity. From our experimental results, our proposed framework outperforms the existing models (i.e., LSTM and Bi-LSTM) with higher recall, lower training time, and higher prediction productivity. © 2019 IEEE.

Keywords

Anomaly Detection, Language model, Monitoring System, preventive maintenance