• Title/Summary/Keyword: Complex Event Processing System

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An implementation of automated ECG interpretation algorithm and system(III) - Detector of atrium and ventricle activity (심전도 자동 진단 알고리즘 및 장치 구현(III) - 심방 및 심실활동 검출기)

  • Kweon, H.J.;Lee, J.W.;Yoon, J.Y.;Choi, S.K.;Lee, J.Y.;Lee, M.H.
    • Proceedings of the KOSOMBE Conference
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    • v.1996 no.05
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    • pp.288-292
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    • 1996
  • This paper describes far the detection of heart event that is, QRS complex and P wave which are result from heart activity. The proposed QRS detection method by using the spatial velocity was identified as having the 99.6% detection accuracy as well as fast processing time. Atrial flutter, coupled P wave, and noncoupled P wave as well as atrial fibrillation could be detected correctly by three different algorithms according to their origination farm. About 99.6% correction accuracy coupled P wave could be obtained and we could be found that most detection errors are caused by establishing wrong search interval.

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Artificial Intelligence Plant Doctor: Plant Disease Diagnosis Using GPT4-vision

  • Yoeguang Hue;Jea Hyeoung Kim;Gang Lee;Byungheon Choi;Hyun Sim;Jongbum Jeon;Mun-Il Ahn;Yong Kyu Han;Ki-Tae Kim
    • Research in Plant Disease
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    • v.30 no.1
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    • pp.99-102
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    • 2024
  • Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.