Paper Abstract and Keywords |
Presentation |
2024-06-05 14:20
An Experiment to Find the Causes of Information System Errors with Image Captioning AI. Akio Fujioka, Shungo Kimura, Rin Yamaku, Miki Shirane, Kazuyuki Iso, Masao Akino (TID), Keiko Tomizawa, Sakura Yamada, Yu Horiuchi, Hitomi Ainoura, Eiji Takahashi (Kyoeikai) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Many companies in Japan promote DX. This clinic provides medical examination services. In this clinic, routine tasks are automated using RPA. However, the information systems stop due to various errors. Since this clinic does not have a full-time information system administrator, the clinic staff must resolve these errors themselves. The increased time required for staff to resolve errors is problematic. Our goal is to enable the clinic staff to resolve the causes of errors using image captioning AI without technical knowledge of information systems. As a first step, we experimented with AI to find the causes of two types of errors in the information systems using RPA. The results showed that the output sentences sometimes correctly explained the causes of the errors, but at other times provided incorrect explanations. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Image Captioning AI / Finding the Cause of the Error / RPA / / / / / |
Reference Info. |
ITE Tech. Rep., vol. 48, pp. 25-28, June 2024. |
Paper # |
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Date of Issue |
2024-05-29 (AIT) |
ISSN |
Online edition: ISSN 2424-1970 |
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