Page 34 - ஸ்டான்போர்ட் பல்கலைக்கழகம் பள்ளி News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Fujitsu and France s Inria Develop New Time-Series AI Technology to Identify Causes of Data Anomalies Fig. 1 TDA-based technology for identifying the causes detecting anomalies Fig. 2 EEG data of delirium state (blue line) and EEG data judged to be normal generated by this technology (red line) In recent years, various kinds of time-series data collected in fields including healthcare, social infrastructure, and manufacturing have been leveraged by AI to perform situational judgment and detect anomalies. In the case of time-series data, however, there are a wide range of factors that can contribute to AI decision-making. This means that even experts find it difficult to notice what kind of changes in the data contributed to an anomaly detection making it difficult to take appropriate measures to prevent their occurrence.

Gen shinozakiFrederic chazalAi models pressFujitsu ltdUniversity of iowaStanford university school of medicineProfessor at stanford universityFujitsu limitedInternational conference on machine learningTopological data analysisThirty eighth international conferenceMachine learningStanford university schoolInria jointly develop technologyAutomatically create anomaly detectingPress release