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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 Shinozaki , Frederic Chazal , Ai Models Press , Fujitsu Ltd , University Of Iowa , Stanford University School Of Medicine , Professor At Stanford University , Fujitsu Limited , International Conference On Machine Learning , Topological Data Analysis , Thirty Eighth International Conference , Machine Learning , Stanford University School , Inria Jointly Develop Technology , Automatically Create Anomaly Detecting , Press Release , Professor Shinozaki , Associate Professor , Stanford University , ஃப்யூஜிட்ஸு லிமிடெட் , பல்கலைக்கழகம் ஆஃப் ஐயுவா , ஸ்டான்போர்ட் பல்கலைக்கழகம் பள்ளி ஆஃப் மருந்து , ப்ரொஃபெஸர் இல் ஸ்டான்போர்ட் பல்கலைக்கழகம் , ஃப்யூஜிட்ஸு வரையறுக்கப்பட்டவை , சர்வதேச மாநாடு ஆன் இயந்திரம் கற்றல் , இயந்திரம் கற்றல் ,