Fujitsu Corporation

Masaru Todoriki

Ken Kobayashi

Yuhei Umeda

Hiroya Inakoshi

Izumi Nitta

Koji Maruhashi

Daisuke Fukuda

Tetsuyoshi Shiota

Shunichi Watanabe

Satoko Iwakura

A machine learning method includes acquiring data including attendance records of employees and information indicating which employee has taken a leave of absence from work, in response to determining that a first employee of the employees has not taken a leave of absence in accordance with the data, generating a first tensor on a basis of an attendance record of the first employee and parameters associated with elements included in the attendance record, in response to determining that a second employee of the employees has taken a leave of absence in accordance with the data, modifying the parameters, and generating a second tensor on a basis of an attendance record of the second employee and the modified parameters, and generating a model by machine learning based on the first tensor and the second tensor.

Machine learning method

A non-transitory computer-readable recording medium stores therein an analysis program that causes a computer to execute a process including: dividing a Betti number sequence into a plurality of Betti number sequences, the Betti number sequence being included in a result of a persistent homology process performed on time series data, the plurality of Betti number sequences corresponding to different dimension of the Betti number sequence; and performing an analysis on each of the plurality of Betti number sequences.

Analysis method, analysis device, and recording medium

A detection device adds, with regard to each of a plurality of sets of time-series data including a plurality of items, a time-shift term to at least any of the plurality of items included in each of the plurality of sets of time-series data. The detection device generates a plurality of attractors from the plurality of sets of time-series data to which the time-shift term has been added. The detection device generates a plurality of Betti sequences from each of the plurality of attractors by executing a persistent homology transformation on each of the plurality of attractors, each of the plurality of Betti sequences indicating a correspondence relationship between a Betti number and a scale value has been used for the persistent homology transformation. The detection device detects a state change in the plurality of sets of time-series data based on a time change in the plurality of Betti sequences.

Computer-readable recording medium, detection method, and detection device

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Kita, Japan

Masaru Todoriki