поведінковий аналіз

The paper investigates the problem of predicting changes in user states (including churn) based on session data using deep neural networks. The paper considers the use of long short-term memory models and convolutional neural networks, as well as the use of byte pair coding for data pre-processing. The functionality of the developed information system for forecasting changes in the state of users and interpreting forecasting models, which combines methods of data analysis, building forecasting models and explaining the results, is analysed.

Simulation model of the mechanical component of the fully differential capacitive MEMS accelerometer has been developed using MATLAB/Simulink environment. The model allows to simulate movement of the proof mass, capacitance changes of the sense capacitors of the sensitive element, sensitivity of the sensor depending on the applied force of acceleration, and to perform the transient analysis of the integrated device at the system level of computeraided design.