long short-term memory

The impact of activation functions on LTSM server load prediction accuracy: machine learning approach

The continuously growing number of users and their requests to the server demands substantial resources to ensure fast responses without delays.  However, server load is inherently unevenly distributed throughout the day, week, or month.  Accurately predicting the required resources and dynamically managing their allocation is crucial, as it can lead to significant cost savings in server maintenance without compromising the user experience.  This study investigates the influence of activation function choice on the forecasting accuracy of Long Short-Term Memory (LSTM) n

Structure of the Information System for Predicting and Interpreting Changes in the State of the Service User

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.