Determining the Number of Effective Distributions Based on Neural Network Ensemble

Since big data streams contain hidden meanings, there is a permanent motivation to store and process them. However, storing and processing this data requires special methods and tools. Today, the most effective approach for this situation is distributed computing mechanisms. However, this approach is economically expensive, since it requires a lot of computing resources. Therefore, users who do not have economic capabilities strive to solve problems with large data streams on a single server. In this situation, a sharp drop in efficiency in terms of time is observed. However, even for a single computing machine, the use of an internal distribution mechanism can lead to efficiency in terms of time. In this case, efficiency depends on several indicators, the most important of which is determining the number of effective distributions. However, determining the number of effective distributions is a complex process. To solve this problem, this research paper considers the use of artificial intelligence algorithms. First of all, the research methodology is developed and processes that are in it are explained. In the next step, Random Forest, XGBoost, Support Vector Regression, and Multiple Linear Regression algorithms are tested to determine the number of effective distributions. In order to improve the accuracy of the study, a multilayer neural network is improved, that is, a neural network ensemble method is developed that combines the above machine learning algorithms. At the end of the study, the research results are presented and explained in detail.

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