machine learning

Analysis of artificial intelligence methods for rail transport traffic noise detection

Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency.

Application of algorithmic models of machine learning to the freight transportation process

The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined.

Forecasting fuel consumption in means of transport with the use of machine learning

Transport is a key factor influencing greenhouse gas emissions. In relation to this, the issues and challenges facing the transport industry were presented. The issues of challenges for the transport industry related to the European Green Deal were discussed. It discussed how the transport system is critical for European companies and global supply chains. The issues related to the exposure of society to costs are presented: greenhouse gas emissions and pollution. The article deals with the issues of managing transport processes in an enterprise.

Is a Dialogue between Philosophy and the Educational Technologies Possible? (Based on the Results of Webinars by Experts of the “SoftServe” Company, 2022)

      Based on analysis of the Tech Summer for Teachers Bootcamp webinars for the educational community organized by the IT Company SoftServe, attention is focused on their interdisciplinary approach, in particular in the teaching of philosophical disciplines. Special attention was paid to the anthropological component in the field of information technologies, artificial intelligence, cybersecurity and virtual communication.

Mobile Information System for Human Nutrition Control

It is acknowledged that each person's life, group of people and nation is formed depending on geographical, economic, political, cultural and religious conditions. Lifestyle is formed as a result of daily repetition and consists of the following factors: nutrition, exercise, the presence of bad habits, moral and spiritual development, and so on. In recent decades, lifestyle has been considered an integral part of well-being, leading to increased research. According to the scientist's study, more than half of health problems are related to diet.

Processes and Elements of Big Data Analisys of Distance Learning Systems

The impact of the pandemic on educational processes in Ukraine is analyzed. The problematic moments observed during distance learning, positive and negative factors of online  education are considered. Factors that can lead to conflict situations in the educational process and complicate the process of collecting and analyzing information are presented. The use of machine learning methods for big data analysis in distance learning systems is proposed.

Stochastic machine learning modeling for the estimation of some uncertain parameters. Case study: Retardation factor in a radionuclide transport model

In the present work, we define a stochastic model using machine learning techniques to generate random fields of some uncertain parameters.  The proposed stochastic model is based on Bayesian inference and aims at reconstituting the parameters of interest and their credible intervals.  The main goal of this work is to define a model that estimates the values of the uncertain parameters known only by their distribution probability functions and some observed spatial measurements.  We note that this type of parameters may be associated with some mathematical models usually traduced by non-lin

Управління мережами мобільного зв’язку 5G за допомогою використання технологій штучного інтелекту

The article is devoted to the problem of excessive traffic of base station cells. In order to reduce the
impact of this problem on the quality of services of mobile network operators, it is proposed to use
artificial intelligence (AI) technology to analyze and predict the load on the network. AI is great for
wireless environments, as it has a lot of data available for analysis and obtaining certain patterns.
The article proposes a model of machine learning and neural network architecture for forecasting
the load on 5G cells.

Density based fuzzy support vector machine: application to diabetes dataset

In this work, we propose a deep prediction diabetes system based on a new version of the support vector machine optimization model.  First, we determine three types of patients (noisy, cord, and interior) basing on specific parameters. Second, we equilibrate the clinical data sets by suppressing noisy and cord patients.  Third, we determine the support vectors by solving an optimization program with a reasonable size.