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The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis.

Recently, deep learning technologies, namely Neural Networks [1], are attracting more and more attention from businesses and the scientific community, as they help optimize processes and find real solutions to problems much more efficiently and economically than many other approaches. In particular, Neural Networks are well suited for situations when you need to detect objects or look for similar patterns in videos and images, making them relevant in the field of information and measurement technologies in mechatronics and robotics.

Gender recognition from voice is a challenging problem in speech processing. This task involves extracting meaningful features from speech signals and classifying them into male or female categories. In this article, was implemented a gender recognition system using Python programming. I first recorded voice samples from both male and female speakers and extracted Mel-frequency cepstral coefficients (MFCC) as features. Then trained, a Support Vector Machine (SVM) classifier was on these features and evaluated its performance using accuracy, precision, recall, and F1-score metrics.

As a result of the research, one of the approaches to building an intelligent information system based on the recommendation of products to users with a solution to the cold start problem is described and modeled. The conducted research takes into account the advantages and disadvantages of the meth- ods, as well as their compatibility, when combining them, which is an important factor for the speed of the system and the efficiency of the algorithm.

This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it.

This article covers the problem of developing a control system for non-player characters in a multiplayer RPG. Commercial projects in the field of videogames and RPG (Role-Playing Game) projects in particular seldom use machine learning models for the implementation of character behaviour. The most common approach is to use primitive preprogrammed rules, or to implement a finite state machine. Such approaches ruin the immersion of playing with real creatures, since various predefined rules make the characters predictable.

The article is devoted to the study of methods and means of determining objects of radio technical intelligence using machine learning technologies and an ontological approach. A naіve Bayesian classifier was used to identify objects of radio technical intelligence. The Naive Bayes classifier is a machine learning algorithm used to classify objects based on probabilities. In this article, a naive Bayesian classifier is used to determine the classes to which objects of radio technical intelligence belong.

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.

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.

      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.