machine learning

During the research, an information system for voicing Ukrainian-language text was developed based on NLP and machine learning methods. The created information system is implemented in the form of a desktop application, which allows the process of voicing the Ukrainian-language text. The created system included all stages of software development: the design process, the implementation process, and the testing process.

This research presents the development and implementation of information technology for monitoring and analyzing segments of a mobile operator's stores using clustering methods. The study addresses a pertinent issue in marketing and business optimization, namely the enhancement of strategies for the network of mobile communication stores.

Nowadays wind energy is one of the most important and promising sources of environmentally clean renewable energy. Wind turbine blades are among the most expensive components. Depending on the size, their manufacturing costs range between 10 % and 20 % of total manufacturing costs. Moreover, the size of blades has increased in recent years, leading to greater efficiency and energy production, but presenting higher failure probability.

This article presents the design, development, and evaluation of a specialized program for analyzing, developing aggregations of this data, and visualizing large volumes of data. The main goal of this program is to simplify data processing, speed up their analysis, and make it easier to write code for problems with large amounts of data. To achieve this goal, machine learning is used, as well as two repositories.

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.