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Methods of Machine Learning and Design of a System for Determining the Emotional Coloring of Ukrainian-language Content

In the article, the authors analyze the current state of research in the field of emotional analysis of Ukrainian-language content for data mining systems. The main methods and approaches to solving the problem are analyzed. The main machine learning algorithms for analyzing textual content are also considered. As a result of the analysis, the main methods and approaches that can be used to analyze the Ukrainian language were identified and classified. The next step was to design the system's functionality using a structural approach.

Development of a Method for Investigating Cybercrimes by the Type of Ransomware Using Artificial Intelligence Models in the Information Security Management System of Critical Infrastructure

In this article, the authors focused on analyzing the possibilities of using artificial intelligence models for effective detection and analysis of cybercrimes. A comprehensive method using artificial intelligence algorithms, such as Random Forest and Isolation Forest algorithms, is developed and described to detect ransomware, which is one of the main threats to information security management systems (ISMS) in the field of critical infrastructure.

MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING

Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyz- ing data about thermometers. The examination involves the application of a branch of artificial intelligence, in particular machine learning (ML) methods, as a means of automating processes. Such methods facilitate the identification and aggregation of pertinent data, the detection of gaps, and the conversion of unstructured text into an easily analyzable structured format.

MEAT QUALITY RESEARCH USING CLASSIFICATION ALGORITHMS

The food industry is going through constant improvements and is subject to analyzing consumer needs, product quality research is essential to striking this balance. In this regard, meat quality, the most essential food category, should be studied with unbiased methods that give precise and correct results. Classification algorithms are considered one of the main components of developing an objective and reliable method of meat quality assessment.

DEVELOPMENT OF THE MULTIMODAL HANDLING INTERFACE BASED ON GOOGLE API

Today, Artificial Intelligence is a daily routine, becoming deeply entrenched in our lives. One of the most popular and rapidly advancing technologies is speech recognition, which forms an integral part of the broader concept of multimodal data handling. Multimodal data encompasses voice, audio, and text data, constituting a multifaceted approach to understanding and processing information. This paper presents the development of a multimodal handling interface leveraging Google API technologies.

DETERMINATION OF HOPPER FULLNESS OF SMART SCREW PRESS USING MACHINE LEARNING

Problem statement. This research addresses the challenge of accurately determining the fullness of the hopper within a screw press for optimal oil extraction efficiency and quality. Existing weight or volume-based measurement methods can often struggle with determining the feed hopper fullness due to variable oil weights during extraction stages, material heterogeneity, environmental influences and imprecise instrument calibration. Purpose.

ADVANCING VIDEO SEARCH CAPABILITIES: INTEGRATING FEEDFORWARD NEURAL NETWORKS FOR EFFICIENT FRAGMENT-BASED RETRIEVAL

In the context of rapidly increasing volumes of video data, the problem of their efficient search and analysis becomes more acute. This research aims to develop and test an innovative system to improve the speed and accuracy of video search, utilizing the capabilities of Deep Convolutional Neural Networks (DCNN) and Feedforward Neural Networks (FFNN).

IMPACT OF USING PREDICTIVE ARTIFICIAL INTELLIGENCE ON CONTRACT DURATION

In a constantly changingbusiness environment, the integration of artificial intelligence (AI) is becoming a fundamental direction in achieving increased revenues and sales volumes for companies. AI and its various applications contribute to identifying patterns in consumer choices, which at the same time contributes to the more effective formation of marketing and sales strategies of companies.

PREDICTION OF THE OCCURRENCE OF STROKE BASED ON MACHINE LEARNING MODELS

The research conducted in the medical domain addressed a topic of significant importance, steadily growing in relevance each year. The study focused on predicting the onset of strokes, a condition posing a grave risk to individuals' health and lives. Utilizing a highly imbalanced dataset posed a challenge in developing machine learning models capable of effectively predicting stroke occurrences. Among the models examined, the Random Forest model demonstrated the most promising performance, achieving precision, recall, and F1-score metrics of 90%.

METHODS OF MACHINE LEARNING IN MODERN METROLOGY

In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data.