artificial intelligence

Purpose. The purpose of the study was to analyze the mechanisms of anti-crisis management of the activities of enterprises in the conditions of international economic activity. The formation of mechanisms for anti-crisis management of enterprise activities was studied, in particular, the development of event development scenarios and action plans in case of their implementation, early warning systems, cost optimization, production efficiency improvement, personnel training for work in crisis situations.

The integration of artificial intelligence into the logistics industry is a rapidly evolving field with the potential to revolutionize the way goods are transported and managed. Artificial intelligence can be used to optimize a wide range of logistics processes, from demand forecasting and route planning to warehouse management and customer service. However, the integration of artificial intelligence also raises a number of technical and ethical issues that need to be addressed to ensure its successful implementation.

The integration of artificial intelligence into the logistics industry is a rapidly evolving field with the potential to revolutionize the way goods are transported and managed. Artificial intelligence can be used to optimize a wide range of logistics processes, from demand forecasting and route planning to warehouse management and customer service. However, the integration of artificial intelligence also raises a number of technical and ethical issues that need to be addressed to ensure its successful implementation.

Annotation. The relevance of the topic is due to the development of artificial intelligence (AI), one
of the most important technological trends of our time, which has a significant impact on human rights
and various aspects of human life. AI is developing extremely fast. This creates new challenges for human
rights that need to be addressed immediately. It is important to understand how technology can change
our society and what measures should be taken to protect human rights.

“E-commerce Market: New Technologies and Development Perspectives” offers an in-depth analysis of the current state of electronic commerce and forecasts its future trends. Focusing on the utilization of cutting-edge technologies, the author examines the impact of mobile devices, artificial intelligence, data analysis, and personalization on the effectiveness of e-commerce. Key factors influencing the future prospects of the market, such as the increasing use of smartphones, the development of interactive technologies, and intensified competition, are highlighted.

A method for recognizing contours of objects in a video data stream is proposed. Data will be uploaded using a video camera in real time and object recognition will be performed. We will use the YOLO network – a method of identifying and recognizing objects in real time. Recognized objects will be recorded in a video sequence showing the contours of the objects.

Phasor diagrams are a powerful tool for  visualizing and understanding the distribution of current, voltage, and power in electrical systems. During Russia's war against Ukraine, our energy industry has become very vulnerable to enemy attacks, and therefore needs a quick and effective recovery. Energy specialists lack software tools for working with the power system, and in the period of development of artificial intelligence, creating such tools is not so difficult.

A system for recognizing objects that are captured in real time on a video camera in a noisy environment that changes to the surrounding conditions has been built. The method of filling the database for mobile military objects was studied. For object recognition, the YOLO v8 neural network is used, which allows you to track moving and identify objects that fall into the video from the video camera. This neural network makes it possible to track objects with a change in scale, during movement with obstacles.

The methods for identifying persons of interest (POI) based on mobile device data has been considered. The problem is relevant and unresolved in the activities of law enforcement, intelligence, and other agencies involved in operational search activities due to the large amount of data stored on mobile devices. Given the complexity and volume of mobile data, traditional analysis methods are often insufficiently effective.

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.