Abstract: Today, mobile devices play an important role in everyone's daily life, and one of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task, it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices, such as quantization and compression of models, optimization of initial calculations. In addition to ways to optimize the machine learning model itself, various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages, and therefore, in the results of the work, it is proposed to use not only a combination of the described options, but also an additional method of parallelization of image processing processes. The article discusses examples of specific tools and frameworks available for optimizing machine learning performance on iOS, and conducted its own experiments to test the effectiveness of various optimization methods. An analysis of the results obtained and a comparison of the performance of the algorithms are also provided. The practical significance of this article is as follows: Improving the performance of machine learning algorithms on iOS mobile devices will lead to more efficient use of computing resources and increase system performance, which is very important in the context of limited computing power and energy resources of mobile devices. Optimization of machine learning performance on the iOS platform contributes to the development of faster and more responsive applications, which will also improve the user experience and allow developers to create new and innovative features and capabilities. Expanding the applicability of machine learning on iOS mobile devices opens up new opportunities for application development in various fields such as pattern recognition, natural language processing, data analysis, and others.

Keywords: parallelization, performance, efficiency, OS Apple, optimization, image recognition, iOS, machine learning, mobile device, neural network

Abstract: The modern development of computer technologies and operating systems is accompanied by an increase in the need for software capable of ensuring the interaction of various programs and applications with each other, regardless of their source environment. In this study, a comparative analysis of two such programs will be conducted – Wine and PortProton. Wine is a program capable of running most applications developed for Windows on Unix-like systems. This is a compatibility layer that allows you to work with Windows applications. There is also a domestic version – PortProton, that offers the launch of Windows applications. This study aims to compare these two programs, analyze their features, advantages and disadvantages, determine which of them is the most convenient and functional for the end user in the context of Windows application emulation. The research methodology involves a comparative analysis of the Wine and PortProton platforms through benchmark testing and checking the performance of Windows applications on Linux. Benchmark testing includes evaluating the performance, stability, and speed of Windows applications on each platform. Due to the lack of scientific sources on the topic of comparing Wine and PortProton in the context of Windows application emulation, this study has a unique character. In conclusion Wine and PortProton successfully cope with the emulation of Windows applications, showing in some moments the best performance due to the optimization of the Linux operating system. PortProton copes best with the task of emulating programs due to stable operation and ease of use. Wine, despite a slight advance in the context of performance and the ability to run several programs at the same time, showed the worst efficiency due to the incorrect operation of some programs and the lack of an intuitive graphical interface. Based on the above conclusions, PortProton can be recommended for most users.

Keywords: Unix, Linux, Technical specifications, Windows, Performance, Wine, Portproton, Application compatibility, Emulation, cross platforms

Abstract: The objects of this study are data storage devices of various types and levels of complexity, as well as the principles of their operation. They are complex technical systems that include many components and are characterized by a high degree of integration. The subject of the research is to study the main characteristics of hard drives and solid-state drives. Their structure, functional features, principles of operation and ways of optimization are important. The purpose of the study is to determine the most effective methods for optimizing the operation of these devices. This includes aspects such as memory management, load balancing, power management, and others. The results of this research can be used to improve data efficiency, improve the performance of data storage systems and create new technologies in this area. This study examines the performance of various disk storage solutions through a series of tests aimed at understanding speed and dependence on external factors. The main conclusions of the study reflect the importance of the integrated use of optimization approaches to improve the speed of reading and writing data. Optimizing the processes of reading and writing data is critically important for modern high-performance computing systems, as well as for applications that require quick access to large amounts of information. The improved techniques used in the course of the study contribute to a significant increase in the performance of data storage devices. They take into account the specifics of various types of storage devices, including hard drives and solid-state drives, and offer optimization approaches that take into account their unique characteristics. Overall, the results of this study provide valuable insights into the principles of optimizing data storage, and they can serve as a basis for developing new strategies and solutions in this important area of information technology. This study represents a significant contribution to the scientific understanding of optimizing data reading and writing processes, and its findings may have long-term implications for the development of data storage technologies.

Keywords: Cache Buffer, Interface, Defragmentation, Reading data, Fragmentation, Efficiency, Optimization, Solid state drives, Hard drives, The file system