Vol 11, No 2 (2024)

This article presents the results of a study aimed at forecasting signals for buying and selling Bitcoin cryptocurrency using machine learning models. The conducted analysis included the study of cryptocurrency features and markets, technical analysis, development of trading strategies, application of mathematical methods based on moving averages, and building classification models for buy or sell signals. The results demonstrate the effectiveness of applying machine learning models in modern trading strategies in the cryptocurrency market.

For the widespread use of information technology in the organization’s business process, which will be aimed at optimizing work and leading to increased productivity and profitability, quality software is needed. Consequently, the design and production of new software (software) requires an accurate analysis of its technical characteristics and, on this basis, will remain one of the pressing tasks in the field of information technology. Therefore, the article discusses an approach for assessing and improving the basic parameters of effective software operation. Reliability, to ensure the required performance, is the main criterion of operation, since it is the ability of a software product to reliably perform specified functions under specified conditions for the required period of time with a sufficiently high probability. The problem of software reliability deserves more and more attention due to the continuous complication of the systems being created, the increase in the range of tasks assigned to them, and, as a conclusion, a significant increase in the complexity and volume of software. New versions are used for those software modules that may experience software failures. To implement the proposed approach, a mathematical model for assessing software reliability is provided. Formulas are presented that are used to calculate the complex reliability parameters of the system under consideration. Relevant examples are shown. For this purpose, the Markov service model was used, that is, the study of queuing systems using a Markov process, which has a discrete set of states. The process of functioning of a multiprocessor computing complex consisting of identical processors is represented by a closed queuing system with waiting.

The modern field of education is characterized by the increasing use of multiple choice tests to assess students’ knowledge and skills. One of the common methods of selecting sentences for such tests is the application of textual data clustering procedures. In this study, a module for sentence selection was developed that includes three steps: preprocessing, sentence parameter computation, and clustering. However, an objective evaluation of the quality of the obtained clusters using the silhouette coefficient and Davis-Boldin index showed that the clustering model used did not give satisfactory results.

The research is conducted to develop and analyze an object filtering model for intelligent recommender systems. The main objective is to solve the problem of orientation in the vast amounts of information accumulated by mankind. The aim of the research is to create an effective tool for systematization and knowledge management, which in turn contributes to the optimization of decision-making processes and interaction with data. The paper focuses on the research and development of an object filtering model for intelligent recommender systems. Within the methodology and research area, the developed model is described in detail and the theoretical and practical aspects of the methodology are analyzed. In this paper, a variant of the problem statement of the research and development of an object filtering model for intelligent recommender systems is presented. In addition, the paper deconstructs the second stage of this problem, emphasizing its importance in the context of achieving system performance. The results of the study are analyzed in detail, highlighting the key points and features of the proposed model. The scope of the study is reviewed, detailing the prospects of applying the results in scientific and practical applications, providing the reader with a deeper understanding of the potential of the proposed model. The object filtering model has a high potential of usefulness for business and manufacturing. This work will be useful for developers and researchers of recommender systems in which users rarely or never interact with the same object.

This article explores the development of a device architecture for non-invasive monitoring of the human body's performance based on ultrasound measurements. The implementation of this architecture is carried out using programmable integrated circuits (FPGAs). Ultrasonic sensors are connected to the FPGA to make measurements. The purpose of the study is a detailed description of the architecture of the device under development. The proposed technical device is capable of detecting pathologies in the human vascular system at a precision stage, which in turn, with surgical treatment, can increase the life expectancy of an individual. Methodology. To ensure the correct functionality of the device with high accuracy, it is necessary to ensure sufficient processing speed of the data coming from each sensor and output of the result. To increase the accuracy of the measured parameters, it is necessary to install ultrasonic sensors in the form of a phased array. Since ultrasonic sensors are analog, it is necessary to use high-frequency digital-to-analog and analog-to-digital converters with high bit depth to obtain high-quality data. These transducers are connected to ultrasonic sensors via signal amplifiers. The proposed architecture provides optimal performance and flexible configuration for measuring ultrasonic signals using sensors. The results of the study. A device demonstrating high data processing speed has been developed and experiments have been conducted on its use. The device has a compact size, which allows you to carry it on yourself without restricting movement. The scope of application. The device is designed to study the work of the heart and human cardiac activity, and is used in the field of healthcare.

The genetic algorithm for numerical optimization (GA) of the metaheuristic class is a method for finding optimal solutions based on the biological principles of natural selection and variability. GA is characterized by high operating speed, resistance to noise in the data, low probability of hitting the local extremum of the multimodal objective function, as well as the simultaneous application of probabilistic and deterministic rules for generating search space points. An alternative to the classical GA is the quantum-inspired genetic algorithm for numerical optimization (QIGA), which has advantages that are unattainable for GA by using the concepts and principles of quantum computing. The article proposes a new approach to the implementation of a quantum-inspired genetic numerical optimization algorithm for searching for the global maximum of the objective function, based on modeling the functioning of the GA by simulating the execution of quantum calculations based on qudit in the conditions of the existence of quantum decoherence in the era of noisy medium-scale quantum algorithms. For this purpose, to carry out quantum operations of rotating the states of multilevel quantum systems, the paper presents a density matrix based on Heisenberg–Weyl operators as an analogue of the Bloch sphere for qudits. The simulation of quantum decoherence is interpreted from the point of view of the influence of extraneous noise emanating from the environment on the qudit and is presented as the use of a normal random variable with zero mathematical expectation and unit variance in quantum gates. At the same time, the work presents detailed pseudocodes of the functioning of both the most modified quantum-inspired genetic algorithm for numerical optimization and its individual operations. Testing is carried out by conducting computational experiments with the implementation of a modified algorithm on two-dimensional and multidimensional functions of test optimization problems, as well as when solving an applied optimization problem of planning hybrid flow production in the manufacturing industry based on financial costs and solving the problem of increasing forecasting accuracy based on compact extreme learning machines. The experimental results demonstrate the superiority of the new algorithm over QIGA and classical optimization algorithms in the accuracy of the solution, the speed of convergence with the target value of the global maximum and the execution time of the algorithm.

Optimization is a very important concept in any field of business, be it retail, finance, automotive or healthcare. The goal of optimization is to find a point or set of points in the search space by minimizing/maximizing the loss/cost function that gives the optimal solution for the problem at hand. In this case, clustering methods, data mining techniques and clustering optimization algorithms are of particular importance. In this context, metaheuristic algorithms, which include the genetic algorithm, become particularly popular and important. Thus, the aim of the paper is to examine the possibilities of using genetic algorithm in the clustering problem for weighted directed graph. Objectives: 1) to consider the peculiarities of using GA in optimization problems; 2) to propose a variant of solving the problem of partitioning some set of ISP users into groups according to a certain set of characteristics using GA; 3) to evaluate the efficiency of the proposed GA in comparison with the algorithm of limit enumeration. Research methods: methods of system analysis, applied and computational mathematics; experimental research; computer and simulation modeling. As a result of the research, the paper proposes an approach for solving the problem of clustering Internet users using a genetic algorithm. To take into account the specificity of the problem and to improve the efficiency of the genetic algorithm, heterogeneous chromosomes were used and modifications were made in the course of classical procedures of crossing and mutation. Conclusions. The developed algorithm was tested for performance in comparison with the limit search algorithm and its significant advantage in this indicator was shown. To take into account the specifics of the task and increase the efficiency of the GA, heterogeneous chromosomes were used. To achieve this, significant modifications were made to the classical procedures of crossing and mutation. The developed algorithm was tested for performance in comparison with the limit search algorithm and its significant advantage in this indicator was shown. The obtained comparison results allow us to assert that already for 150 units of the original set, solving the problem using the limit search method requires a disproportionately large amount of time. While the proposed GA provides a solution for a significantly larger problem dimension in a quite acceptable time.

The plasma-arc electrolytic centrifugal conversion process was developed for processing of crude ore materials and hydrocarbons to end products with simultaneous generation of energy carriers and energy [1]. The developed process may be used with a similar purpose for processing of various wastes. Industrial and household wastes have identical chemistry with common ore and hydrocarbons. Waste ore called “tailings” is comparable to payable ore and, just as in base ore, oxygen comprises about half of its composition. Household wastes for the most part contain organic compounds, including plastic, wood, paper, and form hydrocarbon mixtures containing various metals and nonmetals. Melted waste mixture contains almost every elements of the periodic table with various chemical elements acting as reaction catalysts. The wastes are processed under electric energy generated by hydrogen burning in oxygen. Hydrogen is recovered from hydrocarbon materials contained in household wastes and water contained in the feed. Oxygen is recovered from waste ore. Hydrogen and oxygen are stored in a methanol compound obtained from synthesis gas generated in the course of waste plasma melting.

This work investigates methods for producing ceramic materials based on the Cr₂O₃—SiO₂—Fe₂O₃—CaO—Al₂O₃—MgO—CuO system capable of generating modulated pulsed radiation in the far-infrared spectral region. The possibility of synthesizing such ceramics, in addition to helio-technology, using thermomechanical processing and mechanoactivation of the initial carbonates is considered. A comprehensive analysis of the structure and properties of the obtained materials using X-ray structural, electron microscopic analysis, and other methods has been carried out. It has been established that activation by pulsed infrared radiation generated by the principle of pulsed tunneling effect (PTE) leads to changes in the microstructure of the samples, accompanied by the formation of metastable phases at the interfaces and the generation of radiation.

The paper presents the results of the study of the synthesis and comparative analysis of film-ceramic composites based on functional ceramics obtained by various methods, including thermomechanochemical and sol-gel methods. The influence of activation of the obtained materials by the pulse tunnel effect on their structure and properties is analyzed. Data on the development of plants under composite films in comparison with the control are presented.