Browsing by Author "Halachev P."

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    Methods and algorithms of optimization in computer engineering: review and comparative analysis Métodos y algoritmos de optimización en ingeniería informática: revisión y análisis comparativo
    (2024-02-08) Yakhno V.; Kolumbet V.; Halachev P.; Khambir V.; Ivanenko R.
    Introduction: the main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence. Method: one of its most relevant areas has been highlighted-the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter”-the biological media of the human body-is considered. Results: through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions-the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function-the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal. Conclusions: a comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented.
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    Prediction of e-learning efficiency by neural networks
    (2012-01-01) Halachev P.
    A model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.
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    The impact of quantum computing on the development of algorithms and software El impacto de la computación cuántica en el desarrollo de algoritmos y software
    (2024-01-01) Lemesheva N.; Antonenko H.; Halachev P.; Suprun O.; Tytarchuk Y.
    Introduction: there is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: an extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: this paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes. Conclusion: while there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions.
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    The Influence of Artificial Intelligence on the Automation of Processes in Electronic Commerce La influencia de la inteligencia artificial en la automatización de procesos en el comercio electrónico
    (2024-01-01) Halachev P.
    Objective: this study explores the transformative impact of Artificial Intelligence (AI) on automating business processes in electronic commerce (e-commerce), with a focus on enhancing efficiency and customer experience. Method: the research employs Deep Learning (DL) and Machine Learning (ML) as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories. Techniques such as data preprocessing, normalization, sentiment analysis, and advanced predictive models using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) are utilized. Data collection was conducted using web scraping tools like Beautiful Soup and Scrapy, along with APIs from Amazon and Google. Results: the application of AI in e-commerce has led to significant improvements in inventory control, fraud prevention, and customer relations. ML algorithms have enhanced the estimation of product demand and personalized customer interactions, while DL has strengthened product recommendation systems and fraud detection mechanisms. The findings indicate that AI contributes to a more secure, faster, and smarter operational environment in e-commerce. Conclusion: this research highlights the substantial benefits and broad potential of AI in optimizing e-commerce operations, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.