Факультет комп’ютерних та інформаційних технологій
Постійне посилання на фондhttps://repository.lntu.edu.ua/handle/123456789/49
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Item type:Наукова стаття, Multiprocessing as a Way to Optimize Queries. Advances in Transdisciplinary Engineering(2024) Khrystynets, Natalia; Melnyk, Kateryna; Lavrenchuk, Svitlana; Miskevych, Oksana; Kostiuchko, SerhiiDeveloping an effective web application involves the use of various methods and techniques to ensure fast and efficient processing of requests. Sometimes it is not possible to solve the problem of multiprocessing with a single tool, such as a programming language or framework. This work investigates the use of asynchronous methods of processing requests using queues. Job operation in background and non-background modes relative to the main web process is studied. Analytics are provided to analyze a web application with 13,000 requests to process daily. It is proposed to optimize the processing by using the Laravel framework and the Python server dual-tasking using the Supervisor tool on Linux, as well as using a task scheduler for each task. The paper presents positive findings about this algorithm, which contributes to the efficiency of web development and provides a great user experience on the website. Fast processing of web application requests can be a valuable competitive advantage for a business or organization. Research in this field helps to maintain their high competitiveness. In addition, the study of query processing speed is important in scientific research, as it contributes to the development of new algorithms, optimization methods and technologies.Item type:Наукова стаття, The system of dynamic optimization pricing by machine learning(Greece, 2024) Konotopchyk, Artem; Melnyk, Kateryna; Lavrenchuk, Svitlana; Khrystynets, Nataliia; Melnyk, Pavlo; Melnyk, Pavlo; Bortnyk, KaterynaBased on the real retail data collected over eight months, a number of models are developed to determine the most efficient machine learning algorithm. It is found that the KNeighbors Regressor model demonstrates the best performance for a small number of transactions, achieving a low MSE of 0.00091 and a high R2 of 0.72 in the validation set. For a large number of transactions, Random Forest Regressor and Decision Tree Regressor show the best ability to capture complex relationships and handle nonlinearties in the data, thanks to the ensemble learning technique. Whereas the Linear Regressor and Support Vector Regressor models demonstrated large deviations from the real price on the test data. The results of the study demonstrate the relevance of rtificial intelligence algorithms in dynamic pricing trategies, showing the ability to quickly process huge amounts of data, taking into account numerous factors and changes in market demand.