The subject of research in the article is machine learning models for classifying web-pages by quality and compliance with SEO rules. The goal of the article is improving the efficiency of search engines by establishing and using factors that have the greatest impact on the degree of SEO optimization of web pages. The article solves the following tasks: study of the effectiveness of using machine learning methods to build a classification model that automatically classifies web pages according to the degree of adaptation to SEO optimization recommendations; assessment of the influence of relevant page factors (text on a web page, text in meta tags, links, image, HTML code) on the degree of SEO optimization using the developed classification models. The following methods are used: machine learning methods, classification methods and statistical methods. The following results were obtained: analysis of the effectiveness of the application of machine learning methods to determine the degree of adaptation of a web page to SEO recommendations was carried out; classifiers were trained on a data set of web pages randomly selected from the DMOZ catalog and rated by three independent SEO experts in the categories: “low SEO”, “medium SEO” and “high SEO”; five main classifiers were tested (decision trees, naive Bayes, logistic regression, KNN and SVM), on the basis of which it was revealed that all the studied models received greater accuracy (from 54.69% to 69.67%) than the accuracy of the baseline (48.83%); the results of the experiments confirm the hypothesis about the effectiveness of adapting web pages to SEO recommendations using classification algorithms based on machine learning. Conclusions. It was confirmed that with the help of classification algorithms built on the basis of machine learning and the knowledge of experts, it is possible to effectively adjust web pages to SEO recommendations. The considered methods can be adapted for various search engines and applicable to different languages, provided that a stamping or lemmatization algorithm has been developed for them. The results of the study can be used in the development of automated software to support the work of SEO in audit technologies to identify web pages in need of optimization and in spam detection processes.
An interval mathematical model of a multicriteria task of reengineering physical structures of distributed databases is proposed. The model is built on the basis of the Kolmogorov-Gabor polynomial and the universal utility function of partial criteria. It provides an assessment of options for interval values of cost indicators, access time and network traffic. The use of the universal function of general utility makes it possible to more accurately take into account the preferences of the decision-maker. The use of a rational S-like utility function of partial criteria allows to reduce the time complexity of the procedures for calculating estimates. Practical application of the proposed model will improve the efficiency of automated design technologies for distributed databases.
The subject of research in the article is decision-making support processes in the tasks of optimizing technological processes (TP) at the stages of their design or reengineering. The goal of the work is to improve the efficiency of technologies of automated design of TP due to the development of mathematical models of the tasks of selecting subsets of effective design solutions with intervally specified characteristics of options. The following tasks have been solved in the article: review and analysis of the current state of the problem of supporting decision-making in the tasks of optimization of TP at the stages of their design or reengineering; decomposition of the problem of making project decisions; formalization of the task of comparing intervals for selection of Pareto fronts using comparison indices based on the generalized Hukuhari difference; development of a mathematical model of the problem for the method based on Carlin's lemma; development of a mathematical model of the problem for the method based on Hermeyer's theorem; determination of the Pareto front in the task of optimization of TP by the method of pairwise comparisons. The following methods were used: system approach, theories of systems, theories of usefulness, theories of decision-making, system design, optimization and operations research. Results. The place and connections of the problem of determining the Pareto front in the problem of making project decisions are determined. A formalized interval comparison procedure for the selection of Pareto fronts using Hukuhari total difference comparison indices. Mathematical models of the problem of selection of Pareto fronts using methods based on Carlin's lemma and Hermeyer's theorem have been developed for the case of interval publication with the value of local criteria. An example of the formation of the Pareto front in the problem of optimization of the technological process by the method of pairwise comparison according to the indicators of the duration of the technological cycle, reliability and specified costs is given. Conclusions. The proposed mathematical models expand the methodological bases of the automation of TP design processes. They make it possible to correctly reduce the set of alternative options for construction of TP for the final choice, taking into account the knowledge, experience of designers and factors that are difficult to formalize. The practical use of mathematical models will allow to increase the degree of automation of design or control processes, to reduce the time of decision-making in conditions of incomplete certainty of input data and to guarantee their quality by selecting them only from a subset of effective ones.
The subject of research in the article is the process of supporting decision-making in the tasks of optimizing closed logistics networks at the stage of reengineering. The goal of the work is to improve the efficiency of technologies for the automated design of closed logistics networks due to the improvement of mathematical models of multi-criteria problems of reengineering their topological structures. The following tasks are solved in the article: review and analysis of the current state of the problem of supporting decision-making in the tasks of optimizing logistics networks at the stage of their reengineering; decomposition of the problem of optimization of logistics networks at the main stages of their life cycles; selection of a logical scheme of the reengineering process of the logistics network as a territorially distributed object; development of a mathematical model of the general problem of multi-criteria optimization of logistics networks according to indicators of economy, efficiency, reliability and survivability; selection of models for scalar multi-criteria evaluation of reengineering options, taking into account factors that are difficult to formalize, knowledge and experience of the decision-maker. The following methods are used: system approach, theories of systems, theories of usefulness, theories of decision-making, system design, optimization and operations research. Results. Decomposition of the reengineering problem was carried out on the following tasks: determination of the purpose of reengineering and the principles of network reconstruction; network structure optimization; optimization of the topology of network elements; selection of functioning technology; determination of parameters of elements and vehicles; assessment and selection of the best network construction option. The general mathematical model of the multi-criteria task of reengineering the topological structures of centralized three-level logistics networks based on the indicators of costs, cargo delivery time, reliability and survivability has been improved. Universal functions of general utility and utility of local criteria are proposed to obtain scalar estimates for multiple indicators. Exclusion of part of local criteria and restrictions from the general model allows obtaining models of practically all interesting problems of optimization of logistics networks. Conclusions. The developed complex of mathematical models expands the methodological principles of automating the processes of designing logistics networks, allows for the correct reduction of a set of effective options for their construction for the final choice, taking into account factors that are difficult to formalize, the knowledge and experience of designers. The practical use of the proposed complex of mathematical models will reduce the time and capacity complexity of project decision-making support technologies, and due to the use of the proposed options selection procedures, increase their quality based on a number of functional and cost indicators.
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