There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The article considers the development of a model of scheduling the behavior of an intelligent agent based on its rational activity. The model construction is based on Petri nets and uses the subject area ontology to assess the cost of resources and the relevance of states. It is shown that the problem of minimizing resource costs can be reduced to the asynchronous dynamic programming problem.

Modelling of the Intelligent Agent’s Behavior Scheduler Based on Petri Nets and Ontological Approach

This article is dedicated to the development of standardized methods and software for information resources processing in electronic content commerce systems (ECCS). In this paper an actual scientific problem of methods and tools development and research of information resources processing in ECCS was solved with the use of designed classification, mathematical tools, software and generalized ECCS architecture.

https://ph.pollub.pl/index.php/iapgos/article/download/1186/952

Design and features analysis of generalized electronic content-commerce systems architecture

Review of: "The case for development of an E-cigarette Ontology (E-CigO) to improve quality, efficiency and clarity in the conduct and interpretation of research"

This paper considers one of the stages of designing camouflage concealment means - the identification of characteristic colors of the terrain. Color is an integral part of the visual characteristic of camouflage means intended to conceal personnel, material resources, weapons and military equipment from enemy reconnaissance and destruction means. It is proposed to identify characteristic colors using cluster analysis, which refers to unsupervised machine learning methods. The number of clusters obtained determines the number of colors that will be displayed on the camouflage coating. As a result of the research, mathematical clustering algorithms were analyzed to determine the characteristic colors of the terrain. The need to conduct these studies is due to the lack of a universal way to determine the number of clusters, and is based on the research of other scientists who have determined that for each subject terrain only a certain clustering algorithm works most effectively, which must be determined experimentally. According to the results of the research, it was determined that the optimal algorithm for determining the characteristic colors of the terrain was the k-means++ clustering algorithm.

Clustering Methods Analysis for Terrain Colors Characteristics Determination

The article dwells upon the system for detecting fake news via the Internet based on machine learning methods. In the process of analyzing the accuracy of fakes or non-fakes identification, several algorithms have been chosen using the multilayer parceptron (0.9945/0.9979), the Bayes classifier (0.913/0.998), the random forest (0.933/0.991), the logistic regression (0.9988/0.9965), the k-nearest neighbors (0.999/0.83) and the decision tree (0.9903/0.98) to select the most optimal and accurate one according to the results of the experimental trials. It is highlighted that despite the method of k-nearest neighbors has shown the best results (0.999), it has the worst result when recognizing non-fakes (0.83). It is also proved that in order to identify propaganda and fakes, the best variant is to use the method of k-nearest neighbors (0.999), the logistic regression (0.9988) and the multilayer parceptron (0.9945). The Bayesian classifier (0.998), the multilayer parceptron (0.9979) and the logistic regression (0.9965) are more optimal for identifying real news (not fake news). For the simultaneous recognition of fakes and real news that are not fakes, when analyzing the differences in identification accuracy, the multilayer parceptron (0.0034) and the logistic regression (0.0023) are the best ones. Other algorithms have the following worse results: the decision tree (0.013), the random forest (0.058), the Bayes classifier (0.085) and the k-nearest neighbors (0.169).

Victoria Vysotska

Papers

Modelling of the Intelligent Agent’s Behavior Scheduler Based on Petri Nets and Ontological Approach

Design and features analysis of generalized electronic content-commerce systems architecture

Review of: "The case for development of an E-cigarette Ontology (E-CigO) to improve quality, efficiency and clarity in the conduct and interpretation of research"

Clustering Methods Analysis for Terrain Colors Characteristics Determination

Technology of Fake News Recognition Based on Machine Learning Methods