Authors

A three-level fuzzy cognitive model for region innovation development analysis

Analysis of short unstructured documents using fuzzy significance scales and special procedures for economic information integration

Algorithms and soft for adapting the knowledge base of project management information systems

The effectiveness of design solutions largely depends on the promptness of processing a large amount of data from various sources, which determines the feasibility of using information decision support systems (IDSS) in the field of project management. The peculiarities of information processes in project management greatly complicate or even make it impossible to implement in practice methods for constructing analytical, as well as probabilistic and statistical dependencies between the characteristics of the modeled project management system and the indicators of its internal and external environment. In this regard, as an algorithmic support for IDSS for project management, it is promising to use precedent methods for analyzing information based on knowledge about similar situations previously observed in the practice of project management, and representing knowledge in the form of ontologies. Analysis of practical situations in the field of project management makes it possible to substantiate the expediency of organizing a monitoring procedure for the IDSS knowledge base, based on the results of which decisions on its adaptation are made. The article proposes the main ways of this adaptation: changing the structure and basic elements (first of all, concepts) of ontologies; clarification of the structure of the description of current situations and, therefore, precedents. The developed algorithm for monitoring the IDSS knowledge base on project management for the analysis and identification of typical situations of the feasibility of changing it is described. The algorithm is distinguished by the possibility of developing recommendations on the modification of ontologies based on a fuzzy classification of search results and using precedents relevant to current situations. A procedure is proposed for changing the structure of the description of precedents, taking into account the results of assessing the indices of the fuzzy correspondence of the characteristics of the existing precedents to the characteristics of the project being implemented. A description of a computer program that implements the proposed algorithm and its components, as well as the results of its application are given. Read more...

Anomaly detection in economic indicators based on a neural network with depthwise separable

Anomaly detection is a pressing research problem in many subject areas, the solution of which enables timely management decision-making. This study proposes a method for identifying anomalies in economic indicators characterizing the internal and external environment of a manufacturing organization. This method can be applied in the algorithmic support of business decision support systems. The method is based on the use of an artificial neural network with an autoencoder architecture trained to replicate input data at the output. After training the autoencoder on normal data, the error in reconstructing the input at the output will be small. However, when fed anomalous data, the error will increase, which can serve as an anomaly indicator. The proposed method uses a convolutional autoencoder, so the input data is first converted into images (signatures), for which an original method for their formation is proposed. The method involves representing the historical behavior of each economic indicator as a heat matrix. Each heat matrix forms one channel, and their combination forms a signature, which is then fed to the autoencoder input for further analysis. The autoencoder utilizes depthwise separable convolutions, allowing for autonomous tuning of convolutional filters for individual signature channels. The novelty of the research results lies in the developed method for detecting anomalies in economic indicator arrays, which enables localization of collective and individual anomalies (outliers), as well as in the developed software used to test the method. Computational experiments demonstrated that the method achieves anomaly detection accuracy comparable to some modern models. Read more...