Wyniki 1-2 spośród 2 dla zapytania: authorDesc:"Łukasz Rybak"

Adaptive Decision Support System in Network Centric Warfare Process DOI:10.15199/13.2018.7.10

  The term ‘net’ in the Network Centric Warfare (NCW) should be comprehended as creating connections (relations) between all subjects taking part in the operation in order to share information and provide each other direct cooperation. It is impossible to gain a network centric ability without different use of innovative technical solutions i.e. online techniques (including information safety mechanisms), meeting the needs of battlefield mobile users (by introducing programmable and also intelligent structures of Software Defined Radio or Cognitive Radio) and introducing as well as improving different types of wireless protocols for self-organizing and decentralized Mobile Ad-hoc NETworks (MANET) [1]. The key elements which make it possible to receive a network centric battlefield, thus situational awareness in network centric works are creating safe and effective mechanisms of data acquisition, carrying out the analysis, synthesis as well as aggregation, sharing information on every higher level of data processing, creating Knowledge Bases (KB) and using them effectively in Electronic Warfare (EW). [2, 3]. The Decision Support System (DSS) in network centric activities is based on knowledge and describes chosen methods and techniques of data/information/knowledge management and how significantly their use in the process of commanding maximizes the effectiveness of such a system. The base for the solution, presented in this article is the use of an example of nonparametric regression k-Nearest Neighbours algorithm in the classification process, on the basis of a few chosen distance measures and feature vector similarity, which are described in the conclusion module. Data processing in an adaptive decision support system In a computerized world it is possible to record every single event or state in a form of ordered data structures. In order to dominate on the network centric battlefield it is necessary to proces[...]


  The process of classification in general is correlated with the term of pattern recognition [1]. Dynamic development of ICT sector constantly determinates the increase of the number of processed data. The term mentioned above describes a discipline which in nature deals with processing information in a digital form with the use of ICT devices [2]. In the context of the main problems of this article and great dissertation on processing information, what needs to be underlined is the fact that as part of pattern recognition process there are actions connected with automation of trend and relation detection as well as predictions of statistical features in data sets. The above mentioned actions take place with the use of algorithms and then, the received output data, which define particular regularities in processed data, are used to assign objects to different categories [1]. The assignment process of statistical observation to categories on the basis of their features is defined as statistical classification. This term is derived from interdisciplinary science i.e. machine learning. To correlate the problems above what needs to be carried out is classification of algorithm learning methods. In this article, the authors focused on the classification, which is one of the supervised learning categories. Such a classification means assigning a new sample to one of the predefined classes on the basis of the feature analysis of objects belonging to the learning set, in which each instance has an already assigned class label. Another supervised learning method is regression, in which the problem is to predict a numerical value of a particular variable e.g. share prices [3]. Statistical classification in the context of practical, interdisciplinary uses is a subject of research for many scientists. The described concept in the article [4] i.e. a support system for making clinical decisions in the process of heart diseases diagnostic[...]

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