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This article proposes a technique for using fuzzy interval logic during multi-stage processing of state spaces of complex biophysical objects in the medical system HEALTH. The proposed method of selecting the best diagnoses in the context of fuzzy background information was used to solve the practical problems of computer medical diagnostics. The t...
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... The development of effective methods for structural image recognition using the principles of data granulation [33] and the apparatus of fuzzy logic [22] is promising. This methodology allows us to organize high-quality and high-speed tools for recognizing visual objects using the mechanisms of computational intelligence. ...
The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problem of an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the influence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classification and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The verification of theoretical foundations and evaluation of the effectiveness of the proposed data processing methods for real image bases is performed using the OpenCV library. The applied significance of the work is substantiated according to the criterion of data processing time without reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.
... The difference of NEAOP between control and sample is shown on (Figure 3). NEAOP of the samples are shown in Table 4 and Figure 4. Some other most appropriate details are given in the literature [20][21][22]. ...
ABSTRACT
Varumin (1 and 2) is known as commercially produced drugs in the Republic of Macedonia. It contains different
herbal medicines such as mistletoe, elecampane, corneltree bark, marigold, yarrow, and cynodon and St John’s
wort. In addition, Varumin (1 and 2) has been designed to increase the defending strength (immunity) of
different parts of the human body infected by acute and chronic diseases. Since herbal drugs profiles of
Varumin (1 and 2), the present study aimed to assessing the chemical profile of potent compounds in order to
give a better understanding of possible therapeutical mechanisms in the occurrence of diseases via detecting
compounds present in Varumin (1 and 2). The obtained results revealed that the amount of Rutin and
Quercetin ranged between 63.04 μg/mL to 64.09 μg/mL and 65.93 μg/mL to 66.26 μg/mL respectively. And,
the total flavonoids were found between the range of 1.901 ± 0.08 μg/mL to 2.094 ± 0.08 μg/mL. Finally, the
Non Enzymatic Antioxidant properties (NEAOP) have been studied well and the obtained results were in the
range of 85.91±1.63 μg/mL to 97.95 0.24 μg/mL. All obtained values were found within good agreement with
other workers.
Keywords: Rutin, Quercetin, NEAOP, Varumin (1and 2), Flavonoids, St John’s wort
... The literary sources [10,[12][13][14][15][16] show the possibilities and efficiency of using the apparatus of fuzzy logic in intelligent systems. The practical application of the method of fuzzy analytical hierarchy is described in detail in the article [17]. ...
... The achieved results of the successful implementation of fuzzy tools can be useful for improving fuzzy ontological models which are able to develop in time and space. There is no description of the ontological representation and application of fuzziness in the ontological systems in [10,[12][13][14][15][16][17]. ...
... The information space of complex systems is not sufficiently normalized due to the qualitative and quantitative content. This space is characterized by linguistic variables that expand the space to a fuzzy one [1][2][3][12][13][14]. The ontology is a formal representation of reality; it requires the clear delivery of information, but reality cannot always be clearly expressed, so it is recommended to use fuzzy ontology [11]. ...
Ontologies in artificial intelligence systems are an effective way to represent and integrate knowledge and data. The property of such structures is that any subject area is accurately described in formal language. There is a problem in the research and determination of the adequacy of ontologies under development. The perspective directions are model construction for the development of fuzzy ontologies and also the creation of methods for evaluating adequacy. The achieved results allow one to implement the processes of supporting the development and integration of ontologies of complex systems on the basis of intelligent approaches. The method is proposed to solve the problem of alternative representation and the integration of knowledge and data in artificial intelligence systems. The methodology of improving the model of the hybrid development of fuzzy ontologies is described here; it provides the preliminary modification of models of extensive and intensive progress of ontologies in space and time. The identified features of fuzzy ontology processing allow us to create a procedure for finding and eliminating inadequacies. The software implementation of the application for the integration and presentation of heterogeneous data is carried out. The consumption of Random Access Memory (RAM) for the proposed models is analyzed. The further perspectives of the proposed research are determined in accordance with the principles of classification.
... The literary sources [10,[12][13][14][15][16] show the possibilities and efficiency of using the apparatus of fuzzy logic in intelligent systems. The practical application of the method of fuzzy analytical hierarchy is described in detail in the article [17]. ...
... The achieved results of the successful implementation of fuzzy tools can be useful for improving fuzzy ontological models which are able to develop in time and space. There is no description of the ontological representation and application of fuzziness in the ontological systems in [10,[12][13][14][15][16][17]. ...
... The information space of complex systems is not sufficiently normalized due to the qualitative and quantitative content. This space is characterized by linguistic variables that expand the space to a fuzzy one [1][2][3][12][13][14]. The ontology is a formal representation of reality; it requires the clear delivery of information, but reality cannot always be clearly expressed, so it is recommended to use fuzzy ontology [11]. ...
Ontologies in artificial intelligence systems are an effective way to represent and integrate knowledge and data. The property of such structures is that any subject area is accurately described in formal language. There is a problem in the research and determination of the adequacy of ontologies under development. The perspective directions are model construction for the development of fuzzy ontologies and also the creation of methods for evaluating adequacy. The achieved results allow one to implement the processes of supporting the development and integration of ontologies of complex systems on the basis of intelligent approaches. The method is proposed to solve the problem of alternative representation and the integration of knowledge and data in artificial intelligence systems. The methodology of improving the model of the hybrid development of fuzzy ontologies is described here; it provides the preliminary modification of models of extensive and intensive progress of ontologies in space and time. The identified features of fuzzy ontology processing allow us to create a procedure for finding and eliminating inadequacies. The software implementation of the application for the integration and presentation of heterogeneous data is carried out. The consumption of Random Access Memory (RAM) for the proposed models is analyzed. The further perspectives of the proposed research are determined in accordance with the principles of classification.
... In the current era, computer science is a major subject. It has many real-life applications such as cloud computing [1], artificial intelligence [2], virtualization environment [3], Internet of things [4,5,6,7,8,9,10,11], transportation problem [12,13], shortest path problem [14,15,16,17,18,19,20,21], internet security [22], uncertainty [23,24,25,26] and so on. IT is the mode by which users can store, fetch, communicate and utilize the information. ...
Pipelines are undoubtedly the most blue-chip concept in Windows PowerShell (PS). It creates a significant difference between PowerShell and DOS. The working process of the pipeline is based on several inputs and single outputs. It means that the output of one sub-part becomes the input to the other. Pipeline successively refines the data through several stages for final filtered output. In this paper, we have studied the details of the pipeline and presented a tutorial by considering all the capacities of pipeline in the context of the shell.
Особливості оподаткування інжинірингових послуг в Україні
