The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics' changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.
Abstract:In this paper, a methodology of geoinformation approach to mapping of atmospheric pollution of the air basin of Almaty city is developed. The proposed method of presenting data on pollution in form of an algorithm allows building a map of contamination of the surface layer of the atmosphere closest to the actually observed one. Designed object-oriented method of presentation of environmental pollution in the form of dynamic GIS models can be used when modeling the ecological status of any area, megalopolis, i.e. where spatial data, distributed in time, is used.
Learning objects are one of the innovations in domain of teaching technology. Shared and reusable learning objects are based on the understanding that content should be designed in a modular way rather than designed as a single integrated software. Learning objects are separate and independent objects, allowing each individual to create lessons in accordance with his or her own learning style or method, enabling the realization of customized learning. Since learning objects have descriptive information (metadata), they can be easily searched, so that they can access the learning content on time. The metadata definitions are made in the XML file. For this reason, learning objects can be shared with the use of XML, while the reuse of learning objects cannot be provided because XML is insufficient in the semantic definition of learning objects. Semantic Web (Web 3.0) technologies can be used to produce workable and interpretable web pages. Ontologies are being developed with the use of these technologies. Thanks to the ontology, intelligent learning environments can be developed to access the learning objects that are distributed on the web about each learning acquisition. In this study, according to the curriculum of Computer Engineering, ComputerEngineringCurriculum learning object ontology was defined. The defined learning object ontology provides better sharing and reusability of learning objects. Protégé ontology development editor was used for this purpose. This paper shows that learning environments developed using semantic web technologies and ontologies can offer intelligent solutions for individualized instruction and rapid access to accurate instructional content on the web.
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