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- PublicationMethodology for Similarity Assessment of Relational Data Models and Semantic Ontologies(2017-01-17)
; ; ;Barghorn, Knut ;Merkuryev, YuriBerina, GundegaIn the upcoming age of semantic web there is a large number of relational databases being widely used. When time comes for a legacy relational database to migrate to semantic web or to be integrated with it, an important issue of determining similarity (compatibility) between two data models expressed in different ways arises. The goal of this paper is to describe the methodology for similarity assessment of relational database models and semantic data models and to present an ontology matching tool research prototype. The methodology consists of a set of steps, including transformation rules for data models, whose compatibility must be assessed, to the same ontology representation and applying ontology matching techniques. The methodology enables domain experts to perform a matching task semi-automatically between a relational data model and data model expressed as an ontology. The results of the semi-automatic matching are manually verified by the domain experts. The methodology was approbated using a use case from land administration domain. In the use case compatibility of data model provided by an international standard and a relational database had to be assessed.Scopus© Citations 1 - PublicationPear and apple recognition using deep learning and mobile(2020)
;Kodors, Sergejs ;Gunars Lacis ;Vitaliy ZhukovToms BartulsonsApple and pear are among the most widely grown and economically important fruit species worldwide and in Latvia. In turn, scab diseases caused by ascomycetous fungi Venturiainaequalis and V. pyrina, are economically the most important diseases worldwide. Durable plant resistance has been widely regarded as the preferred disease limitation method due to environmental and food safety concerns. Whereas in cases where the use of pesticides cannot be avoided, their applications should be more precise, more targeted and reduced substantially. One way how to realize it is the smart and precision horticulture that can greatly increase the effectiveness of pesticides and use them more selectively. The smart and precision horticulture relies heavily on new technologies and digitalization, including sensing technologies, software applications, communication systems, telematics and positioning technologies, hardware and software systems, data analytics solutions, as well as knowledge linking biological information to data technologies. The aim of our project – development and implementation of mobile application with deep learning system for early identification and evaluation of apple and pear scab. The specific of project – the image processing must be completed by a mobile device without image upload into GPU cluster. This research presents the comparison of deep learning architectures developed for mobile devices (MobileNet and MobileNetV2). The classification precision and speed of neural networks are compared using open dataset”Fruits-360”. The results are applicable to develop transfer learning and domain adaptation solutions. Meanwhile, decomposition into many simple subtasks can reduce required device resources to complete complex analysis using mobiles, as well as to create trustworthy AI model. The model of MobileNetV2 showed the best results: total accuracy 0.998, Cohen’s Kappa 0.991 and latency 212ms/step.Scopus© Citations 7 - PublicationTRANSVERSAL COMPETENCIES FOR DIGITAL READINESS AND DEVELOPMENT OF HUMAN CAPITAL IN ENGINEERING EDUCATION(2021)
; Velta ĻubkinaThe research is carried out within the framework of Rezekne Academy of Technology, Latvia in cooperation with West Ukrainian National University, Ukraine LV – UA project “Gender aspects of digital readiness and development of human capital in regions” Nr.LV-UA/ 2018/3. Nowadays, educational institutions have to adapt to the new situation, make changes that would promote the transformation of the study process, promote the development of digital readiness and human capital.The aim of the research is to study the need for transversal competencies, innovation, entrepreneurship and development of digital readiness and human capital in engineering studies. The monographic and descriptive method has been applied for studying scientific literature and modeling method for a professional development plan.