Now showing 1 - 5 of 5
  • Publication
    FUNCTIONAL MODELLING OF IT RISK ASSESSMENT SUPPORT SYSTEM
    (2011) ;
    Andrejs Romanovs
    ;
    Yuri Merkuryev
    ;
    Arnis Kleins
    ;
    Pjotrs Dorogovs
    ;
    Ojars Krasts
  • Publication
    AUTOMATION OF FEEDBACK ANALYSIS IN ASYNCHRONOUS E-LEARNING
    (2023)
    - Rohit
    ;
    ; ;
    Atis Kapenieks
    With the recent hit of the Pandemic study process is shifted to E-learning. Measuring the actual progress of a student in Asynchronous e-learning because of machine-human interaction and feedback is also considered a primary issue in this area. With the rapid development of artificial intelligence, computers can capture surroundings. Image processing is a rising technique in Artificial Intelligence (AI).  Recognition of an individual's emotion helps identify the person's inner state. It is easy to measure a student's feedback about the session by doing it. The main functionality of this project is to capture the student's enough frames during the study session and provide the analysis of the average emotions to the administration panel. This prototype was used on 10 minutes of lecture to capture the emotion. The primary goal of this proposed system is to capture the learner's emotion at a specific interval during the e-learning session and provide the feedback of it to the instructor.
  • Publication
    Methodology for Similarity Assessment of Relational Data Models and Semantic Ontologies
    (2017-01-17) ; ;
    Barghorn, Knut
    ;
    Merkuryev, Yuri
    ;
    Berina, Gundega
    In 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