Now showing 1 - 9 of 9
  • Publication
    INFORMATIONAL WARFARE – INFLUENCE ON INFORMATIONAL STRUCTURES
    The concept of information warfare encompasses the use of information and communication technologies to gain an advantage over a potential opponent. The information warfare is the manipulation with the information that trusts the goal, so that the goal should make decisions about its interests in the interests of opponents. Information structures are treated as systems that process different types of information, provide storage and access to users. Such structures may enclose neural networks, self-learning systems etc. They need to be ready to learn, respond to threats and ensure their safety, which is topical in today's information warfare. This paper will address aspects related to the security of information systems from a system theory point of view. The knowledge base of information structures can be elements of artificial intelligence, which security must be protected against various threats. The authors considers artificial neural networks to be one of the potential threats in the context of information warfare.
    Scopus© Citations 1
  • Publication
    APPLE AND PEAR SCAB ONTOLOGY
    (2021) ; ;
    Toms Bartulsons
    ;
    Olga Sokolova
    ;
    Lienīte Litavniece
    ;
    Anna Nikolajeva
    An important issue in horticulture is ensuring plant disease, such as scab, prevention and treatment. Apple and pear are among the most widely grown (approximately 43% of all fruit tree area [1]) and economically important fruit crops specified worldwide and in Latvia. Scab diseases caused by ascomycetous fungi Venturia inaequalis and V.pyrina are economically the most important diseases worldwide. Research projects have produced research data covering various aspects of plant-pathogen interactions, but there is no internal linkage analysis, as well as implementation of other types of data (such as environmental and meteorological data, etc.). Establishing such a data integration system would allow the identification of new regularities in plant-pathogen interactions, and provide mechanisms for disease control decisions. Semantic analysis is one of information technology approaches to finding relationships in data. The product of analysis is ontology. There are plant disease ontologies which provide classification of diseases and describe their reasons. However, there is no ontology which describes a specific plant and relations among its farming parameters and disease probability. Such an ontology for apple and pear scab is presented in this paper. The constructed ontology can be applied to develop guidelines or digital expert systems.
    Scopus© Citations 1
  • 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
  • Publication
    METHOD FOR COLOR LASER MARKING PROCESS OPTIMIZATION WITH THE USE OF GENETIC ALGORITHMS
    Optimization of color laser marking process mostly depends on effective identification of optimal values of laser marking parameters. This is a difficult combinatorial optimization problem, which is still essential for companies that use laser marking systems. The study proposes a new approach to the process optimization through the use of genetic algorithms, carrying out preliminary experimental investigation, analyzing the laser marking results, and presenting possible improvements to the current implementation of genetic algorithms.
    Scopus© Citations 9
  • Publication
    Assessment of Name Based Algorithms for Land Administration Ontology Matching
    (2015-03-13) ; ;
    Aldis Rausis
    ;
    Jazeps Buls
    Abstract The purpose of this paper is to tackle semantic heterogeneity problem between land administration domain ontologies using name based ontology matching approach. The majority of ontology matching solutions use one or more string similarity measures to determine how similar two concepts are. Due to wide variety of available general purpose techniques it is not always clear which ones to use for a specific domain. The goal of this research is to evaluate several most applicable string similarity measures for use in land administration domain ontology matching. To support the research ontology matching tool prototype is developed, where the proposed algorithms are implemented. The practical results of ontology matching for State Land Service of Latvia are presented and analyzed. Matching of Land Administration Domain Model international standard, present Latvian Land Administration ontology is conducted.
    Scopus© Citations 5