Now showing 1 - 10 of 13
  • Publication
    A Metamodel Based Approach for UML Notated Domain Specific Modelling Language
    (2011)
    Arnis Kleins
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    Yuri Merkuryev
    ;
    Ojars Krasts
    This paper focuses on a metamodel based approach to Unified Modelling Language (UML systems modelling and simulation. The approach allows creating a system model by operating with artefacts from the problem domain. As a novelty for UML modelling, especially for simulation purposes, the presented meta-model is enriched by a set of stochastic attributes of modelled activities. Modelling process is ensured by developing UML based Domain Specific Language (DSL) that is suitable for the metamodel, where UML diagrams are complemented with attributes necessary for model simulation. A modelling tool prototype was developed with Microsoft Visual Studio using Microsoft Visualization and Modelling SDK. Elaborated models are stored in a modelbase which conforms to the described metamodel. Relevant DEVS simulation software will be developed for ability to run those models and analyse gathered results. The given approach facilitates increases of the productivity in development of domain specific modelling and simulation tools up to 10 times.
    Scopus© Citations 2
  • Publication
    Real Estate Monitoring System Based on Remote Sensing and Image Recognition Technologies
    (2017)
    Kodors, Sergejs
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    Aldis Rausis
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    Aivars Ratkevics
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    Janis Zvirgzds
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    ;
    Ivonna Ansone
    Geoinformation are changing fast, therefore a change detection of real estate must be processed in short time. The increasing resolution of sensed geospatial data creates critically important to develop high performance computing solutions to process geospatial information. The topic of scientific work is the real estate monitoring system based on image recognition and remote sensing technologies. System's practical application is automatic building recognition from LiDAR data using saliency based method, vector map generation and change detection in actual cadastral maps. The scientific work describes high performance computing solution and gives its performance comparison with traditional method.
    Scopus© Citations 11
  • 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
    Mathematical Model of the Distribution of Laser Pulse Energy
    (2016)
    Pavels Narica
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    ; ;
    Pavels Cacivkins
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    Method allows for modelling of the complex process of laser pulse energy distribution over flat work surface. The process of calculating the correct result does not use common lasing formulas but instead employs the mathematical model of matrix multiplication of three input matrices representing a pulse model, a line model, and a plane model. The pulse model represents the distribution of planar energy densities within the laser pulse. The line model represents the distribution of pulses within the line. The plane model represents the distribution of lines within the plane. Because mathematical model is implemented within a spreadsheet processor, its size can be adjusted as needed and it can be instantiated multiple times for simultaneous modelling of different input parameters.
  • 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
    Mathematical Model of Forecasting Laser Marking Experiment Results
    (2016)
    Pavels Narica
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    ; ;
    Pavels Cacivkins
    ;
    Method allows for modelling of the anticipatory results of colour laser marking experiments. The process of calculating expected results takes into consideration the construction specifics of laser system being used and displays the results in compact form of a set of parameter matrices that have their values conditionally formatted as colour maps for easy identification of complex patterns. The complete set of all the related parameter matrices, both technical and derived, as well as the specific relations between them form the mathematical model of forecasting laser marking experiment results. Because the mathematical model is implemented within spreadsheet processor, it can be instantiated multiple times for any number of experiments.
  • Publication
    E-LEARNING: DEVELOPING TOMORROW'S EDUCATION
    E-learning refers to the term to deliver education or training using digital resources. Computer-based learning, which is considered the keystone of today's E-learning concept, was born in the 80s. Earlier E-learning provides education using only text as with the development in technology it allows adding various forms, i.e., Graphical Text, Images, Video Conferencing etc. In today's time, the concept of is E-learning growing at a rapid pace. Improved bandwidth and growing technology helped in pushing the expansion of E-learning. Along with the university, large corporate companies are also resorting to E-learning. E-learning provides many advantages as compare to Instructor-led training (ILT). E-learning saves the times of travel as physical presence is not required. Education can be provided from anywhere at any time. E-learning is cost-effective also as the course, once developed, can be modified easily. There can some concern which can be faced by the trainer and leaners in future. Adopting E-learning will be a step towards saving the environment. It will be environmentally friendly as tablets will replace books; paper notes will be replaced with digital messages. Digital tools will help to reduce the burden of a student. Artificial Intelligence is a prevalent concept in computer science. A branch of AI, known as a Neural Network, is based on the human brain. The research's main aim is to review existing methods and analyse further possibilities of E-learning systems with neural networks.
  • Publication
    AUTOMATION OF FEEDBACK ANALYSIS IN ASYNCHRONOUS E-LEARNING
    (2023)
    Rohit
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    ; ;
    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
    CORAS for Threat and Risk Modeling in Social Networks
    (2015)
    Aleksandrs Larionovs
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    ;
    As more users joining social networks possibility of threats is growing, as the information can be reached by expanding number of individuals that increases the possibility that the information “package” will find way to subjects with the appropriate degree of sensitivity to the information – vulnerability. Therefore, the risk management process and, in particular, the risk identification and analysis of key characteristics should be performed. Presented paper describes usage of CORAS methodology for modelling of social network risks.
    Scopus© Citations 6
  • Publication
    Machine Learning Technology Overview In Terms Of Digital Marketing And Personalization
    (2021) ;
    Nikolajeva, Anna
    The research is dedicated to artificial intelligence technology usage in digital marketing personalization. The doctoral theses will aim to create a machine learning algorithm that will increase sales by personalized marketing in electronic commerce website. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Learning algorithms learn on their own based on previous experience and generate their sequences of learning experiences, to acquire new skills through self-guided exploration and social interaction with humans. An entirely personalized advertising experience can be a reality in the nearby future using learning algorithms with training data and new behaviour patterns appearance using unsupervised learning algorithms. Artificial intelligence technology will create website specific adverts in all sales funnels individually.