Now showing 1 - 10 of 72
  • Publication
    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
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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
    Scopus© Citations 6
  • Publication
    The Concept of Ontology for Numerical Data Clustering
    Classical clustering algorithms have been studied quite well, they are used for the numerical data grouping in similar structures - clusters. Similar objects are placed in the same cluster, different objects – in another cluster. All classical clustering algorithms have common characteristics, their successful choice defines the clustering results. The most important clustering parameters are following: clustering algorithms, metrics, the initial number of clusters, clustering validation criteria. In recent years there is a strong tendency of the possibility to get the rules from clusters. Semantic knowledge is not used in classical clustering algorithms. This creates difficulties in interpreting the results of clustering. Currently, the possibilities to use ontology increase rapidly, that allows to get knowledge of a specific data model. In the frames of this work the ontology concept, prototype development for numerical data clustering, which includes the most important characteristics of clustering performance have been analyzed.
    Scopus© Citations 1
  • Publication
    The Choice of Metrics for Clustering Algorithms
    Methods of data analysis and automatic processing are treated as knowledge discovery. In many cases it is necessary to classify data in some way or find regularities in the data. That is why the notion of similarity is becoming more and more important in the context of intelligent data processing systems. It is frequently required to ascertain how the data are interrelated, how various data differ or agree with each other, and what the measure of their comparison is. An important part in detection of similarity in clustering algorithms plays the accuracy in the choice of metrics and the correctness of the clustering algorithms operation.
    Scopus© Citations 25
  • Publication
    Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. Many educational courses operate with models that were previously available only in mathematics and physics disciplines. As a possible solution, there could be the use of package Matlab Simulink in realization of different algorithms both for engineering disciplines and economic studies. The paper presents examples of using simulation modelling in the educational research processes.
  • Publication
    Application of Fuzzy Rule Base Design Method
    In many classification tasks the final goal is usually to determine classes of objects. The final goal of fuzzy clustering is also the distribution of elements with highest membership functions into classes. The key issue is the possibility of extracting fuzzy rules that describe clustering results. The paper develops a method of fuzzy rule base designing for the numerical data, which enables extracting fuzzy rules in the form IFTHEN. To obtain the membership functions, the fuzzy c-means clustering algorithm is employed. The described methodology of fuzzy rule base designing allows one to classify the data. The practical part contains implementation examples.
  • Publication
    Many educational courses operate with models that were previously available only in mathematics or other learning disciplines. As a possible solution, there could be the use of package IBM SPSS Statistics and Modeler in realization of different algorithms for IT studies. Series of research were carried out in order to demonstrate the suitability of the IBM SPSS for the purpose of visualization of various simulation models of some data mining disciplines – particularly cluster analysis. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic and clustering. Clustering methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies, clustering algorithms and real examples are demonstrated.
  • Publication
    Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.
  • Publication
    Educational experience shows that during the research process researchers perceive graphical information better than analytical relationships. Many economic courses operate with models that were previously available only in mathematics and physics disciplines. As a possible solution, there could be the use of the package Matlab Simulink in the realization of different algorithms both for engineering disciplines and economic studies. The article substantiates the usefulness of implementing the simulation models during the early stage of the research, when in parallel to acquiring analytical relations, simulation models may be introduced. The aim of the article is to show Matlab Simulink suitability for the purpose of visualizing simulation models of various economic disciplines.  To reach the aim, the following research tasks have been set: identification of Matlab Simulink possibilities for simulation of economic processes; demonstrate visualization models on the basis of examples; visualization of time series model using Latgale unemployment rate data. The article presents examples of using simulation modeling in the economic research processes - optimal tax rate searching and time series application. Common research methods are used in this research: descriptive research method, statistical method, mathematical modeling.