Grabusts, Pēteris

Thumbnail Image
Preferred name
Grabusts, Pēteris
Official Name
Grabusts, Pēteris
Alternative Name
Peter Grabusts
Pēteris Grabusts
Main Affiliation
Email
peteris.grabusts@rta.lv
Scopus Author ID
55835004700
Researcher ID
AAU-5594-2021

Research Output

Filters

Reset filters

Settings

Now showing 1 - 5 of 5
  • Publication
    Decision Tree Creation Methodology Using Propositionalized Attributes
    (2016)
    Grabusts, Pēteris 
    ;
    Arkādijs Borisovs
    ;
    Ludmila Aleksejeva
    The aim of the article is to analyse and thoroughly research the methods of construction of the decision trees that use decision tree learning with statement propositionalized attributes. Classical decision tree learning algorithms, as well as decision tree learning with propositionalized attributes have been observed. The article provides the detailed analysis of one of the methodologies on the importance of using the decision trees in knowledge presentation. The concept of ontology use is offered to develop classification systems of decision trees. The application of the methodology would allow improving the classification accuracy.
  • Publication
    AUTOMATION OF FEEDBACK ANALYSIS IN ASYNCHRONOUS E-LEARNING
    (2023)
    Rohit
    ;
    Grabusts, Pēteris 
    ;
    Teilāns, Artis 
    ;
    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
    Ontology-Based Classification System Development Methodology
    (2015)
    Grabusts, Pēteris 
    ;
    Arkady Borisov
    ;
    Ludmila Aleksejeva
    The aim of the article is to analyse and develop an ontology-based classification system methodology that uses decision tree learning with statement propositionalized attributes. Classical decision tree learning algorithms, as well as decision tree learning with taxonomy and propositionalized attributes have been observed. Thus, domain ontology can be extracted from the data sets and can be used for data classification with the help of a decision tree. The use of ontology methods in decision tree-based classification systems has been researched. Using such methodologies, the classification accuracy in some cases can be improved.
  • Publication
    Clustering Methodology for Time Series Mining
    (2009)
    Grabusts, Pēteris 
    ;
    Arkady Borisov
    A time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.<
  • Publication
    Path Planning Methods in Chemical Engineering
    (2015)
    Edvards Valbahs
    ;
    Grabusts, Pēteris 
    ;
    Ilo Dreyer
    Usually, when the practical motion planning and the shortest path are discoursed, mainly the limited number of tasks is observed. Almost all the tasks associated with the path from one point in 2D or 3D space to another point can be attributed to the usual issue in the practical application. Motion planning and the shortest path have vivid and indisputable importance as human activity in such areas as logistics and robotics. In our work we would like to draw particular attention to the field of application seems to be unnoticeable for the task such as motion planning and the shortest path problem. Due to quite simple examples used, we would like to show that the task of motion planning can be used for simulation and optimization of multi-staged and restricted processes which are presented in chemical engineering accordingly. In the article the simulation and optimization of three important chemical-technological processes for the chemical industry are discussed. The work done gave us the possibility to work out software for simulation and optimization of processes that in some cases facilitates and simplifies the work of professionals engaged in the field of chemical engineering.