Результаты поиска по 'Bayesian network':
Найдено статей: 3
  1. Kondratyev M.A.
    Forecasting methods and models of disease spread
    Computer Research and Modeling, 2013, v. 5, no. 5, pp. 863-882

    The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods — regression and autoregressive models; machine learning-based approaches — Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).

    Keywords: morbidity forecasting, point-to-point estimates, regression models, ARIMA, hidden Markov models, method of analogues, exponential smoothing, SIR, Rvachev–Baroyan model, cellular automata, populationbased models, agent-based models.
    Views (last year): 71. Citations: 19 (RSCI).
  2. Makarov I.S., Bagantsova E.R., Iashin P.A., Kovaleva M.D., Gorbachev R.A.
    Development of and research on machine learning algorithms for solving the classification problem in Twitter publications
    Computer Research and Modeling, 2023, v. 15, no. 1, pp. 185-195

    Posts on social networks can both predict the movement of the financial market, and in some cases even determine its direction. The analysis of posts on Twitter contributes to the prediction of cryptocurrency prices. The specificity of the community is represented in a special vocabulary. Thus, slang expressions and abbreviations are used in posts, the presence of which makes it difficult to vectorize text data, as a result of which preprocessing methods such as Stanza lemmatization and the use of regular expressions are considered. This paper describes created simplest machine learning models, which may work despite such problems as lack of data and short prediction timeframe. A word is considered as an element of a binary vector of a data unit in the course of the problem of binary classification solving. Basic words are determined according to the frequency analysis of mentions of a word. The markup is based on Binance candlesticks with variable parameters for a more accurate description of the trend of price changes. The paper introduces metrics that reflect the distribution of words depending on their belonging to a positive or negative classes. To solve the classification problem, we used a dense model with parameters selected by Keras Tuner, logistic regression, a random forest classifier, a naive Bayesian classifier capable of working with a small sample, which is very important for our task, and the k-nearest neighbors method. The constructed models were compared based on the accuracy metric of the predicted labels. During the investigation we recognized that the best approach is to use models which predict price movements of a single coin. Our model deals with posts that mention LUNA project, which no longer exist. This approach to solving binary classification of text data is widely used to predict the price of an asset, the trend of its movement, which is often used in automated trading.

    Keywords: сryptocurrency, Twitter, machine learning, natural language processing, vectorization, dense model, logistic regression, random fores classifier, KNN, naive Bayes classifier.
  3. Gadzhiev R.I.
    Estimation of probabilistic model of employee labor process
    Computer Research and Modeling, 2012, v. 4, no. 4, pp. 969-975

    The mathematical estimation model for employee labor process, built on the basis of Bayesian network is presented in the article. The great attention is given to the estimation of qualitative characteristics of labor product. Usage of described model is supposed in the companies with the management employee workflows system.

    Keywords: estimation of labor, workflows modeling, Bayesian network, probabilistic modeling.
    Views (last year): 1.

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