Search Results - (( problem using bayesian algorithm ) OR ( java application testing algorithm ))

Search alternatives:

Refine Results
  1. 1
    Classification System for Heart Disease Using Bayesian Classifier

    Classification System for Heart Disease Using Bayesian Classifier by Magendram, Anusha

    Published 2007
    “…In this system a Bayesian algorithm was used in order to implement the system. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

    Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition by Yahya, Anwar Ali, Mahmod, Ramlan, Ramli, Abd Rahman

    Published 2010
    “…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  3. 3
    A Comparison Between Levenberg-Marquardt (LM) Intelligent System And Bayesian Regularization (BR) Intelligent System For Flow Regime Classification

    A Comparison Between Levenberg-Marquardt (LM) Intelligent System And Bayesian Regularization (BR) Intelligent System For Flow Regime Classification by Sa'ad, Mohamad Iqbal

    Published 2006
    “…ECT measured the different capacitance value of fluid and produced the data for the classification problem. Multilayed Perceptron (MLP), a type of artificial neural network (ANN) which is widely used in a classification problem is developed using MATLAB 7®. …”
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  4. 4
    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images

    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images by Farzan, Ali, Ramli, Abdul Rahman, Mashohor, Syamsiah, Mahmud, Rozi

    Published 2010
    “…A two-fold fuzzy segmentation algorithm based on Bayesian method is proposed in this paper. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  5. 5
    Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition

    Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition by Ali Yahya, Anwar

    Published 2007
    “…The results are compared with the results of static Bayesian networks and naïve bayes. The results confirm the merits of using dynamic Bayesian networks for dialogue act recognition. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    Statistical approach on grading: mixture modeling

    Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

    Published 2006
    “…A solution to this problem is using the Markov Chain Monte Carlo method namely Gibbs sampler algorithm. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  7. 7
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  8. 8
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  9. 9
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  10. 10
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  11. 11
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  12. 12
    Extreme air pollutant data analysis using classical and Bayesian approaches

    Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

    Published 2015
    “…The univariate and bivariate extreme processes have been considered extensively using a frequentist perspective and recently there has been an increasing interest in the application of Bayesian methods to EV problems. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  13. 13
    The use of the Bayesian approach in the formation of the student's competence in the ICT direction

    The use of the Bayesian approach in the formation of the student's competence in the ICT direction by Seisenbekova, Perizat, Shayakhmetova, Asem, Othman, Mohamed

    Published 2019
    “…To effectively use the Bayesian approach, a high-quality software product is needed that implements the mathematical ideas of Bayesian networks in practice. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  14. 14
    Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms

    Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms by Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza

    Published 2024
    “…Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  15. 15
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
    Get full text
    Get full text
    Final Year Project
    Save to List
    Saved in:
  16. 16
    Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation

    Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation by Rahimizadeh, Hamid

    Published 2009
    “…A useful property of the statistical classifier like Bayesian is that, it is optimal in the sense that it minimizes the expected mis classification rate. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  17. 17
    Statistical approach on grading the student achievement via normal mixture modeling

    Statistical approach on grading the student achievement via normal mixture modeling by Md. Desa, Zairul Nor Deana, Mohamad, Ismail, Mohd. Khalid, Zarina, Md. Zin, Hanafiah

    Published 2006
    “…A solution to this problem is using the Markov Chain Monte Carlo approach namely Gibbs sampler algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  18. 18
    Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data

    Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

    Published 2018
    “…Thus, this thesis aims at improving the efficiency of RF by providing a probabilistic framework using Bayesian reasoning. The modification comprises of two main modelling problems: high-dimensionality and missing data. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  19. 19
    A bayesian via laplace approximation on log-gamma model with censored data

    A bayesian via laplace approximation on log-gamma model with censored data by Yusuf, Madaki Umar, Abu Bakar, Mohd Rizam, Husain, Qasim Nasir, Ibrahim, Noor Akma, Arasan, Jayanthi

    Published 2016
    “…Methods/Analysis: Alternatively, Bayesian estimation by MCMC simulation using the Random-walk Metropolis algorithm was applied, using AIC and BIC comparison makes it the smallest and great choice for fitting the survival models and simulations by Markov Chain Monte Carlo Methods. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  20. 20
    Extreme Gradient Boosting (XGBoost) and Random Forest (RF) Hybrid Ensemble with Bayesian Optimization in Landslide Susceptibility Mapping

    Extreme Gradient Boosting (XGBoost) and Random Forest (RF) Hybrid Ensemble with Bayesian Optimization in Landslide Susceptibility Mapping by Dorothy, Anak Martin Atok

    Published 2025
    “…The generated models have certain shortcomings, especially when it comes to the problems of overfitting and overestimation. Therefore, the objective of this study is to assess and enhance the performance of Extreme Gradient Boosting (XGBoost) and Random Forest (RF) in predicting the susceptibility of landslides through the combination of the two models and the use of the Bayesian Optimization (BO) algorithm. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:

Search Tools: