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  1. 1
    Fuzzy modeling using Bat Algorithm optimization for classification

    Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

    Published 2018
    “…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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    Undergraduates Project Papers
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  2. 2
    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm

    Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

    Published 2018
    “…The reversible jump Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain the Bayesian estimator. …”
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  3. 3
    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. …”
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  4. 4
    Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data

    Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data by Rostami, Mohammad

    Published 2016
    “…In this research, application of extreme value theory within a Bayesian framework using the Metropolis Hastings algorithm and the slice sampler algorithm as an alternative approach, has been introduced. …”
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    Thesis
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  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. …”
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    Thesis
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  6. 6
    Bayesian inference for the bivariate extreme model

    Bayesian inference for the bivariate extreme model by Mohd Amin, Nor Azrita, Adam, Mohd Bakri

    Published 2016
    “…Using simulation study, the capability of MTM algorithm to analyze the posterior distribution is implement. …”
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    Conference or Workshop Item
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  7. 7
    Experimental analysis on the anti spam effectiveness - commercial, Bayesian and Ngram algortihm / Ahmad Kamal Ramli

    Experimental analysis on the anti spam effectiveness - commercial, Bayesian and Ngram algortihm / Ahmad Kamal Ramli by Ramli, Ahmad Kamal

    Published 2008
    “…By using standard series of text messages which consists of spam and ham words, N-Gram algorithm performed very well. …”
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    Thesis
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  8. 8
    Prediction of football club winning rate using Bayesian model algorithm / Adam Khairul Anuar

    Prediction of football club winning rate using Bayesian model algorithm / Adam Khairul Anuar by Khairul Anuar, Adam

    Published 2023
    “…Recommendations include continuous model training, detailed features, collaboration with experts, and exploring alternative algorithms. In essence, this research contributes to football analytics, offering a reliable Bayesian model for match outcome prediction.…”
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  9. 9
    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
    “…MTM algorithm is an extension of MH algorithm, designed to improve the convergence of MH algorithm by performing parallel computation. …”
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  10. 10
    Simulation algorithm of bayesian approach for choice-conjoint model

    Simulation algorithm of bayesian approach for choice-conjoint model by Zulhanif

    Published 2011
    “…Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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  11. 11
    Bayesian Network Classifiers for Damage Detection in Engineering Material

    Bayesian Network Classifiers for Damage Detection in Engineering Material by Mohamed Addin, Addin Osman

    Published 2007
    “…The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). …”
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  12. 12
    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
    “…Hence involving problem specific information and expert knowledge in designing segmentation algorithms seems to be useful. A two-fold fuzzy segmentation algorithm based on Bayesian method is proposed in this paper. …”
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  13. 13
    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. …”
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    Thesis
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  14. 14
    Learning to Filter Text in Forum Malay Message using Naive Bayesian Technique

    Learning to Filter Text in Forum Malay Message using Naive Bayesian Technique by Ab. Halim, Norhadila

    Published 2006
    “…The paper explains about me use of the basic naive Bayesian algorithm to classify forum messages whether clean or bad where clean message has no bad words, while bad message contains at least one bad word. …”
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    Final Year Project
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  15. 15
    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
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  16. 16
    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
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  17. 17
    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
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  18. 18
    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
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  19. 19
    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
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  20. 20
    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex

    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

    Published 2012
    “…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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