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  1. 1
    Multi-floor indoor location estimation system based on wireless local area network

    Multi-floor indoor location estimation system based on wireless local area network by Chua, Tien Han

    Published 2007
    “…The most probable match is selected and returned as estimated location based on Bayesian filtering algorithm. Estimated location is reported as physical location and symbolic location. …”
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  2. 2
    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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  3. 3
    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
    “…The methodology includes preliminary studies, model prototyping, and evaluation using data from sources like Kaggle. …”
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  4. 4
    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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  5. 5
    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. …”
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  6. 6
    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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  7. 7
    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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  8. 8
    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. …”
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  9. 9
    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
    “…These studies will help in choosing the right training algorithm for classification problem involved. …”
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    Monograph
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  10. 10
    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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  11. 11
    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
    “…The estimation of unknown PDF is a common problem and in this study Gaussian kernel function which is most widely used nonparametric density estimation method has been used for PDF calculation. …”
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  12. 12
    Feature extraction using active appearance model algorithm with Bayesian classification approach

    Feature extraction using active appearance model algorithm with Bayesian classification approach by Nuruzzaman, Mohammad, Hussain, Azham, Mohamad Tahir, Hatim, Abu Seman, Mohamad Amir

    Published 2013
    “…This study enhances invariant recognition of human faces and analysis to improve face verification and identification performance using Active Appearance Model (AAM) for feature extraction with Bayesian classification approach. …”
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  13. 13
    Statistical approach on grading: mixture modeling

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

    Published 2006
    “…The Gibbs sampler algorithm is applied using the WinBUGS programming package. …”
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  14. 14
    Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates

    Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates by Ehteram M., Graf R., Ahmed A.N., El-Shafie A.

    Published 2023
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  15. 15
    Slice sampling technique in Bayesian extreme of gold price modelling

    Slice sampling technique in Bayesian extreme of gold price modelling by Rostami, Mohammad, Adam, Mohd Bakri, Ibrahim, Noor Akma, Yahya, Mohamed Hisham

    Published 2013
    “…In this paper, a simulation study of Bayesian extreme values by using Markov Chain Monte Carlo via slice sampling algorithm is implemented. …”
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  16. 16
    Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

    Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP by Kakudi, Habeebah Adamu, Loo, Chu Kiong, Moy, Foong Ming, Masuyama, Naoki, Pasupa, Kitsuchart

    Published 2019
    “…We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. …”
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  17. 17
    Bayesian logistic regression model on risk factors of type 2 diabetes mellitus

    Bayesian logistic regression model on risk factors of type 2 diabetes mellitus by Chiaka, Emenyonu Sandra

    Published 2016
    “…The Bayesian logistic regression methods made use of the metropolis hasting (Random walk algorithm) and the Gibbs sampler with the incorporation of non-informative flat prior and non-informative non-flat prior distributions to obtain the posterior distribution for each coefficient of the variables. …”
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  18. 18
    Identifying the Ideal Number Q-Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment

    Identifying the Ideal Number Q-Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment by Chuan, Zun Liang, Azlyna, Senawi, Wan Nur Syahidah, Wan Yusoff, Noriszura, Ismail, Tan, Lit Ken, Mu, Wen Chuan

    Published 2018
    “…In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. …”
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  19. 19
    River segmentation using satellite image contextual information and Bayesian classifier

    River segmentation using satellite image contextual information and Bayesian classifier by Yousefi, Paria, Jalab, Hamid Abdullah, Ibrahim, R.W., Mohd Noor, Nurul Fazmidar, Ayub, M.N., Gani, Abdullah

    Published 2016
    “…The algorithm has two phases: creating the profile to separate river area via evaluated morphological erosion and dilation, namely, a training map; and improving the river’s image segmentation using the Bayesian rule algorithm in which two consecutive filters swipe false positive (non-water area) along the image. …”
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  20. 20
    A bayesian network approach to identify factors affecting learning of Additional Mathematics

    A bayesian network approach to identify factors affecting learning of Additional Mathematics by Ong, Hong Choon, Kumarenthiran A/L Chandrasekaran

    Published 2015
    “…Bayesian network is used to identify the relationship between the factors in the study and to analyze the data as it is able to represent the variables as nodes and the relationships as directed arcs. …”
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