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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

“…In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks' random variables. …”
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Bayesian inference for the bivariate extreme model by Mohd Amin, Nor Azrita, Adam, Mohd Bakri

“…Using simulation study, the capability of MTM algorithm to analyze the posterior distribution is implement. …”
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

“…All the algorithm for the engine has been developed by using Java script language. …”
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Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

“…Two MCMC techniques are considered for the inferences namely Metropolis-Hastings (MH) algorithm and the Multiple-try Metropolis (MTM) algorithm. …”
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Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

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

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

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

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

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

“…Two different brain MRI datasets are used to evaluate the algorithm. …”
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A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System by Roy R.B., Rokonuzzaman M., Amin N., Mishu M.K., Alahakoon S., Rahman S., Mithulananthan N., Rahman K.S., Shakeri M., Pasupuleti J.

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

“…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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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

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Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming by Ayodele B.V., Mustapa S.I., Alsaffar M.A., Cheng C.K.

“…This study investigates the applicability of the Leven�Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. …”

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

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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.

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

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

“…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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Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming by Ayodele, Bamidele V., Siti Indati, Mustapa, Alsaffar, May Ali, Cheng, C. K.

“…This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. …”
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Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP by Kakudi, Habeebah Adamu, Loo, Chu Kiong, Moy, Foong Ming, Masuyama, Naoki, Pasupa, Kitsuchart

“…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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