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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

“…The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. 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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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition by Ali Yahya, Anwar

“…Motivating by these drawbacks, this research proposes a new model of dialogue act recognition in which dynamic Bayesian machine learning is applied to induce dynamic Bayesian networks models from task-oriented dialogue corpus using sets of lexical cues selected automatically by means of new variable length genetic algorithm. …”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

“…Multiple cases are developed using different optimally selected input variable vectors to train and test the back propagation neural network (BP-NN) and the hybrid model. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…The firefly algorithm remains a feasible alternative for shallow architectural network models, while metaheuristic algorithms such as the Particle swarm algorithm and Bat algorithm are better options for deeper architectural network models. …”
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Neural network based model predictive control for a steel pickling process by Kittisupakorn, P., Thitiyasook, P., Hussain, Mohd Azlan, Daosud, W.

“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.

“…This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. …”

Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm by Nasruddin, Sholahudin, Satrio P., Mahlia T.M.I., Giannetti N., Saito K.

Variable Global Optimization min-sum (VGOMS) algorithm of decodeand-forward protocol for the relay node in the cooperative channel by Hushairi, Zen, Jamaah, Suut, Al-Khalid, Bin Hj Othman, Khairuddin, Bin Ab Hamid

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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio... by Daba, Layth Muwafaq Abdulhussein

“…Additionally, a simulator is used to evaluate the proposed methodology under different deployment scenarios and network conditions. …”
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models by Hilal, Yousif Yakoub

“…Finally, this research concluded that a genetic algorithm is useful for selecting input variables in oil palm production. …”
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Enhancing riverine load prediction of anthropogenic pollutants: harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin, Khairunnisa, Ul-Saufie, Ahmad Zia, Senin, Syahrul Fithry, Zainudin, Zaki, Rashid, Ammar Mohd, Abu Bakar, Noor Fitrah, Anas Abd Wahid, Muhammad Zakwan, Azha, Syahida Farhan, Abd Wahab, Mohd Firdaus, Wang, Lei, Sahar, Farisha Nerina, Osman, Mohamed Syazwan

“…This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. …”
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Soil Pore Water Pressure and River Suspended Sediment Modelling using Artificial Neural Networks by Ul Mustafa, Muhammad Raza

“…These two variables were investigated on the premise that (i) sufficient field data on the time series of these two dependant variables and associated independent variables were obtainable; (ii) it was concluded from literature review that no attempt has yet been made to predict soil pore water pressure responses to rainfall using ANN and the issue has great relevance to slope stability and hill-slope hydrological studies (iii) even though attempts for suspended sediment prediction using ANN have been reported in literature, hardly any attempt has been made to assess the suitability of different ANN training algorithms in predicting river suspended sediment concentration.…”
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Neighbour-based on-demand routing algorithms for mobile ad hoc networks by Ejmaa, Ali Mohamed E.

“…All the three proposed algorithms are evaluated using discrete event simulation, in particular Network Simulator tool (NS2), and compared with the latest routing algorithm (NCPR ) and fundamental algorithm (AODV) using five performance metrics. …”
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Prediction of monthly rainfall at Senai, Johor using artificial immune system and deep learning neural network by Noor Rodi, Nur Syazwani, Abdul Malek, Marlinda, Zaini, Nur’Atiah A., Ismail, Amelia Ritahani, M. Hisham, Mohd Hizwan

“…MLP is a deep learning algorithm used in the Artificial Neural Network (ANN). …”
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Automatic control of flotation process using computer vision by Saravani, Ali Jahed

“…A froth model correlating the image variables to process variables and a prediction system estimating the metallurgical parameters based on image variables were then developed by using a neural network structure. …”
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Linear regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography / Yong Yan Yin by Yong, Yan Ling

“…In addition, an inter-observer variability test was performed and has shown that the proposed algorithm has comparable variability against manual luminal area estimations by expert human observers. …”
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Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri by Mohd Asri, Azinuddin

“…In contrast, machine learning is used to calculate the accuracy assessment of dependent between independent variables. …”
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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms by Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.

“…In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. …”

DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS by FIRAS BASIM, ISMAIL ALNAIMI

“…The encoding and optimization process using genetic algorithms has been applied successfully. …”
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