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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.

“…The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. …”
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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.

“…GA and correlation analysis are used for the selection of the most influential input variables for the training and testing of the hybrid model. …”
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Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model by Yaacob, Mohd. Shafiek, Jamaluddin, Hishamuddin

“…In this paper, major properties of an adaptive fuzzy model as a system identifier when trained by the back-propagation algorithm are discussed. …”
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Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

“…As for the implementation of MPCA in feature extraction for BOD and COD, there were only 4 inputs required to explain at least 99.999% variability for both analyses. Altogether, for BOD, the BR algorithm with 60% training and 12 hidden nodes gives R=0.7825 whereas for COD, the BR algorithm with 70% training and 10 hidden nodes gives R=0.6716. …”
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Multidimensional Minimization Training Algorithms for Steam Boiler Drum Level Trip Using Artificial Intelligence Monitoring System by Ismail, F. B., Al-Kayiem, Hussain H.

“…The one hidden layer with one neuron using BFG training algorithm provides the best optimum neural network structure. …”
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Application of Hybrid Evolutionary Algorithm and thematic map for rule set generation and visualization of chlorophyta abundance at Putrajaya lake / Lau Chia Fong by Lau, Chia Fong

“…HEA is run on the training set in order to provide insights on the relationships between input variables and the algae abundance. …”
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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

An optimum drill bit selection technique using artificial neural networks and genetic algorithms to increase the rate of penetration by Momeni, M., Hosseini, S.J., Ridha, S., Laruccia, M.B., Liu, X.

“…This paper discusses bit selection by employing a method of combining Artificial Neural Network (ANN) and the computation of Genetic Algorithm (GA). …”
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Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

“…Recursive Feature Elimination (RFE) was employed for feature selection, and we trained seven supervised classifiers. …”
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Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit by Shah N.N.H., Razak N.N.A., Razak A.A., Abu-Samah A., Suhaimi F.M., Jamaluddin U.

“…The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. …”

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

“…Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. …”
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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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Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanopeprintss using artificial neural network (ANN) by Khandanlou, Roshanak, Fard Masoumi, Hamid Reza, Ahmad @ Ayob, Mansor, Shameli, Kamyar, Basri, Mahiran, Kalantari, Katayoon

“…The performed experiments were designed into two data sets including training, and testing sets. To acquire the optimum topologies, ANN was trained by quick propagation (QP), Batch Back Propagation (BBP), Incremental Back Propagation (IBP), genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithms for testing data set. …”
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Reinforcement learning-based target tracking for unmanned aerial vehicle with achievement rewarding and multistage traning by Ahmed Abo Mosali, Najm Addin Mohammed

“…Fifth, a novel agent selection algorithm was developed to enable the selection of the best agent and avoid under-fitting and over-fitting. …”
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An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

“…Additionally, the research restricts the number of variables through feature selection to enhance the performance of the algorithm. …”
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Artificial neural network modelling of photodegradation in suspension of manganese doped zinc oxide nanoparticles under visible-light irradiation by Abdollahi, Yadollah, Zakaria, Azmi, Sairi, Nor Asrina, Matori, Khamirul Amin, Masoumi, Hamid Reza Fard, Sadrolhosseini, Amir Reza, Jahangirian, Hossein

“…To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. …”
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"Application of Neural Network in developing Virtual Analyzer of Reformate Research Octane Number" by Suharin, Zuraihan Selina

“…The selected key variables in predicting Research Octane Number are, teed flow rate, recycle flow rate, coil outlet temperature of furnace and equivalent temperature bed of reactors. 1068 sample data points are used tor modeling the Research Octane Number which then are divided selectively intro three sections; training, validation and testing data. …”
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Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN) by Khandanlou, Roshanak, Masoumi, Hamid Reza Fard, Ahmad @ Ayob, Mansor, Shameli, Kamyar, Basri, Mahiran, Kalantari, Katayoon

“…The performed experiments were designed into two data sets including training, and testing sets. To acquire the optimum topologies, ANN was trained by quick propagation (QP), Batch Back Propagation (BBP), Incremental Back Propagation (IBP), genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithms for testing data set. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit by Norliyana, Nor Hisham Shah, Normy Norfiza, Abdul Razak, Athirah, Abdul Razak, Asma’, Abu-Samah, Fatanah, M. Suhaimi, Ummu Kulthum, Jamaludin

“…The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. …”
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