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  1. 1
    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system

    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

    Published 2022
    “…In this study, the Deep Belief Network algorithm was optimized and constructed to design an effective NIDS anomaly model. …”
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  2. 2
    Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia

    Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia by Zaini, Farah Anishah, Sulaima, Mohamad Fani, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

    Published 2024
    “…Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. …”
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  3. 3
    Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia

    Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia by Sulaima, Mohamad Fani, Zaini, Farah Anishah, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie

    Published 2024
    “…Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short‑term daily electricity load in Peninsular Malaysia. …”
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  4. 4
    Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations

    Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations by Goheannee

    Published 2014
    “…In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. …”
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  5. 5
    A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction

    A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction by Yousif S.T., Ismail F.B., Al-Bazi A.

    Published 2025
    Subjects: “…Feed forward neural network-based particle swarm optimization approach…”
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  6. 6
    The impact of executive function and aerobic exercise recognition in obese children under deep learning

    The impact of executive function and aerobic exercise recognition in obese children under deep learning by JING, XIN, ABDULLAH, BORHANNUDIN, ABU SAAD, HAZIZI, YANG, XIANGKUN

    Published 2025
    “…Initially, a motion recognition model based on STN and Lucas–Kanade optical flow algorithm optimization was constructed. …”
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  7. 7
    Feedforward neural network for solving particular fractional differential equations

    Feedforward neural network for solving particular fractional differential equations by Admon, Mohd Rashid

    Published 2024
    “…Then, a single hidden layer of FNN based on Chelyshkov polynomials with an extreme learning machine algorithm (SHLFNNCP-ELM) is constructed for solving FDEsC. …”
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  8. 8
    Hierarchical multi-agent system in traffic network signalization with improved genetic algorithm

    Hierarchical multi-agent system in traffic network signalization with improved genetic algorithm by Tan, Min Keng, Chuo, Helen Sin Ee, Chin, Renee Ka Yin, Yeo, Kiam Beng, Teo, Kenneth Tze Kin

    Published 2019
    “…A dynamic modeling technique is proposed using Q-learning (QL) algorithm to online observe and learn the inflow-outflow traffic behaviors and extract the model parameters to update the evaluation model used in the fitness function of genetic algorithm (GA). …”
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  9. 9
    A multi-depot vehicle routing problem with stochastic road capacity and reduced two-stage stochastic integer linear programming models for rollout algorithm

    A multi-depot vehicle routing problem with stochastic road capacity and reduced two-stage stochastic integer linear programming models for rollout algorithm by Anuar, Wadi Khalid, Lee, Lai Soon, Seow, Hsin Vonn, Pickl, Stefan

    Published 2021
    “…As a result, it is shown that a base policy consisting of an exact optimal decision at each decision epoch can be obtained constructively through these reduced two-stage stochastic integer linear programming models. …”
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  10. 10
    Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

    Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass by Mohammad, Saleem Ethaib, Omar, Rozita, Mustapa Kamal, Siti Mazlina, Awang Biak, Dayang Radiah, S., Syafiie

    Published 2018
    “…The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. …”
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  11. 11
    Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty

    Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Tiong S.K., Hossain M.J.

    Published 2024
    “…The RUL prediction uncertainty with a 95% confidence interval (CI) is also analyzed. The GSA algorithm optimizes the hyperparameters of the LSTM network to construct an optimal model. …”
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  12. 12
    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach

    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

    Published 2022
    “…This study constructs the flow of DNN based method with the K-Means algorithm. …”
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  13. 13
    Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao

    Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao by He , Biao

    Published 2024
    “…Third, the overbreak prediction model is further integrated with metaheuristic algorithms, aiming to identify the optimal blasting parameters that can minimize overbreak. …”
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  14. 14
    A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction

    A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction by Hammid, Ali Thaeer, M. H., Sulaiman, Awad, Omar I.

    Published 2018
    “…The objective of this study, first, a firefly algorithm (FA) based on the k-fold cross-validation of BPNN has been suggested to predict data for keeping rapid learning and prevents the exponential increase in operating parts. …”
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  15. 15
    Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat

    Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat by Chia , Yu Huat

    Published 2024
    “…This is due to the data generated from the numerical model possess the pattern for the ML algorithm ease of prediction. …”
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  16. 16
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…Widespread models like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Strategy (ES) and Population-Based Incremental Learning (PBIL) dealing with the specified problems are also explored and compared. …”
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  17. 17
    Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms

    Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms by Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.

    Published 2024
    “…Also, the model performance was characterized based on the number of input variables utilized. …”
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  18. 18
    A dynamic eLearning prediction modelbased on incomplete activities of eLearning system

    A dynamic eLearning prediction modelbased on incomplete activities of eLearning system by Chayanukro, Songsakda

    Published 2020
    “…Therefore, the objectives of this study are: a) to analyze the eLearning activities that affect learning outcome; b) to construct a learning outcome prediction model for eLearning usage; c) to synthesize a dynamic eLearning prediction model based on incomplete activities of eLearning systems; and d) to evaluate the dynamic eLearning prediction model based on advantage, accuracy, and effectiveness. …”
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  19. 19
    The enhanced BPNN-NAR and BPNN-NARMA models for Malaysian aggregate cost indices with outlying data

    The enhanced BPNN-NAR and BPNN-NARMA models for Malaysian aggregate cost indices with outlying data by Ahmad Kamaruddin, Saadi, Md Ghani, Nor Azura, Mohamed Ramli, Norazan

    Published 2015
    “…The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. …”
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  20. 20
    Optimization of multi-agent traffic network system with Q-Learning-Tune fitness function

    Optimization of multi-agent traffic network system with Q-Learning-Tune fitness function by Tan, Min Keng

    Published 2019
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