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  1. 1
    Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi

    Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi by Dejamkhooy, Abdolmajid, Asefi, Sajjad

    Published 2018
    “…In the next step, the parameter identification as an optimization problem is solved by Moth-flame optimization, which is a novel nature-inspired heuristic algorithm. Simulation results and their comparison with Particle Swarm Optimization based method show high performance and good ability of the proposed method in PMSM parameter estimation.…”
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  2. 2
    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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  3. 3
    Hybrid optimization approach to estimate random demand

    Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

    Published 2012
    “…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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  4. 4
    Use of AR Block Processing for Estimating the State Variables of Power System

    Use of AR Block Processing for Estimating the State Variables of Power System by Mohd Nor, Nursyarizal, Jegatheesan, Ramiah, Perumal, Nallagownden

    Published 2008
    “…Samples of the local utility network for 103-bus parameter are collected and simulated using Burg and Modified Covariance algorithm to estimate the state variables. Simulation results to support the proposed method are also presented and compared with WLS method.…”
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  5. 5
    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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  6. 6
    Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications

    Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications by Shanmugam Y., Narayanamoorthi R., Ramachandaramurthy V.K., Bernat P., Shrestha N., Son J., Williamson S.S.

    Published 2025
    “…The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”
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  7. 7
    Information Theoretic-based Feature Selection for Machine Learning

    Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

    Published 2018
    “…The second test evaluates IFS in a controlled study using simulated datasets. Moreover, the third test used ten natural domain datasets obtained from UCI Repository, in about fifteen different experiments, using three to four different Machine Learning Algorithms for performance evaluation. …”
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  8. 8
    Robust correlation feature selection based support vector machine approach for high dimensional datasets

    Robust correlation feature selection based support vector machine approach for high dimensional datasets by Baba, Ishaq Abdullahi, Mohammed, Mohammed Bappah, Jillahi, Kamal Bakari, Umar, Aliyu, Hendi, Hasan Talib

    Published 2025
    “…The second step utilizes the estimates of weights from the first step to select the most important variables for the model. …”
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  9. 9
    Adapting perturbation voltage for variable speed micro-hydro using particle swarm optimization (PSO)

    Adapting perturbation voltage for variable speed micro-hydro using particle swarm optimization (PSO) by Kit, Guan Lim, Mohd Izzat Fikri Md Zainal, Min, Keng Tan, Ahmad Razani Haron, Chang, Yii Chai, Teo, Kenneth Tze Kin

    Published 2022
    “…MicroHydro that operates in variable speed mode are sensitive to the changes of flow rate and proved to have wide operating point. …”
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  10. 10
    EZ-SEP: extended Z-SEP routing protocol with hierarchical clustering approach for wireless heterogeneous sensor network

    EZ-SEP: extended Z-SEP routing protocol with hierarchical clustering approach for wireless heterogeneous sensor network by Nurlan, Zhanserik, Zhukabayeva, Tamara, Othman, Mohamed

    Published 2021
    “…In addition, EZ-SEP is weighted up using various estimation schemes such as base station repositioning, altering the field density, and variable nodes energy for comparison with the previous parent algorithm. …”
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  11. 11
    Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy

    Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  12. 12
    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani

    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

    Published 2018
    “…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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  13. 13
    Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data

    Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data by Sleabi, Waleed Dhhan

    Published 2016
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  14. 14
    Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data

    Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

    Published 2018
    “…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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  15. 15
    Development of robust procedures for partial least square regression with application to near infrared spectral data

    Development of robust procedures for partial least square regression with application to near infrared spectral data by Silalahi, Divo Dharma

    Published 2021
    “…The nonlinear solutions coupled with some improved robust methods such as Diagnostic Robust Generalized Potential (DRGP) method and GM6-Estimator are also introduced. …”
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  16. 16
    Determining malaria risk factors in Abuja, Nigeria using various statistical approaches

    Determining malaria risk factors in Abuja, Nigeria using various statistical approaches by Segun, Oguntade Emmanuel

    Published 2018
    “…Data collected were used for the multilevel analysis, Markov Chain Monte Carlo (MCMC) simulation via WinBUGS algorithm and influence diagrams for BBNs. …”
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  17. 17
    Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid

    Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid by Gaeid, Khalaf Salloum

    Published 2012
    “…The fault detection algorithm identifies the time and location of each fault. …”
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  18. 18
    Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data

    Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data by Baba, Ishaq Abdullahi

    Published 2022
    “…The proposed method inherits the robustness properties of the original RFCH estimators. …”
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  19. 19
    Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique

    Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique by Hardev Singh, Jitvinder Dev Singh

    Published 2015
    “…There are many types of motion estimation method but the most used method is the block matching method which is the fixed block matching and the variable block matching. …”
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  20. 20
    Simulated Kalman Filter algorithms for solving optimization problems

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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