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  1. 1

    A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy by Qing, Zhang, Abdullah, Abdul Rashid, Choo, Wei Chong, Ali, Mass Hareeza

    Published 2022
    “…This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.…”
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    Article
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    Effort Estimation Model for Function Point Measurement by Koh, Tieng Wei

    Published 2007
    “…This research work has generated an algorithmic effort estimation model for function points measurement. …”
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    Thesis
  5. 5

    Reproducing kernel Hilbert space method for cox proportional hazard model by Abdul Manaf, Nur'azah

    Published 2016
    “…Then, we apply the kernel method to the survival data. Finally, we propose an algorithm of minimization of the loss function in the general Cox model. …”
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    Thesis
  6. 6

    Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller by Zaridah, Mat Zain

    Published 2010
    “…The design schemes of modeling adaptive and predictive FLC (APFLC) is described as follow: Basic FLC, Predictive FLC (PFLC) and APFLC. …”
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    Thesis
  7. 7

    Power System State Estimation In Large-Scale Networks by NURSYARIZAL MOHD NOR, NURSYARIZAL

    Published 2010
    “…The gain and the Jacobian matrices associated with the basic algorithm require large storage and have to be evaluated at every iteration, resulting in more computation time. …”
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    Thesis
  8. 8

    Machining characteristics of hastelloy c-2000 in end milling using artificial intelligence approach by Nurul Hidayah, Razak

    Published 2012
    “…Artificial Neural network (ANN) prediction model was developed with back propagation algorithm with the use of multilayer perceptron and activation function of hyperbolic tangent. …”
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    Thesis
  9. 9

    Optimal planning and design of hybrid renewable energy system for rural healthcare facilities / Olatomiwa Lanre Joseph by Olatomiwa Lanre , Joseph

    Published 2016
    “…Followed by development of prediction algorithm for solar radiation using soft-computing methodologies. …”
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    Thesis
  10. 10

    Computing the autopilot control algorithm using predictive functional control for unstable model by H. A., Kasdirin, J. A., Rossiter

    Published 2009
    “…One basic Ballistic Missile model (10) is used as an unstable model to formulate the control law algorithm using PFC. …”
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    Conference or Workshop Item
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    Voting algorithms for large scale fault-tolerant systems by Karimi, Abbas

    Published 2011
    “…Using a proper test-bed, after 10000 run times, we compared our newly proposed algorithm with the basic algorithm in terms of reliability and availability. …”
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    Thesis
  13. 13

    Mining The Basic Reproduction Number (R0) Forecast For The Covid Outbreak by Rajogoval, Illayakantthan

    Published 2022
    “…All the classification accuracies obtained were above the baseline accuracy. The COVID-19 Basic Reproduction Number, R0 a predictive model is developed using a linear regression classification algorithm to predict the COVID-19 Basic Reproduction Number, Robased on the actual COVID-19 Basic Reproduction Number, R0. …”
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    Monograph
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    A hybrid prediction model for pipeline corrosion using Artificial Neural Network with Particle Swarm Optimization by Ee, L.K., Aziz, I.A.

    Published 2018
    “…The basic ANN Model will be improved by integrating the Particle Swarm Optimization (PSO) algorithm to achieve a better and optimal performance. …”
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    Article
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    A hybrid prediction model for pipeline corrosion using Artificial Neural Network with Particle Swarm Optimization by Ee, L.K., Aziz, I.A.

    Published 2018
    “…The basic ANN Model will be improved by integrating the Particle Swarm Optimization (PSO) algorithm to achieve a better and optimal performance. …”
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    Article
  17. 17

    Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive by Al-Bared, M.A.M., Mustaffa, Z., Armaghani, D.J., Marto, A., Yunus, N.Z.M., Hasanipanah, M.

    Published 2021
    “…However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures. …”
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    Article
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    Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive by Al-Bared, M.A.M., Mustaffa, Z., Armaghani, D.J., Marto, A., Yunus, N.Z.M., Hasanipanah, M.

    Published 2021
    “…However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures. …”
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    Article
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    Predicting Flow Rate of V-Shape Customm Tank Using Derivative Free Recursive Algorithm by Tawfeiig, Huzzaifa, Asirvadam , Vijanth Sagayan, Saad , Nordin

    Published 2011
    “…The study involve MATLAB SIMULINK simulation program for the custom tank along with different prediction models. The obtained results showed that introducing Multilayer Perceptron (MLP) Neural Network architecture improve the prediction significantly where different algorithms, Recursive Kalman Filter (RKF) and Extended Kalman Filter (EKF) have been used simultaneously to estimate fluid height and output flow. …”
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    Citation Index Journal
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    Neural Network – A Black Box Model by Kuok, Kuok King, Chan, Chiu Po, Md. Rezaur, Rahman, Khairul Anwar, Mohamad Said, Chin Mei, Yun

    Published 2024
    “…To date, ANN has been successfully adopted in streamflow prediction, rainfall-runoff modeling, groundwater modeling, water quality modeling, and water demand forecasting.…”
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    Book Chapter