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  1. 1
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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  2. 2
    Octane number prediction for gasoline blends using convolution neural network / Zhu Yue

    Octane number prediction for gasoline blends using convolution neural network / Zhu Yue by Zhu , Yue

    Published 2021
    “…Simulation result show that RBF algorithm gives the best performance. …”
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    Thesis
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  3. 3
    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm

    Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

    Published 2013
    “…In some cases, the prediction errors of Taguchi ANN model was larger than 10 even with Levenberg Marquardt training algorithm. To overcome such problem, a hybrid genetic algorithm-based Taguchi ANN (GA-Taguchi ANN) has been developed. …”
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  4. 4
    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…The development of the algorithm starts by reviewing the characteristic of an autonomous systems. …”
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  5. 5
    Power simulators – a survey / K. Keerthivasan ...[et al.]

    Power simulators – a survey / K. Keerthivasan ...[et al.] by Keerthivasan, K., Deve, V. Sharmila, Krishnaveni, L., Jerome, Jovitha, Ramanujam, R.

    Published 2012
    “…The Power Simulator also termed as Dispatcher Training Simulator (DTS) is used for imparting training in power system operation and allied areas. …”
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  6. 6
    REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION

    REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION by P ISKANDAR, YULITA HANUM

    Published 2006
    “…In order to overcome latency problem, this research is an attempt to suggest a new prediction algorithm based on heuristic that could be used to develop a more effective and general system for virtual training applications. …”
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  7. 7
    Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources

    Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

    Published 2021
    “…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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  8. 8
    Case study : an effect of noise in character recognition system using neural network

    Case study : an effect of noise in character recognition system using neural network by Mohamad, Esmawaty

    Published 2003
    “…The theoretical foundation of this algorithm will be studied and summarized. Simulation experiment results on training and testing data will be recorded and discussed.…”
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  9. 9
    Virtual Models for Real-World Learning: The Development and Evaluation of a Patient Simulator for Eye Disability Diagnosis

    Virtual Models for Real-World Learning: The Development and Evaluation of a Patient Simulator for Eye Disability Diagnosis by Muhammad Khuarizmi bin Mohd Rapi

    Published 2024
    thesis::master thesis
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  10. 10
    A new machine learning-based hybrid intrusion detection system and intelligent routing algorithm for MPLS network

    A new machine learning-based hybrid intrusion detection system and intelligent routing algorithm for MPLS network by Mohammad Azmi Ridwan, Dr.

    Published 2023
    “…The dataset development for both algorithms is carried out via simulations in Graphical Network Simulator 3 (GNS3). …”
    text::Thesis
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  11. 11
    Performance of MIMO space-time coded system and training based channel estimation for MIMO-OFDM system

    Performance of MIMO space-time coded system and training based channel estimation for MIMO-OFDM system by Abdolee, Reza

    Published 2006
    “…Secondly, it has focused on training based channel estimation algorithm for MIMO-OFDM system. …”
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  12. 12
    Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks

    Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks by Desta, Zahlay F., K.S., Ramarao, Taj, Mohammed Baloch

    Published 2008
    “…The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively.…”
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    Conference or Workshop Item
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  13. 13
    Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks

    Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks by Desta, Zahlay F., K.S., Rama Rao

    Published 2009
    “…The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively.…”
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  14. 14
    Mobile machine vision for railway surveillance system using deep learning algorithm

    Mobile machine vision for railway surveillance system using deep learning algorithm by Kit, Guan Lim, Daniel Siruno, Min, Keng Tan, Chung, Fan Liau, Sha, Huang, Tze, Kenneth Kin Teo

    Published 2021
    “…In this paper, object detection model is developed and implemented with deep learning algorithm. …”
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    Proceedings
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  15. 15
    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM

    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

    Published 2011
    “…In this thesis, the approach has been analyzed and algorithms that simulate the new approach have been mapped out.…”
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  16. 16
    E-Handrawn Calculator

    E-Handrawn Calculator by Mohamad, Syamimi

    Published 2008
    “…The purpose of this project is to demonstrate an application of back-propagation network (comparison of training their algorithms and transfer function) in order to developing e-Hand-Drawn Calculator. …”
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    Final Year Project
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  17. 17
    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)

    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA) by Mohiuddin, A. K. M., Ashour, Ahmed Aly Ibrahim Shaaban, Yap, Haw Shin

    Published 2008
    “…This paper involves engine modeling in 1D software simulation environment, GT-Power. GT-Power is one of the CAE tools available in GT-SUITE offers the only true "virtual engine/power train" tool, capable of integrated simulations of the total engine and power train system. …”
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    Proceeding Paper
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  18. 18
    Enhanced conjugate gradient methods for training MLP-networks

    Enhanced conjugate gradient methods for training MLP-networks by Izzeldin, H., Asirvadam , Vijanth Sagayan, Saad , Nordin

    Published 2010
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  19. 19
    Artificial neural network and inverse solution method for assisted history matching of a reservoir model

    Artificial neural network and inverse solution method for assisted history matching of a reservoir model by Negash, B.M., Vel, A., Elraies, K.A.

    Published 2017
    “…This allows to directly simulate the trained neural network and avoid the use of objective function and optimization algorithm. …”
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  20. 20
    Neural network algorithm-based fall detection modelling

    Neural network algorithm-based fall detection modelling by Mohd Yusoff, Ainul Husna, Koh, Cheng Zhi, Ngadimon, Khairulnizam, Md Salleh, Salihatun

    Published 2020
    “…The simulated result shows that the training model of Type 2 is the best model with a training result of 6.1551mse, 40 epochs, time 0.06s, and 0.92742 accuracy. …”
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