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  1. 1
    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer

    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

    Published 2017
    “…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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  2. 2
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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  3. 3
    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment

    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

    Published 2021
    “…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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  4. 4
    Smart appointment organizer for mobile application / Mohd Syafiq Adam

    Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

    Published 2009
    “…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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  5. 5
    Backpropagation neural network training algorithm analysis / Shahrul Azmi Rosli

    Backpropagation neural network training algorithm analysis / Shahrul Azmi Rosli by Rosli, Shahrul Azmi

    Published 2010
    “…This paper presents the analysis of Backpropagation Neural Network Training Algorithms in Artificial Neural Network (ANN) using MATLAB and demonstrates the analysis of training algorithms using the dataset of concrete compressive strength.…”
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  6. 6
    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

    Published 2014
    “…Different equations are used to guide the network for providing an accurate result with less training and testing error. …”
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  7. 7
    River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia

    River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia by Mustafa, M.R., Rezaur, R.B., Saiedi, Saied, Isa, M.H.

    Published 2012
    “…It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. …”
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  8. 8
    Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification

    Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification by Nuaimi, Zakaria Noor Aldeen Mahmood Al, Abdullah, Rosni

    Published 2017
    “…By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks. …”
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  9. 9
    Hybrid honey badger algorithm with artificial neural network (HBA-ANN) for website phishing detection

    Hybrid honey badger algorithm with artificial neural network (HBA-ANN) for website phishing detection by Muhammad Arif, Mohamad, Muhammad Aliif, Ahmad, Zuriani, Mustaffa

    Published 2024
    “…HBA as metahueristic algorithm is used to optimize the network training process of ANN to improve their performances. …”
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  10. 10
    An Improvement on Extended Kalman Filter for Neural Network Training

    An Improvement on Extended Kalman Filter for Neural Network Training by Tsan, Ken Yim

    Published 2005
    “…This study explored the training of a neural network inference system using the extended Kalman filter (EKF) learning algorithm. …”
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  11. 11
    Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks

    Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks by Roslan, Nurfarah Arina

    Published 2022
    “…Following the best selection of neural network algorithm, BR algorithm is further trained using 50-70% training with 10-28 hidden neurons. …”
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  12. 12
    Training functional link neural network with ant lion optimizer

    Training functional link neural network with ant lion optimizer by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2020
    “…This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. …”
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  13. 13
    Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey

    Memory efficient BFGS neural-network learning algorithms using MLP-network: a survey by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George W

    Published 2004
    “…This paper surveys various implementation of a memory efficient second order (Broyden, Fletcher, Goldfard and Shanno) BFGS training algorithms which includes novel optimal memory (OM) BFGS neural network training algorithm, proposed by the present authors, which optimises performance in relation to available memory. …”
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  14. 14
    Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification

    Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification by Al Nuaimi, Zakaria Noor Aldeen Mahmood, Abdullah, Rosni

    Published 2017
    “…By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks.Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima.Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues.Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. …”
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  15. 15
    BAT-BP: A new BAT based back-propagation algorithm for efficient data classification

    BAT-BP: A new BAT based back-propagation algorithm for efficient data classification by Mohd. Nawi, Nazri, M. Z., Rehman, Hafifi, Nurfarian, Khan, Abdullah, Siming, Insaf Ali

    Published 2016
    “…Classification datasets from UCI machine learning repository are used to train the network. The simulation results show that the efficiency of BPNN training process is highly enhanced when combined with BAT algorithm.…”
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  16. 16
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

    Published 2019
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  17. 17
    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification

    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification by Abusnaina, Ahmed A., Abdullah, Rosni

    Published 2013
    “…Traditional training algorithms have some drawbacks such as local minima and its slowness.Therefore, evolutionary algorithms are utilized to train neural networks to overcome these issues.This research tackles the ANN training by adapting Mussels Wandering Optimization (MWO) algorithm.The proposed method tested and verified by training an ANN with well-known benchmarking problems.Two criteria used to evaluate the proposed method were overall training time and classification accuracy.The obtained results indicate that MWO algorithm is on par or better in terms of classification accuracy and convergence training time.…”
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  18. 18
    Performance of packet filtering using back propagation algorithm

    Performance of packet filtering using back propagation algorithm by M.I.Buhari, M. H. Habaebi, Burhanuddin Mohd.Ali

    Published 2004
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  19. 19
    Fast sequential learning methods on RBF-network using decomposed training algorithms

    Fast sequential learning methods on RBF-network using decomposed training algorithms by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George W

    Published 2004
    “…This work investigates novel sequential learning methods applied on a decomposed form of training algorithms using radial basis function (RBF) network. …”
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  20. 20
    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
    “…This research focuses on the use of binaryencoded genetic algorithm (GA) to implement efficient search strategies for the optimal architecture and training parameters of a multilayer feed-forward ANN. …”
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