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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
    Particle swarm optimization for neural network learning enhancement

    Particle swarm optimization for neural network learning enhancement by Abdull Hamed, Haza Nuzly

    Published 2006
    “…In Backpropagation Neural Network (BPNN), there are many elements to be considered such as the number of input, hidden and output nodes, learning rate, momentum rate, bias, minimum error and activation/transfer functions. …”
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  6. 6
    The effect of adaptive parameters on the performance of back propagation

    The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

    Published 2012
    “…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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  7. 7
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…In ANN, there are many elements need to be considered, and these include the number of input nodes, hidden nodes, output nodes, learning rate, momentum rate, bias parameter, minimum error and activation/transfer functions. …”
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  8. 8
    Green network planning and operational power consumption optimization in LTE-A using artificial intelligence

    Green network planning and operational power consumption optimization in LTE-A using artificial intelligence by Al-Samawi, Aida Ismail Ahmed

    Published 2015
    “…Moreover, the optimum rate of active relay is a function of the traffic pattern, average relay station load factor, their derivatives, and the relative RS to BS capacity factor. …”
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  9. 9
    Development of a scalable video compression algorithm

    Development of a scalable video compression algorithm by Khalifa, Othman Omran, Issa, Sinzobakwira, Olanweraju, Rashidah Funke, Al Khazmi, El Mahdi A

    Published 2012
    “…To improve the perceptual quality of coded video in a computation-constrained scenario, through controlling per-frame complexity, a rate-distortion optimised (RDO) rate control algorithm for encoding low bit rate video helped to achieve the target bitrates and PSNR using Lagrangian multiplier function. …”
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  10. 10
    Identification of vocal and voice disorders

    Identification of vocal and voice disorders by Murugesa Pandiyan, Paulraj, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Mohd Rizon, Mohammed Juhari, Prof. Dr., Sivanandam, S. N., Muthusamy, Hariharan, Dr.

    Published 2011
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  11. 11
    Wireless network power optimization using relay stations blossoming and withering technique

    Wireless network power optimization using relay stations blossoming and withering technique by Al-Samawi, Aida, Sali, A., Nissirat, Liyth Ahmad, Noordin, Nor Kamariah, Othman, Mohamed, Hashim, Fazirulhisyam

    Published 2017
    “…In this paper, a new relay switching perspective is introduced for relay blossoming and withering algorithm. First, relay switching is considered as a function of time representing the rate of active relays. …”
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  12. 12
    Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant

    Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant by Rahmat, Mohd Fua'ad, Rahmat, Mohd Fuaad, Abdul Wahab, Norhaliza, Che Razali, Mashitah, Gaya, Muhammad Sani, Samsudin, Sharatul Izah

    Published 2014
    “…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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  13. 13
    HF-AQM: An efficient active queue management scheme for wireless local area network environment

    HF-AQM: An efficient active queue management scheme for wireless local area network environment by Hassan, Suhaidi, Hassan, Atheer F., Arif, Suki

    Published 2017
    “…An Active Queue Management (AQM) is a proactive scheme that controls the network congestion by avoiding the congestion before it happened.When implementing AQM in wireless networks several contemporary issues must be considered, such as interference, collisions, multipath-fading, propagation distance, shadowing effects and route failure, and whether the wireless networks is WLAN or other type.Therefore, the needs for AQM algorithm that can perform in WLAN network as good and efficient as in wired network become so crucial.This paper proposes a new AQM algorithm called Hybrid Fair AQM (HF-AQM) that can achieve better fairness and higher utilization in WLAN environment by hybridizing queue delay and input rate to measure network congestion. …”
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  14. 14
    An efficient anomaly intrusion detection method with evolutionary neural network

    An efficient anomaly intrusion detection method with evolutionary neural network by Sarvari, Samira

    Published 2020
    “…Although activation functions are important for MLP to learn but for nonlinear complex functional mappings it has complicated calculation which reduces the accuracy of classification. …”
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  15. 15
    Smart fall detection by enhanced SVM with fuzzy logic membership function

    Smart fall detection by enhanced SVM with fuzzy logic membership function by Harum, Norharyati, Khalil, Mohamad Kchouri, Hazimeh, Hussein, Obeid, Ali

    Published 2023
    “…Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. …”
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  16. 16
    Forecasting of photovoltaic output using hybrid particle swarm optimization-artificial neural network model / Muhamad Faizol Adli Abdullah

    Forecasting of photovoltaic output using hybrid particle swarm optimization-artificial neural network model / Muhamad Faizol Adli Abdullah by Abdullah, Muhamad Faizol Adli

    Published 2010
    “…In Backpropagation Neural Network (BPNN), there are many elements to be considered such as the number of input, hidden and output nodes, learning rate, momentum rate, bias, minimum error and activation/transfer functions. …”
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  17. 17
    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
    “…Particularly, GA is utilized to determine the optimal number of hidden layers, number of neurons in each hidden layer, type of training algorithm, type of activation function of hidden and output neurons, initial weight, learning rate, momentum term, and epoch size of a multilayer feed-forward ANN. …”
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  18. 18
    Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm

    Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm by Zainuddin, Zarita

    Published 2001
    “…These factors include the choice of initial weights, the choice of activation function and target values, and the two backpropagation parameters, the learning rate and the momentum factor.…”
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  19. 19
    A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

    A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout by Kauthar, Mohd Daud, Mohd Saberi, Mohamad, Zalmiyah, Zakaria, Rohayanti, Hassan, Zuraini, Ali Shah, Safaai, Deris, Zuwairie, Ibrahim, Suhaimi, Napis, Sinnott, Richard O.

    Published 2019
    “…Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. …”
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  20. 20
    A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

    A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout by Mohd Daud, Kauthar, Mohamad, Mohd Saberi, Zakaria, Zalmiyah, Hassan, Rohayanti, Ali Shah, Zuraini, Deris, Safa'ai, Ibrahim, Zuwairie, Napis, Suhaimi, Sinnott, Richard O.

    Published 2019
    “…Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. …”
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