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  1. 1

    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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    Monograph
  2. 2

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

    Published 2006
    “…Findings indicated that heuristic-based algorithm is an accurate prediction method to compensate latency in virtual training. …”
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    Thesis
  3. 3

    Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection by Chia, Yee Shin

    Published 2015
    “…Subsequently, the integration of embedded correlation-based filtering algorithm has further increased the classification accuracy of training process and testing process by 4.93% and 14.73% respectively. …”
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    Thesis
  4. 4

    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

    Published 2021
    “…Prior to the development of ANN-based WQI prediction model, the BR algorithm was chosen with two-, three-, four-, five- and six-neuron architectures for 60% and 70% training. …”
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    Monograph
  5. 5

    Development of high quality speech compression system for Quranic recitation based on modified CELP algorithm by Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2013
    “…Instead of random Gaussian signal, we trained the codebook using LBG algorithm for Surah Al-Fatihah, and used the trained codebook for the whole Quran. …”
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    Proceeding Paper
  6. 6

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

    Published 2019
    “…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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    Thesis
  7. 7

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

    Published 2017
    “…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
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    Article
  8. 8

    SUDOKU HELPER by Abdul Razak, Muhammad Asyraf

    Published 2015
    “…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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    Final Year Project
  9. 9

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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    Thesis
  10. 10

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

    Published 2020
    “…This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. The algorithm is trained by network training function; LM, SCG and RP by collocation with threshold-based setting value. …”
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    Article
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  12. 12

    Hybrid bat algorithm-artificial neural network for modeling operating photovoltaic module temperature: article / Noor Rasyidah Hussin by Hussin, Noor Rasyidah

    Published 2014
    “…As a conclusion, the MAPE obtained for the Bat Algorithm based Multi-Layer Feedforward Neural Network is 4.79 in %.…”
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    Article
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  15. 15

    Analysis of training function for NNARX in solar radiation prediction modeling by Mohd Rizman, Sultan Mohd, Juliana, Johari, Fazlina, Ahmat Ruslan, Noorfadzli, Abdul Razak, Salmiah, Ahmad, Ahmad Syahiman, Mohd Shah

    Published 2022
    “…Each Training Function algorithm will be used in modeling development and their prediction output will be compared with the actual output. …”
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    Conference or Workshop Item
  16. 16

    Railway shortest path planner application using ant colony optimization algorithm / Muhammad Hassan Firdaus Ruslan by Ruslan, Muhammad Hassan Firdaus

    Published 2017
    “…Users need to input source and destination station and the output will be the proposed shortest path and other train information’s such as train types, train lines and stations involves. …”
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    Thesis
  17. 17

    Accelerated mine blast algorithm for ANFIS training for solving classification problems by Mohd Salleh, Mohd Najib, Hussain, Kashif

    Published 2016
    “…ANFIS accuracy depends on the parameters it is trained with. Keeping in view the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm, many have trained ANFIS using metaheuristic algorithms. …”
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    Article
  18. 18

    Hybrid bat algorithm hybrid-artificial neural network for modeling operating photovoltaic module temperature / Noor Rasyidah Hussin by Hussin, Noor Rasyidah

    Published 2014
    “…As a conclusion, the MAPE obtained for the Bat Algorithm based Multi-Layer Feedforward Neural Network is 4.79 in %.…”
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    Thesis
  19. 19

    Optimization of RFID network planning for monitoring railway mechanical defects based on gradient-based Cuckoo search algorithm by Talib, Nihad Hasan

    Published 2020
    “…In conclusion, this study presented a novel hybrid evolutionary algorithm based on the combination of AHP with GBCS to specify optimal RFID reader positions and amount based on the working train station domain. …”
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    Thesis
  20. 20

    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding by Mahmoud, Omer, Anwar, Farhat, Salami, Momoh Jimoh Emiyoka

    Published 2007
    “…Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. …”
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    Article