Search Results - (( java application using algorithm ) OR ( parameter learning process algorithm ))

Refine Results
  1. 1

    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm by Fatma Susilawati, Mohamad, Mumtazimah, Mohamad, Sarhan, AlDuais

    Published 2020
    “…The learning rate and momentum factor are the are the most significant parameter for increasing the efficiency of the BBP algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  2. 2

    Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

    Published 2023
    “…Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperature…”
    Article
  3. 3

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
    Get full text
    Get full text
    Final Year Project
  4. 4
  5. 5
  6. 6
  7. 7

    Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar, A., Kanthasamy, R., Sait, H.H., Zwawi, M., Algarni, M., Ayodele, B.V., Cheng, C.K., Wei, L.J.

    Published 2022
    “…The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. …”
    Get full text
    Get full text
    Article
  8. 8

    Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

    Published 2023
    “…Deep reinforcement learning is a self-learning algorithm through trial and error and experience, without any prior knowledge. …”
    Get full text
    Get full text
    Get full text
    Thesis
  9. 9
  10. 10

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

    Published 2012
    “…The results show that the proposed algorithm extensively improves the learning process of conventional Back Propagation algorithm.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  11. 11

    Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

    Published 2023
    “…Carbon nanotubes; Electrocatalysts; Electrooxidation; Forestry; Formic acid; Gaussian distribution; Learning algorithms; Palladium; Parameter estimation; Regression analysis; Support vector machines; Formic acid electrooxidation; Fuel cell application; Gaussian kernel functions; Gaussian process regression; Interaction effect; Machine learning algorithms; Performance; Process parameters; Regression trees; Support vector machine regressions; Sensitivity analysis…”
    Article
  12. 12

    A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter by Ur Rehman, M.J., Dass, S.C., Asirvadam, V.S.

    Published 2018
    “…Within the parameter learning steps, the MCMC algorithm requires to perform state estimation for which the target distribution is constructed by using the Ensemble Kalman filter (EnKF). …”
    Get full text
    Get full text
    Article
  13. 13
  14. 14

    Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

    Published 2021
    “…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15
  16. 16

    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

    Published 2022
    “…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. Machine learning algorithms have widely been adopted recently to enhance the performance of IDSs. …”
    Get full text
    Get full text
    Thesis
  17. 17

    Real-Time Video Processing Using Native Programming on Android Platform by Saipullah, Khairul Muzzammil

    Published 2012
    “…However for the Android platform that based on the JAVA language, most of the software algorithm is running on JAVA that consumes more time to be compiled. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  18. 18

    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  19. 19
  20. 20