Search Results - (( its application a algorithm ) OR ( model application learning algorithm ))*

Search alternatives:

Refine Results
  1. 1
    A modified generalized RBF model with EM-based learning algorithm for medical applications

    A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

    Published 2006
    “…Moreover, GRBF trained by the new algorithm has an apparent statistical meaning. Experimental results show potentials for real-life applications.…”
    Get full text
    Get full text
    Get full text
    Proceeding Paper
    Save to List
    Saved in:
  2. 2
    Ensemble dual recursive learning algorithms for identifying flow with leakage

    Ensemble dual recursive learning algorithms for identifying flow with leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  3. 3
    SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA

    SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA by AHMED IZZELDIN, HUZAIFA TAWFEIG

    Published 2012
    “…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  4. 4
    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market

    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

    Published 2023
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    A deep reinforcement learning hybrid algorithm for the computational discovery and characterization of small proteins utilizing mycobacterium tuberculosis as a model

    A deep reinforcement learning hybrid algorithm for the computational discovery and characterization of small proteins utilizing mycobacterium tuberculosis as a model by Ouwabunmi, Babalola AbdulHafeez

    Published 2025
    “…This study presents the development and evaluation of a novel hybrid machine learning algorithm that integrates the strengths of Random Forest and Gradient Boosting models to enhance the prediction of smORFs. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023

    Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023 by Soomro, Anam, Zakariyah, Habeebullah, Aftab, S.M.A., Muflehi, Mohamad, Shah, Asadullah, Meraj, Syeda

    Published 2024
    “…This study conducts a systematic literature review (SLR) on the prediction of loan defaults using machine learning algorithms (MLAs) from 2020 to 2023. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  7. 7
    Multi-Backpropagation network

    Multi-Backpropagation network by Wan Ishak, Wan Hussain, Siraj, Fadzilah, Othman, Abu Talib

    Published 2002
    “…In most cases, Neural Network considered large amount of data, as it will be teach to learn or memorize the data as the knowledge. The learning mechanism for Neural Network is its learning algorithm. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  8. 8
    Advances of metaheuristic algorithms in training neural networks for industrial applications

    Advances of metaheuristic algorithms in training neural networks for industrial applications by Chong H.Y., Yap H.J., Tan S.C., Yap K.S., Wong S.Y.

    Published 2023
    “…Backpropagation; Gradient methods; Neural networks; Artificial neural network models; Complex applications; Exploration and exploitation; Gradient-based learning; Industry applications; Meta heuristic algorithm; Meta-heuristic search algorithms; Near-optimal solutions; Optimization…”
    Article
    Save to List
    Saved in:
  9. 9
    Classification model for water quality using machine learning techniques

    Classification model for water quality using machine learning techniques by Azilawati, Rozaimee, Azrul Amri, Jamal, Azwa, Abdul Aziz

    Published 2015
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  10. 10
    Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms

    Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms by Mohd Romlay, Muhammad Rabani, Rashid, Muhammad Mahbubur, Toha @ Tohara, Siti Fauziah, Mohd Ibrahim, Azhar

    Published 2019
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  11. 11
    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 Back Propagation algorithm or its variation on Multilayered Feedforward Networks is widely used in many applications. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  12. 12
    Alternate methods for anomaly detection in high-energy physics via semi-supervised learning

    Alternate methods for anomaly detection in high-energy physics via semi-supervised learning by Md. Ali, Mohd. Adli, Badrud’din, Nu’man, Abdullah, Hafidzul, Kemi, Faiz

    Published 2020
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  13. 13
    A review of hybrid deep learning applications for streamflow forecasting

    A review of hybrid deep learning applications for streamflow forecasting by Ng K.W., Huang Y.F., Koo C.H., Chong K.L., El-Shafie A., Najah Ahmed A.

    Published 2024
    Subjects:
    Review
    Save to List
    Saved in:
  14. 14
    Machine learning: tasks, modern day applications and challenges

    Machine learning: tasks, modern day applications and challenges by Aljuaid, Lamyaa Zaed, Koh, Tieng Wei, Sharif, Khaironi Yatim

    Published 2019
    “…These machine learning algorithms are a collection of complex mathematical models and human intuitions. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  15. 15
    Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee

    Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee by Chong , Hue Yee

    Published 2023
    “…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  16. 16
    Deep learning model for predicting and detecting overlapping symptoms of cardiovascular diseases in hospitals of UAE

    Deep learning model for predicting and detecting overlapping symptoms of cardiovascular diseases in hospitals of UAE by Abbas Alhadeethy, Najwa Fadhil, Khedher, Akram M Z M, Shah, Asadullah

    Published 2012
    “…The DL algorithm and technique of an ML domain is often meditated by a variety of neural networks (NN). …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  17. 17
    Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms

    Rainfall-rinoff model based on ANN with LM, BR and PSO as learning algorithms by Mohd Romlay, Muhammad Rabani, Rashid, Muhammad Mahbubur, Toha @ Tohara, Siti Fauziah, Mohd Ibrahim, Azhar

    Published 2019
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  18. 18
    Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm

    Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

    Published 2018
    “…The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets. © 2016, The Natural Computing Applications Forum.…”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  19. 19
    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing

    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

    Published 2024
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  20. 20
    A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction

    A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction by Hammid, Ali Thaeer, M. H., Sulaiman, Awad, Omar I.

    Published 2018
    “…The objective of this study, first, a firefly algorithm (FA) based on the k-fold cross-validation of BPNN has been suggested to predict data for keeping rapid learning and prevents the exponential increase in operating parts. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: