Search Results - (( variable applying learning algorithm ) OR ( java applications usage algorithm ))

Refine Results
  1. 1

    An Effective Fast Searching Algorithm for Internet Crawling Usage by Chia, Zhen Hon, Nor Azhar, Ahmad

    Published 2016
    “…The search algorithm is a crucial part in any internet applications. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  2. 2

    Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition by Ali Yahya, Anwar

    Published 2007
    “…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
    Get full text
    Get full text
    Thesis
  3. 3
  4. 4
  5. 5

    Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China by Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.

    Published 2024
    “…Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. …”
    Article
  6. 6
  7. 7

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…The experimental result shows that using Java 2, this method improves the speed of PrefixSpan up to almost two orders of magnitude as well as the memory usage to more than one order of magnitude.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  8. 8

    An evolutionary based features construction methods for data summarization approach by Rayner Alfred, Suraya Alias, Chin, Kim On

    Published 2015
    “…Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. …”
    Get full text
    Get full text
    Research Report
  9. 9

    Feature selection for high dimensional data: An evolutionary filter approach. by Yahya, Anwar Ali, Osman, Addin, Ramli, Abdul Rahman, Balola, Adlan

    Published 2011
    “…Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. …”
    Get full text
    Get full text
    Get full text
    Article
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14

    Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

    Published 2018
    “…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
    Get full text
    Get full text
    Get full text
    Thesis
  15. 15
  16. 16

    A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem by Sze, Jeeu Fong, Salhi, S., Wassan, N.

    Published 2016
    “…In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large neighbourhood search (LNS) as a diversification strategy is proposed and applied to the capacitated vehicle routing problem. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17
  18. 18
  19. 19
  20. 20

    A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

    Published 2023
    “…The stacked ensemble deep learning method applied was proven robust with a performance accuracy, precision, recall, and F1 score at 95.69%, 94.96%, 92.92%, and 93.88% respectively. …”
    Get full text
    Get full text
    Get full text
    Thesis