Search Results - (( code classification matching algorithm ) OR ( parallel optimization window algorithm ))

Search alternatives:

  • Showing 1 - 8 results of 8
Refine Results
  1. 1
    Solving multi-objective dynamic vehicle routing problem with time windows using multi-objective algorithm

    Solving multi-objective dynamic vehicle routing problem with time windows using multi-objective algorithm by Khoo, Thau Soon

    Published 2022
    “…Our algorithm uses non-fitness evolutionary distributed parallelized adaptive large neighbourhood search (NEDPALNS). …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
    Save to List
    Saved in:
  2. 2
    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
    Article
    Save to List
    Saved in:
  3. 3
    An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding

    An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

    Published 2020
    “…In the classic Bag of visual words model, the Fuzzy c-means algorithm is replaced with K-means and the accuracy of SIFT matching is increased. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  4. 4
    CUDA implementation of fractal image compression

    CUDA implementation of fractal image compression by Al Sideiri A., Alzeidi N., Al Hammoshi M., Chauhan M.S., AlFarsi G.

    Published 2023
    Article
    Save to List
    Saved in:
  5. 5
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…For next, instead of k-means clustring, Fuzzy cmeans clustering is combined with Spatial Pyramid Matching image representation to improve the accuracy of classification results. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    Development of appoinment scheduling agent using distributed constraint satisfaction (DisCS)

    Development of appoinment scheduling agent using distributed constraint satisfaction (DisCS) by Mohd. Azmi, Nurulhuda Firdaus, Eri, Zeti Darleena, Ibrahim, Nuzulha Khilwani, Ahmad Rusli, Rozana Diana

    Published 2008
    “…The aim of this development is to optimize the reservation schedule towards efficiency schedule. …”
    Get full text
    Get full text
    Get full text
    Monograph
    Save to List
    Saved in:
  7. 7
    Online teleoperation of writing manipulator through graphics processing unit based accelerated stereo vision

    Online teleoperation of writing manipulator through graphics processing unit based accelerated stereo vision by Abu Raid, Fadi Imad Osman

    Published 2021
    “…These algorithms are then parallelized using Compute Unified Device Architecture CUDA C language to run on Graphics Processing Unit GPU for hardware acceleration. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  8. 8
    Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali

    Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali by M. Ali, Musab A.

    Published 2016
    “…The subsequent step is using the DAUB3 wavelet transform for feature extraction along with the application of an additional step for biometric template security that is the Non-invertible transform (cancelable biometrics method) and finally utilizing the Support Vector Machine (Non-linear Quadratic kernel) for matching/classification. The experimental results showed that the recognition rate achieved are of 99.9% on Bath-A data set, with a maximum decision criterion of 0.97.…”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:

Search Tools: