Showing 1 - 20 results of 157 for search '(((( experimental data algorithm ) OR ( element data algorithm ))) OR ( level fusion algorithm ))', query time: 0.14s Refine Results
  1. 1
  2. 2

    Decision-level fusion for single-view gait recognition with various carrying and clothing conditions by Al-Tayyan, Amer

    Published 2017
    “…Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. …”
    Get full text
    article
  3. 3
  4. 4
  5. 5

    Allocating data to distributed-memory multiprocessors by genetic algorithms by Mansour, Nashat

    Published 2016
    “…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
    Get full text
    Get full text
    Get full text
    article
  6. 6

    Variable Selection in Data Analysis: A Synthetic Data Toolkit by Mitra, Rohan

    Published 2024
    “…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
    Get full text
    article
  7. 7
  8. 8

    Web Based Online Hybrid Teaching Method of Network Music Course by Abu Zitar, Raed

    Published 2022
    “…Based on Web data mining, an improved algorithm of hybrid hierarchical recommendation algorithm and genetic algorithm is used in the experiment, and compared with the other two algorithms in the experiment. …”
    Get full text
  9. 9

    Properties of simulated annealing and genetic algorithms for mapping data to multicomputers by Mansour, Nashat

    Published 1997
    “…We experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. …”
    Get full text
    Get full text
    Get full text
    article
  10. 10

    General applicability of genetic and simulated annealing algorithms for data mapping by Mansour, Nashat

    Published 1995
    “…We experimentally analyze the general applicability of genetic algorithms (GA) and simulated annealing algorithms (SA) for mapping data to multicomputers. …”
    Get full text
    Get full text
    Get full text
    conferenceObject
  11. 11
  12. 12

    An enhanced k-means clustering algorithm for pattern discovery in healthcare data by Haraty, Ramzi A.

    Published 2015
    “…This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. …”
    Get full text
    Get full text
    Get full text
    Get full text
    article
  13. 13
  14. 14

    Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations by Mansour, Nashat

    Published 1992
    “…Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. …”
    Get full text
    Get full text
    Get full text
    article
  15. 15
  16. 16
  17. 17

    Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm by Abu Zitar, Raed

    Published 2022
    “…As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity.…”
    Get full text
  18. 18
  19. 19
  20. 20

    Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

    Published 2023
    “…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”