Search Results - (( cover selection based algorithm ) OR ( java application optimized algorithm ))

Refine Results
  1. 1

    Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite by Suali, Anjila J., Nuraminah Ramli, Rozmie Razif Othman, Hasneeza Liza Zakaria, Iszaidy Ismail, Nor Shahida Mohd Jamail, Rimuljo Hendradi, Nurol Husna Che Rose

    Published 2025
    “…Given the impracticality of testing every possible interaction due to time, budget, and resource constraints, combinatorial testing, which is t-way testing, is adopted to cover parameter interactions efficiently. This research focuses on the Input-Output Based Relations (IOR) testing strategy, which optimizes the test suite size by selecting critical parameters and employing “don’t care” values for non-essential inputs. …”
    Get full text
    Get full text
    Get full text
    Article
  2. 2
  3. 3

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
    Get full text
    Get full text
    Conference or Workshop Item
  4. 4

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
    Get full text
    Get full text
    Final Year Project
  5. 5
  6. 6

    Minimization of Test Cases and Fault Detection Effectiveness Improvement through Modified Reduction with Selective Redundancy Algorithm by Nikfal, Shima

    Published 2007
    “…Among these selected test cases, the algorithm identifies the redundant test cases based on definition occurrence, if they cover a similar combination of branch coverage except in one branch and also if the test cases cover a similar definition occurrence . …”
    Get full text
    Get full text
    Thesis
  7. 7

    Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm by Noor Azam, Muhammad Harith, Ridzuan, Farida, Mohd Sayuti, M Norazizi Sham

    Published 2023
    “…Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selection that is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique. …”
    Get full text
    Get full text
    Get full text
    Article
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12

    A novel and high capacity audio steganography algorithm based on adaptive data embedding positions by Shahadi H.I., Jidin R., Way W.H.

    Published 2023
    “…The WBM is preceded by preparing the cover audio in order to select the bits-positions that can possibly be used for embedding from each detail coefficient based on coefficient amplitude then copy the contents of the selected bits-positions and arrange them in blocks of bits. …”
    Article
  13. 13

    Regression test case selection & prioritization using dependence graph and genetic algorithm by Musa, Samaila, Md Sultan, Abu Bakar, Abd Ghani, Abdul Azim, Baharom, Salmi

    Published 2014
    “…The approach is based on optimization of selected test case from dependency analysis of the source codes. …”
    Get full text
    Get full text
    Article
  14. 14

    Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
    Get full text
    Get full text
    Thesis
  16. 16

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
    Get full text
    Get full text
    Article
  17. 17

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
    Get full text
    Get full text
    Article
  18. 18

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…Genetic Algorithm (GA) is one of the most popular optimization solutions. …”
    Get full text
    Get full text
    Thesis
  19. 19

    Fuzzy genetic algorithms for combinatorial optimisation problems by Varnamkhasti, Mohammad Jalali

    Published 2012
    “…The female chromosome is selected by standard tournament selection while the male chromosome is selected based on the hamming distance from the selected female chromosome, fitness value or the active genes. …”
    Get full text
    Get full text
    Thesis
  20. 20

    Ant colony optimization for rule induction with simulated annealing for terms selection by Saian, Rizauddin, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item