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Showing 1 - 20 results of 394 for search '(( tests using algorithm ) OR ((( elements learning algorithm ) OR ( neural coding algorithm ))))', query time: 0.14s Refine Results
  1. 1
    Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms

    Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms by Harmanani, H. M.

    Published 2007
    “…The method is based on a genetic algorithm that efficiently explores the testable design space and finds a sub-optimal test registers assignment for each k-test session. …”
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  2. 2
    An incremental approach for test scheduling and synthesis using genetic algorithms

    An incremental approach for test scheduling and synthesis using genetic algorithms by Harmanani, H.

    Published 2017
    “…This paper presents a new and an efficient method for concurrent BIST synthesis and test scheduling. This method maximizes concurrent testing of modules while performing the allocation of functional units, test registers, and multiplexers. …”
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  3. 3
    Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory

    Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory by Nadia Rasheed (22997257)

    Published 2025
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  4. 4
    A method for test scheduling using an evolutionary approach. (c2000)

    A method for test scheduling using an evolutionary approach. (c2000) by Noujaim, Jad

    Published 2000
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  5. 5
    An incremental approach for test synthesis and scheduling using genetic algorithms. (c2002)

    An incremental approach for test synthesis and scheduling using genetic algorithms. (c2002) by Hajar, Aouni

    Published 2002
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  6. 6
    A hybrid distributed test generation method using deterministic and genetic algorithms

    A hybrid distributed test generation method using deterministic and genetic algorithms by Harmanani, H.

    Published 2017
    “…The algorithm is parallelized based on a cluster of workstations using the message passing interface (MPI) library. …”
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  7. 7
    Power-constrained system-on-a-chip test scheduling using a genetic algorithm

    Power-constrained system-on-a-chip test scheduling using a genetic algorithm by Harmanani, Haidar M.

    Published 2006
    “…This paper presents an efficient approach for the test scheduling problem of core-based systems based on a genetic algorithm. …”
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  8. 8
    A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network

    A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network by Xiaofang Pan (1895950)

    Published 2019
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  9. 9
    Empirical comparison of regression test selection algorithms

    Empirical comparison of regression test selection algorithms by Mansour, Nashat

    Published 2001
    “…The empirical results show that the five algorithms can be used for different requirements of regression testing. …”
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  10. 10
    Metaheuristic Algorithm for State-Based Software Testing

    Metaheuristic Algorithm for State-Based Software Testing by Haraty, Ramzi A.

    Published 2018
    “…We formulate the testing problem as an optimization problem and use a simulated annealing (SA) metaheuristic algorithm to generate test cases as sequences of events while keeping the test suite size reasonable. …”
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  11. 11
    Natural optimization algorithms for optimal regression testing

    Natural optimization algorithms for optimal regression testing by Mansour, Nashat

    Published 1997
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  12. 12
    Tensile Test Optimization Using the Design of Experiment and Soft Computing

    Tensile Test Optimization Using the Design of Experiment and Soft Computing by Mehdi Moayyedian (14880358)

    Published 2023
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  13. 13
    A comparative study of five regression testing algorithms

    A comparative study of five regression testing algorithms by Mansour, N.

    Published 1997
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  14. 14
    Test case generation for path coverage. (c2000)

    Test case generation for path coverage. (c2000) by Salame, Miran

    Published 2000
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  15. 15
    Evolutionary algorithms for state justification in sequential automatic test pattern generation

    Evolutionary algorithms for state justification in sequential automatic test pattern generation by El-Maleh, Aiman H.

    Published 2005
    “…Sequential circuit test generation using deterministic, fault-oriented algorithms is highly complex and time consuming. …”
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  16. 16
    Metaheuristic algorithm for testing web 2.0 applications. (c2012)

    Metaheuristic algorithm for testing web 2.0 applications. (c2012) by Zeitunlian, Hratch Michel

    Published 2012
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  17. 17
    Simulated annealing and improved genetic algorithms for path testing. (c1997)

    Simulated annealing and improved genetic algorithms for path testing. (c1997) by Joumaa, Rania

    Published 1997
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  18. 18
    A multiobjective optimization method for the SOC Test Time, TAM, and power optimization using a strength pareto evolutionary algorithm

    A multiobjective optimization method for the SOC Test Time, TAM, and power optimization using a strength pareto evolutionary algorithm by Harmanani, Haidar M.

    Published 2017
    “…This paper presents a multiobjective optimization approach for the SOC test scheduling problem. The method uses a Strength Pareto Evolutionary Algorithm that minimizes the overall test application time in addition to power, wrapper design and TAM assignment. …”
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  19. 19
    Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms

    Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms by Zhongqiang Ma (13765801)

    Published 2023
    “…In addition, these new algorithms, compared against basic versions of other metaheuristics using classical benchmark problems, show competitive performances. …”
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  20. 20
    Nonlinear analysis of shell structures using image processing and machine learning

    Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

    Published 2023
    “…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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