Search Results - (( java application optimization algorithm ) OR ( dynamic selection system algorithm ))

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  1. 1
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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  2. 2
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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    Monograph
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  3. 3
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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    Thesis
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  4. 4
    Resource management in grid computing using ant colony optimization

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high.This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system.The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism.The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element.The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job.EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form.The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. …”
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  5. 5
    Visdom: Smart guide robot for visually impaired people

    Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

    Published 2025
    “…An Android mobile application, developed using Java and the java.net.Socket library, provides an intuitive and accessible user interface for seamless interaction with the robot. …”
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    Final Year Project / Dissertation / Thesis
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  6. 6
    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices

    Optimization of blood vessel detection in retina images using multithreading and native code for portable devices by Tran, Duc Ngoc, Hussin, Fawnizu Azmadi, Yusoff, Mohd Zuki

    Published 2013
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  7. 7
    Performance evaluation of real-time multiprocessor scheduling algorithms

    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. …”
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  8. 8
    Route Optimization System

    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). …”
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    Final Year Project
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  9. 9
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
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  10. 10
    Model structure selection for a discrete-time non-linear system using genetic algorithm

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

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. 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. …”
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  11. 11
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

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

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. 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. …”
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  12. 12
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

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

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. 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. …”
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  13. 13
    Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model

    Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model by Yaacob, Mohd. Shafiek, Jamaluddin, Hishamuddin

    Published 2001
    “…The standard rule-based fuzzy models were used to identify discrete-time nonlinear dynamic systems. The method of selection of the input variables, the number of rules, and the learning rate are briefly discussed. …”
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  14. 14
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

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

    Published 2004
    “…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. 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. …”
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  15. 15
    Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition

    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. …”
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  16. 16
    Application of memetic algorithm in modelling discrete-time multivariable dynamics systems

    Application of memetic algorithm in modelling discrete-time multivariable dynamics systems by Ahmad, Robiah, Jamaluddin, Hishamuddin, Hussain, Mohd. Azlan

    Published 2008
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  17. 17
    Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

    Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers by Al-Gubri, Qusay, Mohd Ariff, Mohd Aifaa

    Published 2017
    “…This paper proposed a novel algorithm based on advanced feature selection technique for decision tree (DT) classifier to assess the dynamic security in power system. …”
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  18. 18
    Performance analysis of two component carrier selection algorithms in the downlink LTE-A

    Performance analysis of two component carrier selection algorithms in the downlink LTE-A by Mohd. Ramli, Huda Adibah, Ismail, Ahmad Fadzil, Abdullah, Khaizuran, Sandrasegaran, Kumbesan

    Published 2013
    “…Simulation results demonstrate the efficacy of the Random CC selection algorithm (in dynamic allocation) in optimizing the system capacity without compromising the QoS of the GBR application. …”
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  19. 19
    Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat

    Adaptive resource allocation algorithms with QoS support based on network conditions using fuzzy logic system for IEEE 802.11n networks / Bakeel Hussein Naji Maqhat by Naji Maqhat, Bakeel Hussein

    Published 2016
    “…The final contribution is an efficient selection mechanism scheme for contending stations to access the channel called an Access Channel Selection based Fuzzy (ACSF) expert system for WLAN. …”
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  20. 20
    Discrete-Time System Identification Based On Novel Information Criterion Using Genetic Algorithm

    Discrete-Time System Identification Based On Novel Information Criterion Using Genetic Algorithm by Abd Samad, Md Fahmi, Mohd Nasir, Abdul Rahman

    Published 2017
    “…Model structure selection is a problem in system identification which addresses selecting an adequate model i.e. a model that has a good balance between parsimony and accuracy in approximating a dynamic system. …”
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