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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
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  2. 2
    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
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  3. 3
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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  4. 4
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    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. …”
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  5. 5
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  6. 6
    Examination timetabling using genetic algorithm case study: KUiTTHO

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

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  7. 7
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  8. 8
    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
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  9. 9
    Batch mode heuristic approaches for efficient task scheduling in grid computing system

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…Many algorithms have been implemented to solve the grid scheduling problem. …”
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  10. 10
    MAZE ROBOT: APPLYING AUTONOMOUS VEHICLE NAVIGATION ALGORITHM WITH EVENT-DRIVEN PROGRAMMING

    MAZE ROBOT: APPLYING AUTONOMOUS VEHICLE NAVIGATION ALGORITHM WITH EVENT-DRIVEN PROGRAMMING by ABDUL MALEK, NADHIRA

    Published 2011
    “…Hardware and software co-design method is used to construct the mobile robot. The basic navigation algorithm was developed using finite state machine (FSM). …”
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  11. 11
    Implementation of Autonomous Vehicle Navigation Algorithms Using Event-Driven Programming

    Implementation of Autonomous Vehicle Navigation Algorithms Using Event-Driven Programming by Saman, Abu Bakar Sayuti, Sebastian , Patrick, Malek, Nadhira, Hasidin, Nurul Zahidah

    Published 2012
    “…By using FSM to describe the behaviour of a navigating mobile robot, an equivalent algorithm can be developed. …”
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  12. 12
    Design Of Robot Motion Planning Algorithm For Wall Following Robot

    Design Of Robot Motion Planning Algorithm For Wall Following Robot by Ali Hassan, Muhamad Khairul

    Published 2006
    “…Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. …”
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  13. 13
    Neuro-Fuzzy Algorithm Implemented In Altera’s FPGA For Mobile Robot’s Obstacle Avoidance Mission.

    Neuro-Fuzzy Algorithm Implemented In Altera’s FPGA For Mobile Robot’s Obstacle Avoidance Mission. by Mahyuddin, Muhammad Nasiruddin, Chan, Zhi Wei, Arshad, Mohd Rizal

    Published 2009
    “…This paper presents the designed obstacle avoidance program for mobile robot that incorporates a neuro-fuzzy algorithm using Altera™ Field Programmable Gate Array (FPGA) development DE2 board. …”
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  14. 14
    Implementation of an autonomous mobile robot navigation algorithm using C language

    Implementation of an autonomous mobile robot navigation algorithm using C language by Subra Mullisi, Shafeq Marwan

    Published 2009
    “…The objective of the project is to develop navigation algorithm for the autonomous mobile robot. …”
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  15. 15
    Classroom finder system with student availability, space and time constraint

    Classroom finder system with student availability, space and time constraint by Loh, Yong Bin

    Published 2024
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  16. 16
    Implementation of autonomous vehicle navigation algorithms using event-driven programming

    Implementation of autonomous vehicle navigation algorithms using event-driven programming by Saman, Abu Bakar Sayuti, Sebastian , Patrick, Malek, Nadhira, Hasidin, Nurul Zahidah

    Published 2012
    “…By using FSM to describe the behaviour of a navigating mobile robot, an equivalent algorithm can be developed. …”
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  17. 17
    Thalassaemia detection using CBR Algorithm via mobile device

    Thalassaemia detection using CBR Algorithm via mobile device by Nur Faezah, Omar

    Published 2011
    “…This thesis proposes a Case-Based Reasoning model for medical diagnosis,particularly for Thalassaemia diagnosis. CBR Algorithm is an algorithm that can solve a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation in a new situation.CBR is suit for solving the problem in Thalassaemia cases.The model is designed and prototype is developed to test the diagnosis accuracy of the model.T his application is focus on Beta Thalassaemia and Haemoglobin E Trait only.For platform, this application will be using the mobile device such as PDA. …”
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  18. 18
    A new mobile botnet classification based on permission and API calls

    A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

    Published 2024
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  19. 19
    Mobile game application development using evolutionary algorithms

    Mobile game application development using evolutionary algorithms by Ong, Jia Hui

    Published 2014
    “…Hence it has opened up a whole new market with its mass among users and attracted a large number of smartphone application developers. Evolutionary Algorithms have never been used on mobile applications, thus it gives a novel idea of utilizing Evolutionary Algorithms in developing applications typically in games for mobile platforms. …”
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  20. 20
    Smart student timetable planner

    Smart student timetable planner by Wong, Xin Tong

    Published 2025
    “…Course data is managed in CSV format, parsed into JSON for fast processing, while sessionStorage and localStorage handle user data within active sessions. A Genetic Algorithm forms the core scheduling engine, generating optimized timetables that respect both hard constraints, such as avoiding clashes, and soft constraints, such as personal preferences.The final output of this project is a functional web-based timetable planner that successfully enhances scheduling efficiency, reduces the likelihood of errors, and improves the overall academic planning experience. …”
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