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  1. 1
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
    “…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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  2. 2
    An Educational Tool Aimed at Learning Metaheuristics

    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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  3. 3
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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  4. 4
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  5. 5
    Solving Assembly Line Balancing Problem Using Genetic Algorithm With Heuristics-Treated Initial Population

    Solving Assembly Line Balancing Problem Using Genetic Algorithm With Heuristics-Treated Initial Population by Chong, Kuan Eng, Omar, Mohamed K, Abu Bakar, Nooh

    Published 2008
    “…Although genetic algorithm (GA) has been widely used to address assembly line balancing problems (ALBP), not much attention has been given to the population initialization procedure. …”
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  6. 6
    MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters

    MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar

    Published 2012
    “…In this paper, we propose a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. …”
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  7. 7
    Assessing the chaotic map population initializations for sine cosine algorithm using the case study of pairwise test suite generation

    Assessing the chaotic map population initializations for sine cosine algorithm using the case study of pairwise test suite generation by Din, Fakhrud, Kamal Zuhairi, Zamli, Abdullah, Nasser

    Published 2022
    “…In this work, we propose to assess the effectiveness of pseudo random (i.e., Random) as well as three chaotic map initializations (i.e., sine map, circle map, and logistic map) for SCA using the pairwise test case generation as our case study. …”
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  8. 8
    Handover initiation algorithm in hierarchical macrocell/femtocell In LTE network / Abdul Azim Mad Diah

    Handover initiation algorithm in hierarchical macrocell/femtocell In LTE network / Abdul Azim Mad Diah by Mad Diah, Abdul Azim

    Published 2013
    “…Therefore, in this paper the initiation algorithm are proposed when a user enters the coverage area of the femtocell or macrocell. …”
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  9. 9
    Combining approximation algorithm with genetic algorithm at the initial population for NP-complete problem

    Combining approximation algorithm with genetic algorithm at the initial population for NP-complete problem by Razip, H., Zakaria, M.N.

    Published 2018
    “…In Genetic Algorithm (GA), the prevalent approach to population initialization are heuristics and randomization. …”
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  10. 10
    A firefly algorithm based hybrid method for structural topology optimization

    A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

    Published 2020
    “…The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. …”
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  11. 11
    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly

    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

    Published 2019
    “…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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  12. 12
    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

    Published 2020
    “…This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing.…”
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  13. 13
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  14. 14
    Clustering ensemble learning method based on incremental genetic algorithms

    Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

    Published 2012
    “…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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  15. 15
    Autonomous and deterministic supervised fuzzy clustering

    Autonomous and deterministic supervised fuzzy clustering by Lim, K.M., Loo, C.K., Lim, W.S.

    Published 2010
    “…This algorithm implements k-means to initialize the fuzzy model. …”
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  16. 16
    Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster

    Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi

    “…In this paper has been proposed a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. …”
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  17. 17
    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line

    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line by Noroziroshan, Alireza, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Napsiah

    Published 2010
    “…A heuristic algorithm was introduced to generate the initial population for intelligence based genetic algorithm. …”
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  18. 18
    An Integrated RRT*SMART-A* Algorithm for solving the Global Path Planning Problem in a Static Environment

    An Integrated RRT*SMART-A* Algorithm for solving the Global Path Planning Problem in a Static Environment by Suwoyo, Heru, Adriansyah, Andi, Andika, Julpri, Ubaidillah, Abu

    Published 2023
    “…In short, by involving A*, the exploration process for generating the Smart-RRT*’s initial path can be supported. …”
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  19. 19
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by SUWOYO, HERU, ADRIANSYAH, ANDI, ANDIKA, JULFRI, SHAMSUDIN, ABU UBAIDAH, ZAKARIA, MOHAMAD FAUZI

    Published 2023
    “…In short, by involving A*. the exploration process for generating the .mart-RRT*'s initial path can be supported. …”
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  20. 20
    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT

    AN INTEGRATED RRT*SMART-A* ALGORITHM FOR SOLVING THE GLOBAL PATH PLANNING PROBLEM IN A STATIC ENVIRONMENT by Suwayo, Heri, Adrishah, Andi, Andika, Juleri, Shamdudin, Abu Ubaidah, Zakaria, Mohamad Fauzi

    Published 2023
    “…In short, by involving A*. the exploration process for generating the .mart-RRT*'s initial path can be supported. …”
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