Search Results - (( developing effective identification algorithm ) OR ( java implementation learning algorithm ))

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  1. 1

    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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    Thesis
  2. 2

    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 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 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

    Feature identification in a real surface metrology analysis by means of Double Iteration Sobel (DIS) / Ainaa Farhanah Mohd Razali by Mohd Razali, Ainaa Farhanah

    Published 2022
    “…The aim of this development is to increase the accuracy and stability of the edges features identification to be close to human perception on the actual edge location. …”
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  6. 6

    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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  7. 7

    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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  8. 8

    Identification of continuous-time hammerstein system using sine cosine algorithm by E. F., Junis, J. J., Jui, Mohd Helmi, Suid, Mohd Ashraf, Ahmad

    Published 2019
    “…This paper presents the development of identification of continuous-time Hammerstein systems based on Sine Cosine Algorithm (SCA). …”
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    Talkout : Protecting mental health application with a lightweight message encryption by Gavin Teo Juen

    Published 2022
    “…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2021
    “…Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. …”
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  15. 15

    Identifying the influential spreaders in multilayer interactions of online social networks by Al-Garadi, M.A., Varathan, Kasturi Dewi, Ravana, S.D., Ahmed, E., Chang, V.

    Published 2016
    “…The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. …”
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    Statistical Based Real-Time Selective Herbicide Weed Classifier by Ahmed, Irshad, Abdul Muhamin , Naeem, Muhammad, Islam, Azween, Abdullah

    Published 2007
    “…The developed system has been tested on the weeds in the lab along with the prior algorithm based on population variance, which have shown that the system is very effective in weed identification and efficient than the algorithm based on population variance. …”
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  18. 18

    A novel discrete wavelet transform-based graphical language classifier for identification of high-impedance fault in distribution power system by Veerasamy, Veerapandiyan, Abdul Wahab, Noor Izzri, Vinayagam, Arangarajan, Othman, Mohammad Lutfi, Ramachandran, Rajeswari, Inbamani, Abinaya, Hizam, Hashim

    Published 2020
    “…This paper proposes a discrete wavelet transform (DWT)-based Graphical Language classifier algorithm for identification of high-impedance fault (HIF) in medium voltage (MV) distribution network of 13.8 kV. …”
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  19. 19

    A Framework for Green Energy Resources Identification and Integration Supported by Real-Time Monitoring, Control, and Automation Applications by Far Chen, Jong

    Published 2025
    “…Experimental results confirmed the effectiveness of the proposed methodologies. In conclusion, the proposed methodologies demonstrate the potential to overcome barriers to green energy implementation, fostering sustainable development in Sarawak. …”
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  20. 20

    Gas Identi cation by Using a Cluster-k-Nearest-Neighbor by Brahim Belhaouari, samir

    Published 2009
    “…Therefore for that reason we develop another algorithm for clustering space which contributes a higher accuracy compares to K-means cluster with less subclass number, higher stability and bounded time of classification with respect to the variable data size. …”
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