Search Results - (( java _ learning algorithm ) OR ( java implementation clustering algorithm ))

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  1. 1

    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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    Thesis
  2. 2

    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

    Published 2009
    “…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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    Conference or Workshop Item
  3. 3

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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    Thesis
  4. 4

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

    Published 2014
    “…A simulator for desktop grid environment has been developed using Java as the implementation language due to its wide popularity. …”
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    Final Year Project
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    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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    Final Year Project / Dissertation / Thesis
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    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
  9. 9

    Teaching and learning via chatbots with immersive and machine learning capabilities by Nantha Kumar Subramaniam

    Published 2019
    “…These chatbots support learning of Java via problem-solving steps through “learning by doing”. …”
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    Conference or Workshop Item
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    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

    Published 2006
    “…MALESAbrain1-3 is an intelligent algorithm which originally is designed for problem-based learning (PBL) environment. …”
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    Article
  12. 12

    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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    Thesis
  13. 13

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

    Published 2020
    “…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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    Conference or Workshop Item
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    Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system by Beg, Abul Hashem

    Published 2011
    “…The PLSR architecture model, workflow and algorithms are described. The PLSR has been developed using Java Programming language. …”
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    Thesis
  16. 16

    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 support vector machines (SVM) algorithm obtained the overall best results of 94.5% accuracy, 91.8% precision, 91.7% recall, and 91.1% f-Measure while the naïve bayes (NB) algorithm obtained the best AUC score of 0.944 with the tweet data of Dato Seri Anwar. …”
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    Thesis
  17. 17

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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    Thesis
  18. 18

    Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

    Published 2022
    “…The features extracted of time domain and frequency domain in vibration and acoustic will use as database of a Support Vector Machine (SVM) algorithms by using MATLAB R2021a. The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. …”
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    Monograph
  19. 19

    AI powered asthma prediction towards treatment formulation: an android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Md Muzahid, Abu Jafar, Sarker, Md Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md

    Published 2022
    “…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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    Article
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    Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

    Published 2024
    “…In this paper, the stacking ensemble method is used to increase the accuracy of the machine learning model for LSM where the base (first-level) learners use five ML algorithms namely decision tree (DT), k-nearest neighbor (KNN), AdaBoost, extreme gradient boosting (XGB) and random forest (RF). …”
    Conference Paper