Search Results - (( technology implementation mining algorithm ) OR ( java segmentation using algorithm ))

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  1. 1

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

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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    Thesis
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    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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    Thesis
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    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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    Book
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    ANALYSIS OF CUSTOMER SERVICE BUSINESS PROCESS USING DATA MINING by ABDUL RAHIM, NURALIAH

    Published 2020
    “…Hence, this paper contribute to justify by the basic concepts of data mining, described the selected types and models of algorithms, and the process of data mining by using R Tools.…”
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    Final Year Project
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    Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

    Published 2005
    “…The scale-up experiment showed that the proposed algorithm is more scalable than the other existing algorithms. …”
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    Thesis
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    Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan by Meisam , Gordan

    Published 2020
    “…One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. …”
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    Thesis
  15. 15

    Data Classification and Its Application in Credit Card Approval by Thai , VinhTuan

    Published 2004
    “…This project is involved with identification of the available algorithms used in data classification and the implementation of C4.5 decision tree induction algorithm in solving the data classifying task. …”
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    Final Year Project
  16. 16

    Analysis using data mining techniques: the exploration and review data of diabetes patients / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Othman, Jamal, Johan, Elly Johana, Mohd Mydin, Azlina, Wan Mohamad, Wan Anisha

    Published 2025
    “…Therefore, it is advisable for future studies to implement robust classification algorithms, such as ensemble methods, to effectively manage and extract potential insights.…”
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    Article
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    Classification of Immunosignature Using Random Forests for Cancer Diagnosis by Zarzar, Mouayad, Razak, Eliza, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah

    Published 2015
    “…To attain this essential research purpose, a minimum set of genes that can assure higher performance in classification using data mining algorithms need to be detected. The evolution of authoritative immunofingerprint mining technology is exerting a growing influence on comprehensive cancer diagnosis biology. …”
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    Proceeding Paper
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    Profiling network traffic of Sultan Idris Shah building (BSIS) using data mining technique / Rusmawati Ishak by Ishak, Rusmawati

    Published 2018
    “…Orange is a tool that being used in implementing K-Means Clustering Algorithm that could be seen as the most suitable solution to find a network trend pattern of user accessing the Internet and to produce with profiling network. …”
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    Thesis
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    Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with Orange Data Mining by Ang, Yuhao, Mohd Shafri, Helmi Zulhaidi, Al-Habshi, Mohammed Mustafa

    Published 2025
    “…Furthermore, reliefF-Gradient boosting and random forest algorithms achieved promising overall accuracy of 97.4% and 96.9%, respectively after implementing filtering and feature selection techniques. …”
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    Article
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