Search Results - (( availability classification using algorithm ) OR ( java application mining algorithm ))

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  1. 1

    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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    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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    Article
  5. 5

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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    Conference or Workshop Item
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    Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

    Published 2024
    “…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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    Article
  8. 8

    Performance comparison of CNN and LSTM algorithms for arrhythmia classification by Hassan, S.U., Zahid, M.S.M., Husain, K.

    Published 2020
    “…However, there is a lack of study that analyzes the performance comparison of CNN and LSTM algorithms for arrhythmia classification. In this paper, the performance of CNN and LSTM algorithms for arrhythmia classification is compared for a publicly available dataset. …”
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    Conference or Workshop Item
  9. 9

    Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification by Hijazi, Musab, Zeki, Akram M., Ismail, Amelia Ritahani

    Published 2023
    “…One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm . …”
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    Article
  10. 10

    Gene subset selection for lung cancer classification using a multi-objective strategy by Mohamad, Mohd. Saberi, Omatu, Sigeru, Deris, Safaai, Yoshioka, Michifuci

    Published 2008
    “…However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and many of the genes are not relevant to the classification. …”
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    Article
  11. 11

    Comparative analysis of text classification algorithms for automated labelling of quranic verses by Adeleke, Abdullah, Samsudin, Noor Azah, Mustapha, Aida, Mohd Nawi, Nazri

    Published 2017
    “…In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. …”
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    Article
  12. 12

    Support vector classification of remote sensing images using improved spectral Kernels by Md. Sap, Mohd. Noor, Kohram, Mojtaba

    Published 2008
    “…Results show that these kernels do in fact improve classification accuracy and use the prior information available in imagery to a better degree.…”
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    Article
  13. 13

    Machine learning-based leukemia classification using gene expression for accurate diagnosis by Mahmoud, Amena, Talpur, Kazim Raza, Saini, Shilpa, Talpur, Bandeh Ali, Shah, Asadullah, Zaki, John

    Published 2025
    “…The proposed techniques is then compared with the existing approaches using the same dataset. It is observed that the proposed model for leukemia classification has an accuracy of 97% using SVM algorithm whereas 94% is using Logistic regression algorithm.…”
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    Proceeding Paper
  14. 14

    Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

    Published 2016
    “…MLP usually requires a large amount of available measures in order to achieve good classification accuracy. …”
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    Thesis
  15. 15

    Performances of machine learning algorithms for binary classification of network anomaly detection system by Nawir, M., Amir, A., Lynn, O.B., Yaakob, N., Ahmad, R.B.

    Published 2018
    “…Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. …”
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    Conference or Workshop Item
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    DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH by LUONG, TRUNG TUAN

    Published 2005
    “…The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. …”
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    Final Year Project
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    The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification by Faizol, Bin Mohd Suria

    Published 2020
    “…However, none of the available works had proposed BFOA as a classification algorithm despite of its good performance. …”
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    Thesis
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    Improved building roof type classification using correlation-based feature selection and gain ratio algorithms by Norman, M., Mohd Shafri, Helmi Zulhaidi, Pradhan, Biswajeet, Yusuf, B.

    Published 2017
    “…The classification results using SVM classifier produced an overall accuracy of 83.16%. …”
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    Classification of brain tumors: using deep transfer learning by Husin, Nor Azura, Husam, Mohamed, Hussin, Masnida

    Published 2023
    “…To achieve the goal, a modified GoogleNet model was used. Various learning algorithms were tested. The experiment also examined transfer learning and data augmentation. …”
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    Article
  20. 20

    SVM for network anomaly detection using ACO feature subset by Mehmood, T., Rais, H.B.M.

    Published 2016
    “…But irrelevant and redundant features are the obstacle for classification algorithm to build an efficient detection model. …”
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    Conference or Workshop Item