Search Results - (( java implication based algorithm ) OR ( programming process bayes algorithm ))

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    Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak by Ishak, Najihah Salsabila

    Published 2021
    “…A classifier model is developed using Naive Bayes characteristics. A comparison between built-in Scikit Learn Naive Bayes algorithm, and the scratch Naive Bayes algorithm is used to measure its effectiveness in terms of accuracy. …”
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    Thesis
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    Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu by Pala, Tuba, Camurcu, Ali Yilmaz

    Published 2014
    “…Multilayer Perceptron algorithm has been the best algorithm with the highest success percentage in both of the programs; Decision Trees has been the algorithm which has the lowest success percentage again in both of the programs. …”
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    Article
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    A stylometry approach for blind linguistic steganalysis model against translation-based steganography by Mohd Lokman, Syiham

    Published 2023
    “…Meanwhile, SVM is the best classifier for stego text detection with significantly low processing time performance…”
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    Thesis
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    Predicting students’ STEM academic performance in Malaysian secondary schools using educational data mining by Termedi @ Termiji, Mohammad Izzuan

    Published 2023
    “…Four different data mining classification algorithms which are Random Forest, PART, J48 and Naive Bayes will be used on the dataset. …”
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    Thesis
  15. 15

    Customer sentiment analysis through social media feedback by Siti Nur Syamimi, Mat Zain

    Published 2022
    “…The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. …”
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    Undergraduates Project Papers
  16. 16

    Prediction of novel doping agent using an in silico model that integrates chemical, biological and phenotypic data by Jamil, Nurul Amalina

    Published 2016
    “…In this study, two different training sets, termed as biological and phenotypic were compiled and three molecular descriptors (MACCS, ECFP4, FCFP4) and two machine learning algorithms (Naive Bayes and Decision Tree) were employed to build the predictive models. …”
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    Student Project