Search Results - (( developing interpretive bayes algorithm ) OR ( java implication based algorithm ))

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  1. 1

    Development of machine learning-based algorithm to determine the condition in transformer oil by Mohsen Al-Katheri, Hussein Hasan

    Published 2021
    “…Three different types of ML algorithm have achieved high accuracy of 93.0%, 95.4% and 97.7% support vector machine (SVM), Naïve Bayes algorithm (NB), K-nearest neighbour (KNN) respectively. …”
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    Thesis
  2. 2

    Text-based emotion prediction system using machine learning approach by Ahmad Fakhri, Ab. Nasir, Eng, Seok Nee, Chun, Sern Choong, Ahmad Shahrizan, Abdul Ghani, Anwar, P. P. Abdul Majeed, Asrul, Adam, Mhd, Furqan

    Published 2020
    “…Text-based emotion prediction system to interpret and understand human emotions was successfully developed.…”
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    Conference or Workshop Item
  3. 3

    Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias by Alias, Annasuha Atie Atirah

    Published 2023
    “…Thus, this project aims to design, develop, and evaluate web-based application utilizing sentiment analysis, specifically employing the Naïve Bayes algorithm, to identify and analyze suicidal ideation within Twitter posts. …”
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    Thesis
  4. 4

    Analysing machine learning models to detect disaster events using social media by Faris Azni Azlan, Mr.

    Published 2023
    “…To simulate the examining process further, a fuzzy algorithm is developed to automatically rate the severity of a disaster as described in each message in disaster environment. …”
    text::Thesis
  5. 5

    Household overspending model amongst B40, M40 and T20 using classification algorithm by Zulaiha Ali, Othman, Azuraliza, Abu Bakar, Nor Samsiah, Sani, Jamaludin, Sallim

    Published 2020
    “…The results show that the decision tree through J48 algorithm has produced the easiest rule to be interpreted. …”
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    Article
  6. 6

    Sentiment analysis of customer review for Tina Arena Beauty by Amri, Nur Najwa Shahirah

    Published 2025
    “…Three machine learning algorithms Naive Bayes, Random Forest, and Support Vector Machine (SVM) were evaluated, and SVM achieved the highest accuracy and was selected as the final classifier. …”
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    Student Project
  7. 7
  8. 8

    A novel approach based on machine learning and public engagement to predict water-scarcity risk in urban areas by Hanoon, Sadeq Khaleefah, Abdullah, Ahmad Fikri, M. Shafri, Helmi Z., Wayayok, Aimrun

    Published 2022
    “…The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. …”
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    Article
  9. 9

    Study of hand gesture recognition using impulse radio ultra wideband (IRUWB) radar sensor by Terence Jerome Daim

    Published 2023
    “…Data collection experiments yield a diverse dataset of hand gestures, including variations in speed, essential for algorithm development. The developed algorithms interpret raw IR-UWB radar sensor data and associate it with specific hand gestures, addressing the core objective of gesture recognition. …”
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    Thesis
  10. 10

    Evolving fuzzy grammar for crime texts categorization by Mohd Sharef, Nurfadhlina, Martin, Trevor

    Published 2015
    “…Results show that the EFG algorithm produces results that are close in performance with the other ML methods while being highly interpretable, easily integrated into a more comprehensive grammar system and with lower model retraining adaptability time.…”
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    Article
  11. 11

    Physiological signals as predictors of mental workload: Evaluating single classifier and ensemble learning models by Nailul, Izzah, Sutarto, Auditya Purwandini, Hendi, Ade, Ainiyah, Maslakhatul, Muhammad Nubli, Abdul Wahab

    Published 2023
    “…A comprehensive evaluation was conducted on several ML algorithms, including both single (Support Vector Machine – SVM, and Naïve Bayes) and ensemble learning (Gradient Boost and AdaBoost) classiers and incorporating selected features and validation approaches. …”
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    Article
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