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  1. 1
    Sentiment analysis regarding marital issues using Naive Bayes algorithm / Farah Nabila Mohd Razali

    Sentiment analysis regarding marital issues using Naive Bayes algorithm / Farah Nabila Mohd Razali by Mohd Razali, Farah Nabila

    Published 2025
    “…This study explores the application of sentiment analysis using the Naive Bayes algorithm to understand public perceptions of marital issues, particularly factors contributing to the rising divorce rate. …”
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  2. 2
    Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm

    Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm by Yusof, Norzihani, Rosidi, Siti Aishah Rosidi, Ibrahim, Nuzulha Khilwani Ibrahim, Ahmed Ali, Ahmed El-Mogtaba Bannga

    Published 2020
    “…From the results, the different value of accuracy for both SVM and Naïve Bayes Algorithm was 2.4%. The Naïve Bayes Algorithm displayed better result comparing to SVM. …”
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  3. 3
    A comparative study in classification techniques for unsupervised record linkage model

    A comparative study in classification techniques for unsupervised record linkage model by Ektefa, Mohammadreza, Sidi, Fatimah, Ibrahim, Hamidah, A. Jabar, Marzanah, Memar, Sara

    Published 2011
    “…Duplicate detection and classification of records in two real world datasets, namely Cora and Restaurant is experimented by Support Vector Machines, Naïve Bayes, Decision Tree and Bayesian Networks which are regarded as some prominent classification techniques. …”
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  4. 4
    Sentiment analysis regarding childcare issues using Naive Bayes Algorithm / Alis Farhana Zulkipeli

    Sentiment analysis regarding childcare issues using Naive Bayes Algorithm / Alis Farhana Zulkipeli by Zulkipeli, Alis Farhana

    Published 2025
    “…This study applies the Naive Bayes algorithm for sentiment analysis to assess public perceptions of childcare issues, particularly child abandonment and accidents. …”
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  5. 5
    Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification

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

    Published 2023
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  6. 6
    Diabetes Diagnosis And Level Of Care Fuzzy Rule-Based Model Utilizing Supervised Machine Learning For Classification And Prediction

    Diabetes Diagnosis And Level Of Care Fuzzy Rule-Based Model Utilizing Supervised Machine Learning For Classification And Prediction by Mohd Aris, Teh Noranis, Abu Bakar, Azuraliza, Mahiddin, Normadiah, Zolkepli, Maslina

    Published 2024
    “…In this study, ten supervised machine learning algorithms namely the J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet, AdaBoostM1, Random Forest, Multilayer Perceptron, Bagging and Stacking are applied for a simulated diabetes fuzzy dataset, verified by medical experts. …”
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  7. 7
    Image clustering comparison of two color segmentation techniques

    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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  8. 8
    An experimental evaluation of case slicing as a new classification technique

    An experimental evaluation of case slicing as a new classification technique by Shiba, Omar A. A., Sulaiman, Md. Nasir, Ahmad, Fatimah, Mamat, Ali

    Published 2003
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  9. 9
    Predicting Customer Behaviour on Buying Life Insurance using Machine Learning

    Predicting Customer Behaviour on Buying Life Insurance using Machine Learning by Fabu Ayub A Ghafar, Norhana Abd Rahim, Yumn Suhaylah Yusoff

    Published 2026
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  10. 10
    Driver drowsiness detection using different classification algorithms

    Driver drowsiness detection using different classification algorithms by Nor Shahrudin, Nur Shahirah, Sidek, Khairul Azami

    Published 2020
    “…Hence, this paper present and prove the reliability of ECG signal for drowsiness detection in classifying high accuracy ECG data using different classification algorithms.…”
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  11. 11
    Intrusion Detection Systems, Issues, Challenges, and Needs

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
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  12. 12
    Boosting and bagging classification for computer science journal

    Boosting and bagging classification for computer science journal by Wibawa, Aji Prasetya, Putri, Nastiti Susetyo Fanany, Al Rasyid, Harits, Nafalski, Andrew, Hashim, Ummi Rabaah

    Published 2023
    “…Therefore, a method of categorization is provided to solve this issue. Classification is a machine-learning technique that groups data based on the supplied label class. …”
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  13. 13
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    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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  14. 14
    Phishing image spam classification research trends: Survey and open issues

    Phishing image spam classification research trends: Survey and open issues by John Abari, Ovye, Mohd Sani, Nor Fazlida, Khalid, Fatimah, Mohd Yunus Bin Sharum, Mohd Yunus, Mohd Ariffin, Noor Afiza

    Published 2020
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  15. 15
    Analysing the performance of classification algorithms on diseases datasets

    Analysing the performance of classification algorithms on diseases datasets by Lydia E.L., Sharmil N., Shankar K., Maseleno A.

    Published 2023
    “…The proposed classification algorithms measure the diseases using the disease datasets which estimates the accurate prediction. …”
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  16. 16
    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab

    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

    Published 2020
    “…The algorithms include Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), Classification And Regression Tree (CART), Random Forest (RF). …”
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  17. 17
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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  18. 18
    Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner

    Sentiment Analysis of Omicron COVID-19 Variant using Naïve Bayes Classifier and RapidMiner by Dayu, Wijaya, Leon A., Abdillah

    Published 2023
    “…Then, the comments will be manually labeled followed by classification using the Nave Bayes algorithm and RapidMiner software. …”
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  19. 19
    Analyzing customer reviews for ARBA Travel using sentiment analysis

    Analyzing customer reviews for ARBA Travel using sentiment analysis by Abdullah, Nurulain

    Published 2025
    “…Among these, Naive Bayes achieved the highest performance with an accuracy of 93.67% and an F1-score of 93.54%, making it the most effective model for sentiment classification in this context. …”
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  20. 20
    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.]

    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.] by Mohd Fuzi, Mohd Faris, Mohd Shahirudin, Syamir, Abd Halim, Iman Hazwam, Jamaluddin, Muhammad Nabil Fikri

    Published 2023
    “…The malware classification was determined by testing and training the supervised ML algorithms using the extracted features from the malware dataset. …”
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