Search Results - (( sequence classification modified algorithm ) OR ( code classification mining algorithm ))

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  1. 1
    Parallel implementation of genomic sequences classification using modified gabor wavelet transform on multicore systems

    Parallel implementation of genomic sequences classification using modified gabor wavelet transform on multicore systems by Gunawan, Teddy Surya, Ambikairajah, Eliathamby

    Published 2012
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  2. 2
    Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer

    Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer by Tuerxun, Adilijiang

    Published 2017
    “…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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  3. 3
    Comparative analysis for topic classification in juz Al-Baqarah

    Comparative analysis for topic classification in juz Al-Baqarah by Rahman, Mohamad Izzuddin, Samsudin, Noor Azah, Mustapha, Aida, Abdullahi Oyekunle, Adeleke

    Published 2018
    “…The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. …”
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  4. 4
    Parallel implementation of genomic sequences classification using modified Gabor wavelet transform on multicore systems

    Parallel implementation of genomic sequences classification using modified Gabor wavelet transform on multicore systems by Gunawan, Teddy Surya, Ambikairajah, Eliathamby

    Published 2012
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  5. 5
    Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System

    Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System by Muhammad Qasim, Khan

    Published 2019
    “…This model implements Non-Recursive Phasor Estimation (NRPE) algorithm. Secondly, the study proposes an index for faulty node classification (FNC) based on the positive sequence voltage (PSV) measurements of the pre-fault and post-fault conditions. …”
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  6. 6
    Classification of metamorphic virus using n-grams signatures

    Classification of metamorphic virus using n-grams signatures by A Hamid, Isredza Rahmi, Md Sani, Nur Sakinah, Abdullah, Zubaile, Mohd Foozy, Cik Feresa, Kipli, Kuryati

    Published 2020
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  7. 7
    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool

    Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

    Published 2018
    “…No EEG studies in Malaysia has been done on school children to study their emotional behaviour while learning. Classification and prediction are the functions provided by the data mining techniques that suit in EEG signal processing. …”
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  8. 8
    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
    “…In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. …”
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  9. 9
    A fuzzy approach for early human action detection / Ekta Vats

    A fuzzy approach for early human action detection / Ekta Vats by Ekta, Vats

    Published 2016
    “…In order to perform early human action detection, the conventional classification problem is modified into frame-by-frame level classification. …”
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  10. 10
    An enhanced android botnet detection approach using feature refinement

    An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

    Published 2019
    “…The experimental and statistical tests show that 97.28% accuracy achieved by Random Forest machine classifier, it performs well as compared to other classification algorithms. Based on the test results, various open research issues which need to be addressed in future studies are highlighted.…”
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