Search Results - (( java application mining algorithm ) OR ( vowel classification problems algorithm ))

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  1. 1
    Multinomial logistic regression probability ratio-based feature vectors for Malay vowel recognition

    Multinomial logistic regression probability ratio-based feature vectors for Malay vowel recognition by Atanda, Abdulwahab Funsho

    Published 2021
    “…The performance of Malay vowel recognition (MVR) like any multiclass classification problem depends largely on Feature Vectors (FVs). …”
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  2. 2
    Voiceless Bangla vowel recognition using sEMG signal

    Voiceless Bangla vowel recognition using sEMG signal by Sheikh Shanawaz, Mostafa, Mohiuddin, Ahmad, Mohd Abdur, Rashid

    Published 2016
    “…After that, these prominent features subset is applied in the Artificial Neural Network for vowel classification. This novel Bangla vowel classification method can offer a significant contribution in voice synthesis as well as in speech communication. …”
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  3. 3
    Rule-based embedded HMMs phoneme classification to improve Qur’anic recitation recognition

    Rule-based embedded HMMs phoneme classification to improve Qur’anic recitation recognition by Mohammed Ali Alqadasi, Ammar, Sunar, Mohd Shahrizal, Turaev, Sherzod, Abdulghafor, Rawad Abdulkhaleq Abdulmolla, Salam, Md Sah, Alashbi, Abdulaziz Ali Saleh, Ahmed Salem, Ali, Ali, Mohammed A. H.

    Published 2022
    “…This paper introduces a Rule-Based Phoneme Duration Algorithm to improve phoneme classification in Qur’anic recitation. …”
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  4. 4
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  5. 5
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  6. 6
    Study and Implementation of Data Mining in Urban Gardening

    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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  7. 7
    Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani

    Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani by Mirhassani, Seyedmostafa

    Published 2015
    “…In the next step, based on the cepstral features provided by the filterbanks a hierarchical phoneme classification is performed. Systems using the provided features were evaluated in phoneme recognition/classification task. …”
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  8. 8
    Mining Sequential Patterns Using I-PrefixSpan

    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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  9. 9
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  10. 10
    Mining Sequential Patterns using I-PrefixSpan

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

    Published 2008
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  11. 11
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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