Search Results - (( gram extraction method algorithm ) OR ( frames extraction path algorithm ))

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  1. 1

    Local DTW coefficients and pitch feature for back-propagation NN digits recognition by Sudirman, R., Salleh, Shahruddin Hussain, Salleh, Sh-Hussain

    Published 2006
    “…The LPC features vectors are aligned between the source frames to the template using our DTW frame fixing (DTW-FF) algorithm. …”
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    Conference or Workshop Item
  2. 2

    Local DTW Coefficients and Pitch Feature for Back-Propagation NN Digits Recognition by Sudirman, Rubita, Salleh, Sh-Hussain, Salleh, Shaharuddin

    Published 2006
    “…The LPC features vectors are aligned between the source frames to the template using our DTW frame fixing (DTW-FF) algorithm. …”
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    Conference or Workshop Item
  3. 3

    NN with DTW-FF Coefficients and Pitch Feature for Speaker Recognition by Sudirman, Rubita, Salleh, Sh-Hussain, Salleh, Shaharuddin

    Published 2006
    “…This paper proposes a new method to extract speech features in a warping path using dynamic programming (DP). …”
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    Article
  4. 4
  5. 5

    Effective query structuring with ranking using named entity categories for XML retrieval by Roko, Abubakar

    Published 2016
    “…The method employs Semantic Tags Extraction (STSE) algorithm to extract semantic tags of an element and Element Enrichment (EERM) algorithm to enrich the elements. …”
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    Thesis
  6. 6

    Reduced Set Kernel Principal Component Analysis (Rskpca) Algorithm for Palm Print Based Mobile Biometric System by Ibrahim, Noor Salwani

    Published 2015
    “…A new approach in feature extraction called Reduced-Set Kernel Principal Component Analysis (RSKPCA) is proposed to speed up the processing in feature extraction. …”
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    Thesis
  7. 7

    Intelligent deep machine learning cyber phishing URL detection based on BERT features extraction by Muna Elsadig, Ashraf Osman Ibrahim Elsayed, Shakila Basheer, Manal Abdullah Alohali, Sara Alshunaifi, Haya Alqahtani, Nihal Alharbi, Wamda Nagmeldin

    Published 2022
    “…Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. …”
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    Article
  8. 8

    Slam-based mapping for object recognition by Loh, Wan Ying

    Published 2018
    “…The SIFT (Scale-Invariant Feature Transform) is used to extract features from the current frame and match with the object database to identify and recognize the object whenever the robot come across the object. …”
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    Final Year Project / Dissertation / Thesis
  9. 9

    Object tracing from synthetic fluid spray through instance segmentation by Md Refat Khan, Pathan

    Published 2024
    “…The resulting objects were then analyzed using a customized nearest neighbor algorithm to calculate their correspondence between frames and a BFS algorithm was used to trace their movement path. …”
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    Thesis
  10. 10

    Improvement on rooftop classification of worldview-3 imagery using object-based image analysis by Norman, Masayu

    Published 2019
    “…Therefore, the LiDAR derived data were combined with WV-3 image using different fusion methods such as layer stacking (LS), Gram–Schmidt (GS), and PC spectral sharpening (PCSS). …”
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    Thesis
  11. 11

    Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study by Mujtaba, Ghulam, Shuib, Liyana, Raj, Ram Gopal, Rajandram, Retnagowri, Shaikh, Khairunisa

    Published 2018
    “…Methods: For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. …”
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    Article
  12. 12

    Semi-supervised learning for sentiment classification with ensemble multi-classifier approach by Aribowo, Agus Sasmito, Basiron, Halizah, Abd Yusof, Noor Fazilla

    Published 2022
    “…The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. …”
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    Article