Search Results - (( using vector method algorithm ) OR ( automatic classification techniques algorithm ))

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  1. 1

    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
    “…In order to utilize the supervised classification algorithms without consuming a lot of time for labeling data manually, a two step method which selects the training data automatically has been proposed in previous studies. …”
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    Article
  2. 2

    Plant leaf recognition algorithm using ant colony-based feature extraction technique by Ghasab, Mohammad Ali Jan

    Published 2013
    “…The aim of this research is to contribute to the measurement of physiological dimensions of plant leaves by the proposed Auto-Measure algorithm to operate in an automatical manner which inherently requires an improvement in automatic feature extraction. …”
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    Thesis
  3. 3

    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

    Published 2024
    “…The third proposed segmentation algorithm involves two pipelines for feature extraction: split & merge methods and Contextual Encoding Network (EncNet) with an attention mechanism. …”
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    Thesis
  4. 4

    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
    “…Objectives: Automatic text classification techniques are useful for classifying plaintext medical documents. …”
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    Article
  5. 5

    Quranic diacritic and character segmentation and recognition using flood fill and k-nearest neighbors algorithm by Alotaibi, Faiz E A L

    Published 2019
    “…For classification, KNN used before and after enhancement technique based on essential vector with our dataset, the diacritic accuracy was 96.4286% after enhancement, which is better than the 87.5020% in detecting. …”
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    Thesis
  6. 6

    Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image by Shojanoori, Razieh

    Published 2016
    “…The method of maximum likelihood classification and support vector machines leads to the lowest classification accuracy since these algorithms extract only the spectral information of each pixel and consequently fail to utilize spatial, color and textural information.…”
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    Thesis
  7. 7

    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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    Thesis
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    Towards Practical Face Recognition System Employing Row-Based Distance Method In 2dpca Based Algorithms by Al-Arashi, Waled Hussein Mohammed

    Published 2014
    “…In the midst of these techniques, subspace methods are considered the most popular and powerful techniques. …”
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    Thesis
  11. 11

    HEP-2 CELL FEATURE EXTRACTION USING WAVELET AND INDEPENDENT COMPONENT ANALYSIS by NUR THANIA AWATIF BINTI MOHAMAD ROSDI, NUR THANIA AWATIF BINTI MOHAMAD ROSDI

    Published 2013
    “…Then Support Vector Machine (SVM) is used to perform the classifications parts using the features extracted from both methods. …”
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    Final Year Project
  12. 12

    A preliminary study on automated freshwater algae recognition and classification system / Hayat Mansoor Abdullah by Mansoor Abdullah, Hayat

    Published 2012
    “…Then, Image segmentation applied by using canny edge detection algorithm with specific morphological operation to isolate the image objects components. …”
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    Thesis
  13. 13

    Multi-sensor fusion based on multiple classifier systems for human activity identification by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Alo, Uzoma Rita, Al-garadi, Mohammed Ali

    Published 2019
    “…To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. …”
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    Article
  14. 14

    Enhanced extreme learning machine for general regression and classification tasks by Mahmood, Saif F

    Published 2020
    “…The method is developed for regression task by using mean/ median of ELM training errors which is then used as threshold for separating the training data and converting the continuous targets to binary. …”
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    Thesis
  15. 15

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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    Thesis
  16. 16

    Classification of transient disturbance using Wavelet based support vector machine / Fahteem Hamamy Anuwar by Anuwar, Fahteem Hamamy

    Published 2012
    “…After the feature extraction stage, Support Vector Machine (SVM) is used to classify the transient disturbance waveforms. …”
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    Thesis
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    An enhanced gated recurrent unit with auto-encoder for solving text classification problems by Zulqarnain, Muhammad

    Published 2020
    “…Classification has become an important task for categorizing documents automatically based on their respective groups. …”
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    Thesis
  18. 18

    Automatic classification of medical x-ray images by Zare, M.R., Seng, W.C., Mueen, A.

    Published 2013
    “…Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. …”
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    Article
  19. 19

    Predictive analytics for the sentiment of malaysian place of interest using machine learning models by Qiryn Adriana, Kharul Zaman

    Published 2023
    “…The data was then divided into training and testing sets, and was trained using three different supervised learning algorithms, namely Support Vector Machine, Random Forest, and Naive Bayes. …”
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    Undergraduates Project Papers
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