Search Results - (( image classification cell algorithm ) OR ( java implementation mining algorithm ))

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  1. 1
    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
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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  2. 2
    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
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  3. 3
    An Investigation of Image and Video Classification Algorithm for White Blood Cells Detection in Real Time View

    An Investigation of Image and Video Classification Algorithm for White Blood Cells Detection in Real Time View by Abu Kasim, Nabeela

    Published 2015
    “…The new approach used in separating it by using image classification algorithm to separate the white blood cells from the blood capillaries and it will be done live from a video. …”
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  4. 4
    Scalable approach for mining association rules from structured XML data

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  5. 5
    Mining association rules from structured XML data

    Mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  6. 6
    HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC

    HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC by Jamil, Nur Farahim

    Published 2014
    “…A working classification algorithm is developed by using MATLAB and the Fuzzy Logic Toolbox to differentiate and classify the staining pattern of HEp-2 cell images. …”
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  7. 7
    FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC

    FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC by ABANG SAZALI, DAYANG FARZANAI

    Published 2013
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  8. 8
    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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  9. 9
    HEp-2 cell images classification based on statistical texture analysis and fuzzy logic

    HEp-2 cell images classification based on statistical texture analysis and fuzzy logic by Jamil, N.F.B., Faye, I., May, Z.

    Published 2014
    “…This paper proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. …”
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  10. 10
    Blood cell image segmentation using unsupervised clustering techniques

    Blood cell image segmentation using unsupervised clustering techniques by Tuan Muda, Tuan Zalizam, Abdul Salam, Rosalina

    Published 2009
    “…The aim of our research is to develop an effective algorithm for segmentation of the blood cell images. …”
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  11. 11
    Classification red blood cells using support vector machine

    Classification red blood cells using support vector machine by Akrimi J.A., Suliman A., George L.E., Ahmad A.R.

    Published 2023
    “…Cells; Classification (of information); Cytology; Diagnosis; Feature extraction; Image processing; Image segmentation; Imaging techniques; Medical imaging; Support vector machines; Classifier algorithms; Clinical diagnosis; Confusion matrices; Image processing technique; Mean Filte; Red blood cell; Retrieval systems; SVM; Blood…”
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  12. 12
    An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

    An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving by Jing, Rui Tang, Mat Isa, Nor Ashidi

    Published 2014
    “…In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.…”
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  13. 13
    Comparative Analysis of Cervical Cell Classification Using Machine Learning Algorithms

    Comparative Analysis of Cervical Cell Classification Using Machine Learning Algorithms by Wan Azani Mustafa, Khalis Khiruddin, Khairur Rijal Jamaludin, Firdaus Yuslan Khusairi, Shahrina Ismail

    Published 2026
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  14. 14
    Modified Piecewise Linear Mapping Contrast Enhancement And Local Otsu Segmentation Methods For Hep-2 Cell Images

    Modified Piecewise Linear Mapping Contrast Enhancement And Local Otsu Segmentation Methods For Hep-2 Cell Images by Baharom, Mohamad Shahrul Affendi

    Published 2019
    “…However, some of the image suffers from texture’s cell loss. On the other hand, the proposed segmentation method capable to segment cell and isolate most of the combined cell. …”
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  15. 15
    Classification Of Cervical Cancer Stage From Pap Smear Tests

    Classification Of Cervical Cancer Stage From Pap Smear Tests by Sendal, Ken Irok

    Published 2019
    “…The performance of the proposed classification algorithm gave satisfactory results of accuracy, 91.9% for KNN classification and 95.0% for SVM classification.…”
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  16. 16
    Red blood cells classification with sharpening segmentation and mask R-CNN

    Red blood cells classification with sharpening segmentation and mask R-CNN by Arianti, Nunik Destria, Muda, Azah Kamilah, Ahmad, Norashikin

    Published 2023
    “…This paper proposes classification using sharpening segmentation combined with the algorithm mask R-CNN to increase the accuracy of calculating the number of RBCs. …”
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  17. 17
    Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab

    Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab by Wahhab, Hayan Tareq Abdul

    Published 2015
    “…Tettamanti Research Center for childhood leukemia and hematological diseases, Italy. The image segmentation addressed several key issues in blast cells segmentation including, the blast cell localization, sub-imaging, color variation and segregation of touching cells. …”
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  18. 18
    An Intelligent Detection System for Rheumatoid Arthritis (RA) Disease using Image Processing

    An Intelligent Detection System for Rheumatoid Arthritis (RA) Disease using Image Processing by Hajyyev, Abdyrahym

    Published 2014
    “…The algorithm tested on the publicly available Mivia Hep-2 Cell image dataset. …”
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  19. 19
    Lung cancer medical images classification using hybrid CNN-SVM

    Lung cancer medical images classification using hybrid CNN-SVM by Abdulrazak Yahya, Saleh, Chee, Ka Chin, Vanessa, Penshie, Hamada Rasheed Hassan, Al-Absi

    Published 2021
    “…This algorithm is capable of automatically classifying and analyzing each lung image to check if there is any presence of cancer cells or not. …”
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  20. 20
    Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia

    Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia by Kiu, Siew Ming

    Published 2018
    “…Overlapping cell identification and classification of microscopic blood cell image is proposed to increase the accuracy of the number of automated counting and the percentage of the overall accuracy. …”
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