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  1. 1
    A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani

    A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

    Published 2016
    “…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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  2. 2
    MRI brain tumor detection methods using contourlet transform based on time adaptive self-organizing map

    MRI brain tumor detection methods using contourlet transform based on time adaptive self-organizing map by Ali Farzamnia, Seyed Hamidreza Hazaveh, Seyede Safieh Siadat, Ervin Gubin Moung

    Published 2023
    “…In this article, we propose a novel method that uses contourlet transform and time adaptive self-organizing map, optimized by the whale optimization algorithm, in order to distinguish between benign and malignant brain tumors in MRI images. …”
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  3. 3
    Development of rheumatoid arthritis classification from electronic image sensor using ensemble method

    Development of rheumatoid arthritis classification from electronic image sensor using ensemble method by Sharon, H., Elamvazuthi, I., Lu, C.-K., Parasuraman, S., Natarajan, E.

    Published 2020
    “…The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. …”
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  4. 4
    FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES

    FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES by MD HASIM, SITI MASTURA

    Published 2013
    “…Image segmentation will be done after pre-processing completed to further enhance the image according to its edge or region to be used for the input image. Different methods of features extraction will be used and compared for better outcome. …”
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  5. 5
    Detection of acute leukaemia cells using variety of features and neural networks

    Detection of acute leukaemia cells using variety of features and neural networks by A. S., Abdul Nasir, Mohd Yusoff, Mashor, Prof. Dr., H., Rosline

    Published 2011
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  6. 6
    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm

    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm by Mohamed, M. E, Samir, B. B., Azween, Abdullah

    Published 2010
    “…A new non classification algorithm was developed based on the danger theory model of human immune system (HIS).The abstract model of system algorithm is inspired from HIS cell mechanism mainly, the Dendritic cell behavior and T-cell mechanisms. …”
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    Citation Index Journal
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  7. 7
    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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  8. 8
    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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  9. 9
    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition by Nur Farahaina, Idris, Mohd Arfian, Ismail

    Published 2021
    “…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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  10. 10
    Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia

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

    Published 2022
    “…This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. …”
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  11. 11
    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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  12. 12
    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
    “…In total, 601 features were extracted from each blast cell, and its nucleus: 31of these were shape-based features, while 534 were texture-based features and 36 were color-based features. …”
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  13. 13
    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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  14. 14
    New Algorithm of Location Model based on Robust Estimators and Smoothing Approach

    New Algorithm of Location Model based on Robust Estimators and Smoothing Approach by Hamid, Hashibah

    Published 2017
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  15. 15
    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms

    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

    Published 2021
    “…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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  16. 16
    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System by Hormozi, Shahram Golzari

    Published 2011
    “…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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  17. 17
    Classification of Diabetes Mellitus using Ensemble Algorithms

    Classification of Diabetes Mellitus using Ensemble Algorithms by Noor, N.A.B.S., Elamvazuthi, I., Yahya, N.

    Published 2021
    “…The objective of this study is to perform DM classification using various machine learning algorithms. …”
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  18. 18
    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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  19. 19
    Validation on an enhanced dendrite cell algorithm using statistical analysis

    Validation on an enhanced dendrite cell algorithm using statistical analysis by Mohamad Mohsin, Mohamad Farhan, Hamdan, Abdul Razak, Abu Bakar, Azuraliza, Abd Wahab, Mohd Helmy

    Published 2017
    “…In practice, most of studies apply a straightforward approach for evaluation where appropriate performance metrics such as classification accuracy is selected, computes the mean and its variance over several repetitive experiments, and then compares it with the base algorithm or other comparative approach. …”
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  20. 20
    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
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