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  1. 1
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
    “…It is a code with double shield protection that prevents unauthorized extraction or damaging the remote database in the server side due to malicious SQL injection. …”
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  2. 2
    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms

    Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms by Rachmad, Iqbal, Tri Basuki, Kurniawan, Misinem, ., Edi Surya, Negara, Tata, Sutabri

    Published 2024
    “…A critical step in developing such intelligent systems is the feature extraction process. Feature extraction is essential in classification, especially for data sources in the form of images. …”
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  3. 3
    Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan

    Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan by Ruslan, Muhammad Rushamir Hakimi

    Published 2017
    “…This study has shown how the Negative Selection Algorithm can diagnose the skin cancer based on the input image and extracted data that has been provided. …”
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    Thesis
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  4. 4
    Genetic algorithm based ensemble framework for sentiment analysis

    Genetic algorithm based ensemble framework for sentiment analysis by Lai, Po Hung

    Published 2018
    “…Machine Learning classification is commonly used in sentiment analysis and it requires plain text documents to be transformed to analyzable data through feature extraction and selection. Feature extraction produces various representations of plain text documents whereas feature selection selects the features that are useful and relevant to the classification task. …”
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  5. 5
    Comparing the performance of FCBF, Chi-Square and relief-F filter feature selection algorithms in educational data mining

    Comparing the performance of FCBF, Chi-Square and relief-F filter feature selection algorithms in educational data mining by Zaffar, M., Hashmani, M.A., Savita, K.S.

    Published 2019
    “…There are many feature selection algorithms, however three filter feature selection algorithms FCBF, Chi-Square, and ReliefF are selected due their better performance, and applied on three different studentâ��s data sets. …”
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  6. 6
    Comparing the performance of FCBF, Chi-Square and relief-F filter feature selection algorithms in educational data mining

    Comparing the performance of FCBF, Chi-Square and relief-F filter feature selection algorithms in educational data mining by Zaffar, M., Hashmani, M.A., Savita, K.S.

    Published 2019
    “…There are many feature selection algorithms, however three filter feature selection algorithms FCBF, Chi-Square, and ReliefF are selected due their better performance, and applied on three different studentâ��s data sets. …”
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  7. 7
    Ant colony optimization for rule induction with simulated annealing for terms selection

    Ant colony optimization for rule induction with simulated annealing for terms selection by Saian, Rizauddin, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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  8. 8
    A new ant based rule extraction algorithm for web classification

    A new ant based rule extraction algorithm for web classification by Ku-Mahamud, Ku Ruhana, Saian, Rizauddin

    Published 2011
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    Monograph
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  9. 9
    Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms

    Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms by Anuar N.K., Bakar A.A., Ahmad A.R., Yussof S., Rahim F.A., Ramli R., Ismail R.

    Published 2023
    “…Data Analytics; Data mining; Decision making; Feature extraction; Machine learning; Predictive analytics; Privacy by design; Features selection; Fine grains; No leakages; Predictive modeling; Privacy preserving; Learning algorithms…”
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  10. 10
    Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme

    Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme by Ashour, Mohammed Waleed, Khalid, Fatimah, Al-Obaydee, Mohammed

    Published 2013
    “…To overcome these problems, an efficient feature extraction and selection technique is required which extracts and reduces the number of selected features and thus improves the classification accuracy. …”
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  11. 11
    Development Of Fall Risk Clustering Algorithm In Older People

    Development Of Fall Risk Clustering Algorithm In Older People by Wong, Kam Kang

    Published 2020
    “…The proposed algorithm consists of several stages, includes data pre-processing, feature selection, feature extraction, clustering and characteristic interpretation. …”
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    Final Year Project / Dissertation / Thesis
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  12. 12
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In cluster generating process, the developed BBSI algorithm was used to select the best band combination for generating cluster by using Iterative self- Organizing Data Analysis (ISODATA) technique. …”
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  13. 13
    Performance comparison of feature selection methods for prediction in medical data

    Performance comparison of feature selection methods for prediction in medical data by Mohd Khalid, Nur Hidayah, Ismail, Amelia Ritahani, Abdul Aziz, Normaziah, Amir Hussin, Amir 'Aatieff

    Published 2023
    “…This study analyzes filter, wrapper, and embedded feature selection methods for medical data with the predictive machine learn- ing algorithm, Random Forest and CatBoost. …”
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    Proceeding Paper
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  14. 14
    Artificial Bee Colony With Differential Evolution Algorithm For Feature Extraction And Selection Of Mass Spectrometry Data

    Artificial Bee Colony With Differential Evolution Algorithm For Feature Extraction And Selection Of Mass Spectrometry Data by Mohamed Yusoff, Syarifah Adilah

    Published 2016
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  15. 15
    Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding

    Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding by Sarwar, Talha, Mohd Noor, Noorhuzaimi Karimah, Miah, M. Saef Ullah

    Published 2022
    “…In this context, keyphrase extraction algorithms can extract information from news articles. …”
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  16. 16
    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…Feature extraction and selection reduces the number of features. …”
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  17. 17
    Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

    Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion by Zafar, R., Dass, S.C., Malik, A.S.

    Published 2017
    “…In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. …”
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  18. 18
    Bayesian Network Classifiers for Damage Detection in Engineering Material

    Bayesian Network Classifiers for Damage Detection in Engineering Material by Mohamed Addin, Addin Osman

    Published 2007
    “…The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). …”
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  19. 19
    Malware Classification and Detection using Variations of Machine Learning Algorithm Models

    Malware Classification and Detection using Variations of Machine Learning Algorithm Models by Andi Maslan, Andi Maslan, Abdul Hamid, Abdul Hamid

    Published 2025
    “…Types of attacks can be Ping of Death, flooding, remote-controlled attacks, UDP flooding, and Smurf Attacks. Attack data was obtained from the ClaMP dataset, which has an unbalanced data set, and has very high noise, so it is necessary to analyze data packets in network logs and optimize feature extraction which is then analyzed statistically with machine learning algorithms. …”
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  20. 20
    The framework of weighted subset-hood Mamdani fuzzy rule based system rule extraction (MFRBS-WSBA) for forecasting electricity load demand

    The framework of weighted subset-hood Mamdani fuzzy rule based system rule extraction (MFRBS-WSBA) for forecasting electricity load demand by Mansor, Rosnalini, Mat Kasim, Maznah, Othman, Mahmod

    Published 2016
    “…Fuzzy rules are very important elements that should be taken consideration seriously when applying any fuzzy system.This paper proposes the framework of Mamdani Fuzzy Rule-based System with Weighted Subset-hood Based Algorithm (MFRBS-WSBA) in the fuzzy rule extraction for electricity load demand forecasting.The framework consist of six main steps: (1) Data Collection and Selection; (2) Preprocessing Data; (3) Variables Selection; (4) Fuzzy Model; (5) Comparison with Other FIS and (6) Performance Evaluation. …”
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