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  1. 1
    A malware analysis and detection system for mobile devices / Ali Feizollah

    A malware analysis and detection system for mobile devices / Ali Feizollah by Ali, Feizollah

    Published 2017
    “…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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  2. 2
    Detection and extraction features for signatures images via different techniques

    Detection and extraction features for signatures images via different techniques by Fatma Susilawati, M., Alsuhimat, F.M., Iqtait, M.

    Published 2019
    “…Three techniques have been applied using (UTSig) dataset; the results showed that the BRISK algorithm got the best result among the feature detection algorithm in terms of accuracy and the FAST algorithm got the best result among the feature detection algorithm in terms of run time.…”
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  3. 3
    Enhanced feature selections of Adaboost training for face detection using genetic algorithm

    Enhanced feature selections of Adaboost training for face detection using genetic algorithm by Mohd. Zin, Zalhan

    Published 2007
    “…Often some of these features are irrelevant and do not contribute directly to the face detection techniques. …”
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  4. 4
    Feature selection in intrusion detection, state of the art: A review

    Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

    Published 2016
    “…These input features give information to the learning algorithms which used in intrusion detection system in the form of the detection method. …”
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  5. 5
    Facial Feature Extraction Based on Improved Harris Corner Detection Algorithm

    Facial Feature Extraction Based on Improved Harris Corner Detection Algorithm by Bagherian, Elhaam

    Published 2011
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  6. 6
    An amplitude independent muscle activity detection algorithm based on adaptive zero crossing technique and mean instantaneous frequency of the sEMG signal

    An amplitude independent muscle activity detection algorithm based on adaptive zero crossing technique and mean instantaneous frequency of the sEMG signal by Hameed, Husamuldeen Khalid, Wan Hasan, Wan Zuha, Shafie, Suhaidi, Ahmad, Siti Anom, Jaafar, Haslina

    Published 2017
    “…The algorithm does not employ any amplitude features in the detection process and employs only frequency features of the sEMG signal; therefore it is amplitude independent and can detect muscle activities in signals that have low signal to noise ratio. …”
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  7. 7
    Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost)

    Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) by Mohd. Zin, Zalhan, Khalid, Marzuki, Yusof, Rubiyah

    Published 2007
    “…Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. …”
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  8. 8
    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi

    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

    Published 2019
    “…Further, the shortage of efficient feature selection techniques gives rise to low accuracy in anomaly detection. …”
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  9. 9
    SVM for network anomaly detection using ACO feature subset

    SVM for network anomaly detection using ACO feature subset by Mehmood, T., Rais, H.B.M.

    Published 2016
    “…But irrelevant and redundant features are the obstacle for classification algorithm to build an efficient detection model. …”
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  10. 10
    Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques

    Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

    Published 2011
    “…The transductive conformal prediction and outlier detection have been employed for feature selection algorithm. …”
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  11. 11
    K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm

    K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm by Al-Hafiz, Ali Raheem, Jabir, Adnan J., Subramaniam, Shamala

    Published 2025
    “…From the results, an accuracy of 99% is achieved using the voting ensemble technique with feature selection compared with 77.3% without feature selection. …”
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  12. 12
    Feature selection algorithms for Malaysian dengue outbreak detection model

    Feature selection algorithms for Malaysian dengue outbreak detection model by Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali

    Published 2017
    “…Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. …”
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  13. 13
    Classification Algorithms and Feature Selection Techniques for a Hybrid Diabetes Detection System

    Classification Algorithms and Feature Selection Techniques for a Hybrid Diabetes Detection System by Al-Hameli, Bassam Abdo, Alsewari, Abdulrahman A., Alraddadi, Abdulaziz Saleh, Aldhaqm, Arafat

    Published 2021
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  14. 14
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…Recently, various techniques based on different algorithms have been developed. …”
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  15. 15
    Bio-inspired for Features Optimization and Malware Detection

    Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

    Published 2018
    “…This technique shows that the use of Android permissions is a potential feature for malware detection. …”
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  16. 16
    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 state-of-the-art shows that most of the feature reduction techniques, if not all, which have been implemented for the damage detection are feature selection not extraction. …”
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  17. 17
    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

    Published 2023
    “…Android (operating system); Android malware; Classification (of information); Feature Selection; Learning systems; Mobile security; Android apps; Classification models; Feature weight; Features selection; Machine learning algorithms; Machine-learning; Malware detection; Malwares; Memory usage; Selection techniques; Learning algorithms…”
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  18. 18
    Feature-based face recognition system using utilized artificial neural network

    Feature-based face recognition system using utilized artificial neural network by Chai, Tong Yuen

    Published 2010
    “…A proposed algorithm which combines RGB color map and corner detection techniques will detect the mouth corners. …”
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  19. 19
    Bio-inspired for Features Optimization and Malware Detection

    Bio-inspired for Features Optimization and Malware Detection by Razak, Mohd Faizal Ab, Anuar, Nor Badrul, Othman, Fazidah, Firdaus, Ahmad, Afifi, Firdaus, Salleh, Rosli

    Published 2018
    “…This technique shows that the use of Android permissions is a potential feature for malware detection. …”
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  20. 20
    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). …”
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