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  1. 1
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. …”
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  2. 2
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  3. 3
    Performance evaluation for different intrusion detection system algorithms using machine learning

    Performance evaluation for different intrusion detection system algorithms using machine learning by Zarir, Mustafa Nadhim

    Published 2018
    “…The aim of the Intrusion Detection System (IDS) is to monitor the network system for any sort of attacks. …”
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  4. 4
    An enhancement of classification technique based on rough set theory for intrusion detection system application

    An enhancement of classification technique based on rough set theory for intrusion detection system application by Noor Suhana, Sulaiman

    Published 2019
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  5. 5
    Integrating genetic algorithms and fuzzy c-means for anomaly detection

    Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

    Published 2005
    “…Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. …”
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  6. 6
    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
    “…Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. …”
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  7. 7
    Extended development of a Computer Aided Detection (CAD) system for brain bleed in CT / Muhammad Illyas Abdul Muhji

    Extended development of a Computer Aided Detection (CAD) system for brain bleed in CT / Muhammad Illyas Abdul Muhji by Muhammad Illyas, Abdul Muhji

    Published 2018
    “…Previous study by Leong show the useful of CAD system but her study is not fully automated. Thus the main objective of this study is to implement an automatic algorithm to previous algorithm to make it fully automatic system. …”
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  8. 8
    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
    “…In this paper, three widely used feature detection algorithms, SURF, BRISK and FAST, these algorithms are compared to calculate the processing time and accuracy for set of signatures correctly. …”
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  9. 9
    Obstacle Detection And Avoidance System

    Obstacle Detection And Avoidance System by Yew, Chang Chern

    Published 2018
    “…The algorithm on how the robot car responses when obstacle is detected is also designed. …”
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  10. 10
    Missing tags detection algorithm for radio frequency identification (RFID) data stream

    Missing tags detection algorithm for radio frequency identification (RFID) data stream by Zainudin, Nur 'Aifaa

    Published 2019
    “…Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). …”
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  11. 11
    Face detection using artificial neural network approach

    Face detection using artificial neural network approach by Khalid, Marzuki, Jumari, Khairol Faisal, Nazeer, Shahrin Azuan, Omar, Nazaruddin

    Published 2007
    “…The system also applies the adaboost learning algorithm to select a small number of critical visual features from a very large set of potential features. …”
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  12. 12
    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
    “…Experiments on a set of images from BioID face database proved that by using GA to search on a large number of feature types and sets, the proposed technique referred to as GABoost was able to obtain the cascades of boosted classifiers for the face detection system that can give higher detection rates (94.25%), lower false positive rates (55.94%) and less training time (6.68 hours).…”
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  13. 13
    Evaluation of fall detection classification approaches

    Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

    Published 2012
    “…An accurate automatic fall detection system can help elderly people be safe in every situation. …”
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  14. 14
    Hybrid Classification Algorithm For Denial Of Service Attack Detection Using Rough Set Theory And Artificial Immune

    Hybrid Classification Algorithm For Denial Of Service Attack Detection Using Rough Set Theory And Artificial Immune by Fatin Norsyafawati, Mohd Sabri

    Published 2024
    thesis::master thesis
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  15. 15
    Rough set discretization: equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system

    Rough set discretization: equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system by Noor Suhana, Sulaiman, Rohani, Abu Bakar

    Published 2017
    “…Significant discretization technique suit to the Intrusion Detection System (IDS) data need to determine in IDS framework, since IDS data consist of huge records that need to be examined in system. …”
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  16. 16
    Rough set discretization: Equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system

    Rough set discretization: Equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system by Noor Suhana, Sulaiman, Rohani, Abu Bakar

    Published 2016
    “…Significant discretization technique suit to the Intrusion Detection System (IDS) data need to determine in IDS framework, since IDS data consist of huge records that need to be examined in system. …”
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  17. 17
    Intrusion Detection Systems, Issues, Challenges, and Needs

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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  18. 18
    Hybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system

    Hybrid of rough set theory and Artificial Immune Recognition System as a solution to decrease false alarm rate in intrusion detection system by Sabri F.N.M., Norwawi N.M., Seman K.

    Published 2024
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  19. 19
    Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi

    Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi by Ahmad Tarmizi, Muhammad Danial

    Published 2020
    “…The predictive model success in detecting and classifying the snake. The model achieve 96.89 % of accuracy percentage in training set and 96% of accuracy from testing set. …”
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  20. 20
    Swarm negative selection algorithm for electroencephalogram signals classification

    Swarm negative selection algorithm for electroencephalogram signals classification by Sahel Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita

    Published 2009
    “…Therefore, there is a strong need to automate this process. Such automated systems must rely on robust and effective algorithms for detection and prediction. …”
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