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  1. 1
    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
    “…In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. …”
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  2. 2
    Weighting method for modal parameter based damage detection algorithms

    Weighting method for modal parameter based damage detection algorithms by Fayyadh, M.M., Abdul Razak, H.

    Published 2011
    “…This study proposes weighting method for damage detection algorithms based on the bending mode shapes for beam structures. …”
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  3. 3
    Methods of intrusion detection in information security incident detection: a comparative study

    Methods of intrusion detection in information security incident detection: a comparative study by Tan, Fui Bee, Yau, Ti Dun, M. N. M., Kahar

    Published 2018
    “…The objective of this paper is to study and make a comparison among the available intrusion detections algorithms and methods in the intrusion detections. …”
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  4. 4
    Multi road marking detection system for autonomous car using hybrid- based method

    Multi road marking detection system for autonomous car using hybrid- based method by Shah, Khan Bahadur

    Published 2018
    “…The proposed system consists of a combination of Inverse Perspective Transform method, an image enhancement method and edge detection method. …”
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  5. 5
    Assessment of CDA and Morphlink-C Algorithms method in crack detection using [Multirotor DJ1 phantom 3] image / Muhammad Hafizi Abd Rahaim

    Assessment of CDA and Morphlink-C Algorithms method in crack detection using [Multirotor DJ1 phantom 3] image / Muhammad Hafizi Abd Rahaim by Abd Rahaim, Muhammad Hafizi

    Published 2018
    “…These data model then was evaluated their availability of processed image to detect crack using CDA and MorphLink-C algorithms. …”
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  6. 6
    Comparison between reverse and traditional screening algorithms for syphilis diagnosis in Hospital Universiti Sains Malaysia

    Comparison between reverse and traditional screening algorithms for syphilis diagnosis in Hospital Universiti Sains Malaysia by Sidek, Nurul Azira

    Published 2021
    “…Introduction: The global incidence of syphilis increased drastically throughout the world, including Malaysia. Screening algorithm with high sensitivity and specificity should be available to detect syphilis accurately. …”
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  7. 7
    Detection of in-car-abandoned children via deep learning algorithm / Mohd Farhan Mohd Pauzi

    Detection of in-car-abandoned children via deep learning algorithm / Mohd Farhan Mohd Pauzi by Mohd Pauzi, Mohd Farhan

    Published 2022
    “…This problem occurs due to lacking of existing system in detecting children image in a car. Therefore, this study aims to detect the existence of "in-car-abandoned children" using deep learning algorithm. …”
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  8. 8
    Experimental evaluation of data fusion algorithm for residual generation in detecting UAV servo actuator fault

    Experimental evaluation of data fusion algorithm for residual generation in detecting UAV servo actuator fault by Sahwee Z., Rahman N.A., Sahari K.S.M.

    Published 2023
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  9. 9
    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer

    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

    Published 2023
    “…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. …”
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  10. 10
    Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level

    Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

    Published 2024
    thesis::doctoral thesis
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  11. 11
    Unsupervised Anomaly Detection with Unlabeled Data Using Clustering

    Unsupervised Anomaly Detection with Unlabeled Data Using Clustering by Chimphlee, Witcha, Abdullah, Abdul Hanan, Md. Sap, Mohd. Noor

    Published 2005
    “…We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. …”
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  12. 12
    An intelligent DDoS attack detection tree-based model using Gini index feature selection method

    An intelligent DDoS attack detection tree-based model using Gini index feature selection method by Bouke, Mohamed Aly, Abdullah, Azizol, ALshatebi, Sameer Hamoud, Abdullah, Mohd Taufik, Atigh, Hayate El

    Published 2023
    “…This paper proposes a novel intelligent DDoS attack detection model based on a Decision Tee (DT) algorithm and an enhanced Gini index feature selection method. …”
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  13. 13
    Towards robust skin colour detection and tracking

    Towards robust skin colour detection and tracking by Gamage, Nuwan, Akmeliawati, Rini, Chow, Kuang Ye

    Published 2009
    “…Accuracy and performance of skin detection and tracking highly depends on the skin model in use and the tracking method. …”
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    Proceeding Paper
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  14. 14
    An ensemble feature selection method to detect web spam

    An ensemble feature selection method to detect web spam by Oskouei, Mahdieh Danandeh, Razavi, Seyed Naser

    Published 2018
    “…An ensemble feature selection method has been proposed in this paper to detect web spam. …”
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  15. 15
    Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine

    Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine by Fathi Mahdi Elsiddig Haroun, Mr.

    Published 2023
    “…The SVM algorithm has been used to detect high- and low-density vegetation regions from the extracted ROI. …”
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  16. 16
    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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  17. 17
    Methods and challenges in shot boundary detection: a review

    Methods and challenges in shot boundary detection: a review by Abdulhussain, Sadiq H., Ramli, Abd Rahman, Saripan, M. Iqbal, Mahmmod, Basheera M., Al-Haddad, Syed Abdul Rahman, Wissam, A. Jassim

    Published 2018
    “…The recent increase in the number of videos available in cyberspace is due to the availability of multimedia devices, highly developed communication technologies, and low-cost storage devices. …”
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  18. 18
    Development of automatic obscene images filtering using deep learning

    Development of automatic obscene images filtering using deep learning by Awad, Abdelrahman Mohamed, Gunawan, Teddy Surya, Habaebi, Mohamed Hadi, Ismail, Nanang

    Published 2021
    “…The whole recognition process can be divided into two stages, including fine detection and focus detection. The fine detection includes skin color detection with YCbCr and HSV color spaces and accurate face detection using the Adaboost algorithm with Haar-like features. …”
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  19. 19
    LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO

    LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO by MAHABADI, SEYED ALI KASAEIZADEH MAHABADI

    Published 2010
    “…Two stages of preprocessing which are noise reduction and edge detection have been compared with respect to different methods available. …”
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  20. 20
    Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image

    Object-based imagery analysis for automatic urban tree species detection using high resolution satellite image by Shojanoori, Razieh

    Published 2016
    “…The main goal of this research is to build generic rule from World View-2 satellite imagery in conjunction with spectral, spatial, color and textural information, which is extracted from available training data for tree species detection. …”
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