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  1. 1
    Wormhole attack detection mechanism in mobile ad hoc network using neighborhood information and path tracing algorithm

    Wormhole attack detection mechanism in mobile ad hoc network using neighborhood information and path tracing algorithm by Enshaei, Mehdi

    Published 2015
    “…Path Tracing (PT) Algorithm was proposed to detect and prevent exposed wormhole attacks in MANET. …”
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  2. 2
    QoS evaluation of different TCPs congestion control algorithm using NS2

    QoS evaluation of different TCPs congestion control algorithm using NS2 by Tarmizi, M., Albagul, A., Khalifa, Othman Omran, Wahyudi, Martono

    Published 2006
    “…The second is RED (Random Early Detection), which drops incoming packets at a certain probability.…”
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  3. 3
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  4. 4
    Performance study of large block forward error correction with random early detection queue policy

    Performance study of large block forward error correction with random early detection queue policy by Almomani, Omar, Ghazali, Osman, Hassan, Suhaidi

    Published 2009
    “…In this paper, we report an investigation on performance of the forward error correction (FEC) with random early detection (RED) queuing policy. FEC is a technique that uses redundant packet to reconstruct the dropped packet, while RED is an active queue management algorithm. …”
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  5. 5
    Rain streak removal using emboss and spatial-temporal depth filtering technique in video keyframes

    Rain streak removal using emboss and spatial-temporal depth filtering technique in video keyframes by Shariah, Sawsan Kamel

    Published 2012
    “…The results obtained were satisfying, where the algorithm were able to detect and remove the rain streaks without losing the aliveness of the scene. …”
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  6. 6
    CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets

    CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets by Alaidaros, Hashem Mohammed

    Published 2017
    “…In addition, it is also aimed to improve the resource consumption of the packet-based detection approach. CHID applied attribute wrapper features evaluation algorithms that marked malicious flows for further analysis by the packet-based detection. …”
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  7. 7
    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS

    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS by AZMAN, MOHAMAD AFIQ

    Published 2014
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  8. 8
    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS

    DEVELOPMENT OF NODE RELIABILITY DETECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS by AZMAN, MOHAMAD AFIQ

    Published 2014
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  9. 9
    Automated traffic counting data collection and analysis

    Automated traffic counting data collection and analysis by Low, Anand Hong Ren

    Published 2021
    “…However, the accuracy is dropped to 50% when the condition is dark. This is due to the system is unable to detect the vehicle in such condition.…”
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  10. 10
    Prevention And Detection Mechanism For Security In Passive Rfid System

    Prevention And Detection Mechanism For Security In Passive Rfid System by Khor, Jing Huey

    Published 2013
    “…A GUI is created in a form of JAVA application to display data detected from tag. …”
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  11. 11
    An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability

    An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability by Bouke, Mohamed Aly, Abdullah, Azizol

    Published 2023
    “…Our study uses three well-known intrusion detection datasets: NSL-KDD, UNSW-NB15, and KDDCUP99. …”
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  12. 12
    Adaptive unified neural network for dynamic power quality compensation

    Adaptive unified neural network for dynamic power quality compensation by Ghazanfarpour, Behzad, Mohd Radzi, Mohd Amran, Mariun, Norman, Shoorangiz, Reza

    Published 2013
    “…For compensating the fault, another controller plant is implemented that uses Levenberg-Marquardt backpropagation algorithm. …”
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  13. 13
    Automatic generation of content security policy to mitigate cross site scripting

    Automatic generation of content security policy to mitigate cross site scripting by Mhana, Samer Attallah, Din, Jamilah, Atan, Rodziah

    Published 2016
    “…The algorithm is implemented as a plugin. It does not interfere with the web application original code. …”
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  14. 14
    Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications

    Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications by Lim , Chee Peng

    Published 2016
    “…In this report, the research on welding defect detection and classification using radiograph images is presented. …”
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  15. 15
    Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease

    Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease by Temidayo Oluwatosin Omotehinwa, David Opeoluwa Oyewola, Ervin Gubin Moung

    Published 2024
    “…This aimed to optimize the Light Gradient-Boosting Machine (LightGBM) algorithm to enhance its performance and accuracy in the early detection of CHD, providing a reliable, cost-effective, and non-invasive diagnostic tool. …”
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  16. 16
    Performance enhancement of DCCP TCP - like over long delay link networks

    Performance enhancement of DCCP TCP - like over long delay link networks by Awang Nor, Shahrudin, Hassan, Suhaidi, Ghazali, Osman, M. Kadhum, Mohammed, Omar, Mohd Hasbullah

    Published 2012
    “…Instead of halving the current congestion window when congestion events are detected, the reduction of current congestion window drop has been shown to improve the DCCP TCP-like throughput with minimal drop packet percentage.…”
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  17. 17
    Cloud-based lightweight detection of hardhat compliance based on YOLOv5 in power construction site

    Cloud-based lightweight detection of hardhat compliance based on YOLOv5 in power construction site by Wanbo, Luo

    Published 2025
    “…Furthermore, it achieved approximately 50 Frames per Second (FPS) using a live camera, meeting the real-time detection requirements. …”
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  18. 18
    ACCURACY ENHANCEMENT OF OMR FOR EXAM MARKING WITHOUT PRE-SET TEMPLATE USING IMAGE PROCESSING

    ACCURACY ENHANCEMENT OF OMR FOR EXAM MARKING WITHOUT PRE-SET TEMPLATE USING IMAGE PROCESSING by Tow, Jingyi

    Published 2019
    “…However, studies so far have achieved relatively low accuracy and poor consistency unless a fixed OMR form design is used. Accuracy drops with more OMR questions. Therefore, this study investigate means to improve OMR marking accuracy using enhanced algorithm designed for OMR marking. …”
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  19. 19
    Improving robotic grasping system using deep learning approach

    Improving robotic grasping system using deep learning approach by Mohannad K. H., Farag

    Published 2020
    “…This research aimed to develop a Deep Learning grasp detection model and a slip detection algorithm and integrating them into one innovative robotic grasping system. …”
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  20. 20
    Indoor occupancy detection using machine learning and environmental sensors / Akindele Segun Afolabi ... [et al.]

    Indoor occupancy detection using machine learning and environmental sensors / Akindele Segun Afolabi ... [et al.] by Afolabi, Akindele Segun, Akinola, Olubunmi Adewale, Odetoye, Oyinlolu Ayomidotun, Adetiba, Emmanuel

    Published 2025
    “…In this paper, we experimentally determined the Machine Learning (ML) models that are most robust for use in indoor occupancy detection. This is important because the activities of human subjects in an ML environment are capable of disrupting the data available to some deployed ML models, which might cause the performance of such models to drop. …”
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