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  1. 1
    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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  2. 2
    Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management

    Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management by Khan, Fazeel Ahmed, Abubakar, Adamu

    Published 2024
    “…This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). …”
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  3. 3
    CUDA implementation of fractal image compression

    CUDA implementation of fractal image compression by Al Sideiri A., Alzeidi N., Al Hammoshi M., Chauhan M.S., AlFarsi G.

    Published 2023
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  4. 4
    Comparative analysis for topic classification in juz Al-Baqarah

    Comparative analysis for topic classification in juz Al-Baqarah by Rahman, Mohamad Izzuddin, Samsudin, Noor Azah, Mustapha, Aida, Abdullahi Oyekunle, Adeleke

    Published 2018
    “…The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. …”
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  5. 5
    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms

    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

    Published 2020
    “…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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  6. 6
    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line

    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

    Published 2020
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  7. 7
    Wearable based-sensor fall detection system using machine learning algorithm

    Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

    Published 2021
    “…As for future work, the plan is to convert the code to an app to run on the smartphone so it can be commercialized.…”
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  8. 8
    Dynamic force-directed graph with weighted nodes for scholar network visualization

    Dynamic force-directed graph with weighted nodes for scholar network visualization by Mohd. Aris, Khalid Al-Walid, Ramasamy, Chitra, Mohd Aris, Teh Noranis, Zolkepli, Maslina

    Published 2022
    “…The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. …”
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  9. 9
    XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection

    XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

    Published 2024
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  10. 10
    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider

    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider by Omer Fadlelmola Elkhider, Samih

    Published 2011
    “…The studies show beyond technical algorithms, physical aspects plays a big rule in client server model. …”
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