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  1. 1
    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks

    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

    Published 2025
    thesis::doctoral thesis
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  2. 2
    Incremental learning for large-scale stream data and its application to cybersecurity

    Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

    Published 2015
    “…To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. …”
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  3. 3
    Mobile application for real time baby sign language recognition using YOLOv8 / Siti Aishah Idris and Ahmad Firdaus Ahmad Fadzil

    Mobile application for real time baby sign language recognition using YOLOv8 / Siti Aishah Idris and Ahmad Firdaus Ahmad Fadzil by Idris, Siti Aishah, Ahmad Fadzil, Ahmad Firdaus

    Published 2024
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  4. 4
    Mobile Learning: An Application Prototype for AVL Tree Learning Object

    Mobile Learning: An Application Prototype for AVL Tree Learning Object by Ibrahim, Mohammad Noor, Mahamad, Saipunidzam, Chua, Edrea Ning Wei

    Published 2010
    “…The rapid growth of mobile phone technologies encourages many researchers to explore, design and develop mobile learning applications for tertiary students. …”
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  5. 5
    Framework for pedestrian walking behaviour recognition to minimize road accident

    Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

    Published 2021
    “…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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  6. 6
    Framework for pedestrian walking behaviour recognition to minimize road accident

    Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

    Published 2021
    “…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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  7. 7
    Mobile Learning: An Application Prototype for AVL Tree Learning Object

    Mobile Learning: An Application Prototype for AVL Tree Learning Object by Ibrahim, Mohammad Noor, Mahamad, Saipunidzam, Chua, Edrea Ning Wei

    Published 2010
    “…The rapid growth of mobile phone technologies encourages many researchers to explore, design and develop mobile learning applications for tertiary students. …”
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  8. 8
    The algorithm is ready to be a Shaykh Murabbi: can we define the social media ethics sometime soon?

    The algorithm is ready to be a Shaykh Murabbi: can we define the social media ethics sometime soon? by Paramboor, Jafar

    Published 2021
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  9. 9
    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
    “…In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. An application called ‘AndroSensor’ on a smartphone, that retrieves real-time data from accelerometer, gyroscope and gravity sensors, is used as the input signals. …”
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  10. 10
    Exploratory research on the impact of Mysejahtera application on to the elderly based on user experience / Nur Aisya Roslan and Saiful Bahar Salim

    Exploratory research on the impact of Mysejahtera application on to the elderly based on user experience / Nur Aisya Roslan and Saiful Bahar Salim by Roslan, Nur Aisya, Salim, Saiful Bahar

    Published 2024
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  11. 11
    Content-based indexing of low resolution documents

    Content-based indexing of low resolution documents by Md Nor, Danial

    Published 2016
    “…The algorithm has the capability of reducing any shortcoming associated with normalisation in initial fusion technique. …”
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  12. 12
    Innovative smart phone learning system for graphical systems within covid-19 pandemic

    Innovative smart phone learning system for graphical systems within covid-19 pandemic by Tawafak R.M., Alfarsi G., Jabbar J.

    Published 2023
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  13. 13
    Distracted driver detection with deep convolution neural networks

    Distracted driver detection with deep convolution neural networks by Basubeit, Omar Gumaan Saleh

    Published 2023
    text::Final Year Project
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  14. 14
    Detecting emotions and depression through voice

    Detecting emotions and depression through voice by Gunawan, Teddy Surya

    Published 2021
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  15. 15
    Malware visualizer: A web apps malware family classification with machine learning

    Malware visualizer: A web apps malware family classification with machine learning by Mohd Zamri, Osman, Ahmad Firdaus, Zainal Abidin, Rahiwan Nazar, Romli

    Published 2021
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  16. 16
    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development by Yusoff, Aiman, Kamarudin, Noraziahtulhidayu, Nabil Ali Al-Emad, Nabil Ali Al-Emad, Sapuan, Khusairi

    Published 2023
    “…The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. …”
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  17. 17
    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development by Yusoff, Aiman, Kamarudin, Noraziahtulhidayu, Al-Emad, Nabil Ali, Sapuan, Khusairi

    “…The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. …”
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  18. 18
    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development by Yusof, Aiman, Kamarudin, Noraziahtulhidayu, Al-Emad, Nabil Ali, Sapuan, Khusairi

    Published 2023
    “…The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. …”
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  19. 19
    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development by Yusof, Aiman, Kamarudin, Noraziahtulhidayu, Al-Emad, Nabil Ali, Sapuan, Khusairi

    Published 2023
    “…The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. …”
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  20. 20
    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

    Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development by Yusoff, Aiman, Kamarudin, Noraziahtulhidayu, Al-Emad, Nabil Ali, Sapuan, Khusairi

    Published 2023
    “…The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. …”
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