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  1. 1
    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid

    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

    Published 2022
    “…This study aims to propose the best machine learning algorithm for predicting mobile network performance. …”
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    Thesis
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  2. 2
    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…From the reviews, it is evident that autonomous system is set to handle finite number of encountered states using finite sequences of actions. In order to learn the optimized states-action policy the self-learning algorithm is developed using hybrid AI algorithm by combining unsupervised weightless neural network, which employs AUTOWiSARD and reinforcement learning algorithm, which employs Q-learning. …”
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  3. 3
    Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques

    Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

    Published 2011
    “…In machine learning algorithm, choosing the most relevant features for each attack is a very important requirement, especially in mobile ad hoc networks where the network topology dynamically changes. …”
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  4. 4
    Mobile phone customers churn prediction by using Elman and Jordan recurrent neural network with reinforcement learning algorithm / Muhammad Syahir Mohd Ribuan

    Mobile phone customers churn prediction by using Elman and Jordan recurrent neural network with reinforcement learning algorithm / Muhammad Syahir Mohd Ribuan by Mohd Ribuan, Muhammad Syahir

    Published 2011
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    Student Project
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  5. 5
    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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  6. 6
    An Analysis of Node/Peer Discovery Approach and Routing Algorithms in Mobile & Traditional Peer-to-Peer Networks

    An Analysis of Node/Peer Discovery Approach and Routing Algorithms in Mobile & Traditional Peer-to-Peer Networks by Abdullahi, Mohamed Omar

    Published 2012
    “…Lessons learned from fixed networks have been applied in cellular network. …”
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  7. 7
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…The simulation is implemented with iFogSim and java programming language. …”
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    Article
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  8. 8
    Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm

    Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm by Tilwari, Valmik, Dimyati, Kaharudin, Hindia, Mhd Nour, Fattouh, Anas, Amiri, Iraj

    Published 2019
    “…The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. …”
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    Article
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  9. 9
    Mobile machine vision for railway surveillance system using deep learning algorithm

    Mobile machine vision for railway surveillance system using deep learning algorithm by Kit, Guan Lim, Daniel Siruno, Min, Keng Tan, Chung, Fan Liau, Sha, Huang, Tze, Kenneth Kin Teo

    Published 2021
    “…In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). …”
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    Proceedings
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  10. 10
    Self-organizing network technique for resource allocation and mobility management in LTE femtocell network / Labeeb Mohsin Abdullah

    Self-organizing network technique for resource allocation and mobility management in LTE femtocell network / Labeeb Mohsin Abdullah by Abdullah, Labeeb Mohsin

    Published 2015
    “…To meet these demands, me 4‘h generation LTE mobile network operators has deployed femtocell access points next to the conventional base station structure. …”
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  11. 11
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  12. 12
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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    Monograph
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  13. 13
    Extreme learning machine for user location prediction in mobile environment

    Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media A., Abu Osman, Md. Tap

    Published 2011
    “…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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  14. 14
    Mobility-aware Offloading Decision For Multi-access Edge Computing In 5g Networks

    Mobility-aware Offloading Decision For Multi-access Edge Computing In 5g Networks by Saeid Jahandar, Lida Kouhalvandi, Ibraheem Shayea, Mustafa Ergen, Marwan Hadri Azmi, Hafizal Mohamad

    Published 2024
    journal::journal article
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  15. 15
    Review on ubiquitous education system with multi-agent synchronization on mobile learning application environment

    Review on ubiquitous education system with multi-agent synchronization on mobile learning application environment by Mwinyi, Amir Kombo, Syed Mohamed, Syed Abdul Rahman Al Haddad, Abdullah, Rusli, Hashim, Shaiful Jahari

    Published 2012
    “…Sync agent which is Multi-agent system is a promising technique which, we believe that, this approach has a potential of increasing the performance of the network and easy learning process by speed up the update process of the mobile learning contents.…”
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    Conference or Workshop Item
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  16. 16
    Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication

    Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication by Ramalingam, Mritha, Sultanuddin, S. J., Nithya, N., Raj, T. F. Michael, Kumar, T. Rajesh, Prasad, S. J. Suji, Al-Ammar, Essam A., Siddique, M. H., Udayakumar, Sridhar

    Published 2022
    “…In this paper, a deep learning algorithm was proposed to ensure the voice call quality of the cellular communication networks. …”
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  17. 17
    Extreme learning machine for user location prediction in mobile environment

    Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media Anugerah, Md. Tap, Abu Osman

    Published 2011
    “…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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  18. 18
    Age And Gender Recognition Mobile App

    Age And Gender Recognition Mobile App by Wee, Quo Lung

    Published 2023
    “…After reviewing existing age and gender recognition mobile app, Convolutional Neural Network (CNN), one of the deep learning algorithms is implement in this proposed system. …”
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    Final Year Project Report / IMRAD
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  19. 19
    Video content adaptation based on user preferences and network bandwidth / Badariyah Bakhtiar

    Video content adaptation based on user preferences and network bandwidth / Badariyah Bakhtiar by Bakhtiar, Badariyah

    Published 2007
    “…Offering mobile services to nomadic users involved the limited display and networking capacity of the mobile devices. …”
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  20. 20
    Artificial intelligent power prediction for efficient resource management of WCDMA mobile network

    Artificial intelligent power prediction for efficient resource management of WCDMA mobile network by Tee Y.K., Tinng S.K., Koh J., David Y.

    Published 2023
    “…The output of SVR will be used by WCDMA mobile network to decide on new service admission. …”
    Conference Paper
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