Search Results - predicting users preference ((optimization algorithm) OR (optimisation algorithm))

Search alternatives:

  • Showing 1 - 5 results of 5
Refine Results
  1. 1
    Temporal integration based factorization to improve prediction accuracy of collaborative filtering

    Temporal integration based factorization to improve prediction accuracy of collaborative filtering by Al-Qasem, Al-Hadi Ismail Ahmed

    Published 2016
    “…The TemporalMF++ approach relies on the k-means algorithm and the bacterial foraging optimization algorithm. …”
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm

    Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm by Shah, Abdul Salam, Mohamad Nasir, Haidawati, Fayaz, Muhammad, Lajis, Adidah, Ullah, Israr, Shah, Asadullah

    Published 2020
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  3. 3
    Stock indicator scanner customization tool using deep reinforcement learning

    Stock indicator scanner customization tool using deep reinforcement learning by Cheong, Desmond YongHong

    Published 2022
    “…User cannot use the indicator that they prefer to make the prediction. …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
    Save to List
    Saved in:
  4. 4
    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks

    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks by Mubarak, Mohammed Awadh Ahmed Ben

    Published 2013
    “…It introduces four main schemes to optimize the handover mechanism. The first scheme is Mobile Station Movement Direction Prediction (MMDP) based handover scanning that reduces the redundant scanning activities, scanning delay and signalling overhead. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  5. 5
    Grey number based methodology for non-homogeneous preference elicitation in fuzzy risk analysis management

    Grey number based methodology for non-homogeneous preference elicitation in fuzzy risk analysis management by Abu Bakar, Ahmad Syafadhli, Ku Khalif, Ku Muhammad Naim, Yaakob, Abdul Malek, Gegov, Alexander, Mohamad Amin, Ahmad Zaki

    Published 2019
    Get full text
    Get full text
    Get full text
    Book Section
    Save to List
    Saved in:

Search Tools: