Search Results - (( mobile location model algorithm ) OR ( level classification issues algorithm ))*

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  1. 1
    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
    “…This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. …”
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  2. 2
    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
    “…This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. …”
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  3. 3
    A coalition model for efficient indexing in wireless sensor network with random mobility / Hazem Jihad Ali Badarneh

    A coalition model for efficient indexing in wireless sensor network with random mobility / Hazem Jihad Ali Badarneh by Hazem Jihad , Ali Badarneh

    Published 2021
    “…First, the high dependability on multi-attributes (location and time) of packets in random mobile sensors. …”
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    Thesis
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  4. 4
    Particle filter approach for estimating indoor mobile user location using IEEE 802.11 signals (F1090)

    Particle filter approach for estimating indoor mobile user location using IEEE 802.11 signals (F1090) by Mantoro, Teddy, Ayu, Media Anugerah, Raman, Shakiratul Husna, Md. Latiff , Nurul Hidayati

    Published 2011
    “…This approach shows a promising result in tracking user location indoor using particle filter algorithm.…”
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    Proceeding Paper
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  5. 5
    Dynamic area coverage algorithms for static and mobile wireless sensor network environments using voronoi techniques

    Dynamic area coverage algorithms for static and mobile wireless sensor network environments using voronoi techniques by Ceesay, Omar M.

    Published 2011
    “…In order to ensure the shortest path movement of mobile nodes to target locations, a Matrix Row/Column Elimination model is proposed. …”
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  6. 6
    Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling

    Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

    Published 2023
    Conference Paper
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  7. 7
    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
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  8. 8
    A new multicast-based architecture to support host mobility in IPv6

    A new multicast-based architecture to support host mobility in IPv6 by M. Ali, Borhanuddin, Habaebi, Mohamed Hadi, AI-Talib, Sahar

    Published 2003
    “…KEYWORDS:. Mobile IPv6, hash algorithm, .multicast group.…”
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  9. 9
    Wifi positioning system / Amalina Abdul Halim

    Wifi positioning system / Amalina Abdul Halim by Abdul Halim, Amalina

    Published 2006
    “…The aim of this project is to develop an outdoor positioning system that can estimate the location of mobile devices based on signal strength that broadcast by access point. …”
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    Student Project
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  10. 10
    Off-the-shelf indoor localization system using radio frequency for wireless local area network

    Off-the-shelf indoor localization system using radio frequency for wireless local area network by Alhammadi, Abdulraqeb Shaif Ahmed

    Published 2018
    “…Finally, the proposed Bayesian graphical model based on fingerprinting location algorithm is compared with Madigan model. …”
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  11. 11
    A new multicast-based architecture to support host mobility in IPv6

    A new multicast-based architecture to support host mobility in IPv6 by Mohd Ali, Borhanuddin, Habaebi, Mohamed Hadi, Al-Talib, Sahar Abdul Aziz

    Published 2003
    “…As the mobile node moves to a new location, it joins the multicast group through the new location and prunes through the old location. …”
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  12. 12
    Performance evaluation of Location-Sensitive Handoff using positioning method / Azuwa Ali and Saiful Fadzli Salian

    Performance evaluation of Location-Sensitive Handoff using positioning method / Azuwa Ali and Saiful Fadzli Salian by Ali, Azuwa, Salian, Saiful Fadzli

    Published 2009
    “…This project investigate the performance of handoff process with the help of location information which is called Location-Sensitive Handoff (L-SH) using the location positioning method. …”
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    Research Reports
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  13. 13
    GSM-WCDMA hybrid localization techniques in multilayer heterogeneous network / Muhammad Naqiuddin Hassan

    GSM-WCDMA hybrid localization techniques in multilayer heterogeneous network / Muhammad Naqiuddin Hassan by Hassan, Muhammad Naqiuddin

    Published 2013
    “…There is a serious problem faced by rescue workers to identify exact location of emergency callers from mobile network. …”
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  14. 14
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. …”
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  15. 15
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. …”
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  16. 16
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. …”
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  17. 17
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. …”
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  18. 18
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. …”
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  19. 19
    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab

    An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

    Published 2020
    “…Firstly, intra-severity classification intended to identify the level of severity of the dysarthric speakers. …”
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  20. 20
    Enhanced handover decision algorithm in heterogeneous wireless network

    Enhanced handover decision algorithm in heterogeneous wireless network by Abdullah, Radhwan Mohammed, Ahmad Zukarnain, Zuriati

    Published 2017
    “…It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. …”
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