Search Results - probable distribution ((sensor algorithm) OR (bees algorithm))

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  1. 1
    A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems

    A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems by Tam, Jun Hui, Ong, Zhi Chao, Ismail, Zubaidah, Ang, Bee Chin, Khoo, Shin Yee

    Published 2019
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  2. 2
    Energy Efficient LEACH (EE-LEACH) Routing Algorithm for Wireless Sensor Networks

    Energy Efficient LEACH (EE-LEACH) Routing Algorithm for Wireless Sensor Networks by Pillay, Kosheila Sundram

    Published 2019
    “…Therefore, this research work proposes an energy-efficient LEACH (EE-LEACH) algorithm to elect CHs based on residual energy, RSSI, and random probability to distribute the load evenly among the CHs. …”
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  3. 3
    Enhanced ABD-LSSVM for energy fuel price prediction

    Enhanced ABD-LSSVM for energy fuel price prediction by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2013
    “…This paper presents an enhanced Artificial Bee Colony (eABC)based on Lévy Probability Distribution (LPD) and conventional mutation. …”
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  4. 4
    Recent Efficient Iterative Algorithms on Cognitive Radio Cooperative Spectrum Sensing to Improve Reliability and Performance

    Recent Efficient Iterative Algorithms on Cognitive Radio Cooperative Spectrum Sensing to Improve Reliability and Performance by Akbari, M., Reza, A.W., Noordin, K.A., Dimyati, K., Manesh, M.R., Hindia, M.N.

    Published 2016
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  5. 5
    Enhanced ABC-LSSVM For Energy Fuel Price Prediction

    Enhanced ABC-LSSVM For Energy Fuel Price Prediction by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2014
    “…This paper presents an enhanced Artifi cial Bee Colony (eABC) based on Lévy Probability Distribution (LPD) and conventional mutation. …”
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  6. 6
    Energy efficient cluster head distribution in wireless sensor networks

    Energy efficient cluster head distribution in wireless sensor networks by Siew, Zhan Wei

    Published 2013
    “…For network clustering, the distribution of CH selection directly influences the networks lifetime. …”
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  7. 7
    Determining penetration limit of central distributed generation topology in radial distribution networks

    Determining penetration limit of central distributed generation topology in radial distribution networks by Suliman, Mohamed Saad Abdelgadir

    Published 2021
    “…The biogeography based optimization method has been proven to have better performance than artificial bee colony, genetic algorithm, particle swarm optimization, hybrid of particle swarm optimization and constriction factor approach, and hybrid of ant colony optimization and artificial bee colony methods in terms of active power loss reduction. …”
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  8. 8
    Class binarization with self-adaptive algorithm to improve human activity recognition

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…In comparison, WISDM utilizes an accelerometer sensor embedded in Android smartphone. Meanwhile, PAMAP2 utilizes an accelerometer sensor equipped with three Inertial Measurement Unit (IMU) devices attached to three different placements. …”
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  9. 9
    Battery management optimization and lifecycle impact analysis for microgrid operation with V2G implementation / Muhammad Sufyan

    Battery management optimization and lifecycle impact analysis for microgrid operation with V2G implementation / Muhammad Sufyan by Muhammad , Sufyan

    Published 2019
    “…The proposed energy management approach is solved using firefly algorithm, artificial bee colony, harmony search algorithm and particle swarm optimization. …”
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  10. 10
    EFFICIENT DYNAMIC ADDRESSING BASED ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS

    EFFICIENT DYNAMIC ADDRESSING BASED ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS by ARSHAD, MUHAMMAD AYAZ ARSHAD

    Published 2011
    “…Besides long propagation delays and high error probability, continuous node movement also makes it difficult to manage the routing information during the process of data forwarding. …”
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  11. 11
    Prediction of rice biomass using machine learning algorithms

    Prediction of rice biomass using machine learning algorithms by Radhwane, Derraz

    Published 2022
    “…The Q-TESI, C-TESI, and L-TESI overcame the LN-TESI in retaining the features’ original probability distribution, minimising the augmentation loss, reducing the VIF, increasing the rs, and decreasing the DNN under- and overfitting. …”
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