Search Results - (( parallel distribution detection algorithm ) OR ( data distribution sensor algorithm ))

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  1. 1

    Hybrid anti-islanding algorithm for utility interconnection of distributed generation by Abdolrasol, M.G.M., Mekhilef, Saad

    Published 2009
    “…This algorithm is based on two detection schemes; namely active and passive. …”
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  2. 2

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. …”
    Article
  3. 3

    Multithreaded Scalable Matching Algorithm For Intrusion Detection Systems by Hnaif, Adnan Ahmad Abdelfattah

    Published 2010
    “…Therefore, the performance of the existing algorithms needs to be improved both in sequential and parallel to enhance the speed of the detection engine used in SNORT-NIDS. …”
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  4. 4

    Effect Of The Addition Of Wastepaper To Concrete Mix by Shukeri, Ritzawaty Mohamad

    Published 2009
    “…Therefore, the performance of the existing algorithms needs to be improved both in sequential and parallel to enhance the speed of the detection engine used in SNORT-NIDS. …”
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  5. 5

    Scheduled activity energy-aware distributed cluster- based routing algorithm for wireless sensor networks with non-uniform node distribution by Nokhanji, Nooshin

    Published 2014
    “…The main and definitive target of routing protocols in Wireless Sensor Networks (WSN) is passing data from the sensor nodes to the sink in most energy efficient manner besides considering the accuracy of data. …”
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    Thesis
  6. 6

    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

    Published 2023
    “…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. …”
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  7. 7

    Tumor growth prediction using parallel computing: numerical solutions based on multi-dimensional partial differential equation (PDE) by Alias, Norma, Islam, Md. Rajibul

    Published 2010
    “…This study focuses on the implementation of parallel algorithm for the simulation of tumor growth using two dimensional Helmholtz’s wave equation on a distributed parallel computing system. …”
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    Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: a review paper by Qadori, Huthiafa Q., Ahmad Zukarnain, Zuriati, Mohd Hanapi, Zurina, Subramaniam, Shamala

    Published 2017
    “…Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. …”
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    Efficient k-coverage scheduling algorithms for wireless sensor networks / Ahmed Abdullah Saleh Al-Shalabi by Saleh Al-Shalabi, Abdullah

    Published 2014
    “…An efficient k-coverage algorithm for sensors with fixed sensing range (Maximum Layers Scheduling algorithm - MLS) is demonstrated. …”
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  13. 13

    A spawn mobile agent itinerary planning approach for energy-efficient data gathering in wireless sensor networks by Q. Qadori, Huthiafa, Ahmad Zulkarnain, Zuriati, Mohd Hanapi, Zurina, Subramaniam, Shamala

    Published 2017
    “…Moreover, each distributed MA has to carry the processing code (data aggregation code) to collect the sensory data and return back to the sink with the accumulated data. …”
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  14. 14

    An Improved Distributed Scheduling Algorithm for Wireless Sensor Networks by Sheikh, Muhammad Aman, Drieberg , Micheal, Zain Ali, Noohul Basheer

    Published 2012
    “…Simulation results show that the IDSA significantly outperforms a representative distributed random slot assignment algorithm (DRAND).…”
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  15. 15

    Parallel system for abnormal cell growth prediction based on fast numerical simulation by Alias, Norma, Islam, Md. Rajibul, Shahir, Rosdiana, Hamzah, Norhafizah, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Ludin, Eliana, Azami, Masrin

    Published 2010
    “…The multi-dimensional abnormal cell has produced the numerical analysis and understanding results at the target area for the potential improvement of detection and monitoring the growth. The development of the prediction system is the combinations of the parallel algorithms, open source software on Linux environment and distributed multiprocessor system. …”
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  16. 16

    Machine learning in botda fibre sensor for distributed temperature measurement by Nur Dalilla binti Nordin

    Published 2023
    “…To make the sensor more reliable, temperature data must be collected over the length of the cable, or distributed data rather than point data. …”
    text::Thesis
  17. 17

    Energy efficient geographical and power based clustering algorithm for heterogeneous wireless sensor networks by Jahan, Mohammad Saukat, Sali, Aduwati, Azarbad, Bahman, Usaha, Wipawee, A. Rasid, Mohd Fadlee, Mohd Ali, Borhanuddin

    Published 2011
    “…Also, a large number of nodes are self-organized by a distributed cluster formation technique. Moreover, a randomized technique is used to rotate the local cluster-heads base on power label in order to evenly distribute the energy load among the sensors in the network. …”
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  18. 18

    MCRP: multiple chain routing protocol for energy efficiency in homogeneous wireless sensor networks by Kareem, Husam, Hashim, Shaiful Jahari, Sali, Aduwati, Taha, Omer W., Kazim, Issraa J., Shukri, Shaimaa

    Published 2019
    “…In most of the applications, the sensor nodes are distributed in a hostile area and nodes in WSNs are energy constrained, once it is installed, it would be difficult to recharge the energy source of those sensor nodes. …”
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    Optimizing wireless sensor networks: A survey of clustering strategies and algorithms by Hamim, Sakib Iqram, Azamuddin, Ab Rahman

    Published 2024
    “…Wireless Sensor Networks (WSNs) are essential for real-time data collection and monitoring in various fields, such as environmental sensing, healthcare, industrial automation, and military surveillance. …”
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