Search Results - (( simulation integration drops algorithm ) OR ( java implication based algorithm ))

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  1. 1
    IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks

    IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks by Gumaida, Bassam, Ibrahim, Adamu Abubakar

    Published 2024
    “…The proposed algorithm, named Intelligent Water Drops with Simulated Annealing (IWDSA), combines two powerful optimization methods: Intelligent Water Drops (IWD) and Simulated Annealing (SA). …”
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  2. 2
    Collaboration algorithms between intermediaries for supporting flexible supply chain management

    Collaboration algorithms between intermediaries for supporting flexible supply chain management by Mohd Tamrin, Mohd Izzuddin, Tengku Sembok, Tengku Mohd, Kartiwi, Mira

    Published 2013
    “…The authors introduce Integrative Information Management Architecture (IIMA) running on collaboration algorithms which create semi automated intermediary processes to support management team address integration challenges. …”
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  3. 3
    Vehicle pick-up and drop-off schedule optimization in a university setting

    Vehicle pick-up and drop-off schedule optimization in a university setting by Teo, Chun Kit

    Published 2024
    “…Lateness will be removed using a lateness waiting time rollback mechanism. A simulated annealing-based multi-directional iterative local search algorithm is employed for solution optimization. …”
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    Final Year Project / Dissertation / Thesis
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  4. 4
    A simulation study of a new rate-and-queue-based active queue management algorithm

    A simulation study of a new rate-and-queue-based active queue management algorithm by Ali Asghar, Jomah Adham, Razman, Mat Tahar

    Published 2012
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  5. 5
    Traffic scheduling and link sharing mechanisms for controlled load service / Chin Yun Choong

    Traffic scheduling and link sharing mechanisms for controlled load service / Chin Yun Choong by Chin , Yun Choong

    Published 2000
    “…The main factors that determine whether a packet scheduling algorithm is able to support integrated services or differentiated services on Internet is the ability to provide bandwidth guarantee. …”
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  6. 6
    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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  7. 7
    Adaptive Quality Of Service Call Admission Control With User Mobility Prediction For Multimedia Traffic Over Wireless Networks

    Adaptive Quality Of Service Call Admission Control With User Mobility Prediction For Multimedia Traffic Over Wireless Networks by Prihandoko

    Published 2003
    “…The proposed user mobility prediction algorithm integrates the Received Signal Strength (RSS) measurements for the mobile terminal's intra-cell movement and aggregate history of mobile terminals for inter-cell movement. …”
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  8. 8
    Resource Allocation and Mobility Prediction Algorithms for Multimedia Wireless Cellular Networks

    Resource Allocation and Mobility Prediction Algorithms for Multimedia Wireless Cellular Networks by Al-Sanabani, Maher Ali

    Published 2008
    “…The simulation results show that the proposed scheme enhances the estimation of the target cell. …”
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  9. 9
    Improving Adaptive Quality of Service for Multimedia Wireless Networks Using Hierarchical Networks Approach

    Improving Adaptive Quality of Service for Multimedia Wireless Networks Using Hierarchical Networks Approach by Kandasamy, Saravanan

    Published 2004
    “…When compared with the scheme proposed Prihandoko in the literature, the simulation results show that our proposed scheme reduces the new call blocking probabilities, the handoff dropping probabilities and reduces significantly the probability of terminating calls…”
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  10. 10
    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
    “…This artificial intelligent call admission control (CAC) was validated using a dynamic WCDMA mobile network simulator. A few comparative results in downlink have shown that our integrated support vector regression assists genetic algorithm (SVRaGA) is capable of predicting next interval power consumption at Node B with low prediction error and improving the quality of service (QoS) by reducing dropped calls. � 2008 IEICE.…”
    Conference Paper
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  11. 11
    Predictive modelling of wind-influenced dynamic fire spread probability in tank farm due to domino effect by integrating numerical simulation with ANN

    Predictive modelling of wind-influenced dynamic fire spread probability in tank farm due to domino effect by integrating numerical simulation with ANN by Malik, Asher Ahmed, Nasif, Mohammad Shakir, Arshad, Ushtar, Mokhtar, Ainul Akmar, Mohd Tohir, Mohd Zahirasri, Al-Waked, Rafat

    Published 2023
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  12. 12
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
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  13. 13
    An integrated priority-based cell attenuation model for dynamic cell sizing

    An integrated priority-based cell attenuation model for dynamic cell sizing by Amphawan, Angela, Omar, Mohd Nizam, Din, Roshidi

    Published 2012
    “…Real-time predicted mobile traffic from the EFuNN structure was used to control the size of all the cells.Results obtained demonstrate the robustness of the integrated module in recognizing the temporal pattern of the mobile traffic and dynamically controlling the cell size in order to reduce the number of calls dropped.…”
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  14. 14
    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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  15. 15
    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method

    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

    Published 2024
    thesis::doctoral thesis
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  16. 16
    Energy-efficient medium access control strategy for cooperative wireless networks

    Energy-efficient medium access control strategy for cooperative wireless networks by Sami, Mahmoud

    Published 2016
    “…By doing so, the unlicensed users obtain greater opportunity for data transmission, thus increasing their performance. The simulation and analytical results indicate that the CC-TDMA significantly improves the throughput and Packet Drop Rate (PDR) of both licensed and unlicensed users compared to conventional TDMA.…”
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  17. 17
    Tunable Sub-Nanosecond Ultra Wideband Narrow Pulse Generator For Microwave Imaging

    Tunable Sub-Nanosecond Ultra Wideband Narrow Pulse Generator For Microwave Imaging by Zalzala, Ali Mahdi Jaafar

    Published 2016
    “…The aforementioned pulse data has been simulated in a locally developed image reconstruction algorithm (EDAS) to detect hypothetical objects and the resultant images show significant quality enhancement in comparison to a Gaussian pulse (or its derivative) with an equivalent duration. …”
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  18. 18
    Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field

    Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field by Toh, Cheng Chuan

    Published 2018
    “…Theoretically,β and α is parameters that used to vary the NMF2D algorithm in order to yield high SDR value. Experimentally,for the simulation results,the highest SDR value for β-divergence NMF2D is SDR = 16.69dB at β = 0.8 and n = 100.For α-divergence NMF2D,the highest SDR value is SDR = 17.85dB at α = 1.5 and n = 100.Additional of sparseness constraints toward β-divergence NMF2D and α-divergence NMF2D lead to even higher SDR value.There are SDR = 17.06dB for sparse β-divergence NMF2D at λ = 2.5 and SDR = 17.99dB for sparse α-divergence NMF2D at λ = 5. …”
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