Search Results - (( java segmentation using algorithm ) OR ( basic using greedy algorithm ))

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  1. 1
    Automated bilateral negotiation with incomplete information in the e-marketplace.

    Automated bilateral negotiation with incomplete information in the e-marketplace. by Jazayeriy, Hamid

    Published 2011
    “…The MGT algorithm is useful to explore the properties of the Pareto-optimal offers. …”
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  2. 2
    Nearest greedy for solving the waste collection vehicle routing problem: A case study

    Nearest greedy for solving the waste collection vehicle routing problem: A case study by Mat, Nur Azriati, Benjamin, Aida Mauziah, Abdul-Rahman, Syariza, Wibowo, Antoni

    Published 2017
    “…This paper presents a real case study pertaining to an issue related to waste collection in the northern part of Malaysia by using a constructive heuristic algorithm known as the Nearest Greedy (NG) technique. …”
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  3. 3
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  4. 4
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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  5. 5
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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  6. 6
    Heuristic resource allocation algorithm for controller placement in multi-control 5G based on SDN/NFV architecture

    Heuristic resource allocation algorithm for controller placement in multi-control 5G based on SDN/NFV architecture by Ibrahim, Abeer Abdalla Zakaria, Hashim, Fazirulhisyam, Noordin, Nor Kamariah, Sali, Aduwati, Navaie, Keivan, Fadul, Saber Mohammed Elnour

    Published 2021
    “…It is based on the K-center problem, to solve the capacitated controller placement problem (CCPP), which acts as a resource location problem, in which the location and number of controllers can be allocated to maximize resources. A Greedy Randomized Search (GRS) algorithm was used to solve the dynamic assignment of nodes to controllers to achieve load balancing. …”
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  7. 7
    UiTM Transportation Unit bus trip scheduling system / Noorzawanah Ab Rahim

    UiTM Transportation Unit bus trip scheduling system / Noorzawanah Ab Rahim by Ab Rahim, Noorzawanah

    Published 2009
    “…The user interface application has been developed by using Microsoft visual basic 6.0 and Microsoft access as it's database. …”
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  8. 8
    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective

    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

    Published 2013
    “…In the recent era, researchers used different routing algorithms to decrease the power consumption in order to increase the wireless sensor network life. …”
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  9. 9
    A phonetically rich and balanced lexical corpus using zipfian distribution for an under resourced language / Aminath Farshana

    A phonetically rich and balanced lexical corpus using zipfian distribution for an under resourced language / Aminath Farshana by Aminath , Farshana

    Published 2018
    “…The performance of the proposed method was also evaluated by comparing the results with an existing benchmark method (greedy algorithm). The results show that the sentences selected using proposed method cover all the phonetic units and is 14 times smaller than the corpus developed using the benchmark method. …”
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