Search Results - (( using optimization _ algorithm ) OR ( problem segmentation using algorithm ))

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  1. 1

    Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy by Bhandari, A.K., Singh, V.K, Kumar, A., Singh, G.K.

    Published 2014
    “…To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. …”
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  2. 2

    Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation by Alia, Osama Moh’d Radi

    Published 2011
    “…This thesis aims to solve these problems using an efficient metaheuristic algorithm, known as the Harmony Search (HS) algorithm. …”
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  3. 3
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    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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  5. 5

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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  6. 6

    Route optimization using shortest path method / Muhamad Faisal Amin Shakri by Shakri, Muhamad Faisal Amin

    Published 2025
    “…This study investigated the implementation and comparison between well known shortest path method which are Dijkstra’s algorithm, Bellman-Ford, and A* algorithm. This study starts with constructing the weighted graph from the simulation case using Google Maps, the intersection of the junction will be the vertices and road segments are edges, weighted are distances. …”
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  7. 7

    Data fitting by G1 rational cubic Be´zier curves using harmony search by Mohamed, Najihah, Majid, Ahmad Abd, Piah, Abd Rahni Mt

    Published 2015
    “…HS is suitable for multivariate non-linear optimization problem. It is mainly achieved by data fitting using rational cubic Be´ zier curves with G1 continuity for every joint of segments of the whole data sets. …”
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  8. 8

    Application of shuffled frog leaping algorithm for economic dispatch with multiple fuel options / Muhammad Akif Abu Bakar by Abu Bakar, Muhammad Akif

    Published 2016
    “…This thesis discusses about an alternative approach towards solving economic dispatch problem with multiple fuel options using shuffled frog leaping algorithm (SFLA). …”
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  9. 9

    Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image by Zainal Azali, Muhammad Nazmi

    Published 2024
    “…The adaptability of the algorithm is demonstrated through a 3D surface reconstruction using a final disparity map. …”
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    Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation by Rahimizadeh, Hamid

    Published 2009
    “…Object extraction and object recognition are typical applications that use segmentation as a low level image processing. …”
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  13. 13

    Data Fitting by G1 Rational Cubic Bézier Curves Using Harmony Search by Najihah, Mohamed, Ahmad, Abd Majid, Abd Rahni, Mat Piah

    Published 2015
    “…HS is suitable for multivariate non-linear optimization problem. It is mainly achieved by data fitting using rational cubic Bézier curves with G1 continuity for every joint of segments of the whole data sets. …”
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  14. 14

    K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data by Kayalvily, Tabianan, Shubashini, Velu, VInayakumar, Ravi

    Published 2022
    “…In order to process the collected data and segment the customers, an learning algorithm is used which is known as K-Means clustering. …”
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  15. 15

    Acltshe-Amts: A New Adaptive Brain Tumour Enhancement And Segmentation Approaches by Alkhafaji, Ali Fawzi Mohammed Ali

    Published 2024
    “…Multilevel thresholding is one of the prominent approaches used for brain image segmentation. Currently, when applying multilevel thresholding for brain tumor segmentation, two important problems must be carefully addressed. …”
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  16. 16

    An Enhanced Wavelet Neural Network Model For Epileptic Seizure Detection And Prediction by Lai , Kee Huong

    Published 2016
    “…The contribution of this thesis is threefold. Firstly, the HS algorithm is used in the feature selection stage. The HS algorithm, which is originally used for optimization problems involving real numbers, is modi�ed and employed in the task of feature selection, which involves binary values.…”
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  17. 17

    Analyzing CT images for detecting lung cancer by applying the computational intelligence-based optimization techniques by Pethuraj, Mohamed Shakeel, Mohd Aboobaider, Burhanuddin, Salahuddin, Lizawati

    Published 2022
    “…The gathered image noise is removed by applying the mean filter, and the affected regions are segmented with the help of the Butterfly Optimization Algorithm-based K-Means Clustering (BOAKMC)algorithm. …”
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  18. 18

    Dynamic Economic Dispatch For Power System by Hussein, Saif Tahseen

    Published 2016
    “…Also, time-segmentation of the solution, or as known as a period by period solution, always results in sub-optimality, while, only by solving the optimization problem in totality can lead to an optimal solution. …”
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  19. 19

    An Optimized Semantic Segmentation Framework for Human Skin Detection by Huong, Audrey, Ngu, Xavier

    Published 2024
    “…Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. …”
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  20. 20

    Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

    Published 2019
    “…The elements extracted from the confusion matrix parameters (i.e. accuracy, specificity, sensitivity, AUC, precision and f-score) are used in benchmarking the optimal performance of classification algorithms. …”
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