Search Results - (( code classification matching algorithm ) OR ( java applications cloud algorithm ))

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  1. 1
    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
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  2. 2
    An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding

    An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

    Published 2020
    “…In the classic Bag of visual words model, the Fuzzy c-means algorithm is replaced with K-means and the accuracy of SIFT matching is increased. …”
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  3. 3
    CUDA implementation of fractal image compression

    CUDA implementation of fractal image compression by Al Sideiri A., Alzeidi N., Al Hammoshi M., Chauhan M.S., AlFarsi G.

    Published 2023
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  4. 4
    Securing cloud data system (SCDS) for key exposure using AES algorithm

    Securing cloud data system (SCDS) for key exposure using AES algorithm by Thabet Albatol, Mohammed Samer Hasan

    Published 2021
    “…After that we use to build securing cloud data system (SCDS) for key exposure using the AES algorithm for encrypting and decrypting our website cloud data and files. …”
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  5. 5
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…For next, instead of k-means clustring, Fuzzy cmeans clustering is combined with Spatial Pyramid Matching image representation to improve the accuracy of classification results. …”
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  6. 6
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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  7. 7
    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider

    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider by Omer Fadlelmola Elkhider, Samih

    Published 2011
    “…The approach of the mobile cloud is clearly occurring with web host servers expands thus large number of data around the world stored in huge servers, consumers of mobile devices and small computing devices have high performance expectations usually, therefore mobile cloud computing theorizes that the cloud will soon become a disruptive force in the mobile world, eventually becoming the dominant way in which mobile applications operates. …”
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  8. 8
    A malware analysis and detection system for mobile devices / Ali Feizollah

    A malware analysis and detection system for mobile devices / Ali Feizollah by Ali, Feizollah

    Published 2017
    “…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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  9. 9
    Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali

    Biometric identification and recognition for iris using failure rejection rate (FRR) / Musab A. M. Ali by M. Ali, Musab A.

    Published 2016
    “…The subsequent step is using the DAUB3 wavelet transform for feature extraction along with the application of an additional step for biometric template security that is the Non-invertible transform (cancelable biometrics method) and finally utilizing the Support Vector Machine (Non-linear Quadratic kernel) for matching/classification. The experimental results showed that the recognition rate achieved are of 99.9% on Bath-A data set, with a maximum decision criterion of 0.97.…”
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