Search Results - (( java implementation matching algorithm ) OR ( program segmentation mining algorithm ))

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  1. 1
    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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  2. 2
    Framework for stream clustering of trajectories based on temporal micro clustering technique

    Framework for stream clustering of trajectories based on temporal micro clustering technique by Abdulrazzaq, Musaab Riyadh

    Published 2018
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  3. 3
    Multi-floor indoor location estimation system based on wireless local area network

    Multi-floor indoor location estimation system based on wireless local area network by Chua, Tien Han

    Published 2007
    “…During location estimation, current histogram of RSS at unknown location will be compared to the database. The most probable match is selected and returned as estimated location based on Bayesian filtering algorithm. …”
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  4. 4
    An Agent-Based System with Personalization and Intelligent Assistance Services for Facilitating Knowledge Sharing

    An Agent-Based System with Personalization and Intelligent Assistance Services for Facilitating Knowledge Sharing by Mohd Sharef, Nurfadhlina

    Published 2006
    “…KSFaci is embedded in web environment and is implemented using Java Servlet and runs under Apache server. …”
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    Validation of deep convolutional neural network for age estimation in children using mandibular premolars on digital panoramic dental imaging / Norhasmira Mohammad

    Validation of deep convolutional neural network for age estimation in children using mandibular premolars on digital panoramic dental imaging / Norhasmira Mohammad by Mohammad, Norhasmira

    Published 2022
    “…The semi-automated dental staging system developed in this study is based on the Malay children’s population and uses a brain-inspired learning algorithm termed "deep learning". The methodology is comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental radiographs, segmentation, and classification of mandibular premolars according to Demirjian's staging system using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. …”
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