Search Results - (( java segmentation using algorithm ) OR ( learning program mass algorithm ))

Search alternatives:

  • Showing 1 - 7 results of 7
Refine Results
  1. 1
    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.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  2. 2
    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. …”
    Get full text
    Get full text
    Get full text
    Book
    Save to List
    Saved in:
  3. 3
    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
    Get full text
    Get full text
    Get full text
    Get full text
    Proceeding Paper
    Save to List
    Saved in:
  4. 4
    The Development And Evaluation Of Personalized Learning Material Based On A Profiling Algorithm For Polytechnic Students In Learning Algebra

    The Development And Evaluation Of Personalized Learning Material Based On A Profiling Algorithm For Polytechnic Students In Learning Algebra by Mohamed Mokmin, Nur Azlina

    Published 2016
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  5. 5
    Demystifying Artificial Intelligence (AI) for Early Childhood and Elementary Education: A Case Study of Perceptions of AI of State of Missouri Educators

    Demystifying Artificial Intelligence (AI) for Early Childhood and Elementary Education: A Case Study of Perceptions of AI of State of Missouri Educators by Arnone, K., Hutson, J., Woodruff, K.

    Published 2023
    “…Yet with all the potential benefits that AI and machine learning (ML) may provide students, there remains a general reticence to adopt this technology because of misconceptions and perceptions that elementary educators will need expensive equipment, robust support from IT, or to retool and learn programming or coding. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  6. 6
    Application of artificial neural network in discriminating the agarwood oil quality using significant chemical compounds / Mohd Hezri Fazalul Rahiman … [et al.]

    Application of artificial neural network in discriminating the agarwood oil quality using significant chemical compounds / Mohd Hezri Fazalul Rahiman … [et al.] by Rahiman, Mohd Hezri Fazalul, Ismail, Nurlaila, Taib, Mohd Nasir, Mohd Ali, Nor Azah, Tajuddin, Saiful Nizam

    Published 2014
    “…The work involves of selected agarwood oil from low and high quality, the extraction of chemical compounds using GC-MS and Z-score to identify the significant compounds as input to the network. The ANN programming algorithm was developed and computed automatically via Matlab software version R2010a. …”
    Get full text
    Get full text
    Get full text
    Book Section
    Save to List
    Saved in:
  7. 7
    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. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:

Search Tools: