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  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.…”
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  2. 2
    Adaptive glioblastoma detection using evolutionary-based algorithm / Nurul Amira Mohd Ali

    Adaptive glioblastoma detection using evolutionary-based algorithm / Nurul Amira Mohd Ali by Mohd Ali, Nurul Amira

    Published 2020
    “…Artificial Bee Colony algorithm has been selected as the algorithm used in segmenting the brain tumor from MRI image. …”
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  3. 3
    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images

    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images by Farzan, Ali, Ramli, Abdul Rahman, Mashohor, Syamsiah, Mahmud, Rozi

    Published 2010
    “…Two different brain MRI datasets are used to evaluate the algorithm. …”
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  4. 4
    Segmentation of MRI brain images using statistical approaches

    Segmentation of MRI brain images using statistical approaches by Balafar, Mohammad Ali

    Published 2011
    “…The Gaussian Mixture Model (GMM) is a clustering algorithm that is commonly used for brain MRI segmentation. …”
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  5. 5
    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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  6. 6
    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

    Published 2015
    “…This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. …”
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  7. 7
    Brain Machine Interface Controlled Robot Chair

    Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

    Published 2010
    “…The BMI controls the joystick of the robot chair using a shared control algorithm. Real-time experiments are also presented using 10 trained and 5 untrained subjects to validate the applicability of the brain machine interface. …”
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  8. 8
    BRAIN SOURCE LOCALIZATION TECHNIQUE FOR EEG SIGNALS BASED ON ENHANCED MULTIPLE SPARSE PRIORS

    BRAIN SOURCE LOCALIZATION TECHNIQUE FOR EEG SIGNALS BASED ON ENHANCED MULTIPLE SPARSE PRIORS by MUNSIF, ALI JATOI

    Published 2017
    “…The brain source localization information is used to diagnose various brain disorders such as epilepsy, schizophrenia, stress, depression and Alzheimer. …”
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  9. 9
    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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  10. 10
    EEG-based brain source localization using visual stimuli

    EEG-based brain source localization using visual stimuli by Jatoi, M.A., Kamel, N., Malik, A.S., Faye, I., Bornot, J.M., Begum, T.

    Published 2016
    “…Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. …”
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  11. 11
    Investigation Of Edge Detection Techniques Based On Brain Tumor Images

    Investigation Of Edge Detection Techniques Based On Brain Tumor Images by Rosnan, Murni Nur Athirah

    Published 2018
    “…The edge detection operators used to detect the brain tumor are Robert zero-crossing, Sobel operator, Prewitt operator, Canny operator and modified Canny algorithm. …”
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  12. 12
    Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

    Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion by Zafar, R., Dass, S.C., Malik, A.S.

    Published 2017
    “…In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. …”
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  13. 13
    Brain machine interface based wheelchair control with minimal subject training

    Brain machine interface based wheelchair control with minimal subject training by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abdul Hamid, Adom, Ramachandran, Nagarajan

    Published 2009
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  14. 14
    Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm

    Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm by Abdurrahim, Salem Hamed

    Published 2004
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  15. 15
    High performance simulation for brain tumors growth using parabolic equation on heterogeneous parallel computer systems

    High performance simulation for brain tumors growth using parabolic equation on heterogeneous parallel computer systems by Pheng, H. S., Alias, Norma, Mohd. Said, Norfarizan

    Published 2007
    “…The implementation of parallel algorithm based on parallel computing system is used to capture the growth of brain tumour. …”
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  16. 16
    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

    Published 2015
    “…This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. …”
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  17. 17
    The visualization of three dimensional brain tumors' growth on distributed parallel computer systems

    The visualization of three dimensional brain tumors' growth on distributed parallel computer systems by Alias, Norma, Masseri, Mohd. Ikhwan Safa, Islam, Md. Rajibul, Khalid, Siti Nurhidayah

    Published 2009
    “…The discretization of the three-dimensional parabolic equations for the brain tumor’s growth mathematical model using a numerical finite-difference method will be implemented from the previous study of two dimensional model and thus a parallelization of algorithm simulation to computational resources based on high-performance computing systems will be used to generate the growth of the brain tumor in three dimensional. …”
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  18. 18
    High performance simulation for brain tumours growth using parabolic equation on heterogeneous parallel computer system

    High performance simulation for brain tumours growth using parabolic equation on heterogeneous parallel computer system by Pheng H. S., Norma Alias, Norfarizan Mohd Said

    Published 2007
    “…The implementation of parallel algorithm based on parallel computing system is used to capture the growth of brain tumour. …”
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  19. 19
    Brain Tumour Classification using Deep Learning with Residual Attention Network: A Comparative Study

    Brain Tumour Classification using Deep Learning with Residual Attention Network: A Comparative Study by Abdulrazak Yahya, Saleh, Sashwini A/P S, Thiagaraju

    Published 2021
    “…The algorithm performance is evaluated based on training accuracy, testing accuracy, validation accuracy, and validation loss metrices. …”
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  20. 20
    Brain tumor quantification equation: modeled on complete step response algorithm

    Brain tumor quantification equation: modeled on complete step response algorithm by Aboaba, Abdulfattah A., Hameed, Shihab A., Khalifa, Othman Omran, Hassan Abdalla Hashim, Aisha, Harun, Rahmat, Zain, Norzaini Rose Mohd

    Published 2012
    “…What is presented here is a novel brain tumor quantification method based on step response algorithm utilizing a model which itself was based on step response model resulting in smart and rapid quantification of brain tumor volume.…”
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