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  1. Iza Sazanita Isa, Muhammad Aiman Zahir, Siti Azura Ramlan, Wang, Li-Chih, Siti Noraini Sulaiman
    ESTEEM Academic Journal, 2021;17(1):12-25.
    MyJurnal
    Dyslexia is developed by neurobiological in origin which is categorized as
    learning disorder that affect the ability to read, spell, write and speak. The
    most common dyslexia symptom can easily be identified through the
    handwriting pattern. There are many intelligence and computational
    methods that have been proposed, and they have provided various and
    different performance to evaluate the proposed system ability. However,
    system performances are varied and nonstandardized in each assesment on
    dyslexic children to validate the presence of dyslexia symptom. The recent
    deep learning models have been employed to improve the assesment
    performance and (the models/ they have shown) shows significant output to
    detect and classify the present of dyslexia symptoms among school children.
    Therefore, there is a crucial need in deep learning, specifically for
    Convolutional Neural Network ( CNN) to validate performances of different
    networks, so that the most performed CNN could be a bench mark in
    evaluation to detect such symptom. This study aims to compare different deep
    learning networks specifically the CNN models to validate its performance
    in terms of the capability to classify dyslexic handwriting among school
    children. This study is proposed to compare different CNN models such as
    CNN-1, CNN-2, CNN-3 and LeNet-5. The proposed methods to compare the
    CNN performances are developed by using Jupyter notebook as platform.
    Meanwhile, keras is the higher-level API framework to provide a more
    flexible way for defining models. It specifically allows to define multiple
    input or output models as well as models that share layers. The tensorflow is
    also used for machine learning applications such as neural networks. Before
    that, the dataset of handwriting image is preprocessed by the augmentation
    process which includes the rotation of all images. CNN models have shown
    significant performance and provided sufficient results of performance with
    more than 87% of accuracy in classifying the potential dyslexia symptom
    based on handwritten images.
  2. Nurul Huda Ishak, Puteri Nur Syahirah Mohamad Mustafa, Iza Sazanita Isa, Siti Solehah Md Ramli, Nur Darina Ahmad
    ESTEEM Academic Journal, 2021;17(2):112-123.
    MyJurnal
    Repair and maintenance in power distribution is an important factor that
    affects the continuous productivity services and power efficiency in electrical
    supply systems. Thermographic inspection has been often used as a
    maintenance tool, as it allows detection of early-stage failure from the system
    in electrical distribution. Failure in the system can lead to catastrophic
    failure like a high-voltage arc fault. The presence of fault is caused by the
    higher temperature of the instrument that leads to the formation of hotspots.
    The use of infrared inspection is useful in detecting the hotspot that is hardly
    noticeable. It helps to overcome the problems that arise during operation
    and maintenance in the distribution systems. In this research, a fault
    detection system is proposed with the application of Artificial Neural
    Network (ANN) in identifying faults on electrical equipment. This method
    was trained by using the temperature parameter on the IR images taken from
    TNB Distribution. As a result, it will lead to faults detection. Thus, the
    purpose of this project is to ensure the correct recommendation of corrective
    actions in the maintenance procedure of the electrical system. The actions to
    the detection of faults taken are based on the results of the temperature
    measured. The neural network training performance for the temperature of
    hotspot detection was developed with a minimum error of 0.00084165 MSE
    at epoch 39. The study shows the best-fitting allows detection of early-stage
    failure. It can be concluded that the current method in conducting the
    prediction process by using Thermographic inspection is suitable for
    electrical equipment based on the training result.
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