Artificial neural network based system for distorted image recognition

Hossain, Mostafiz Mehebuba; Haque, Mahmudul; Arifuzzaman, Md.; Hossain, S. M. Zakir

doi:10.1049/icp.2021.0852

Cited by 1 publication

(2 citation statements)

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“…Updating processes of weights and bias: Step 8. Update bias and its weight j = 1, 2, ..., p for output layer Y k for k = 1, 2, ..., m using Equation (16).…”

Section: Methodology a The Procedures Of Backpropagation Ann Algorithmmentioning

confidence: 99%

“…The results showed that ANN backpropagation distinguished batik motives between Lampung motives and from outside Lampung with an accuracy rate of 92 percent and the error rate of 8 percent. Hossain et al [16] utilized ANN and developed an intelligent model-based backpropagation to recognize predefined Latin characters subsequently. Bravo-López et al [17] explored an ANN model for landslide susceptibility evaluation at a high land in Ecuador.…”

Section: Related Workmentioning

confidence: 99%

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Applying Artificial Neural Network Based on Backpropagation Method for Indonesian Sign Language Recognition

Irvanizam,

Horatius,

Sofyan

2023

IJCDS

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Sign-language is a nonverbal language utilized by hearing loss and speech disorder people to interact and speak with each other. Sometimes people call it a visual-language utilizing hand gestures and changes in hand shape to express intentions and thoughts. In Indonesia, people use two sign language types commonly. They are Indonesian Signal System (ISS) and Indonesian Sign Language (ISL). In this paper, we investigated research on the recognition of ISL alphabets by proposing an application using the Artificial Neural Network algorithm with the principle of the Backpropagation method for dealing with this research. Firstly, we performed preprocessing the image data sequentially by splitting the dataset, labeling the dataset based on the alphabet types, scaling the images into a size of 150x150 pixels, and segmentizing the datasets to eliminate noise in the image to yield color, grayscale, binary segmentations, and image edge detection. Later on, we split the dataset and distributed them into training and testing sets with two different proportions, 70:30 and 80:20 percent. After applying the algorithm, we obtained the highest accuracy at 80:20 data proportions using an epoch of 700, a learning rate of 0.001, a batch size of 25, and some hidden nodes of 100 was 98.41 percent.

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“…Updating processes of weights and bias: Step 8. Update bias and its weight j = 1, 2, ..., p for output layer Y k for k = 1, 2, ..., m using Equation (16).…”

Section: Methodology a The Procedures Of Backpropagation Ann Algorithmmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%