Issues Related to Parameter Estimation in Model Accuracy Assessment

Henderson, Thomas C.; Boonsirisumpun, Narong

doi:10.1016/j.procs.2013.05.366

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…To improve the classification accuracy of objects, a standard image components database is introduced. The Bayes Classifier [10]- [12], a prevailing algorithm to provide The contours of industry components usually consist of several basic shapes [13], e.g., rectangles, circles, triangles and polygons. In this research, these four primary attributes are defined to represent the components (refer to Fig.…”

Section: B Principle Of the Ann-cic Modelmentioning

confidence: 99%

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A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

Yang¹,

Li²,

Rasul³

2021

IJMLC

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The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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Section: B Principle Of the Ann-cic Modelmentioning

confidence: 99%

“…The accuracy is calculated by using Equation (10). Table III presents the experiment results.…”

Section: Counting Verificationmentioning

confidence: 99%