Log-sigmoid Activation Function based MLP Network for Aggregate Classi fi cation
Mechanical sifting and manual grading have conventionally been utilised to assess the grade of aggregates. Nonetheless, such evaluations require a range of mechanical, chemical, and physical examinations, typically conducted manually, resulting in a process that is tedious, subjective, and labour-...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://journalarticle.ukm.my/26788/1/34.pdf http://journalarticle.ukm.my/26788/ https://www.ukm.my/jkukm/volume-3701-2025/ |
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| Summary: | Mechanical sifting and manual grading have conventionally been utilised to assess the grade of aggregates.
Nonetheless, such evaluations require a range of mechanical, chemical, and physical examinations, typically
conducted manually, resulting in a process that is tedious, subjective, and labour-intensive. This research aims to
provide an image-based classifi cation system for the categorisation of aggregates. An artifi cial neural network (ANN)
has been used to analyse the acquired images and categorise their shapes. The composite images are obtained and
utilised as the input parameter for prediction prior to the thresholding step. The Log-sigmoid (Logsig) activation
function, utilised in a Multilayer Perceptron (MLP) network, exhibits a lower mean square error (MSE) and superior
regression performance relative to the Pureline activation functions. The Logsig-based network has a MSE of 1.7473
and a regression capability of 0.9521. |
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