Shape of aggregates classification by using MLP network based training algorithm and activation function

Mohd Sabri, Mohd Salman and Ahmad Jamil, Syahrull Hi-Fi Syam and Adnan, Ja'afar and Hashim, Fakroul Ridzuan (2023) Shape of aggregates classification by using MLP network based training algorithm and activation function. In: International Conference on X-Rays and Related Techniques in Research and Inductry (ICXRI 2023), 23 - 24 Agust 2023, Dorsett Grand Subang Hotel, Subang Jaya, Selangor. (Submitted)

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Abstract

Mechanical sifting and hand grading have long been used to assess the quality of aggregates. It must pass a range of mechanical, chemical, and physical testing to produce superior aggregates; these tests are typically performed manually and are sluggish, arbitrary, and time-consuming. This work aims to develop an image-based classification system that can categorise aggregates. An artificial neural network was used to reprocess the image after it had been taken in order to classify its shapes. In comparison to Backpropagation (BP) training techniques, the Bayesian Regularization (BR) methodology offers better performance with reduced mean square error (MSE) and higher regression. The LM training approach using the Multilayer Perceptron (MLP) network-based MSE offers the maximum regression and the lowest mean square error (MSE). The BR-trained network has 1.4235 MSE and 0.9760 regression capabilities

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP), Bayesian Regularization (BR), network-based, aggregate classification
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Mr Shahrim Daud
Date Deposited: 08 Mar 2024 03:26
Last Modified: 08 Mar 2024 03:26
URI: http://ir.upnm.edu.my/id/eprint/392

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