Shape aggregate classification using MLP based activation function

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 to classify its shapes. The aggregate images will be captured and before the threshold process take place and be the input parameter for prediction. The Multilayer Perceptron (MLP) network-based Tansig activation function offers the lowest mean square error (MSE) and the maximum regression among the Logsig and Pureline) activation functions. Tansig’s based network has a 0.0479 MSE and 0.9582 regression capacity.