Pengkelasan Aggregat Menggunakan Rangkaian Neural Berhirarki - Othman, Ahmad Nafis (Universiti Sains Malaysia, 2006)
Aggregate must be classify into good shape and not depend on the types. Nowadays, the classification of the aggregate is done manually. This technique is not practical because it take a lot of time and high skill to get the result. Good classification of the aggregate is important in roads construction. The good structure of the road could minimize the rate of accident. This project used hierarchal neural network to classify the aggregate automatically. This neural network has it advantages because it can classify the aggregate to its category and shapes. The aggregates are classified into good and bad shape. There are several types of aggregates which are angular, cubical, irregular and elongated. Hopefully the classification of the hierarchal neural network can be used to classify the aggregate perfectly.