Hybrid RSM-fuzzy modeling for hardness prediction of TiAlN coatings

Jaya, A.; Muhamad, Mohd Razali; Rahman, Md. Nizam Abd.; Napiah, Z. A. F. M.; Hashim, Siti Zaiton Mohd; Haron, Habibollah

doi:10.1109/isda.2011.6121674

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…They are connected with each other by variable weights to be determined. [5,[20][21][22][23] The study ANN network, determined the number of input nodes and output nodes in the input and output layer by the number of input and output variables. The input layer receives information from external sources and passes this information to the network for processing.…”

Section: Development Of Ann Classification Modelmentioning

confidence: 99%

Classification of gender using neural network and Support Vector Machine from bone morphology properties of monkey

Sahadun

Haron

Kadir

2013

2013 IEEE Conference on Open Systems (ICOS)

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Forensic anthropologists are often asked to evaluate partial skeletal remains or severely damaged. The purpose of this research is comparing the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for gender classification. The performance of both models in classifying the gender data is compared and validated using the Ryan and Shaw Dataset (RSD) in terms of accuracy, sensitivity and specificity percentage. These measurements were taken on 226 femurs (126 females and 100 males) and 216 humerus (126 females and 90 males) of old world monkey primates. The comparison result shows that the ANN classifier outperforms SVM classifier for correctly classifying the bone morphology properties in gender data in term of accuracy and specificity percentage value; 71 % and 56% respectively. The conclusion of Artificial Neural Network (ANN) is a powerful classification model that improves the accuracy rate of gender classification models for skeletal remains.

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