PSO-FuzzyNN techniques in gender classification based on bovine bone morphology properties
This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affec...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2019
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/85229/1/NorAzizahAli2019_PSO-FuzzyNNTechniquesinGenderClassification.pdf http://eprints.utm.my/id/eprint/85229/ https://dx.doi.org/10.11113/ijic.v9n1.215 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This simulation project aims to solve forensic anthropology issues by using the computational method. The positive identification on gender is such a potential field to be explored. Basically, gender identification in forensic anthropology by comparative skeletal anatomy by atlas and crucially affect the identification accuracy. The simulation identification method was studied in order to determine the best model, which reduce the total costs of the post-mortem as an objective. The computational method on simulation run improves the identification accuracy as proven by many studies. Fuzzy K-nearest neighbours classifier (FuzzyNN) is such a computational intelligence method and always shows the best performance in many fields including forensic anthropology. Thus, this intelligent identification method was implemented within the determining for best accuracy. The result of this proposed model was compared with raw data collection and standard collections datasets; Goldman Osteometric dataset and Ryan and Shaw Dataset (RSD) as a benchmark for the identification policy. To improve the accuracy of FuzzyNN classifier, Particle Swarm Optimization (PSO) feature selection was used as the basis for choosing the best features to be used by the selected FuzzyNN classification model. The model is called PSO-FuzzyNN and has been developed by MATLAB and WEKA tools platform. Comparisons of the performance measurement namely the percentage of the classification accuracy of the model were performed. The result show potential the proposed PSO-FuzzyNN method demonstrates the capability to the obtained highest accuracy of identification. |
|---|