@article{MAKHILLJEAS2018132417278,
    title = {Lateral Cephalogram Analysis Using Wighted Rough Neural Network for
Sex Determination},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {24},
    pages = {10455-10460},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.10455.10460},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.10455.10460},
    author = {P. and},
    keywords = {K-NN,SVM,Daubechies,PCA,feature selection,feature extraction,Gender classification,BPN and WRNN},
    abstract = {Cephalometric investigation in forensic science, concerned with the recognition, identification,
individualization and assessment of physical confirmation. This study portrays the different soft computing
algorithms for horizontal cephalogram picture based sexual orientation classification. In this study, we proposed
another classification strategy called Weighted Rough Neural Network (WRNN). The Weiner filter has been
utilized for preprocessing to lessen clamor in a picture. Programmed landmark identification for cephalogram
pictures utilizing single fixed appearance model. The fifty one landmark points are extracted from skull image.
Then principal component analysis and Daubechies wavelets are applied for feature selection. At the end
chosen features are ordered according to the sexual orientation by applying Support Vector Machine (SVM),
K-Nearest Neighbor (K-NN), Back Propagation Neural Network (BPN) with proposed Weighted Rough Neural
Network (WRNN) strategies. The comparative examination is performed among these techniques by utilizing
the quantitative measures. From the after effects of the present investigation, it might be concluded that parallel
cephalogram examination utilizing WRNN can be utilized as a dependable instrument in sex assurance.}
    }