• Vedant sachdeva
• Kamlesh Dutta

Gender identification is a crucial aspect of medico-legal examinations. Various anatomical structures, such as the pelvis, long bones with their epiphysis and metaphysis, skull, pubis, paranasal sinuses, foramen magnum, maxillary sinuses, and teeth, are utilized for this purpose. Anthropologists rely on skeletal biomarkers that exhibit gender-specific differences to identify sex. Extensive research has been conducted to estimate sex using nearly every bone in the human skeleton, with numerous comparative studies across different populations to assess the accuracy of gender determination. The process of gender prediction from anthropometric attributes involves several phases: pre-processing, feature extraction, and classification. This research introduces a hybrid optimization algorithm, combining genetic and particle swarm optimization (PSO) algorithms. A random forest model is employed for classification. The proposed model is implemented and evaluated against existing machine learning models, including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest.

Machine learning, particle swarm optimization, genetic swarm optimization, random forest

"Hybrid Optimization Algorithm for Gender Prediction from Anthropometric Attributes Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.j701-j709, June-2024, Available :http://www.jetir.org/papers/JETIR2406974.pdf

"Hybrid Optimization Algorithm for Gender Prediction from Anthropometric Attributes Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppj701-j709, June-2024, Available at : http://www.jetir.org/papers/JETIR2406974.pdf