Work

Evaluating Machine Learning Models for Gender Detection Using Palm Images: A Comparative Approach

Year: 2025

Type: article

Abstract: This study examines how machine learning can identify a person's gender based on palm images. It compares two widely used models, Random Forest and Gradient Boosting, to evaluate their effectiveness. The dataset, obtained from Kaggle, was selected for its reliability and diversity. The research goes through essential steps like data preparation, feature selection, model optimization, and performance comparison. It also highlights the advantages and limitations of each model, offering valuable insights for those interested in this field.

Source: International Research Journal on Advanced Engineering and Management (IRJAEM)

DOI: 10.47392/irjaem.2025.0130

Authors: Jahnvi D Rajyaguru, Dr. Falguni Parsana

Institution: Atmiya University

Language: English

Topic: Biometric Identification and Security

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Gender equality

Open Access status: diamond

OpenAlex ID: W4410099853

FWCI:

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