Year: 2026
Type: article
Abstract: The present study examines an approach to the synthesis of a conceptual model for gender prediction based on user input. The predictive capability of the model is grounded in statistical differences observed within specific datasets and in the applicability of a machine-learning classification model. The predictive analysis is governed by a set of parameters, including language, the specifications of the peripheral input device, sample size, and biological indicators. It should be noted that reliable classification of a user's gender is a function of predefined key indicators.
Author: Valentin Atanasov
Institution: Shumen University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Gender equality
Open Access status: diamond
OpenAlex ID: W7225411880