Cognitive Framework to Identify Women Empowerment using Swarm Enhanced Neural Networks

Women are contributing equally with males to the development of a healthy family, society, and nation. One indication of women's empowerment is socioeconomic level. This study is an innovative one concentrated on the classification and comparison of Socio-Economic Status (SES). Women along the evolution of a cognitive model. Many factors affect the socioeconomic level of women; consequently, an artificial neural network (ANN) model using Fire Work Optimization (FWO) is used to detect, forecast, and classify this status. Operating for optimization, FWO uses swarm intelligence. Training ANN for classification goals using FWO. The results of the experiment prove that FWO-ANN performed more efficiently giving 99.34 percent accuracy in classification. Other swarm optimization methods, such as Particle Optimization (PSO) and Cat Swarm Optimization (CSO), were not as accurate or efficient as the current model.

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Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-02-10
DOI
https://doi.org/10.52783/jisem.v10i12s.1811
Primary Topic
Emotional Intelligence and Performance
Type
article
Field-Weighted Citation Impact
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article

Cognitive Framework to Identify Women Empowerment using Swarm Enhanced Neural Networks

N. Sreedevi
Journal of Information Systems Engineering & Management
Emotional Intelligence and Performance
article

Cognitive Framework to Identify Women Empowerment using Swarm Enhanced Neural Networks

N. Sreedevi
article en

Abstract

Women are contributing equally with males to the development of a healthy family, society, and nation. One indication of women's empowerment is socioeconomic level. This study is an innovative one concentrated on the classification and comparison of Socio-Economic Status (SES). Women along the evolution of a cognitive model. Many factors affect the socioeconomic level of women; consequently, an artificial neural network (ANN) model using Fire Work Optimization (FWO) is used to detect, forecast, and classify this status. Operating for optimization, FWO uses swarm intelligence. Training ANN for classification goals using FWO. The results of the experiment prove that FWO-ANN performed more efficiently giving 99.34 percent accuracy in classification. Other swarm optimization methods, such as Particle Optimization (PSO) and Cat Swarm Optimization (CSO), were not as accurate or efficient as the current model.

Journal of Information Systems Engineering & ManagementVol. 10(12s)
Openalex Percentile: Top 97%
Emotional Intelligence and Performance
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