Artificial Intelligence Model with Optimization Technique to Improve Job Autonomy

Abstract

This paper presents a novel approach to enhancing workplace well-being and classification accuracy, specifically tailored to the dynamic and competitive IT industry in India. The research leverages Simulated SeaHorse Optimization (SSHO), a nature-inspired optimization technique, to estimate and improve job autonomy and happiness scores in the workplace. Furthermore, SSHO is combined with Long Short-Term Memory (LSTM) networks to create a robust classification model. The study’s key findings indicate a direct correlation between the number of SSHO iterations and the enhancement of job autonomy and happiness scores, highlighting the potential of SSHO as an effective tool for optimizing these critical workplace factors. Moreover, the SSHO-LSTM model outperforms traditional models, achieving remarkably high accuracy, precision, recall, and F1-Score in classifying data. The practical implications of this research are significant, as it offers a promising approach for organizations to create a more favorable work environment, ultimately contributing to higher job satisfaction and well-being among employees. This paper advances the understanding of optimization techniques, well-being in the workplace, and intrapreneurial characteristics, providing valuable insights for industry professionals and researchers seeking to improve employee experiences in the IT sector. In conclusion, this paper demonstrates the potential of SSHO and SSHO-LSTM as tools to optimize workplace well-being and enhance classification accuracy, making a substantial contribution to the fields of optimization, machine learning, and workplace well-being in the IT industry.