Unlocking the potential of LSTM for accurate salary prediction with MLE, Jeffreys prior, and advanced risk functions

• Published in: PeerJ. Computer science Vol. 10; p. e1875
• Main Authors: Li, Fanghong, Majid, Norliza Abdul, Ding, Shuo
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 22.02.2024; PeerJ, Inc; PeerJ Inc
• Subjects: Academic achievement; Accuracy; Algorithms; Artificial Intelligence; Career development; Career development planning; Careers; College graduates; Complexity; Computational linguistics; Data Mining and Machine Learning; Data Science; Decision trees; Deep learning; Gaussian mixture models; Higher education; Innovations; Integrated approach; Jeffreys prior; Kullback-Leibler; Labor market; Language processing; LSTM; Machine learning; Maximum likelihood estimation; Methods; Model accuracy; Natural language interfaces; Natural language processing; Neural networks; Personality; Personality traits; Prediction models; Probabilistic models; Regression analysis; Risk; Salary prediction; Skills; Social networks; Students; Success; Support vector machines; Wages; Wages & salaries; Maximum likelihood estimation; College graduate; LSTM; Kullback-Leibler; Gaussian mixture models; Salary prediction; Jeffreys prior

This article aims to address the challenge of predicting the salaries of college graduates, a subject of significant practical value in the fields of human resources and career planning. Traditional prediction models often overlook diverse influencing factors and complex data distributions, limiting...