Employee Productivity Monitoring and Anomaly Detection using BERT and Time-Series Transformer with Gaussian Mixture Model

The revolution toward remote and hybrid work atmospheres has shaped challenges in precisely evaluating employee efficiency while preserving ethical and privacy protective monitoring perceives. This study presents a novel framework that integrates Time-Series Transformer, Bidirectional Encoder Representations from Transformers (BERT) and Gaussian Mixture Model (GMM) to examine work appointment, forecast productivity trends, and detect irregularities. BERT is active for appropriate understanding of member action logs, seizing insights from written communications. The Time-Series Transformer measures sequential behavioral data to model long-range dependances and estimate forthcoming efficiency outlines. Provisionally, GMM achieves unsupervised clustering, cataloging employees into rendezvous levels and recognizing peculiarities that may specify fatigue or detachment. To guarantee confidentiality, Federated Learning and Differential Privacy techniques are combined, allowing secure and devolved processing of employee data. Investigational results establish that the proposed model efficiently classifies work engagement levels, foresees output fluctuations, and notices anomalies with accuracy. This study contributes to the development of intelligent, privacy-conscious monitoring schemes that improve workplace competence while addressing ethical apprehensions.