## 4 Discussion  
The analysis focused on calculating a new proposed mathematical formalism for the _WBI_ based on four components: _HRC_ (using average heart rate, minimum heart rate, and maximum heart rate), _PAC_ (total steps taken, distance covered, running distance,calories burned), _SQC_ (deep sleep duration, shallow sleep duration, total wake time), and SI (identified stress levels throughout the working day). We have considered the potential impact of seasonal and temporal variations on well-being metrics, such as fluctuations in _PAC_ and _SQC_ across different times of the year. However, these variations are mitigated in the WBI through the cumulative contributions of multiple factors. These contributions are further smoothed in the final framework, which helps to reduce the direct influence of seasonal or temporal changes. This approach ensures that the well-being assessment provides a representative measure of employee experiences throughout the year. Future research could focus on refining these adjustments to better account for these cyclical effects, providing even more accurate year-round assessments of employee well-being. The _WBI_ is integrated into the WBM using the LbfgsMaximumEntropy algorithm pre-implemented into the MLNET framework. This study conducted 384 tests, modifying the training parameters. The research achieved an optimal configuration with an L1 value of 0.01, L2 value of 0.01, learning rate of 1 \(\times\) 10\({}^{-7}\), and epochs of 20, with the dropout set to false, resulting in a _MacroAccuracy_ of 0.9181, _MicroAccuracy_ of 0.9595, and a _LogLoss_ of 0.4814. The authors recognize recent advancements in ML and predictive modeling, particularly studies on large language models (LLMs) for improved human-computer interaction and predictive frameworks in engineering, such as those applied to electrohydrodynamic (EHD) pumps. These studies showcase the potential of ML to predict key EHD pump parameters, offering benefits like reduced wear, more straightforward handling, and silent operation [69]. Additionally, the Kolmogorov-Arnold Network (KAN) was introduced to predict pressure and flow rates in EHD pumps, with symbolic formulas revealing the nonlinear effects of factors like voltage, apex angle, and electrode gap on pump performance [70]. Our work parallels these advancements using ML-based optimization (the LbfgsMaximumEntropy algorithm) tailored to predict employee well-being. As seen in EHD pump research, incorporating predictive frameworks highlights the potential of adaptable models that could enhance our well-being prediction capabilities. The further integration of LLMs in sentiment analysis, as demonstrated with Azure Text Analytics in this study, can also deepen interpretive accuracy. Future work will explore adaptive modeling approaches to personalize well-being assessments by incorporating complex, interactive ML models to enrich the employee experience. The second AI model evaluated sentiment analysis through Azure Cognitive Services to calculate the _DSC_, representing the employee's dissatisfaction score. This study assessed 2000 text messages, calculating a precision of 99.59% and an accuracy of 99.7%. The _WBI_ and _DSC_ calculate the _EGI_, and if the _EGI_ for the previous 10 days is less than 5, then a KPI evaluation is necessary for the employee. Key results indicate that the _WBI_ reflects employee well-being, with scores ranging from 1 to 10, showcasing variations in employee states across dates. These features collectively contribute to understanding employee well-being and sentiment in a workplace context, particularly within the framework of Industry 5.0. [PERSON] et al. 's results [23] using Azure Text Analytics revealed some limitations in its capabilities. Azure Sentiment Analysis exhibited an average performance metric of approximately 57% when evaluating texts across English and Romanian contexts, highlighting the errors introduced by the cultural differences.