# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms  
The study incorporated several machine learning algorithms to evaluate their performance in predicting employee capability. These algorithms included the following: \begin{table}
\begin{tabular}{l l} \hline \hline
**Task** & **Description** \\ \hline T1 & Financial request \\ T2 & Suggestion for new technical document \\ T3 & Committee minutes \\ T4 & Primary expense claim \\ T5 & Contract deployment \\ T6 & Clearance of tenderer accounts \\ T7 & Technical opinion \\ T8 & Draft tender design \\ T9 & Design of a national tender \\ T10 & Design of an international tender \\ T11 & Implementation of a proposal for inclusion in the NSRF \\ T12 & Examination of supporting documentation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Primary Tasks. * Linear Regression
* Artificial Neural Networks (ANNs)
* Adaptive Neuro-Fuzzy Inference System (ANFIS)
* Support Vector Machine (SVM)
* Gradient Boosting Machine (GBM)
* Bagged Decision Trees (BDTs)
* XGBoost  
The selection of these algorithms represents a well-rounded and strategic approach to identifying the most effective predictive techniques for employee capability assessment in the public sector. Each algorithm was chosen to contribute a unique perspective to the analysis, leveraging its strengths to address different facets of the dataset's complexity. Linear Regression served as a foundational benchmark, providing insights into linear relationships between variables. Its simplicity established a reference point against which the performance of more sophisticated algorithms could be evaluated. Building on this baseline, Artificial Neural Networks extended the analysis to non-linear dynamics. Their multi-layered architecture enabled the capture of intricate patterns in the data, laying the groundwork for understanding more complex relationships. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was introduced to bridge the gap between interpretability and modeling complexity. By combining the transparency of fuzzy logic with the adaptability of neural networks, ANFIS excelled in capturing nuanced and non-linear relationships while addressing uncertainties inherent in employee data. Support Vector Machine added another dimension to the analysis by focusing on maximizing decision boundaries. Its ability to handle linear and non-linear relationships provided valuable comparisons, especially in scenarios involving distinct data classes. The study of ensemble methods began with Gradient Boosting Machine and XGBoost, both of which leverage iterative corrections to residual errors to enhance predictive accuracy. These algorithms excelled at modeling interactions among features, offering insights into hierarchical and complex decision-making processes. Complementing these, Bagged Decision Tree utilized ensemble learning to improve stability and reliability by aggregating outputs from multiple decision trees, effectively reducing variance and providing robustness against overfitting. All the aforementioned algorithms were executed within the **Apache Spark** framework, created at UC Berkeley's AMPLab [36; 37], to leverage its powerful capabilities for large-scale data processing. Spark's hybrid framework fluidly integrates batch and stream processing, outperforming Hadoop's MapReduce engine due to its innovative design [38]. The primary advantage of Spark in this context lies in its in-memory computation model, which significantly accelerates processing by minimizing reliance on disk I/O. Spark's Directed Acyclic Graphs (DAGs) for workflow optimization and resilient distributed datasets (RDDs) for fault tolerance were instrumental in efficiently handling the complex datasets involved in the study [39]. The notable features of Apache Spark, such as its exceptional speed--up to 100 times faster than Hadoop and support for multiple programming languages (Java, Scala, R, and Python)--enabled the algorithms to execute efficiently and at scale [40].