International Journal of Robotics and Machine Learning Technologies

Enterprise-Level Emphasis Operational Analytics: Predicting Healthcare Encounter Resolution Success Using Machine Learning

Nagababu Kandula — 2025-11-27

Enterprise-Level Emphasis represents a critical operational framework within healthcare administration, designed to ensure accurate claims processing and encounter data management by systematically identifying and resolving discrepancies. This study analyzed ELE’S operational performance using descriptive statistics and machine learning regression techniques to predict settlement success rates. Performance data from 20 observations revealed robust operational metrics: average claim processing time of 12.17 hours, error rates of 4.16%, employee performance scores of 79.8%, system uptime of 97.61%, and settlement success rates of 91.53%. Correlation analysis showed strong negative relationships between processing time, error rates, and operational outcomes, while there were positive relationships between employee performance, system uptime, and settlement success. Two ensemble learning models—AdaBoost regression and XGBoost regression—were developed to predict settlement success rates. AdaBoost achieved a training R² of 0.9993 and a testing R² of 0.9548, demonstrating reasonable generalization despite moderate overfitting. XGBoost showed severe overfitting with perfect training performance (R² = 1.0000) but poor testing results (R² = 0.8907), which reduced the performance of AdaBoost on unobserved data. This analysis confirms the operational performance of ELE, while highlighting the importance of model regularization in predictive analyses. The high generalization of AdaBoost makes ELE more suitable for predicting resolution outcomes, although hyperparameter optimization could improve the practical applicability of the two models in healthcare operational management.

Enterprise Sales Compensation Optimization: A Machine Learning Framework for Accurate Payout Forecasting

Raghavendra Sunku — 2025-08-22

: This research develops and evaluates machine learning models for predicting sales compensation payouts based on key performance metrics. Using a comprehensive dataset of sales performance indicators, three regression algorithms were systematically compared to identify the optimal predictive model for compensation administration systems. Research Significance: Sales compensation prediction is critical for organizational budgeting, performance management, and ensuring fair compensation structures. Traditional manual calculation methods are prone to errors and inefficiencies, making automated predictive models essential for modern sales operations. This study addresses the need for accurate, data-driven compensation forecasting systems that can enhance transparency and reliability in sales management processes. Methodology: Algorithm Analysis Three machine learning algorithms were implemented and evaluated: Random Forest Regressor (RFR), AdaBoost Regressor (ABR), and Gradient Boosting Regressor (GBR). Models were trained on historical sales data and validated using standard train-test split methodology. Performance was assessed using multiple regression metrics including R², RMSE, MAE, and additional error measures to ensure comprehensive evaluation. Input Parameters: Sales Volume, Number of Deals, Average Deal Size. Output Parameter: Compensation Payout Results: Gradient Boosting Regressor demonstrated superior performance with perfect training accuracy and excellent generalization capability. The analysis revealed strong correlation between sales volume and compensation, validating performance-based incentive structures. All models showed acceptable predictive accuracy, with GBR providing the most reliable compensation forecasting.

Keywords: Sales compensation prediction, machine learning, gradient boosting, performance metrics, regression analysis, compensation modeling, sales analytics, predictive modeling.

Machine Learning-Based Robotic Control: A Dual Approach Using Linear and Support Vector Regression

Rajender Radharam — 2025-09-25

: The study on Data Robot Implementation explores the development, modeling, and performance evaluation of intelligent robotic systems that combine both traditional control methods and data-driven machine learning approaches. It explores the transition from continuous-time to discrete-time implementations, emphasizing challenges such as stability, computational delays, and measurement effects. The research uses linear regression (LR) and support vector regression (SVR) techniques to model and predict algorithm performance within a robotic system using parameters such as processing speed, sensor accuracy, energy consumption, and algorithm performance. Statistical analysis revealed high model accuracy, with R2 values exceeding 0.98 for both methods, indicating exceptional predictive reliability. LR demonstrated simplicity and interpretability, while SVR demonstrated superior generalization and nonlinear mapping capabilities. Correlation analysis indicates strong positive relationships between system variables, confirming that improved processing capabilities and sensor accuracy significantly improve automation performance. This study underscores the effectiveness of integrating machine learning algorithms into robot control systems to improve automation outcomes, providing a foundation for future implementations in industrial, healthcare, and intelligent manufacturing environments. The results confirm that data-driven modeling provides a robust framework for predicting, adapting, and optimizing robot performance in complex operational environments.