## 5 Conclusions  
In summary, this study has effectively devised a comprehensive framework for the real-time control of energy and monitoring of incubator conditions in neonatal care facilities. The system effectively integrates various hardware components and a highly efficient database management system by utilizing the Internet of Things (IoT) architecture in combination with the MQTT communication protocol. A rigorous and validated predictive energy consumption model was developed, integrating convolutional neural networks (CNN) and long short-term memory (LSTM) techniques. The temperature distribution within the incubator exhibits variation throughout its sides, as indicated by the results of the tests. The measurements obtained from the incubator's sensors can be recorded within a database system. These stored data may be accessible in real-time using a web application, facilitating the monitoring and control functions. During the experimental phase, the long short-term memory (LSTM) approach produced root mean square error (RMSE) and mean absolute error (MAE) values of 42.650 and 33.575, respectively.  
On the other hand, the convolutional neural network (CNN) technique gave RMSE and MAE values of 37.675 and 30.082, respectively. The integration of both convolutional neural network (CNN) and long short-term memory (LSTM) techniques resulted in notable enhancements in the root mean square error (RMSE) and mean absolute error (MAE) values, which reached 32.436 and 25.382, respectively. This exemplifies the integrated methodology's efficacy in augmenting the model's prediction precision. The results indicate that the model successfully enhanced predicted accuracy to a significant degree. Moreover, the technology facilitates the visualization of temperature distribution within the incubator, augmenting the standard of care provided to infants. This study significantly contributes to the progression of neonatal incubator technology, facilitating the development of more efficient and data-informed newborn care solutions in healthcare settings.  
Figure 20: Comparison with other methods: (**a**) elastic net regression; (**b**) support vector regression; (**c**) gradient boosting regression; (**d**) linear regression; (**e**) ridge regression; (**f**) kernel ridge regression.  
**Author Contributions:** Conceptualization, I.K.A.A.A. and D.M.; methodology, I.K.A.A.A., D.M. and P.N.C.; software, I.K.A.A.A.; data collection, I.K.A.A.A.; data analysis I.K.A.A.A.; writing--original draft preparation, I.K.A.A.A., D.M. and P.N.C.; writing--review and editing, I.K.A.A.A., D.M. and P.N.C.; and supervision, I.K.A.A.A., D.M. and P.N.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** Sincere gratitude is extended to Rajamangala University of Technology Thanyaburi and Institute Technology and Business STIKOM Bali for their unwavering support and the invaluable resources that greatly facilitated the successful completion of this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.