Implementing a Synchronization Method between a Relational and a Non-Relational Database

Győrödi, Cornelia A.; Turtureanu, Tudor; Győrödi, Robert Ş.; Zmaranda, Doina R.

doi:10.3390/bdcc7030153

Cited by 1 publication

(1 citation statement)

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“…In this study, the sensor measurement data from the incubator are sent to the broker and then processed by the system for storage in the database. The database application used in this research is MySQL, which is one of the relational database management systems (DBMS) that makes it easy to manage structured data as tables which can have relationships with each other [18,31]. In storing sensor data, the data must go through a filtering process, which is carried out by setting upper and lower threshold values to improve the quality of the data stored in the database.…”

Section: Databasementioning

confidence: 99%

Enhancing Neonatal Incubator Energy Management and Monitoring through IoT-Enabled CNN-LSTM Combination Predictive Model

Aryanto,

Maneetham,

Crisnapati

2023

Applied Sciences

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This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.

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Section: Databasementioning

confidence: 99%