August 2024
A hospital bed availability monitoring system using the Internet of Things
2024, vol.16 , no.3, pp. 13-26
Article [2024-03-02]
Hospitals are considered the backbone of healthcare systems, and their significance is particularly pronounced during emergencies. However, the rapid overpopulation and recent pandemics have caused tremendous shortages of hospital beds, raising major threats to public health. This developmental research study aims to help improve the management of hospital beds through the design, development, testing, and evaluation of an Internet of Things (IoT)-based monitoring system implemented using Agile Scrum methodology. Based on the results of the hypothesis testing, the authors found that the optical sensors installed in 4 hospital rooms, with varying distance readings, are consistently accurate (F (3, 388) = 1.000, p=0.393), hence the authors accepted the proposed null hypothesis. Moreover, the beneficiaries, composed of the public, hospital staff, nurses, and a doctor evaluated the system to be overall very good, very efficient, very suitable, reliable, very usable, and very satisfactory. The outcome of this research aims to contribute to the United Nations’ Sustainable Development Goals (UN-SDG) on good health and well-being (SDG 3).
Ralph Sherwin A. Corpuz, Jofer Usa, John Cedrick Obaña, Justine Nicole Borbe, Den Arwin Salazar, Thea Marie Valeza. A hospital bed availability monitoring system using the Internet of Things . International Journal on Information Technologies and Security, vol.16 , no.3, 2024, pp. 13-26.
Prediction of remaining useful life using deep learning models with regularization
2024, vol.16 , no.3, pp. 3-12
Article [2024-03-01]
Nowadays the use of modern technologies leads to an increase in the complexity of equipment. Failures of equipment lead to enterprises shutdowns or breakdowns not at enterprises. In this regard, there is a need to predict the remaining useful life of equipment with great accuracy in order to replace its parts at a time close to the time of its failures. This increase safety and reliability and reducing maintenance costs. Predictive maintenance of equipment is replacing corrective and preventive maintenance. In this study to improve prediction accuracy of remaining useful life of equipment it is proposed deep learning models using convolutional and long short-term memory neural networks. To solve the problem of overfitting of the neural networks during the training process, the regularization entered into their structures. Experimental results on real data show that the proposed methods can achieve higher stability and accuracy, which are useful for prediction of remaining useful life of aero engines and have high efficiency.
Katerina M. Gritsyuk, Vera I. Gritsyuk . Prediction of remaining useful life using deep learning models with regularization. International Journal on Information Technologies and Security, vol.16 , no.3, 2024, pp. 3-12.