2021
Autores
Souza, V; Maciel, A; Nedel, L; Kopper, R; Loges, K; Schlemmer, E;
Publicação
Journal on Interactive Systems
Abstract
2021
Autores
Schlemmer, E; Oliveira, LC; Menezes, J;
Publicação
Práxis Educacional
Abstract
2021
Autores
Menezes, J; Schlemmer, E; La Rocca, F; Moreira, JA;
Publicação
REVISTA INTERSABERES
Abstract
2021
Autores
Marques Palagi, AM; Schlemmer, E;
Publicação
EmRede - Revista de Educação a Distância
Abstract
2021
Autores
Kurunathan, H; Severino, R; Tovar, E;
Publicação
JOURNAL OF SENSOR AND ACTUATOR NETWORKS
Abstract
Visible Light Communication (VLC) has been emerging as a promising technology to address the increasingly high data-rate and time-critical demands that the Internet of Things (IoT) and 5G paradigms impose on the underlying Wireless Sensor Actuator Networking (WSAN) technologies. In this line, the IEEE 802.15.7 standard proposes several physical layers and Medium Access Control (MAC) sub-layer mechanisms that support a variety of VLC applications. Particularly, at the MAC sub-layer, it can support contention-free communications using Guaranteed Timeslots (GTS), introducing support for time-critical applications. However, to effectively guarantee accurate usage of such functionalities, it is vital to derive the worst-case bounds of the network. In this paper, we use network calculus to carry out the worst-case bounds analysis for GTS utilization of IEEE 802.15.7 and complement our model with an in-depth performance analysis. We also propose the inclusion of an additional mechanism to improve the overall scalability and effective bandwidth utilization of the network.
2021
Autores
Rocha, CN; Rodrigues, F;
Publicação
INTELLIGENT DATA ANALYSIS
Abstract
The emergency department of a hospital plays an extremely important role in the healthcare of patients. To maintain a high quality service, clinical professionals need information on how patient flow will evolve in the immediate future. With accurate emergency department forecasts it is possible to better manage available human resources by allocating clinical staff before peak periods, thus preventing service congestion, or releasing clinical staff at less busy times. This paper describes a solution developed for the presentation of hourly, four-hour, eight-hour and daily number of admissions to a hospital's emergency department. A 10-year history (2009-2018) of the number of emergency admissions in a Portuguese hospital was used. To create the models several methods were tested, including exponential smoothing, SARIMA, autoregressive and recurrent neural network, XGBoost and ensemble learning. The models that generated the most accurate hourly time predictions were the recurrent neural network with one-layer (sMAPE = 23.26%) and with three layers (sMAPE = 23.12%) and XGBoost (sMAPE = 23.70%). In terms of efficiency, the XGBoost method has by far outperformed all others. The success of the recurrent neuronal network and XGBoost machine learning methods applied to the prediction of the number of emergency department admissions has been demonstrated here, with an accuracy that surpasses the models found in the literature.
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