Results 31 to 40 of about 147,000 (286)

Forecasting daily patient outflow from a ward having no real-time clinical data

, 2016
OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.
Gopakumar, Shivapratap, Luo, Wei, Phung, Quoc-Dinh, Tran, Truyen, Venkatesh, Svetha   +4 more
core   +2 more sources

Near real-time prediction of urgent care hospital performance metrics using scalable random forest algorithm: A multi-site development

Healthcare Analytics, 2023
While previous studies have shown the potential value of predictive modelling for emergency care, few models have been implemented for producing near real-time predictions across various demand, utilisation and performance metrics.
Theresia A. Budiman, Charlotte R. James, Nicholas C. Howlett, Richard M. Wood   +3 more
doaj   +1 more source

Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

, 2017
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance.
De Ridder, Fjo, De Schutter, Bart, Lago, Jesus, Vrancx, Peter   +3 more
core   +2 more sources

Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction

Systems Science & Control Engineering
The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid.
Pingping Xie, Yang Liu, Qiuyu Lu, Xu Lin, Yinguo Yang, Xudong Hu   +5 more
doaj   +1 more source

Kernel Density Estimators for Gaussian Mixture Models

Lithuanian Journal of Statistics, 2013
The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
doaj   +1 more source

Trend Prediction of Valve Internal Leakage in Thermal Power Plants Based on Improved ARIMA-GARCH

Energies
Accurate trend prediction of valve internal leakage is crucial for the safe and economical operation of thermal power units. To address the issues of prediction lag and insufficient accuracy in existing methods when dealing with the dynamic changes in ...
Ruichun Hou, Lin Cong, Kaiyong Li, Zihao Guo, Xinghua Yuan, Chengbing He   +5 more
doaj   +1 more source

Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data [PDF]

In the challenging realm of earthquake prediction, the reliability of forecasting systems has remained a persistent obstacle. This study focuses on earthquake magnitude prediction in Indonesia, leveraging supervised machine learning techniques and cloud ...
Haryono, Eko, Pratama, Thomas Oka, Sunarno, Sunarno, Wijatna, Agus Budhie   +3 more
core   +1 more source

A numeric model to simulate solar individual ultraviolet exposure. [PDF]

, 2003
Exposure to solar ultraviolet (UV) light is the main causative factor for skin cancer. UV exposure depends on environmental and individual factors. Individual exposure data remain scarce and development of alternative assessment methods is greatly needed.
Vernez, David, Milon, Antoine, Francioli, Laurent, Bulliard, Jean-Luc, Vuilleumier, Laurent, Moccozet, Laurent   +5 more
core   +4 more sources

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

Frontiers in Public Health, 2022
In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or ...
Dost Muhammad Khan, Muhammad Ali, Nadeem Iqbal, Nadeem Iqbal, Umair Khalil, Hassan M. Aljohani, Amirah Saeed Alharthi, Ahmed Z. Afify   +7 more
doaj   +1 more source

A Hybrid Model High-Order Hesitant Fuzzy Time Series and Multilayer Perceptron with Mean Aggregated Membership Value for Enhanced Air Pollutant Concentration Forecasting

INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH
This study introduces an advanced hybrid model integrating High-Order Hesitant Fuzzy Time Series (HOHFTS) with a Multilayer Perceptron (MLP) to improve the accuracy of air pollutant concentration forecasting.
Kurniawan Willy Prasetyo, Budi Warsito, Bayu Surarso   +2 more
semanticscholar   +1 more source