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Forecasting

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Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms.

PloS one
Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. Howe...

A Systematic Review of Features Forecasting Patient Arrival Numbers.

Computers, informatics, nursing : CIN
Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely a...

Forecasting Pediatric Trauma Volumes: Insights From a Retrospective Study Using Machine Learning.

The Journal of surgical research
INTRODUCTION: Rising pediatric firearm-related fatalities in the United States strain Trauma Centers. Predicting trauma volume could improve resource management and preparedness, particularly if daily forecasts are achievable. The aim of the study is...

Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks.

Scientific reports
Infectious diseases significantly impact both public health and economic stability, underscoring the critical need for precise outbreak predictions to effictively mitigate their impact. This study applies advanced machine learning techniques to forec...

A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning.

BMC medical research methodology
BACKGROUND: The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models ...

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest.

PloS one
In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable energy generation. Machine Learning (ML...

ST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting.

Neural networks : the official journal of the International Neural Network Society
The field of traffic forecasting has been the subject of considerable attention as a critical component in alleviating traffic congestion and improving urban services. Given the regular patterns of human activities, it is evident that traffic flow is...

Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

BMC medical informatics and decision making
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...

Improving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.

Environment international
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2....