AIMC Topic: Forecasting

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An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration.

Journal of environmental management
Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavele...

A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis.

PloS one
Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to t...

A novel recurrent neural network approach in forecasting short term solar irradiance.

ISA transactions
Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar ir...

Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future.

International journal of pharmaceutics
Over the last two centuries, medicines have evolved from crude herbal and botanical preparations into more complex manufacturing of sophisticated drug products and dosage forms. Along with the evolution of medicines, the manufacturing practices for t...

Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

The Journal of surgical research
BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who...

Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

American journal of surgery
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patien...

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

Journal of biomedical informatics
COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age...

A hybrid deep learning technology for PM air quality forecasting.

Environmental science and pollution research international
The concentration of PM is one of the main factors in evaluating the air quality in environmental science. The severe level of PM directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of ...

Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.

PloS one
OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the fut...

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

IEEE journal of biomedical and health informatics
A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism...