AI Medical Compendium Topic:
Time Factors

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Artificial intelligence to investigate predictors and prognostic impact of time to surgery in colon cancer.

Journal of surgical oncology
BACKGROUND AND OBJECTIVES: The role of time to surgery (TTS) for long-term outcomes in colon cancer (CC) remains ill-defined. We sought to utilize artificial intelligence (AI) to characterize the drivers of TTS and its prognostic impact.

Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data.

Big data
Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications. Anomalies or inconsistencies in big data refer to the occurr...

Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention.

Journal of biomedical informatics
OBJECTIVE: In the fields of medical care and research as well as hospital management, time series are an important part of the overall data basis. To ensure high quality standards and enable suitable decisions, tools for precise and generic imputatio...

Data-driven prediction of greenhouse aquaponics air temperature based on adaptive time pattern network.

Environmental science and pollution research international
Greenhouse aquaponics system (GHAP) improves productivity by harmonizing internal environments. Keeping a suitable air temperature of GHAP is essential for the growth of plant and fish. However, the disturbance of various environmental factors and th...

Forecasting the United State Dollar(USD)/Bangladeshi Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange rates.

PloS one
Forecasting a currency exchange rate is one of the most challenging tasks nowadays. Due to government monetary policy and some uncertain factors, such as political stability, it becomes difficult to correctly forecast the currency exchange rate. Prev...

Forecasting shipping index using CEEMD-PSO-BiLSTM model.

PloS one
Shipping indices are extremely volatile, non-stationary, unstructured and non-linear, and more difficult to forecast than other common financial time series. Based on the idea of "decomposition-reconstruction-integration", this article puts forward a...

Identification and quantification of anomalies in environmental gamma dose rate time series using artificial intelligence.

Journal of environmental radioactivity
Gamma dose rate (GDR) monitors are the most widely used tool for continuous monitoring of environmental radioactivity. They are inexpensive to procure and operate, and generally require little maintenance. However, since no spectral information is av...

Quasi-synchronization of drive-response systems with parameter mismatch via event-triggered impulsive control.

Neural networks : the official journal of the International Neural Network Society
In this paper, an event-triggered impulsive control method is proposed to investigate the quasi-synchronization of drive-response systems with parameter mismatch, which integrates the event-triggered control and impulsive control together. The impuls...

Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model.

Water research
Determination of coagulant dosage in water treatment is a time-consuming process involving nonlinear data relationships and numerous factors. This study provides a deep learning approach to determine coagulant dosage and/or the settled water turbidit...