AIMC Topic: Time Factors

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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...

A Neural Learning Approach for a Data-Driven Nonlinear Error Correction Model.

Computational intelligence and neuroscience
A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network ...

Robust exponential stability of discrete-time uncertain impulsive stochastic neural networks with delayed impulses.

Neural networks : the official journal of the International Neural Network Society
This paper is devoted to the study of the robust exponential stability (RES) of discrete-time uncertain impulsive stochastic neural networks (DTUISNNs) with delayed impulses. Using Lyapunov function methods and Razumikhin techniques, a number of suff...

Intra-person multi-task learning method for chronic-disease prediction.

Scientific reports
In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases...

State Causality and Adaptive Covariance Decomposition Based Time Series Forecasting.

Sensors (Basel, Switzerland)
Time series forecasting is a very vital research topic. The scale of time series in numerous industries has risen considerably in recent years as a result of the advancement of information technology. However, the existing algorithms pay little atten...