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Forecasting

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Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network.

IEEE journal of biomedical and health informatics
Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing the mechanisms of disease pathogenesis. However, a blind search for all possible associations between circRNAs and diseases through biological experimen...

A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction.

Environmental science and pollution research international
Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable...

The regulatory environment for artificial intelligence-enabled devices in the United States.

Seminars in vascular surgery
The regulatory environment in the United States has not kept pace with the rapidly developing market for artificial intelligence (AI)-enabled devices. The number of AI-enabled devices has increased year after year. All of these devices are registered...

Artificial intelligence in assisted reproductive technology: how best to optimize this tool of the future.

Fertility and sterility
Artificial intelligence, at a simple level, involves the use of a computer that can perform "human" functions: learning from experience, adjusting to new inputs, and simulating human intelligence performing human tasks. This Views and Reviews brings ...

Recurrent neural network modeling of multivariate time series and its application in temperature forecasting.

PloS one
Temperature forecasting plays an important role in human production and operational activities. Traditional temperature forecasting mainly relies on numerical forecasting models to operate, which takes a long time and has higher requirements for the ...

Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives.

British journal of haematology
Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods...

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting.

Journal of environmental management
Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literatu...

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects.

European radiology experimental
Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmo...

Application of grey feed forward back propagation-neural network model based on wavelet denoising to predict the residual settlement of goafs.

PloS one
To study the residual settlement of goaf's law and prediction model, we investigated the Mentougou mining area in Beijing as an example. Using MATLAB software, the wavelet threshold denoising method was used to optimize measured data, and the grey mo...