AIMC Topic: Neural Networks, Computer

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Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy.

Magnetic resonance in medicine
PURPOSE: To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic ...

Transductive LSTM for time-series prediction: An application to weather forecasting.

Neural networks : the official journal of the International Neural Network Society
Long Short-Term Memory (LSTM) has shown significant performance on many real-world applications due to its ability to capture long-term dependencies. In this paper, we utilize LSTM to obtain a data-driven forecasting model for an application of weath...

Prediction of blood pressure variability using deep neural networks.

International journal of medical informatics
PURPOSE: The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained through medical examination at a hospital. Previous studies have reported the blood pressure variab...

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Artificial intelligence in medicine
Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, hea...

Real-time prediction of tumor motion using a dynamic neural network.

Medical & biological engineering & computing
Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the motion of the target tumor. Real-time repositioning of the treatment beam during radiotherapy requir...

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.

Journal of computer-aided molecular design
Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secret...

An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.

Scientific reports
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of...

Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks.

Food chemistry
The fermentation products of edible fungi are rich in anthraquinones and have a variety of activities, including the antioxidant activity. Because of the large number of combinations, it is very difficult to obtain the optimal multi-strains co-fermen...

A causal discovery algorithm based on the prior selection of leaf nodes.

Neural networks : the official journal of the International Neural Network Society
In recent years, Linear Non-Gaussian Acyclic Model (LiNGAM) has been widely used for the discovery of causal network. However, solutions based on LiNGAM usually yield high computational complexity as well as unsatisfied accuracy when the data is high...

Attention-guided CNN for image denoising.

Neural networks : the official journal of the International Neural Network Society
Deep convolutional neural networks (CNNs) have attracted considerable interest in low-level computer vision. Researches are usually devoted to improving the performance via very deep CNNs. However, as the depth increases, influences of the shallow la...