AIMC Topic: Neural Networks, Computer

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On acoustic fields of complex scatters based on physics-informed neural networks.

Ultrasonics
This paper proposes a modeling method for scattered acoustic fields under complex structures based on Physics-informed Neural Networks (PINNs), with particular attention to the acquisition of training sets and the embedding of physical governing equa...

Deep, deep learning with BART.

Magnetic resonance in medicine
PURPOSE: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI.

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application.

Environmental science and pollution research international
The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of ...

Application of a convolutional neural network to the quality control of MRI defacing.

Computers in biology and medicine
Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large clinical trials dataset with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying faci...

Deblurring of Sound Source Orientation Recognition Based on Deep Neural Network.

Sensors (Basel, Switzerland)
Underwater target detection and identification technology are currently two of the most important research directions in the information disciplines. Traditionally, underwater target detection technology has struggled to meet the needs of current eng...

Analysis and prediction of anion- and temperature responsive behaviours of luminescent Ru(II)-terpyridine complexes by using Boolean, fuzzy logic, artificial neural network and adapted neuro fuzzy inference models.

Dalton transactions (Cambridge, England : 2003)
Anion- and temperature responsive behaviors of three luminescent Ru(II)-terpyridine complexes are utilized here to demonstrate multiple Boolean (BL) and fuzzy logic (FL) operations. Taking advantage of the imidazole NH protons, anion-promoted alterat...

Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data.

Computational intelligence and neuroscience
With the rapid development of e-commerce, recommendation system has become one of the main tools that assists users in decision-making, enhances user's experience, and creates economic value. Since it is difficult to explain the implicit features gen...

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System.

Computational intelligence and neuroscience
The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest...

Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network.

Food research international (Ottawa, Ont.)
Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathog...

Trapezoidal pile-up nuclear pulse parameter identification method based on deep learning transformer model.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Pile-up between adjacent nuclear pulses is unavoidable in the actual detection process. Some scholars have tried to apply deep learning techniques to identify pile-up nuclear pulse parameters. However, traditional deep learning recurrent neural netwo...