AIMC Topic: Neural Networks, Computer

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Applying feature selection and machine learning techniques to estimate the biomass higher heating value.

Scientific reports
The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass ...

Non-inferiority of deep learning ischemic stroke segmentation on non-contrast CT within 16-hours compared to expert neuroradiologists.

Scientific reports
We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who ...

AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images.

Physics in medicine and biology
In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires cer...

Is AI essential? Examining the need for deep learning in image-activated sorting of .

Lab on a chip
Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and c...

Research on cloud manufacturing service recommendation based on graph neural network.

PloS one
There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle t...

Extrapolation of affective norms using transformer-based neural networks and its application to experimental stimuli selection.

Behavior research methods
Data on the emotionality of words is important for the selection of experimental stimuli and sentiment analysis on large bodies of text. While norms for valence and arousal have been thoroughly collected in English, most languages do not have access ...

From lazy to rich to exclusive task representations in neural networks and neural codes.

Current opinion in neurobiology
Neural circuits-both in the brain and in "artificial" neural network models-learn to solve a remarkable variety of tasks, and there is a great current opportunity to use neural networks as models for brain function. Key to this endeavor is the abilit...

A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening.

Environmental science & technology
The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent de...

Learning smooth dendrite morphological neurons by stochastic gradient descent for pattern classification.

Neural networks : the official journal of the International Neural Network Society
This article presents a learning algorithm for dendrite morphological neurons (DMN) based on stochastic gradient descent (SGD). In particular, we focus on a DMN topology that comprises spherical dendrites, smooth maximum activation function nodes, an...

A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques.

Computers in biology and medicine
The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image...