AIMC Topic: Neural Networks, Computer

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Benchmarking structural evolution methods for training of machine learned interatomic potentials.

Journal of physics. Condensed matter : an Institute of Physics journal
When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. We benchmark two other modalities of evolv...

An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene.

Journal of environmental and public health
As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through si...

An Integrated Approach Fusing CEEMD Energy Entropy and Sparrow Search Algorithm-Based PNN for Fault Diagnosis of Rolling Bearings.

Computational intelligence and neuroscience
This paper solves the problem of difficulty in achieving satisfactory results with traditional methods of bearing fault diagnosis, which can effectively extract the fault information and improve the fault diagnosis accuracy. This paper proposes a nov...

We got nuts! use deep neural networks to classify images of common edible nuts.

Nutrition and health
BACKGROUND: Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns.

A novel sEMG data augmentation based on WGAN-GP.

Computer methods in biomechanics and biomedical engineering
The classification of sEMG signals is fundamental in applications that use mechanical prostheses, making it necessary to work with generalist databases that improve the accuracy of those classifications. Therefore, synthetic signal generation can be ...

Image classification combined with faster R-CNN for the peak detection of complex components and their metabolites in untargeted LC-HRMS data.

Analytica chimica acta
Peak detection of untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) data is a key step to identify the metabolic status of the drugable chemicals and extracts from functional foods or herbs. Nevertheless, the existing appro...

Hierarchical binding in convolutional neural networks: Making adversarial attacks geometrically challenging.

Neural networks : the official journal of the International Neural Network Society
We approach the issue of robust machine vision by presenting a novel deep-learning architecture, inspired by work in theoretical neuroscience on how the primate brain performs visual feature binding. Feature binding describes how separately represent...

Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The specific aim of this study is to develop machine learning models as a clinical approach for personalized treatment of osteoporosis. The model performance on outcome prediction was compared between four machine learning a...

ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.

Computers in biology and medicine
Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, ...

Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation.

Computers in biology and medicine
Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutio...