AIMC Topic: Neural Networks, Computer

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A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.

Medical & biological engineering & computing
Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip fracture risk. In this study, we proposed an approach based on deep learning for the fast and automat...

Machine-Learning-Assisted Recognition on Bioinspired Soft Sensor Arrays.

ACS nano
Soft interfaces with self-sensing capabilities play an essential role in environment awareness and reaction. The growing overlap between materials and sensory systems has created a myriad of challenges for sensor integration, including the design of ...

Efficacy of a Deep Learning Convolutional Neural Network System for Melanoma Diagnosis in a Hospital Population.

International journal of environmental research and public health
(1) Background: The purpose of this study was to evaluate the efficacy in terms of sensitivity, specificity, and accuracy of the quantusSKIN system, a new clinical tool based on deep learning, to distinguish between benign skin lesions and melanoma i...

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data.

Sensors (Basel, Switzerland)
Rolling element bearing faults significantly contribute to overall machine failures, which demand different strategies for condition monitoring and failure detection. Recent advancements in machine learning even further expedite the quest to improve ...

Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a CUSUM Control Algorithm.

Sensors (Basel, Switzerland)
Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe oper...

Automated multilabel diagnosis on electrocardiographic images and signals.

Nature communications
The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data. We report the development of a multil...

Fast protein structure comparison through effective representation learning with contrastive graph neural networks.

PLoS computational biology
Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein...

A Multistage Heterogeneous Stacking Ensemble Model for Augmented Infant Cry Classification.

Frontiers in public health
Understanding the reason for an infant's cry is the most difficult thing for parents. There might be various reasons behind the baby's cry. It may be due to hunger, pain, sleep, or diaper-related problems. The key concept behind identifying the reaso...

Fully Convolutional Neural Network Deep Learning Model Fully in Patients with Type 2 Diabetes Complicated with Peripheral Neuropathy by High-Frequency Ultrasound Image.

Computational and mathematical methods in medicine
This study was aimed at exploring the diagnostic value of high-frequency ultrasound imaging based on a fully convolutional neural network (FCN) for peripheral neuropathy in patients with type 2 diabetes (T2D). A total of 70 patients with T2D mellitus...

Personalized Smart Clothing Design Based on Multimodal Visual Data Detection.

Computational intelligence and neuroscience
In the traditional clothing customization system, only the designer participates in the clothing design, and the style is single. In the face of numerous styles, the user just repeatedly arranges and combines the styles, but does not realize the user...