AIMC Topic: Deep Learning

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[Artificial intelligence-based ECG analysis: current status and future perspectives-Part 2 : Recent studies and future].

Herzschrittmachertherapie & Elektrophysiologie
While fundamental aspects of the application of artificial intelligence (AI) to electrocardiogram (ECG) analysis were discussed in part 1 of this review, the present work (part 2) provides a review of recent studies on the practical application of th...

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

NeuroImage
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disord...

A deep learning framework to classify breast density with noisy labels regularization.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervis...

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

Analytical chemistry
The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tan...

Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis.

Sensors (Basel, Switzerland)
Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people's opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared a...

Multi-End Physics-Informed Deep Learning for Seismic Response Estimation.

Sensors (Basel, Switzerland)
As a structural health monitoring (SHM) system can hardly measure all the needed responses, estimating the target response from the measured responses has become an important task. Deep neural networks (NNs) have a strong nonlinear mapping ability, a...

Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment.

Sensors (Basel, Switzerland)
CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal featu...

A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures.

Sensors (Basel, Switzerland)
This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) appro...

Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery.

Sensors (Basel, Switzerland)
The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approach...

Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language.

Sensors (Basel, Switzerland)
Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Biometric analysis, education, security, healthcare, and smart cities are only a few examples of speech recognition applications. Most s...