AIMC Topic: Neural Networks, Computer

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Performance of Artificial Intelligence in Predicting Future Depression Levels.

Studies in health technology and informatics
Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively i...

Predicting Mortality in COVID-19 Patients Using 6 Machine Learning Algorithms.

Studies in health technology and informatics
In late 2019, COVID-19 appeared and has since spread worldwide as the new pandemic, causing more than 6 million deaths. In dealing with this global crisis, the contribution of Artificial Intelligence was also important through the possibilities of cr...

Exhaustive state-specific dissociation study of the N2(Σg+1)+N(S4) system using QCT combined with a neural network method.

The Journal of chemical physics
This work studies the exhaustive rovibrational state-specific collision-induced dissociation properties of the N2+N system by QCT (quasi-classical trajectory) combined with a neural network method based on the ab initio PES recently published by Varg...

[A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed ...

[Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI)...

Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network.

Medicine
UNLABELLED: When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality o...

[Deep learning-based dose prediction in radiotherapy planning for head and neck cancer].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To propose an deep learning-based algorithm for automatic prediction of dose distribution in radiotherapy planning for head and neck cancer.

Synthetic seismocardiogram generation using a transformer-based neural network.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches...

Asymptotic Self-Similar Blow-Up Profile for Three-Dimensional Axisymmetric Euler Equations Using Neural Networks.

Physical review letters
Whether there exist finite-time blow-up solutions for the 2D Boussinesq and the 3D Euler equations are of fundamental importance to the field of fluid mechanics. We develop a new numerical framework, employing physics-informed neural networks, that d...

Machine-learning-based method for fiber-bending eavesdropping detection.

Optics letters
In this Letter, we present a scheme for detecting fiber-bending eavesdropping based on feature extraction and machine learning (ML). First, 5-dimensional features from the time-domain signal are extracted from the optical signal, and then a long shor...