AIMC Topic: Neural Networks, Computer

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Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images.

Studies in health technology and informatics
Self-supervised methods gain more and more attention, especially in the medical domain, where the number of labeled data is limited. They provide results on par or superior to their fully supervised competitors, yet the difference between information...

Characterizing Infant Mortality Using Data Mining - A Case Study in Two Brazilian States - Santa Catarina and Amapá.

Studies in health technology and informatics
Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifical...

Deep Learning and Explainable Artificial Intelligence to Predict Patients' Choice of Hospital Levels in Urban and Rural Areas.

Studies in health technology and informatics
Maldistribution of healthcare resources among urban and rural areas is a significant challenge worldwide. People living in rural areas may have limited access to medical resources, and often neglect their health problems or receive insufficient care ...

Does Enrichment of Clinical Texts by Ontology Concepts Increases Classification Accuracy?

Studies in health technology and informatics
In the medical domain, multiple ontologies and terminology systems are available. However, existing classification and prediction algorithms in the clinical domain often ignore or insufficiently utilize semantic information as it is provided in those...

Adding an Attention Layer Improves the Performance of a Neural Network Architecture for Synonymy Prediction in the UMLS Metathesaurus.

Studies in health technology and informatics
BACKGROUND: Terminology integration at the scale of the UMLS Metathesaurus (i.e., over 200 source vocabularies) remains challenging despite recent advances in ontology alignment techniques based on neural networks.

UMA-Net: an unsupervised representation learning network for 3D point cloud classification.

Journal of the Optical Society of America. A, Optics, image science, and vision
The success of deep neural networks usually relies on massive amounts of manually labeled data, which is both expensive and difficult to obtain in many real-world datasets. In this paper, a novel unsupervised representation learning network, UMA-Net,...

An individualization approach for head-related transfer function in arbitrary directions based on deep learning.

JASA express letters
This paper provides an individualization approach for head-related transfer function (HRTF) in arbitrary directions based on deep learning by utilizing dual-autoencoder architecture to establish the relationship between HRTF magnitude spectrum and ar...

Open set classification strategies for long-term environmental field recordings for bird species recognition.

The Journal of the Acoustical Society of America
Deep learning is one established tool for carrying out classification tasks on complex, multi-dimensional data. Since audio recordings contain a frequency and temporal component, long-term monitoring of bioacoustics recordings is made more feasible w...

Physics-informed neural networks and functional interpolation for stiff chemical kinetics.

Chaos (Woodbury, N.Y.)
This work presents a recently developed approach based on physics-informed neural networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical kinetic problems with governing equations of stiff ordinary differential ...

Noise-mitigation strategies in physical feedforward neural networks.

Chaos (Woodbury, N.Y.)
Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and do not leverage digital concepts with their practically infinite signal...