AIMC Topic: Algorithms

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Contrastive and adversarial regularized multi-level representation learning for incomplete multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Incomplete multi-view clustering is a significant task in machine learning, given that complex systems in nature and society cannot be fully observed; it provides an opportunity to exploit the structure and functions of underlying systems. Current al...

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

Nature methods
It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial...

Explainable Deep Learning-Assisted Self-Calibrating Colorimetric Patches for In Situ Sweat Analysis.

Analytical chemistry
Sweat has emerged as a compelling analyte for noninvasive biosensing technology because it contains a wealth of important biomarkers in hormones, organic biomacromolecules, and various ionic mixtures. These components offer valuable insights and can ...

Multichannel high noise level ECG denoising based on adversarial deep learning.

Scientific reports
This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram (ECG) signals. As it's well known, noise leads to misinterpretations of fetal ECG signals and thus ...

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is ...

Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.

Frontiers in immunology
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model b...

Preferences in AI algorithms: The need for relevant risk attitudes in automated decisions under uncertainties.

Risk analysis : an official publication of the Society for Risk Analysis
Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a pre...

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms.

Food chemistry
This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of m...

Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver.

Clinical radiology
AIM: To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional images (no CM reduction, normal tube voltage, reconstructed with HBIR. To compare images using reduced contrast media (CM), low kilovoltage peak (kVp) ...

Reinforcement learning-based consensus control for MASs with intermittent constraints.

Neural networks : the official journal of the International Neural Network Society
In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-li...