AIMC Topic: Algorithms

Clear Filters Showing 5181 to 5190 of 28713 articles

AI-based open-source software for cephalometric analysis from limited FOV radiographs.

Journal of dentistry
BACKGROUND: Artificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diag...

AI-DPAPT: a machine learning framework for predicting PROTAC activity.

Molecular diversity
Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challeng...

Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

Acta ophthalmologica
PURPOSE: To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation.

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism.

Neural networks : the official journal of the International Neural Network Society
This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employin...

Quality-related fault detection for dynamic process based on quality-driven long short-term memory network and autoencoder.

Neural networks : the official journal of the International Neural Network Society
Fault detection consistently plays a crucial role in industrial dynamic processes as it enables timely prevention of production losses. However, since industrial dynamic processes become increasingly coupled and complex, they introduce uneven dynamic...

Hypergraph contrastive attention networks for hyperedge prediction with negative samples evaluation.

Neural networks : the official journal of the International Neural Network Society
Hyperedge prediction aims to predict common relations among multiple nodes that will occur in the future or remain undiscovered in the current hypergraph. It is traditionally modeled as a classification task, which performs hypergraph feature learnin...

Fractional-order stochastic gradient descent method with momentum and energy for deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel fractional-order stochastic gradient descent with momentum and energy (FOSGDME) approach is proposed. Specifically, to address the challenge of converging to a real extreme point encountered by the existing fractional gradient ...

Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and transfer learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disea...

Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.

Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy.

Gene
The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilize...