AIMC Topic: Algorithms

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Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models.

SLAS technology
Ophthalmic anesthesia the crucial factors in success and safety of ophthalmic surgery, which involves the delicate aspects of pain control, sedation, and patient response. Advances in ophthalmic surgery cause a need for exact and individualized anest...

Artificial Intelligence in Diagnosis of Heart Failure.

Journal of the American Heart Association
Heart failure (HF) is a complex and varied condition that affects over 50 million people worldwide. Although there have been significant strides in understanding the underlying mechanisms of HF, several challenges persist, particularly in the accurat...

[Not Available].

Vertex (Buenos Aires, Argentina)
Introducción: la ideación suicida es el pensamiento de autoeliminación no siempre reportada por los pacientes en test de depresión. El objetivo fue identificar y analizar síntomas depresivos del cuestionario de salud del paciente-9 asociados a ideaci...

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images.

Scientific reports
Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brai...

Optimized driver fatigue detection method using multimodal neural networks.

Scientific reports
Driver fatigue is a significant factor contributing to road accidents, highlighting the need for precise and reliable detection systems. This study introduces a comprehensive approach using multimodal neural networks, leveraging the DROZY dataset, wh...

Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms.

Environmental monitoring and assessment
Soil organic carbon (SOC) plays a crucial role in carbon cycle management and soil fertility. Understanding the spatial variations in SOC content is vital for supporting sustainable soil resource management. In this study, we analyzed the variability...

Lung nodule detection using a multi-scale convolutional neural network and global channel spatial attention mechanisms.

Scientific reports
Early detection of lung nodules is crucial for the prevention and treatment of lung cancer. However, current methods face challenges such as missing small nodules, variations in nodule size, and high false positive rates. To address these challenges,...

Quantitative detection of serum biochemical indexes via UV-Vis-NIRS combined with deep neural networks.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
To achieve rapid, cost-efficient, convenient and accurate detection of five clinical serum biochemical indexes, namely glucose (GLU), triglycerides (TG), total cholesterol (TC), total protein (TP) and albumin (ALB), ultraviolet-visible-near infrared ...

TCH: A novel multi-view dimensionality reduction method based on triple contrastive heads.

Neural networks : the official journal of the International Neural Network Society
Multi-view dimensionality reduction (MvDR) is a potent approach for addressing the high-dimensional challenges in multi-view data. Recently, contrastive learning (CL) has gained considerable attention due to its superior performance. However, most CL...