AIMC Topic: Deep Learning

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SleepPPG-Net2: deep learning generalization for sleep staging from photoplethysmography.

Physiological measurement
. sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent photoplethysmography (PPG)-based deep learning models perform well on local datasets but struggle...

Forecasting China's shipping indices based on modal decomposition and optimized deep learning integrated model.

PloS one
This study proposes an innovative hybrid forecasting model, VMD-CPSO-BiLSTM, which significantly enhances the prediction accuracy of shipping indices in China's maritime sector. The model employs a sophisticated three-phase methodology: (1) decomposi...

Hybrid backdoor attacks for deep code models.

PloS one
Deep code models face security vulnerabilities through backdoor attacks. Previous approaches have primarily relied on single-trigger mechanisms, resulting in limited stealth and vulnerability to defense strategies. This paper proposes a novel hybrid ...

Artificial intelligence revolutionize food detection? Vision, olfaction and taste integrated with machine learning/deep learning in food detection.

Food chemistry
The rapid advancement of artificial intelligence (AI) is profoundly transforming the theoretical framework and technological paradigm of food detection. The study focuses on elucidating the underlying mechanisms of machine learning (ML)- and deep lea...

Wave masking enhances electrocardiogram reconstruction with linear regression.

Scientific reports
Electrocardiogram (ECG) reconstruction involves synthesizing leads from a reduced or alternative lead set. While ECG leads are generally considered linearly related, recording distortions and individual differences make perfect replication difficult,...

Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.

Scientific reports
Deep learning (DL) algorithms have increasingly been applied to predict protein-ligand binding affinity, a critical step in drug design. Yet, many models still struggle to generalize to unseen data, and when coupled with the absence of confidence est...

MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems.

Scientific reports
Recent advances in artificial intelligence have greatly increased the accuracy of computer-assisted diagnosis for serious conditions including brain tumours. However, concerns about data privacy, class imbalance, and the diversity of medical datasets...

Artificial inteligence reading of cystometric traces provides good correlation with human diagnosis.

World journal of urology
AIM: Urodynamic studies are essential for diagnosing lower urinary tract dysfunction but are expert-dependent and time-consuming. Artificial intelligence (AI), notably machine learning (ML) and deep learning (DL) may help automate and standardize int...

Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice.

Clinical oral investigations
OBJECTIVES: The objective of this study is to assess how different annotation methods used during AI model training affect the accuracy of early caries lesion detection, and how the choice of the evaluation reference standard leads to significant dif...

An automated classification of brain white matter inherited disorders (Leukodystrophy) using MRI image features.

Biomedical physics & engineering express
Leukodystrophies are a group of inherited disorders that predominantly and selectively affect the white matter of the central nervous system. Their overlapping clinical and imaging manifestations make a timely and accurate diagnosis challenging. In t...