AIMC Topic: Deep Learning

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Precise energy modeling and green retrofitting optimization of existing buildings based on BIM and deep learning approaches.

PloS one
The construction industry has emerged as a major contributor to global energy consumption and greenhouse gas emissions amidst continuously rising worldwide energy demands. Enhancing building energy efficiency represents a critical intervention for ac...

A fault diagnosis method for rotating machinery components based on enhanced YOLO v8 and integrated attention mechanism.

PloS one
Accurate fault diagnosis of rotating machinery components is the key to ensuring the safe operation of the mechanical system. Aiming at problems such as inaccurate detection of small target fault features and loss of fault information in the process ...

A Transformer-Based Deep Learning Approach to Predicting Air Organic Pollutant-Human Protein Interactions.

Environmental science & technology
Air pollution poses a critical global public health challenge. Molecular-level initiating events, such as pollutant-protein interactions, can trigger cascades of biological responses that may contribute to adverse health effects. However, current met...

Deep Learning Exploration Expands the Natural Diversity of Metallothioneins in the Archaea Domain.

Journal of agricultural and food chemistry
The diversity and functions of metallothioneins (MTs) in Archaea remain poorly understood. This study identifies 180 archaeal MTs from 406 genomes, revealing distinct evolutionary lineages and structural diversity. Phylogenetic analysis suggests a no...

A Deep Learning Model for Efficient Nontargeted Screening of New Psychoactive Substances with Benchtop Nuclear Magnetic Resonance Devices.

Analytical chemistry
Benchtop nuclear magnetic resonance (NMR) devices enable rapid on-site detection of new psychoactive substances (NPS) at customs or mobile checkpoints, addressing the urgent need for real-time screening in combating illicit drug trafficking. Benchtop...

Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.

Scientific reports
Traditional survival predictions for oral squamous cell carcinoma (OSCC) rely on TNM staging, which lacks individualized prognostic value. Clinical factors such as performance status, age, sex, and lifestyle affect outcomes but are underrepresented i...

A hybrid CNN-transformer framework optimized by Grey Wolf Algorithm for accurate sign language recognition.

Scientific reports
This paper introduces the Gray Wolf Optimized Convolutional Transformer Network, a combined deep learning framework aimed at accurately and efficiently recognizing dynamic hand gestures, especially in American Sign Language (ASL). The model integrate...

Transformer-based deep learning enhances discovery in migraine GWAS.

Nature communications
Migraine is a complex neurological disorder with substantial heritability, yet genome-wide association studies (GWAS) have explained only a fraction of its genetic component. We developed InsightGWAS, a Transformer-based model, to enhance genetic dis...

Dynamic reward-augmented ensemble learning for EEG signal classification in major depressive disorder.

Biomedical physics & engineering express
Major Depressive Disorder (MDD) diagnosis through Electroencephalography (EEG) is hindered by the non-stationary characteristics of neural oscillations and the limited adaptability of conventional classification frameworks. Static ensemble models, wh...

Incorporating and quantifying deformable image registration uncertainties in dose accumulation: a feasibility study on the benefit of online adaptive therapy.

Physics in medicine and biology
. Accurate dose accumulation relies on deformable image registration (DIR) to track dose across multiple images. However, DIR introduces uncertainties that can impact cumulative dose distributions. In this study, we present a probabilistic framework ...