AIMC Topic: Algorithms

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Self-learning model fusion for network anomaly detection: A hybrid CNN-LSTM-transformer framework.

PloS one
The rapid evolution of cyber threats poses significant challenges to the adaptability and performance of anomaly detection systems. This study presents an innovative hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), ...

Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment.

PloS one
Cardiovascular disease (CVD) is considered the number one killer disease in the world, underlining the importance of the application of more accurate diagnostic and therapeutic tools. Traditional screening procedures usually do not provide identifica...

Floc image-driven deep learning enhanced by temporal windows and transformers for carbon emission reduction in drinking water treatment plants.

Water research
Using machine learning (ML) and deep learning (DL) algorithms for precise coagulant dosing in drinking water treatment plants (DWTPs) helps ensure drinking water safety and supports greenhouse gas (GHG) emission reduction. The effectiveness of these ...

GADRC: a graph-based approach for drug repositioning with deep residual networks and computational feature-guided undersampling.

Journal of computer-aided molecular design
Drug repositioning (DR) is a highly promising research strategy aimed at discovering new therapeutic indications for existing drugs. Current computational DR methods have become effective tools for uncovering drug-disease associations, yet they suffe...

A surface defect detection method for electronic products based on improved YOLOv11.

PloS one
Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep lea...

Enhanced local feature extraction of lite network with scale-invariant CNN for precise segmentation of small brain tumors in MRI.

PloS one
Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global fea...

Fault detection of high-speed train wheelset bearings based on improved auxiliary classifier generative adversarial networks and VAE.

PloS one
Fault detection in high-speed train wheelset bearings is paramount for ensuring operational safety. However, the scarcity of fault samples limits the accuracy of traditional detection methods. To address this challenge, this paper proposes a supervis...

Hazediff: A training-free diffusion-based image dehazing method with pixel-level feature injection.

PloS one
In the current environmental context, significant emissions generated by industrial and transportation activities, coupled with an unreasonable energy structure, have resulted in recurrent haze phenomena. This consequently leads to degraded image con...

Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband.

PloS one
The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, ...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...