AIMC Topic: Humans

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Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.

Scientific reports
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annea...

Performance metrics outperform physiological indicators in robotic teleoperation workload assessment.

Scientific reports
Robotics holds the potential to streamline the execution of repetitive and dangerous tasks, which are difficult or impossible for a human operator. However, in complex scenarios, such as nuclear waste management or disaster response, full automation ...

Explainable AI improves task performance in human-AI collaboration.

Scientific reports
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remain...

Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning.

Scientific reports
Hospital-acquired infections (HAIs) are serious complication for patients with acute ischemic stroke (AIS), often resulting in poor functional outcomes. However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed t...

Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data.

Scientific reports
Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth. Prompt risk prediction of CAD wou...

Clustering and classification for dry bean feature imbalanced data.

Scientific reports
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that c...

CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention.

Scientific reports
Ossification of the ligamentum flavum (OLF) is the main causative factor of spinal stenosis, but how to accurately and efficiently identify the ossification region is a clinical pain point and an urgent problem to be solved. Currently, we can only re...

Comparative evaluation of feature reduction methods for drug response prediction.

Scientific reports
Personalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared...

Macular patterns of neuronal and visual field loss in recovered optic neuritis identified by machine learning.

Scientific reports
We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF a...

Developing a fully applicable machine learning (ML) based sex classification model using linear cranial dimensions.

Scientific reports
Recent advances in artificial intelligence (AI) and machine learning (ML) applications have elevated accomplishments in various scientific fields, primarily those that benefit the economy and society. Contemporary threats, such as armed conflicts, na...