AIMC Topic: Algorithms

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Ensuring SOTIF: Enhanced object detection techniques for autonomous driving.

Accident; analysis and prevention
Neural networks' insufficient interpretability can lead to unguaranteed Safety of the Intended Functionality (SOTIF) issues when perceptual results are not always met in autonomous driving applications. To address the safety shortcomings in the curre...

Entity replacement strategy for temporal knowledge graph query relaxation.

Neural networks : the official journal of the International Neural Network Society
The temporal knowledge graph (TKG) query enables the retrieval of candidate answer lists by addressing questions that involve temporal constraints, regarded as a crucial downstream task in the realm of the temporal knowledge graph. Existing methods p...

Enhanced Graph Attention Network by Integrating Transformer for Epileptic EEG Identification.

International journal of neural systems
Electroencephalography signal classification is essential for the diagnosis and monitoring of neurological disorders, with significant implications for patient treatment. Despite the progress made, existing methods face challenges such as capturing t...

A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds.

Accident; analysis and prevention
In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing ...

Predicting single-cell protein production from food-processing wastewater in sequencing batch reactors using ensemble learning.

Bioresource technology
Producing single-cell protein (SCP) from food-processing wastewater offers a sustainable approach to resource recovery, animal feed production, and wastewater treatment. Decision-makers need accurate system performance data under variable influent co...

A hybrid machine learning approach using particle swarm optimization for cardiac arrhythmia classification.

International journal of cardiology
BACKGROUND: Precise and rapid identification of cardiac arrhythmias is paramount for delivering optimal patient care. Machine learning (ML) techniques hold significant promise for classifying arrhythmias, yet achieving peak performance often necessit...

Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Computational biology and chemistry
Lung cancer, with its high mortality rate, is one of the deadliest diseases globally. The alarming increase in lung cancer deaths and its widespread prevalence have led to the development of various cancer control research and early detection methods...

Fast and Stable Neonatal Brain MR Imaging Using Integrated Learned Subspace Model and Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.

DEISM: Deep Reconstruction Framework With Self-Calibration Mechanisms for Accelerated Chemical Exchange Saturation Transfer Imaging.

IEEE transactions on bio-medical engineering
The prolonged scan time of chemical exchange saturation transfer (CEST) imaging, caused by multiple data acquisitions over the varying saturation offset frequencies, necessitates accelerated imaging techniques. In this work, the artifact information ...

Imitation Learning for Path Planning in Cardiac Percutaneous Interventions.

IEEE transactions on bio-medical engineering
OBJECTIVE: Mitral regurgitation is a valvular heart disease particularly affecting the aging population. Minimally invasive transcatheter procedures offer benefits over traditional open-chest surgery but require significant operator skill and hand-ey...