AIMC Topic: Neural Networks, Computer

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Towards large nuclear imaging system optical simulations with optiGAN, a generative adversarial network.

Physics in medicine and biology
Optical Monte Carlo (MC) simulations are essential for modeling light transport in radiation detectors used in nuclear imaging and high-energy physics. However, full-system simulations remain computationally prohibitive due to the need to track optic...

AI-powered remote monitoring of brain responses to clear and incomprehensible speech via speckle pattern analysis.

Journal of biomedical optics
SIGNIFICANCE: Functional magnetic resonance imaging provides high spatial resolution but is limited by cost, infrastructure, and the constraints of an enclosed scanner. Portable methods such as functional near-infrared spectroscopy and electroencepha...

Prediction of air temperature and humidity in greenhouses via artificial neural network.

PloS one
Accurate prediction of greenhouse temperature and relative humidity is critical for developing environmental control systems. Effective regulation strategies can help improve crop yields while reducing energy consumption. In this study, Multilayer Pe...

Investigating methods to enhance interpretability and performance in cardiac MRI for myocardial scarring diagnosis using convolutional neural network classification and One Match.

PloS one
Machine learning (ML) classification of myocardial scarring in cardiac MRI is often hindered by limited explainability, particularly with convolutional neural networks (CNNs). To address this, we developed One Match (OM), an algorithm that builds on ...

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.

PloS one
Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images is important for stroke diagnosis, but most studies have focused on lesion...

TempODEGraphNet: predicting user churn using dynamic social graphs and neural ODEs.

PloS one
Research on user churn prediction has been conducted across various domains for a long time. Among these, the gaming domain is characterized by its potential for diverse types of interactions between users. Due to this characteristic, many studies on...

Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks.

PloS one
Accurate pedestrian trajectory prediction is crucial for applications such as autonomous driving and crowd surveillance. This paper proposes the OV-SKTGCNN model, an enhancement to the Social-STGCNN model, aimed at addressing its low prediction accur...

Validation of simulated training sets using a convolutional neural network for isotope identification in urban environments.

PloS one
Real-time isotope identification in urban environments can aid law enforcement by providing additional information about the nature of a potential threat. Neural networks have shown promise in isotope identification but the large range of potential i...

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation.

PloS one
Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using a...

Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data.

Heredity
The ever-increasing availability of high-throughput DNA sequences and the development of numerous computational methods have led to considerable advances in our understanding of the evolutionary and demographic history of populations. Several demogra...