AIMC Topic: Neural Networks, Computer

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A synergistic future for AI and ecology.

Proceedings of the National Academy of Sciences of the United States of America
Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in comp...

Identifying predictors of Day 5 blastocyst utilization rate using an artificial neural network.

Reproductive biomedicine online
RESEARCH QUESTION: Can artificial intelligence identify predictors of an increased Day 5 blastocyst utilization rate (D5BUR), which is one of the most informative key performance indicators in an IVF laboratory?

Artificial neural network-based shelf life prediction approach in the food storage process: A review.

Critical reviews in food science and nutrition
The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, f...

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

Computer methods and programs in biomedicine
BACKGROUND: Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation wor...

Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System.

The American journal of pathology
Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual eva...

Visual modalities-based multimodal fusion for surgical phase recognition.

Computers in biology and medicine
Surgical workflow analysis is essential to help optimize surgery by encouraging efficient communication and the use of resources. However, the performance of phase recognition is limited by the use of information related to the presence of surgical i...

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.

Computers in biology and medicine
BACKGROUND: Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morpholog...

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images.

Computers in biology and medicine
Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This stud...

RASP: Regularization-based Amplitude Saliency Pruning.

Neural networks : the official journal of the International Neural Network Society
Due to the prevalent data-dependent nature of existing pruning criteria, norm criteria with data independence play a crucial role in filter pruning criteria, providing promising prospects for deploying deep neural networks on resource-constrained dev...

Predictive learning by a burst-dependent learning rule.

Neurobiology of learning and memory
Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the prese...