AIMC Topic: Neural Networks, Computer

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Utilizing High-Resolution Imaging and Artificial Intelligence for Accurate Leaf Wetness Detection for the Strawberry Advisory System (SAS).

Sensors (Basel, Switzerland)
In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases s...

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sensors (Basel, Switzerland)
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...

Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks.

BMC medical imaging
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects an individual's behavior, speech, and social interaction. Early and accurate diagnosis of ASD is pivotal for successful intervention. The limited availability of large data...

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds.

Biofabrication
3D (Bio)printing is a highly effective method for fabricating tissue engineering scaffolds, renowned for their exceptional precision and control. Artificial intelligence (AI) has become a crucial technology in this field, capable of learning and repl...

Neural activity shaping in visual prostheses with deep learning.

Journal of neural engineering
The visual perception provided by retinal prostheses is limited by the overlapping current spread of adjacent electrodes. This reduces the spatial resolution attainable with unipolar stimulation. Conversely, simultaneous multipolar stimulation guided...

Understanding natural language: Potential application of large language models to ophthalmology.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mimic natural language comprehension and generation. Their evolution has undergone several waves of in...

A deep learning framework for the early detection of multi-retinal diseases.

PloS one
Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Qu...

CT-based artificial intelligence prediction model for ocular motility score of thyroid eye disease.

Endocrine
PURPOSE: Thyroid eye disease (TED) is the most common orbital disease in adults. Ocular motility restriction is the primary complaint of patients, while its evaluation is quite difficult. The present study aimed to introduce an artificial intelligenc...

PSAR-SR: Patches separation and artifacts removal for improving super-resolution networks.

Neural networks : the official journal of the International Neural Network Society
The success of the ClassSR has led to a strategy of decomposing images being used for large image SR. The decomposed image patches have different recovery difficulties. Therefore, in ClassSR, image patches are reconstructed by different networks to g...

Adaptive self-supervised learning for sequential recommendation.

Neural networks : the official journal of the International Neural Network Society
Sequential recommendation typically utilizes deep neural networks to mine rich information in interaction sequences. However, existing methods often face the issue of insufficient interaction data. To alleviate the sparsity issue, self-supervised lea...