AIMC Topic: Neural Networks, Computer

Clear Filters Showing 8371 to 8380 of 31376 articles

An edge-device-compatible algorithm for valvular heart diseases screening using phonocardiogram signals with a lightweight convolutional neural network and self-supervised learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Detection and classification of heart murmur using mobile-phone-collected sound is an emerging approach to the scale-up screening of valvular heart disease at a population level. Nonetheless, the widespread adoption of arti...

Alternative states in microbial communities during artificial aeration: Proof of incubation experiment and development of recurrent neural network models.

Water research
Artificial aeration, a widely used method of restoring the aquatic ecological environment by enhancing the re-oxygenation capacity, typically relies upon empirical models to predict ecological dynamics and determine the operating scheme of the aerati...

Plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm.

Microscopy research and technique
Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error-prone. Early detection of plan...

Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors.

Medical engineering & physics
On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements bas...

Revisiting multi-view learning: A perspective of implicitly heterogeneous Graph Convolutional Network.

Neural networks : the official journal of the International Neural Network Society
Graph Convolutional Network (GCN) has become a hotspot in graph-based machine learning due to its powerful graph processing capability. Most of the existing GCN-based approaches are designed for single-view data. In numerous practical scenarios, data...

Prediction of cancer recurrence based on compact graphs of whole slide images.

Computers in biology and medicine
Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting ...

Towards a Better Performance in Facial Expression Recognition: A Data-Centric Approach.

Computational intelligence and neuroscience
Facial expression is the best evidence of our emotions. Its automatic detection and recognition are key for robotics, medicine, healthcare, education, psychology, sociology, marketing, security, entertainment, and many other areas. Experiments in the...

Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System.

Anais da Academia Brasileira de Ciencias
The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and ex...

U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome?

Respiratory medicine and research
BACKGROUND: Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disea...

Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.

Journal of thoracic imaging
PURPOSE: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT).