AIMC Topic: Neural Networks, Computer

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Vision-Based Intelligent Perceiving and Planning System of a 7-DoF Collaborative Robot.

Computational intelligence and neuroscience
In this paper, an intelligent perceiving and planning system based on deep learning is proposed for a collaborative robot consisting of a 7-DoF (7-degree-of-freedom) manipulator, a three-finger robot hand, and a vision system, known as IPPS (intellig...

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the rel...

Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system.

Environmental science and pollution research international
The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the ou...

A guide to machine learning for biologists.

Nature reviews. Molecular cell biology
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models ...

Modality-agnostic self-supervised deep feature learning and fast instance optimisation for multimodal fusion in ultrasound-guided interventions.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fast and robust alignment of pre-operative MRI planning scans to intra-operative ultrasound is an important aspect for automatically supporting image-guided interventions. Thus far, learning-based approaches have failed to t...

SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD.

Computers in biology and medicine
COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods bas...

Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation.

Medical physics
PURPOSE: For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)-based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the tr...

Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

Medical physics
BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showi...

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

Sensors (Basel, Switzerland)
Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, "extended NIR", ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to t...

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals.

Sensors (Basel, Switzerland)
The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents in...