AIMC Topic: Pattern Recognition, Automated

Clear Filters Showing 731 to 740 of 1689 articles

A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
The commonly used classifiers for pattern recognition of human motion, like backpropagation neural network (BPNN) and support vector machine (SVM), usually implement the classification by extracting some hand-crafted features from the human biologica...

Improved multi-view privileged support vector machine.

Neural networks : the official journal of the International Neural Network Society
Multi-view learning (MVL) concentrates on the problem of learning from the data represented by multiple distinct feature sets. The consensus and complementarity principles play key roles in multi-view modeling. By exploiting the consensus principle o...

Delta activity encodes taste information in the human brain.

NeuroImage
The categorization of food via sensing nutrients or toxins is crucial to the survival of any organism. On ingestion, rapid responses within the gustatory system are required to identify the oral stimulus to guide immediate behavior (swallowing or exp...

Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.

International journal of computer assisted radiology and surgery
PURPOSE: We present a cross-modality and fully automatic pipeline for labeling of intervertebral discs and vertebrae in volumetric data of the lumbar and thoracolumbar spine. The main goal is to provide an algorithm that is applicable to a wide range...

Medical breast ultrasound image segmentation by machine learning.

Ultrasonics
Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's health. Segmentation of breast ultrasound images into functional tissues can aid tumor localization, brea...

Joint moment-matching autoencoders.

Neural networks : the official journal of the International Neural Network Society
Image transformation between multiple domains has become a challenging problem in deep generative networks. This is because, in real-world applications, finding paired images in different domains is an expensive and impractical task. This paper propo...

Toward a standard ontology of surgical process models.

International journal of computer assisted radiology and surgery
PURPOSE: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing o...

A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care.

Journal of biomedical informatics
Ensuring the health and safety of independent-living senior citizens is a growing societal concern. Researchers have developed sensor based systems to monitor senior citizens' Activity of Daily Living (ADL), a set of daily activities that can indicat...

Predicting Athlete Ground Reaction Forces and Moments From Spatio-Temporal Driven CNN Models.

IEEE transactions on bio-medical engineering
The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded...

Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks.

Computational intelligence and neuroscience
In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the...