AIMC Topic: Pattern Recognition, Automated
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Learning Deep Representations for Video-Based Intake Gesture Detection.
IEEE journal of biomedical and health informatics
Sep 30, 2019
Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors...
A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule.
Neural networks : the official journal of the International Neural Network Society
Sep 27, 2019
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...
Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects.
IEEE journal of biomedical and health informatics
Sep 26, 2019
Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states (medication working) and OFF states (medication has worn off). This paper describes a new data anal...
Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Sep 26, 2019
Binary classification of brain magnetic resonance (MR) images has made remarkable progress and many automated systems have been developed in the last decade. Multiclass classification of brain MR images is comparatively more challenging and has great...
Synchronization in an array of coupled neural networks with delayed impulses: Average impulsive delay method.
Neural networks : the official journal of the International Neural Network Society
Sep 24, 2019
In the paper, synchronization of coupled neural networks with delayed impulses is investigated. In order to overcome the difficulty that time delays can be flexible and even larger than impulsive interval, we propose a new method of average impulsive...
Adversarial training based lattice LSTM for Chinese clinical named entity recognition.
Journal of biomedical informatics
Sep 23, 2019
Clinical named entity recognition (CNER), which intends to automatically detect clinical entities in electronic health record (EHR), is a committed step for further clinical text mining. Recently, more and more deep learning models are used to Chines...
Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network.
Computational and mathematical methods in medicine
Sep 23, 2019
A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocess...
Salience-aware adaptive resonance theory for large-scale sparse data clustering.
Neural networks : the official journal of the International Neural Network Society
Sep 21, 2019
Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additiona...
Rethinking the performance comparison between SNNS and ANNS.
Neural networks : the official journal of the International Neural Network Society
Sep 19, 2019
Artificial neural networks (ANNs), a popular path towards artificial intelligence, have experienced remarkable success via mature models, various benchmarks, open-source datasets, and powerful computing platforms. Spiking neural networks (SNNs), a ca...
Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning.
Neural networks : the official journal of the International Neural Network Society
Sep 19, 2019
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to...
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