AIMC Topic: Pattern Recognition, Automated
Clear Filters Showing 1491 to 1500 of 1671 articles
Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease.
Journal of digital imaging
Aug 1, 2018
This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 60...
A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.
Journal of digital imaging
Aug 1, 2018
A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately a...
Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
Motion classification based on pattern recognition of surface EMG (sEMG) signals is a promising approach for prosthetic control. We present a pattern recognition model that combines long short-term memory (LSTM) network with multiplayer perceptron (M...
Pattern Analysis in Physiological Pulsatile Signals: An Aid to Personalized Healthcare.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2018
We present a system to analyze patterns inside pulsatile signals and discover repetitions inside signals. We measure dominance of the repetitions using morphology and discrete nature of the signals by exploiting machine learning and information theor...
Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine.
The Review of scientific instruments
Jul 1, 2018
Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and ...
Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jun 1, 2018
Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analy...
Action-Driven Visual Object Tracking With Deep Reinforcement Learning.
IEEE transactions on neural networks and learning systems
Jun 1, 2018
In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control track...
How artificial intelligence is changing drug discovery.
Nature
May 1, 2018
An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.
IEEE transactions on biomedical circuits and systems
Apr 1, 2018
Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim t...
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Apr 1, 2018
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynami...
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